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jzq 2024-06-21 16:03:03 +08:00
parent 86fcb0f3e6
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CMakeLists.txt Normal file
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cmake_minimum_required(VERSION 2.8)
project(ORB_SLAM2)
add_definitions(-w)
IF(NOT CMAKE_BUILD_TYPE)
SET(CMAKE_BUILD_TYPE Release)
ENDIF()
MESSAGE("Build type: " ${CMAKE_BUILD_TYPE})
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wall -O3 -march=native ")
# set(CMAKE_CXX_FLAGS "-g -O3 -march=native")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -O3 -march=native")
# Check C++11 or C++0x support
include(CheckCXXCompilerFlag)
CHECK_CXX_COMPILER_FLAG("-std=c++11" COMPILER_SUPPORTS_CXX11)
CHECK_CXX_COMPILER_FLAG("-std=c++0x" COMPILER_SUPPORTS_CXX0X)
if(COMPILER_SUPPORTS_CXX11)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
add_definitions(-DCOMPILEDWITHC11)
message(STATUS "Using flag -std=c++11.")
elseif(COMPILER_SUPPORTS_CXX0X)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++0x")
add_definitions(-DCOMPILEDWITHC0X)
message(STATUS "Using flag -std=c++0x.")
else()
message(FATAL_ERROR "The compiler ${CMAKE_CXX_COMPILER} has no C++11 support. Please use a different C++ compiler.")
endif()
LIST(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake_modules)
find_package(OpenCV 3.0 QUIET)
if(NOT OpenCV_FOUND)
find_package(OpenCV 2.4.3 QUIET)
if(NOT OpenCV_FOUND)
message(FATAL_ERROR "OpenCV > 2.4.3 not found.")
endif()
endif()
find_package(Eigen3 3.1.0 REQUIRED)
find_package(Pangolin REQUIRED)
find_package(PCL REQUIRED)
include_directories(
${PROJECT_SOURCE_DIR}
${PROJECT_SOURCE_DIR}/include
${PCL_INCLUDE_DIRS}
${EIGEN3_INCLUDE_DIR}
${Pangolin_INCLUDE_DIRS}
)
set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${PROJECT_SOURCE_DIR}/lib)
add_library(${PROJECT_NAME} SHARED
src/System.cc
src/Tracking.cc
src/LocalMapping.cc
src/LoopClosing.cc
src/ORBextractor.cc
src/ORBmatcher.cc
src/FrameDrawer.cc
src/Converter.cc
src/MapPoint.cc
src/KeyFrame.cc
src/Map.cc
src/MapDrawer.cc
src/Optimizer.cc
src/PnPsolver.cc
src/Frame.cc
src/KeyFrameDatabase.cc
src/Sim3Solver.cc
src/Initializer.cc
src/Viewer.cc
src/MapObject.cpp
)
target_link_libraries(${PROJECT_NAME}
${OpenCV_LIBS}
${EIGEN3_LIBS}
${PCL_LIBRARIES}
${Pangolin_LIBRARIES}
${PROJECT_SOURCE_DIR}/Thirdparty/DBoW2/lib/libDBoW2.so
${PROJECT_SOURCE_DIR}/Thirdparty/g2o/lib/libg2o.so
)
# Build examples
set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${PROJECT_SOURCE_DIR}/Examples/RGB-D)
add_executable(rgbd_tum
Examples/RGB-D/rgbd_tum.cc)
target_link_libraries(rgbd_tum ${PROJECT_NAME})
# set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${PROJECT_SOURCE_DIR}/Examples/Stereo)
# add_executable(stereo_kitti
# Examples/Stereo/stereo_kitti.cc)
# target_link_libraries(stereo_kitti ${PROJECT_NAME})
# add_executable(stereo_euroc
# Examples/Stereo/stereo_euroc.cc)
# target_link_libraries(stereo_euroc ${PROJECT_NAME})
# set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${PROJECT_SOURCE_DIR}/Examples/Monocular)
# add_executable(mono_tum
# Examples/Monocular/mono_tum.cc)
# target_link_libraries(mono_tum ${PROJECT_NAME})
# add_executable(mono_kitti
# Examples/Monocular/mono_kitti.cc)
# target_link_libraries(mono_kitti ${PROJECT_NAME})
# add_executable(mono_euroc
# Examples/Monocular/mono_euroc.cc)
# target_link_libraries(mono_euroc ${PROJECT_NAME})

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%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 458.654
Camera.fy: 457.296
Camera.cx: 367.215
Camera.cy: 248.375
Camera.k1: -0.28340811
Camera.k2: 0.07395907
Camera.p1: 0.00019359
Camera.p2: 1.76187114e-05
# Camera frames per second
Camera.fps: 20.0
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 1000
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 20
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#---------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.05
Viewer.KeyFrameLineWidth: 1
Viewer.GraphLineWidth: 0.9
Viewer.PointSize:2
Viewer.CameraSize: 0.08
Viewer.CameraLineWidth: 3
Viewer.ViewpointX: 0
Viewer.ViewpointY: -0.7
Viewer.ViewpointZ: -1.8
Viewer.ViewpointF: 500

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%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 718.856
Camera.fy: 718.856
Camera.cx: 607.1928
Camera.cy: 185.2157
Camera.k1: 0.0
Camera.k2: 0.0
Camera.p1: 0.0
Camera.p2: 0.0
# Camera frames per second
Camera.fps: 10.0
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 2000
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 20
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.1
Viewer.KeyFrameLineWidth: 1
Viewer.GraphLineWidth: 1
Viewer.PointSize:2
Viewer.CameraSize: 0.15
Viewer.CameraLineWidth: 2
Viewer.ViewpointX: 0
Viewer.ViewpointY: -10
Viewer.ViewpointZ: -0.1
Viewer.ViewpointF: 2000

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%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 721.5377
Camera.fy: 721.5377
Camera.cx: 609.5593
Camera.cy: 172.854
Camera.k1: 0.0
Camera.k2: 0.0
Camera.p1: 0.0
Camera.p2: 0.0
# Camera frames per second
Camera.fps: 10.0
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 2000
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 20
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.1
Viewer.KeyFrameLineWidth: 1
Viewer.GraphLineWidth: 1
Viewer.PointSize:2
Viewer.CameraSize: 0.15
Viewer.CameraLineWidth: 2
Viewer.ViewpointX: 0
Viewer.ViewpointY: -10
Viewer.ViewpointZ: -0.1
Viewer.ViewpointF: 2000

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%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 707.0912
Camera.fy: 707.0912
Camera.cx: 601.8873
Camera.cy: 183.1104
Camera.k1: 0.0
Camera.k2: 0.0
Camera.p1: 0.0
Camera.p2: 0.0
# Camera frames per second
Camera.fps: 10.0
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 2000
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 20
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.1
Viewer.KeyFrameLineWidth: 1
Viewer.GraphLineWidth: 1
Viewer.PointSize:2
Viewer.CameraSize: 0.15
Viewer.CameraLineWidth: 2
Viewer.ViewpointX: 0
Viewer.ViewpointY: -10
Viewer.ViewpointZ: -0.1
Viewer.ViewpointF: 2000

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%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 517.306408
Camera.fy: 516.469215
Camera.cx: 318.643040
Camera.cy: 255.313989
Camera.k1: 0.262383
Camera.k2: -0.953104
Camera.p1: -0.005358
Camera.p2: 0.002628
Camera.k3: 1.163314
# Camera frames per second
Camera.fps: 30.0
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 1000
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 20
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.05
Viewer.KeyFrameLineWidth: 1
Viewer.GraphLineWidth: 0.9
Viewer.PointSize:2
Viewer.CameraSize: 0.08
Viewer.CameraLineWidth: 3
Viewer.ViewpointX: 0
Viewer.ViewpointY: -0.7
Viewer.ViewpointZ: -1.8
Viewer.ViewpointF: 500

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%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 520.908620
Camera.fy: 521.007327
Camera.cx: 325.141442
Camera.cy: 249.701764
Camera.k1: 0.231222
Camera.k2: -0.784899
Camera.p1: -0.003257
Camera.p2: -0.000105
Camera.k3: 0.917205
# Camera frames per second
Camera.fps: 30.0
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 1000
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 20
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.05
Viewer.KeyFrameLineWidth: 1
Viewer.GraphLineWidth: 0.9
Viewer.PointSize:2
Viewer.CameraSize: 0.08
Viewer.CameraLineWidth: 3
Viewer.ViewpointX: 0
Viewer.ViewpointY: -0.7
Viewer.ViewpointZ: -1.8
Viewer.ViewpointF: 500

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%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 535.4
Camera.fy: 539.2
Camera.cx: 320.1
Camera.cy: 247.6
Camera.k1: 0.0
Camera.k2: 0.0
Camera.p1: 0.0
Camera.p2: 0.0
# Camera frames per second
Camera.fps: 30.0
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 1000
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 20
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.05
Viewer.KeyFrameLineWidth: 1
Viewer.GraphLineWidth: 0.9
Viewer.PointSize:2
Viewer.CameraSize: 0.08
Viewer.CameraLineWidth: 3
Viewer.ViewpointX: 0
Viewer.ViewpointY: -0.7
Viewer.ViewpointZ: -1.8
Viewer.ViewpointF: 500

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/**
* This file is part of ORB-SLAM2.
*
* Copyright (C) 2014-2016 Raúl Mur-Artal <raulmur at unizar dot es> (University of Zaragoza)
* For more information see <https://github.com/raulmur/ORB_SLAM2>
*
* ORB-SLAM2 is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM2 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM2. If not, see <http://www.gnu.org/licenses/>.
*/
#include<iostream>
#include<algorithm>
#include<fstream>
#include<chrono>
#include <unistd.h>
#include<opencv2/core/core.hpp>
#include<System.h>
using namespace std;
void LoadImages(const string &strImagePath, const string &strPathTimes,
vector<string> &vstrImages, vector<double> &vTimeStamps);
int main(int argc, char **argv)
{
if(argc != 5)
{
cerr << endl << "Usage: ./mono_tum path_to_vocabulary path_to_settings path_to_image_folder path_to_times_file" << endl;
return 1;
}
// Retrieve paths to images
vector<string> vstrImageFilenames;
vector<double> vTimestamps;
LoadImages(string(argv[3]), string(argv[4]), vstrImageFilenames, vTimestamps);
int nImages = vstrImageFilenames.size();
if(nImages<=0)
{
cerr << "ERROR: Failed to load images" << endl;
return 1;
}
// Create SLAM system. It initializes all system threads and gets ready to process frames.
ORB_SLAM2::System SLAM(argv[1],argv[2],ORB_SLAM2::System::MONOCULAR,true);
// Vector for tracking time statistics
vector<float> vTimesTrack;
vTimesTrack.resize(nImages);
cout << endl << "-------" << endl;
cout << "Start processing sequence ..." << endl;
cout << "Images in the sequence: " << nImages << endl << endl;
// Main loop
cv::Mat im;
for(int ni=0; ni<nImages; ni++)
{
// Read image from file
im = cv::imread(vstrImageFilenames[ni],CV_LOAD_IMAGE_UNCHANGED);
double tframe = vTimestamps[ni];
if(im.empty())
{
cerr << endl << "Failed to load image at: "
<< vstrImageFilenames[ni] << endl;
return 1;
}
#ifdef COMPILEDWITHC11
std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now();
#else
std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now();
#endif
// Pass the image to the SLAM system
SLAM.TrackMonocular(im,tframe);
#ifdef COMPILEDWITHC11
std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now();
#else
std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now();
#endif
double ttrack= std::chrono::duration_cast<std::chrono::duration<double> >(t2 - t1).count();
vTimesTrack[ni]=ttrack;
// Wait to load the next frame
double T=0;
if(ni<nImages-1)
T = vTimestamps[ni+1]-tframe;
else if(ni>0)
T = tframe-vTimestamps[ni-1];
if(ttrack<T)
usleep((T-ttrack)*1e6);
}
// Stop all threads
SLAM.Shutdown();
// Tracking time statistics
sort(vTimesTrack.begin(),vTimesTrack.end());
float totaltime = 0;
for(int ni=0; ni<nImages; ni++)
{
totaltime+=vTimesTrack[ni];
}
cout << "-------" << endl << endl;
cout << "median tracking time: " << vTimesTrack[nImages/2] << endl;
cout << "mean tracking time: " << totaltime/nImages << endl;
// Save camera trajectory
SLAM.SaveKeyFrameTrajectoryTUM("KeyFrameTrajectory.txt");
return 0;
}
void LoadImages(const string &strImagePath, const string &strPathTimes,
vector<string> &vstrImages, vector<double> &vTimeStamps)
{
ifstream fTimes;
fTimes.open(strPathTimes.c_str());
vTimeStamps.reserve(5000);
vstrImages.reserve(5000);
while(!fTimes.eof())
{
string s;
getline(fTimes,s);
if(!s.empty())
{
stringstream ss;
ss << s;
vstrImages.push_back(strImagePath + "/" + ss.str() + ".png");
double t;
ss >> t;
vTimeStamps.push_back(t/1e9);
}
}
}

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/**
* This file is part of ORB-SLAM2.
*
* Copyright (C) 2014-2016 Raúl Mur-Artal <raulmur at unizar dot es> (University of Zaragoza)
* For more information see <https://github.com/raulmur/ORB_SLAM2>
*
* ORB-SLAM2 is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM2 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM2. If not, see <http://www.gnu.org/licenses/>.
*/
#include<iostream>
#include<algorithm>
#include<fstream>
#include<chrono>
#include<iomanip>
#include <unistd.h>
#include<opencv2/core/core.hpp>
#include"System.h"
using namespace std;
void LoadImages(const string &strSequence, vector<string> &vstrImageFilenames,
vector<double> &vTimestamps);
int main(int argc, char **argv)
{
if(argc != 4)
{
cerr << endl << "Usage: ./mono_kitti path_to_vocabulary path_to_settings path_to_sequence" << endl;
return 1;
}
// Retrieve paths to images
vector<string> vstrImageFilenames;
vector<double> vTimestamps;
LoadImages(string(argv[3]), vstrImageFilenames, vTimestamps);
int nImages = vstrImageFilenames.size();
// Create SLAM system. It initializes all system threads and gets ready to process frames.
ORB_SLAM2::System SLAM(argv[1],argv[2],ORB_SLAM2::System::MONOCULAR,true);
// Vector for tracking time statistics
vector<float> vTimesTrack;
vTimesTrack.resize(nImages);
cout << endl << "-------" << endl;
cout << "Start processing sequence ..." << endl;
cout << "Images in the sequence: " << nImages << endl << endl;
// Main loop
cv::Mat im;
for(int ni=0; ni<nImages; ni++)
{
// Read image from file
im = cv::imread(vstrImageFilenames[ni],CV_LOAD_IMAGE_UNCHANGED);
double tframe = vTimestamps[ni];
if(im.empty())
{
cerr << endl << "Failed to load image at: " << vstrImageFilenames[ni] << endl;
return 1;
}
#ifdef COMPILEDWITHC11
std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now();
#else
std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now();
#endif
// Pass the image to the SLAM system
SLAM.TrackMonocular(im,tframe);
#ifdef COMPILEDWITHC11
std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now();
#else
std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now();
#endif
double ttrack= std::chrono::duration_cast<std::chrono::duration<double> >(t2 - t1).count();
vTimesTrack[ni]=ttrack;
// Wait to load the next frame
double T=0;
if(ni<nImages-1)
T = vTimestamps[ni+1]-tframe;
else if(ni>0)
T = tframe-vTimestamps[ni-1];
if(ttrack<T)
usleep((T-ttrack)*1e6);
}
// Stop all threads
SLAM.Shutdown();
// Tracking time statistics
sort(vTimesTrack.begin(),vTimesTrack.end());
float totaltime = 0;
for(int ni=0; ni<nImages; ni++)
{
totaltime+=vTimesTrack[ni];
}
cout << "-------" << endl << endl;
cout << "median tracking time: " << vTimesTrack[nImages/2] << endl;
cout << "mean tracking time: " << totaltime/nImages << endl;
// Save camera trajectory
SLAM.SaveKeyFrameTrajectoryTUM("KeyFrameTrajectory.txt");
return 0;
}
void LoadImages(const string &strPathToSequence, vector<string> &vstrImageFilenames, vector<double> &vTimestamps)
{
ifstream fTimes;
string strPathTimeFile = strPathToSequence + "/times.txt";
fTimes.open(strPathTimeFile.c_str());
while(!fTimes.eof())
{
string s;
getline(fTimes,s);
if(!s.empty())
{
stringstream ss;
ss << s;
double t;
ss >> t;
vTimestamps.push_back(t);
}
}
string strPrefixLeft = strPathToSequence + "/image_0/";
const int nTimes = vTimestamps.size();
vstrImageFilenames.resize(nTimes);
for(int i=0; i<nTimes; i++)
{
stringstream ss;
ss << setfill('0') << setw(6) << i;
vstrImageFilenames[i] = strPrefixLeft + ss.str() + ".png";
}
}

155
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/**
* This file is part of ORB-SLAM2.
*
* Copyright (C) 2014-2016 Raúl Mur-Artal <raulmur at unizar dot es> (University of Zaragoza)
* For more information see <https://github.com/raulmur/ORB_SLAM2>
*
* ORB-SLAM2 is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM2 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM2. If not, see <http://www.gnu.org/licenses/>.
*/
#include<iostream>
#include<algorithm>
#include<fstream>
#include<chrono>
#include <unistd.h>
#include<opencv2/core/core.hpp>
#include<System.h>
using namespace std;
void LoadImages(const string &strFile, vector<string> &vstrImageFilenames,
vector<double> &vTimestamps);
int main(int argc, char **argv)
{
if(argc != 4)
{
cerr << endl << "Usage: ./mono_tum path_to_vocabulary path_to_settings path_to_sequence" << endl;
return 1;
}
// Retrieve paths to images
vector<string> vstrImageFilenames;
vector<double> vTimestamps;
string strFile = string(argv[3])+"/rgb.txt";
LoadImages(strFile, vstrImageFilenames, vTimestamps);
int nImages = vstrImageFilenames.size();
// Create SLAM system. It initializes all system threads and gets ready to process frames.
ORB_SLAM2::System SLAM(argv[1],argv[2],ORB_SLAM2::System::MONOCULAR,true);
// Vector for tracking time statistics
vector<float> vTimesTrack;
vTimesTrack.resize(nImages);
cout << endl << "-------" << endl;
cout << "Start processing sequence ..." << endl;
cout << "Images in the sequence: " << nImages << endl << endl;
// Main loop
cv::Mat im;
for(int ni=0; ni<nImages; ni++)
{
// Read image from file
im = cv::imread(string(argv[3])+"/"+vstrImageFilenames[ni],CV_LOAD_IMAGE_UNCHANGED);
double tframe = vTimestamps[ni];
if(im.empty())
{
cerr << endl << "Failed to load image at: "
<< string(argv[3]) << "/" << vstrImageFilenames[ni] << endl;
return 1;
}
#ifdef COMPILEDWITHC11
std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now();
#else
std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now();
#endif
// Pass the image to the SLAM system
SLAM.TrackMonocular(im,tframe);
#ifdef COMPILEDWITHC11
std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now();
#else
std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now();
#endif
double ttrack= std::chrono::duration_cast<std::chrono::duration<double> >(t2 - t1).count();
vTimesTrack[ni]=ttrack;
// Wait to load the next frame
double T=0;
if(ni<nImages-1)
T = vTimestamps[ni+1]-tframe;
else if(ni>0)
T = tframe-vTimestamps[ni-1];
if(ttrack<T)
usleep((T-ttrack)*1e6);
}
// Stop all threads
SLAM.Shutdown();
// Tracking time statistics
sort(vTimesTrack.begin(),vTimesTrack.end());
float totaltime = 0;
for(int ni=0; ni<nImages; ni++)
{
totaltime+=vTimesTrack[ni];
}
cout << "-------" << endl << endl;
cout << "median tracking time: " << vTimesTrack[nImages/2] << endl;
cout << "mean tracking time: " << totaltime/nImages << endl;
// Save camera trajectory
SLAM.SaveKeyFrameTrajectoryTUM("KeyFrameTrajectory.txt");
return 0;
}
void LoadImages(const string &strFile, vector<string> &vstrImageFilenames, vector<double> &vTimestamps)
{
ifstream f;
f.open(strFile.c_str());
// skip first three lines
string s0;
getline(f,s0);
getline(f,s0);
getline(f,s0);
while(!f.eof())
{
string s;
getline(f,s);
if(!s.empty())
{
stringstream ss;
ss << s;
double t;
string sRGB;
ss >> t;
vTimestamps.push_back(t);
ss >> sRGB;
vstrImageFilenames.push_back(sRGB);
}
}
}

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%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 517.306408
Camera.fy: 516.469215
Camera.cx: 318.643040
Camera.cy: 255.313989
Camera.k1: 0.262383
Camera.k2: -0.953104
Camera.p1: -0.005358
Camera.p2: 0.002628
Camera.k3: 1.163314
Camera.width: 640
Camera.height: 480
# Camera frames per second
Camera.fps: 30.0
# IR projector baseline times fx (aprox.)
Camera.bf: 40.0
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
# Close/Far threshold. Baseline times.
ThDepth: 40.0
# Deptmap values factor
DepthMapFactor: 5000.0
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 1000
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 20
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.05
Viewer.KeyFrameLineWidth: 1
Viewer.GraphLineWidth: 0.9
Viewer.PointSize:2
Viewer.CameraSize: 0.08
Viewer.CameraLineWidth: 3
Viewer.ViewpointX: 0
Viewer.ViewpointY: -0.7
Viewer.ViewpointZ: -1.8
Viewer.ViewpointF: 500

70
Examples/RGB-D/TUM2.yaml Normal file
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%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 520.908620
Camera.fy: 521.007327
Camera.cx: 325.141442
Camera.cy: 249.701764
Camera.k1: 0.231222
Camera.k2: -0.784899
Camera.p1: -0.003257
Camera.p2: -0.000105
Camera.k3: 0.917205
Camera.width: 640
Camera.height: 480
# Camera frames per second
Camera.fps: 30.0
# IR projector baseline times fx (aprox.)
Camera.bf: 40.0
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
# Close/Far threshold. Baseline times.
ThDepth: 40.0
# Deptmap values factor
DepthMapFactor: 5208.0
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 1000
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 20
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.05
Viewer.KeyFrameLineWidth: 1
Viewer.GraphLineWidth: 0.9
Viewer.PointSize:2
Viewer.CameraSize: 0.08
Viewer.CameraLineWidth: 3
Viewer.ViewpointX: 0
Viewer.ViewpointY: -0.7
Viewer.ViewpointZ: -1.8
Viewer.ViewpointF: 500

69
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%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 535.4
Camera.fy: 539.2
Camera.cx: 320.1
Camera.cy: 247.6
Camera.k1: 0.0
Camera.k2: 0.0
Camera.p1: 0.0
Camera.p2: 0.0
Camera.width: 640
Camera.height: 480
# Camera frames per second
Camera.fps: 30.0
# IR projector baseline times fx (aprox.)
Camera.bf: 40.0
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
# Close/Far threshold. Baseline times.
ThDepth: 40.0
# Deptmap values factor
DepthMapFactor: 5000.0
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 1000
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 20
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.05
Viewer.KeyFrameLineWidth: 1
Viewer.GraphLineWidth: 0.9
Viewer.PointSize:2
Viewer.CameraSize: 0.08
Viewer.CameraLineWidth: 3
Viewer.ViewpointX: 0
Viewer.ViewpointY: -0.7
Viewer.ViewpointZ: -1.8
Viewer.ViewpointF: 500

573
Examples/RGB-D/associations/fr1_desk.txt Normal file
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Examples/RGB-D/associations/fr3_str_tex_near.txt Normal file
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/**
* This file is part of ORB-SLAM2.
*
* Copyright (C) 2014-2016 Raúl Mur-Artal <raulmur at unizar dot es> (University of Zaragoza)
* For more information see <https://github.com/raulmur/ORB_SLAM2>
*
* ORB-SLAM2 is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM2 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM2. If not, see <http://www.gnu.org/licenses/>.
*/
#include <iostream>
#include <algorithm>
#include <fstream>
#include <chrono>
#include <unistd.h>
#include <opencv2/core/core.hpp>
#include <System.h>
using namespace std;
void LoadImages(const string &strAssociationFilename, vector<string> &vstrImageFilenamesRGB,
vector<string> &vstrImageFilenamesD, vector<double> &vTimestamps);
void LoadLabel(const string &strLabelFilename, vector<vector<double>> &vLabel);
int main(int argc, char **argv)
{
if (argc != 5)
{
cerr << endl
<< "Usage: ./rgbd_tum path_to_vocabulary path_to_settings path_to_sequence path_to_association" << endl;
return 1;
}
// Retrieve paths to images
vector<string> vstrImageFilenamesRGB;
vector<string> vstrImageFilenamesD;
vector<double> vTimestamps;
string strAssociationFilename = string(argv[4]);
LoadImages(strAssociationFilename, vstrImageFilenamesRGB, vstrImageFilenamesD, vTimestamps);
// Check consistency in the number of images and depthmaps
int nImages = vstrImageFilenamesRGB.size();
if (vstrImageFilenamesRGB.empty())
{
cerr << endl
<< "No images found in provided path." << endl;
return 1;
}
else if (vstrImageFilenamesD.size() != vstrImageFilenamesRGB.size())
{
cerr << endl
<< "Different number of images for rgb and depth." << endl;
return 1;
}
// Create SLAM system. It initializes all system threads and gets ready to process frames.
ORB_SLAM2::System SLAM(argv[1], argv[2], ORB_SLAM2::System::RGBD, true); // argv[1]: rgbd_tum argv[2]:ORB_voc.txt
// Vector for tracking time statistics
vector<float> vTimesTrack;
vTimesTrack.resize(nImages);
cout << endl
<< "-------" << endl;
cout << "Start processing sequence ..." << endl;
cout << "Images in the sequence: " << nImages << endl
<< endl;
// Main loop
cv::Mat imRGB, imD;
for (int ni = 0; ni < nImages; ni++)
{
vector<vector<double>> vLabel;
// Read image and depthmap from file
imRGB = cv::imread(string(argv[3]) + "/" + vstrImageFilenamesRGB[ni], CV_LOAD_IMAGE_UNCHANGED); // argv[3]:data/dynamic_objects/rgbd_dataset_freiburg3_walking_static
imD = cv::imread(string(argv[3]) + "/" + vstrImageFilenamesD[ni], CV_LOAD_IMAGE_UNCHANGED);
LoadLabel(string(argv[3]) + "/labels/" + to_string(vTimestamps[ni]) + ".txt", vLabel);
double tframe = vTimestamps[ni];
if (imRGB.empty())
{
cerr << endl
<< "Failed to load image at: "
<< string(argv[3]) << "/" << vstrImageFilenamesRGB[ni] << endl;
return 1;
}
#ifdef COMPILEDWITHC11
std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now();
#else
std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now();
#endif
SLAM.TrackRGBD(imRGB, imD, tframe, vLabel);
#ifdef COMPILEDWITHC11
std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now();
#else
std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now();
#endif
double ttrack = std::chrono::duration_cast<std::chrono::duration<double>>(t2 - t1).count();
vTimesTrack[ni] = ttrack;
// Wait to load the next frame
double T = 0;
if (ni < nImages - 1)
T = vTimestamps[ni + 1] - tframe;
else if (ni > 0)
T = tframe - vTimestamps[ni - 1];
if (ttrack < T)
usleep((T - ttrack) * 1e6);
}
// Stop all threads
SLAM.Shutdown();
// Tracking time statistics
sort(vTimesTrack.begin(), vTimesTrack.end());
float totaltime = 0;
for (int ni = 0; ni < nImages; ni++)
{
totaltime += vTimesTrack[ni];
}
cout << "-------" << endl
<< endl;
cout << "median tracking time: " << vTimesTrack[nImages / 2] << endl;
cout << "mean tracking time: " << totaltime / nImages << endl;
// Save camera trajectory
SLAM.SaveTrajectoryTUM("CameraTrajectory.txt");
SLAM.SaveKeyFrameTrajectoryTUM("KeyFrameTrajectory.txt");
return 0;
}
void LoadImages(const string &strAssociationFilename, vector<string> &vstrImageFilenamesRGB,
vector<string> &vstrImageFilenamesD, vector<double> &vTimestamps)
{
ifstream fAssociation;
fAssociation.open(strAssociationFilename.c_str());
while (!fAssociation.eof())
{
string s;
getline(fAssociation, s);
if (!s.empty())
{
stringstream ss;
ss << s;
double t;
string sRGB, sD;
ss >> t;
vTimestamps.push_back(t);
ss >> sRGB;
vstrImageFilenamesRGB.push_back(sRGB);
ss >> t;
ss >> sD;
vstrImageFilenamesD.push_back(sD);
}
}
}
void LoadLabel(const string &strLabelFilename, vector<vector<double>> &vLabel)
{
ifstream fAssociation;
fAssociation.open(strLabelFilename.c_str());
while (!fAssociation.eof())
{
string s;
getline(fAssociation, s);
if (!s.empty())
{
stringstream ss;
ss << s;
double cls, x, y, w, h;
vector<double> label;
ss >> cls;
label.push_back(cls);
ss >> x;
label.push_back(x);
ss >> y;
label.push_back(y);
ss >> w;
label.push_back(w);
ss >> h;
label.push_back(h);
vLabel.push_back(label);
}
}
}

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Examples/ROS/ORB_SLAM2/Asus.yaml Normal file
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%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 535.4
Camera.fy: 539.2
Camera.cx: 320.1
Camera.cy: 247.6
Camera.k1: 0.0
Camera.k2: 0.0
Camera.p1: 0.0
Camera.p2: 0.0
Camera.width: 640
Camera.height: 480
# Camera frames per second
Camera.fps: 30.0
# IR projector baseline times fx (aprox.)
Camera.bf: 40.0
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
# Close/Far threshold. Baseline times.
ThDepth: 40.0
# Deptmap values factor
DepthMapFactor: 1.0
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 1000
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 20
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.05
Viewer.KeyFrameLineWidth: 1
Viewer.GraphLineWidth: 0.9
Viewer.PointSize:2
Viewer.CameraSize: 0.08
Viewer.CameraLineWidth: 3
Viewer.ViewpointX: 0
Viewer.ViewpointY: -0.7
Viewer.ViewpointZ: -1.8
Viewer.ViewpointF: 500

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cmake_minimum_required(VERSION 2.4.6)
include($ENV{ROS_ROOT}/core/rosbuild/rosbuild.cmake)
add_definitions(-w)
rosbuild_init()
IF(NOT ROS_BUILD_TYPE)
SET(ROS_BUILD_TYPE Release)
ENDIF()
MESSAGE("Build type: " ${ROS_BUILD_TYPE})
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wall -O3 -march=native ")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -O3 -march=native")
# Check C++11 or C++0x support
include(CheckCXXCompilerFlag)
CHECK_CXX_COMPILER_FLAG("-std=c++11" COMPILER_SUPPORTS_CXX11)
CHECK_CXX_COMPILER_FLAG("-std=c++0x" COMPILER_SUPPORTS_CXX0X)
if(COMPILER_SUPPORTS_CXX11)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
add_definitions(-DCOMPILEDWITHC11)
message(STATUS "Using flag -std=c++11.")
elseif(COMPILER_SUPPORTS_CXX0X)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++0x")
add_definitions(-DCOMPILEDWITHC0X)
message(STATUS "Using flag -std=c++0x.")
else()
message(FATAL_ERROR "The compiler ${CMAKE_CXX_COMPILER} has no C++11 support. Please use a different C++ compiler.")
endif()
LIST(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/../../../cmake_modules)
find_package(OpenCV 3.0 QUIET)
if(NOT OpenCV_FOUND)
find_package(OpenCV 2.4.3 QUIET)
if(NOT OpenCV_FOUND)
message(FATAL_ERROR "OpenCV > 2.4.3 not found.")
endif()
endif()
find_package(Eigen3 3.1.0 REQUIRED)
find_package(Pangolin REQUIRED)
include_directories(
${PROJECT_SOURCE_DIR}
${PROJECT_SOURCE_DIR}/../../../
${PROJECT_SOURCE_DIR}/../../../include
${Pangolin_INCLUDE_DIRS}
)
set(LIBS
${OpenCV_LIBS}
${EIGEN3_LIBS}
${Pangolin_LIBRARIES}
${PROJECT_SOURCE_DIR}/../../../Thirdparty/DBoW2/lib/libDBoW2.so
${PROJECT_SOURCE_DIR}/../../../Thirdparty/g2o/lib/libg2o.so
${PROJECT_SOURCE_DIR}/../../../lib/libORB_SLAM2.so
${PROJECT_SOURCE_DIR}/../../../lib/libboost_filesystem.so
${PROJECT_SOURCE_DIR}/../../../lib/libboost_system.so
-lboost_system
)
# Node for monocular camera
rosbuild_add_executable(Mono
src/ros_mono.cc
)
target_link_libraries(Mono
${LIBS}
)
# Node for monocular camera (Augmented Reality Demo)
rosbuild_add_executable(MonoAR
src/AR/ros_mono_ar.cc
src/AR/ViewerAR.h
src/AR/ViewerAR.cc
)
target_link_libraries(MonoAR
${LIBS}
)
# Node for stereo camera
rosbuild_add_executable(Stereo
src/ros_stereo.cc
)
target_link_libraries(Stereo
${LIBS}
)
# Node for RGB-D camera
rosbuild_add_executable(RGBD
src/ros_rgbd.cc
)
target_link_libraries(RGBD
${LIBS}
)

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<package>
<description brief="ORB_SLAM2">
ORB_SLAM2
</description>
<author>Raul Mur-Artal</author>
<license>GPLv3</license>
<depend package="roscpp"/>
<depend package="tf"/>
<depend package="sensor_msgs"/>
<depend package="image_transport"/>
<depend package="cv_bridge"/>
</package>

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/**
* This file is part of ORB-SLAM2.
*
* Copyright (C) 2014-2016 Raúl Mur-Artal <raulmur at unizar dot es> (University of Zaragoza)
* For more information see <https://github.com/raulmur/ORB_SLAM2>
*
* ORB-SLAM2 is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM2 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM2. If not, see <http://www.gnu.org/licenses/>.
*/
#include "ViewerAR.h"
#include <opencv2/highgui/highgui.hpp>
#include <unistd.h>
#include <mutex>
#include <thread>
#include <cstdlib>
using namespace std;
namespace ORB_SLAM2
{
const float eps = 1e-4;
cv::Mat ExpSO3(const float &x, const float &y, const float &z)
{
cv::Mat I = cv::Mat::eye(3,3,CV_32F);
const float d2 = x*x+y*y+z*z;
const float d = sqrt(d2);
cv::Mat W = (cv::Mat_<float>(3,3) << 0, -z, y,
z, 0, -x,
-y, x, 0);
if(d<eps)
return (I + W + 0.5f*W*W);
else
return (I + W*sin(d)/d + W*W*(1.0f-cos(d))/d2);
}
cv::Mat ExpSO3(const cv::Mat &v)
{
return ExpSO3(v.at<float>(0),v.at<float>(1),v.at<float>(2));
}
ViewerAR::ViewerAR(){}
void ViewerAR::Run()
{
int w,h,wui;
cv::Mat im, Tcw;
int status;
vector<cv::KeyPoint> vKeys;
vector<MapPoint*> vMPs;
while(1)
{
GetImagePose(im,Tcw,status,vKeys,vMPs);
if(im.empty())
cv::waitKey(mT);
else
{
w = im.cols;
h = im.rows;
break;
}
}
wui=200;
pangolin::CreateWindowAndBind("Viewer",w+wui,h);
glEnable(GL_DEPTH_TEST);
glEnable (GL_BLEND);
pangolin::CreatePanel("menu").SetBounds(0.0,1.0,0.0,pangolin::Attach::Pix(wui));
pangolin::Var<bool> menu_detectplane("menu.Insert Cube",false,false);
pangolin::Var<bool> menu_clear("menu.Clear All",false,false);
pangolin::Var<bool> menu_drawim("menu.Draw Image",true,true);
pangolin::Var<bool> menu_drawcube("menu.Draw Cube",true,true);
pangolin::Var<float> menu_cubesize("menu. Cube Size",0.05,0.01,0.3);
pangolin::Var<bool> menu_drawgrid("menu.Draw Grid",true,true);
pangolin::Var<int> menu_ngrid("menu. Grid Elements",3,1,10);
pangolin::Var<float> menu_sizegrid("menu. Element Size",0.05,0.01,0.3);
pangolin::Var<bool> menu_drawpoints("menu.Draw Points",false,true);
pangolin::Var<bool> menu_LocalizationMode("menu.Localization Mode",false,true);
bool bLocalizationMode = false;
pangolin::View& d_image = pangolin::Display("image")
.SetBounds(0,1.0f,pangolin::Attach::Pix(wui),1.0f,(float)w/h)
.SetLock(pangolin::LockLeft, pangolin::LockTop);
pangolin::GlTexture imageTexture(w,h,GL_RGB,false,0,GL_RGB,GL_UNSIGNED_BYTE);
pangolin::OpenGlMatrixSpec P = pangolin::ProjectionMatrixRDF_TopLeft(w,h,fx,fy,cx,cy,0.001,1000);
vector<Plane*> vpPlane;
while(1)
{
if(menu_LocalizationMode && !bLocalizationMode)
{
mpSystem->ActivateLocalizationMode();
bLocalizationMode = true;
}
else if(!menu_LocalizationMode && bLocalizationMode)
{
mpSystem->DeactivateLocalizationMode();
bLocalizationMode = false;
}
glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT);
// Activate camera view
d_image.Activate();
glColor3f(1.0,1.0,1.0);
// Get last image and its computed pose from SLAM
GetImagePose(im,Tcw,status,vKeys,vMPs);
// Add text to image
PrintStatus(status,bLocalizationMode,im);
if(menu_drawpoints)
DrawTrackedPoints(vKeys,vMPs,im);
// Draw image
if(menu_drawim)
DrawImageTexture(imageTexture,im);
glClear(GL_DEPTH_BUFFER_BIT);
// Load camera projection
glMatrixMode(GL_PROJECTION);
P.Load();
glMatrixMode(GL_MODELVIEW);
// Load camera pose
LoadCameraPose(Tcw);
// Draw virtual things
if(status==2)
{
if(menu_clear)
{
if(!vpPlane.empty())
{
for(size_t i=0; i<vpPlane.size(); i++)
{
delete vpPlane[i];
}
vpPlane.clear();
cout << "All cubes erased!" << endl;
}
menu_clear = false;
}
if(menu_detectplane)
{
Plane* pPlane = DetectPlane(Tcw,vMPs,50);
if(pPlane)
{
cout << "New virtual cube inserted!" << endl;
vpPlane.push_back(pPlane);
}
else
{
cout << "No plane detected. Point the camera to a planar region." << endl;
}
menu_detectplane = false;
}
if(!vpPlane.empty())
{
// Recompute plane if there has been a loop closure or global BA
// In localization mode, map is not updated so we do not need to recompute
bool bRecompute = false;
if(!bLocalizationMode)
{
if(mpSystem->MapChanged())
{
cout << "Map changed. All virtual elements are recomputed!" << endl;
bRecompute = true;
}
}
for(size_t i=0; i<vpPlane.size(); i++)
{
Plane* pPlane = vpPlane[i];
if(pPlane)
{
if(bRecompute)
{
pPlane->Recompute();
}
glPushMatrix();
pPlane->glTpw.Multiply();
// Draw cube
if(menu_drawcube)
{
DrawCube(menu_cubesize);
}
// Draw grid plane
if(menu_drawgrid)
{
DrawPlane(menu_ngrid,menu_sizegrid);
}
glPopMatrix();
}
}
}
}
pangolin::FinishFrame();
usleep(mT*1000);
}
}
void ViewerAR::SetImagePose(const cv::Mat &im, const cv::Mat &Tcw, const int &status, const vector<cv::KeyPoint> &vKeys, const vector<ORB_SLAM2::MapPoint*> &vMPs)
{
unique_lock<mutex> lock(mMutexPoseImage);
mImage = im.clone();
mTcw = Tcw.clone();
mStatus = status;
mvKeys = vKeys;
mvMPs = vMPs;
}
void ViewerAR::GetImagePose(cv::Mat &im, cv::Mat &Tcw, int &status, std::vector<cv::KeyPoint> &vKeys, std::vector<MapPoint*> &vMPs)
{
unique_lock<mutex> lock(mMutexPoseImage);
im = mImage.clone();
Tcw = mTcw.clone();
status = mStatus;
vKeys = mvKeys;
vMPs = mvMPs;
}
void ViewerAR::LoadCameraPose(const cv::Mat &Tcw)
{
if(!Tcw.empty())
{
pangolin::OpenGlMatrix M;
M.m[0] = Tcw.at<float>(0,0);
M.m[1] = Tcw.at<float>(1,0);
M.m[2] = Tcw.at<float>(2,0);
M.m[3] = 0.0;
M.m[4] = Tcw.at<float>(0,1);
M.m[5] = Tcw.at<float>(1,1);
M.m[6] = Tcw.at<float>(2,1);
M.m[7] = 0.0;
M.m[8] = Tcw.at<float>(0,2);
M.m[9] = Tcw.at<float>(1,2);
M.m[10] = Tcw.at<float>(2,2);
M.m[11] = 0.0;
M.m[12] = Tcw.at<float>(0,3);
M.m[13] = Tcw.at<float>(1,3);
M.m[14] = Tcw.at<float>(2,3);
M.m[15] = 1.0;
M.Load();
}
}
void ViewerAR::PrintStatus(const int &status, const bool &bLocMode, cv::Mat &im)
{
if(!bLocMode)
{
switch(status)
{
case 1: {AddTextToImage("SLAM NOT INITIALIZED",im,255,0,0); break;}
case 2: {AddTextToImage("SLAM ON",im,0,255,0); break;}
case 3: {AddTextToImage("SLAM LOST",im,255,0,0); break;}
}
}
else
{
switch(status)
{
case 1: {AddTextToImage("SLAM NOT INITIALIZED",im,255,0,0); break;}
case 2: {AddTextToImage("LOCALIZATION ON",im,0,255,0); break;}
case 3: {AddTextToImage("LOCALIZATION LOST",im,255,0,0); break;}
}
}
}
void ViewerAR::AddTextToImage(const string &s, cv::Mat &im, const int r, const int g, const int b)
{
int l = 10;
//imText.rowRange(im.rows-imText.rows,imText.rows) = cv::Mat::zeros(textSize.height+10,im.cols,im.type());
cv::putText(im,s,cv::Point(l,im.rows-l),cv::FONT_HERSHEY_PLAIN,1.5,cv::Scalar(255,255,255),2,8);
cv::putText(im,s,cv::Point(l-1,im.rows-l),cv::FONT_HERSHEY_PLAIN,1.5,cv::Scalar(255,255,255),2,8);
cv::putText(im,s,cv::Point(l+1,im.rows-l),cv::FONT_HERSHEY_PLAIN,1.5,cv::Scalar(255,255,255),2,8);
cv::putText(im,s,cv::Point(l-1,im.rows-(l-1)),cv::FONT_HERSHEY_PLAIN,1.5,cv::Scalar(255,255,255),2,8);
cv::putText(im,s,cv::Point(l,im.rows-(l-1)),cv::FONT_HERSHEY_PLAIN,1.5,cv::Scalar(255,255,255),2,8);
cv::putText(im,s,cv::Point(l+1,im.rows-(l-1)),cv::FONT_HERSHEY_PLAIN,1.5,cv::Scalar(255,255,255),2,8);
cv::putText(im,s,cv::Point(l-1,im.rows-(l+1)),cv::FONT_HERSHEY_PLAIN,1.5,cv::Scalar(255,255,255),2,8);
cv::putText(im,s,cv::Point(l,im.rows-(l+1)),cv::FONT_HERSHEY_PLAIN,1.5,cv::Scalar(255,255,255),2,8);
cv::putText(im,s,cv::Point(l+1,im.rows-(l+1)),cv::FONT_HERSHEY_PLAIN,1.5,cv::Scalar(255,255,255),2,8);
cv::putText(im,s,cv::Point(l,im.rows-l),cv::FONT_HERSHEY_PLAIN,1.5,cv::Scalar(r,g,b),2,8);
}
void ViewerAR::DrawImageTexture(pangolin::GlTexture &imageTexture, cv::Mat &im)
{
if(!im.empty())
{
imageTexture.Upload(im.data,GL_RGB,GL_UNSIGNED_BYTE);
imageTexture.RenderToViewportFlipY();
}
}
void ViewerAR::DrawCube(const float &size,const float x, const float y, const float z)
{
pangolin::OpenGlMatrix M = pangolin::OpenGlMatrix::Translate(-x,-size-y,-z);
glPushMatrix();
M.Multiply();
pangolin::glDrawColouredCube(-size,size);
glPopMatrix();
}
void ViewerAR::DrawPlane(Plane *pPlane, int ndivs, float ndivsize)
{
glPushMatrix();
pPlane->glTpw.Multiply();
DrawPlane(ndivs,ndivsize);
glPopMatrix();
}
void ViewerAR::DrawPlane(int ndivs, float ndivsize)
{
// Plane parallel to x-z at origin with normal -y
const float minx = -ndivs*ndivsize;
const float minz = -ndivs*ndivsize;
const float maxx = ndivs*ndivsize;
const float maxz = ndivs*ndivsize;
glLineWidth(2);
glColor3f(0.7f,0.7f,1.0f);
glBegin(GL_LINES);
for(int n = 0; n<=2*ndivs; n++)
{
glVertex3f(minx+ndivsize*n,0,minz);
glVertex3f(minx+ndivsize*n,0,maxz);
glVertex3f(minx,0,minz+ndivsize*n);
glVertex3f(maxx,0,minz+ndivsize*n);
}
glEnd();
}
void ViewerAR::DrawTrackedPoints(const std::vector<cv::KeyPoint> &vKeys, const std::vector<MapPoint *> &vMPs, cv::Mat &im)
{
const int N = vKeys.size();
for(int i=0; i<N; i++)
{
if(vMPs[i])
{
cv::circle(im,vKeys[i].pt,1,cv::Scalar(0,255,0),-1);
}
}
}
Plane* ViewerAR::DetectPlane(const cv::Mat Tcw, const std::vector<MapPoint*> &vMPs, const int iterations)
{
// Retrieve 3D points
vector<cv::Mat> vPoints;
vPoints.reserve(vMPs.size());
vector<MapPoint*> vPointMP;
vPointMP.reserve(vMPs.size());
for(size_t i=0; i<vMPs.size(); i++)
{
MapPoint* pMP=vMPs[i];
if(pMP)
{
if(pMP->Observations()>5)
{
vPoints.push_back(pMP->GetWorldPos());
vPointMP.push_back(pMP);
}
}
}
const int N = vPoints.size();
if(N<50)
return NULL;
// Indices for minimum set selection
vector<size_t> vAllIndices;
vAllIndices.reserve(N);
vector<size_t> vAvailableIndices;
for(int i=0; i<N; i++)
{
vAllIndices.push_back(i);
}
float bestDist = 1e10;
vector<float> bestvDist;
//RANSAC
for(int n=0; n<iterations; n++)
{
vAvailableIndices = vAllIndices;
cv::Mat A(3,4,CV_32F);
A.col(3) = cv::Mat::ones(3,1,CV_32F);
// Get min set of points
for(short i = 0; i < 3; ++i)
{
int randi = DUtils::Random::RandomInt(0, vAvailableIndices.size()-1);
int idx = vAvailableIndices[randi];
A.row(i).colRange(0,3) = vPoints[idx].t();
vAvailableIndices[randi] = vAvailableIndices.back();
vAvailableIndices.pop_back();
}
cv::Mat u,w,vt;
cv::SVDecomp(A,w,u,vt,cv::SVD::MODIFY_A | cv::SVD::FULL_UV);
const float a = vt.at<float>(3,0);
const float b = vt.at<float>(3,1);
const float c = vt.at<float>(3,2);
const float d = vt.at<float>(3,3);
vector<float> vDistances(N,0);
const float f = 1.0f/sqrt(a*a+b*b+c*c+d*d);
for(int i=0; i<N; i++)
{
vDistances[i] = fabs(vPoints[i].at<float>(0)*a+vPoints[i].at<float>(1)*b+vPoints[i].at<float>(2)*c+d)*f;
}
vector<float> vSorted = vDistances;
sort(vSorted.begin(),vSorted.end());
int nth = max((int)(0.2*N),20);
const float medianDist = vSorted[nth];
if(medianDist<bestDist)
{
bestDist = medianDist;
bestvDist = vDistances;
}
}
// Compute threshold inlier/outlier
const float th = 1.4*bestDist;
vector<bool> vbInliers(N,false);
int nInliers = 0;
for(int i=0; i<N; i++)
{
if(bestvDist[i]<th)
{
nInliers++;
vbInliers[i]=true;
}
}
vector<MapPoint*> vInlierMPs(nInliers,NULL);
int nin = 0;
for(int i=0; i<N; i++)
{
if(vbInliers[i])
{
vInlierMPs[nin] = vPointMP[i];
nin++;
}
}
return new Plane(vInlierMPs,Tcw);
}
Plane::Plane(const std::vector<MapPoint *> &vMPs, const cv::Mat &Tcw):mvMPs(vMPs),mTcw(Tcw.clone())
{
rang = -3.14f/2+((float)rand()/RAND_MAX)*3.14f;
Recompute();
}
void Plane::Recompute()
{
const int N = mvMPs.size();
// Recompute plane with all points
cv::Mat A = cv::Mat(N,4,CV_32F);
A.col(3) = cv::Mat::ones(N,1,CV_32F);
o = cv::Mat::zeros(3,1,CV_32F);
int nPoints = 0;
for(int i=0; i<N; i++)
{
MapPoint* pMP = mvMPs[i];
if(!pMP->isBad())
{
cv::Mat Xw = pMP->GetWorldPos();
o+=Xw;
A.row(nPoints).colRange(0,3) = Xw.t();
nPoints++;
}
}
A.resize(nPoints);
cv::Mat u,w,vt;
cv::SVDecomp(A,w,u,vt,cv::SVD::MODIFY_A | cv::SVD::FULL_UV);
float a = vt.at<float>(3,0);
float b = vt.at<float>(3,1);
float c = vt.at<float>(3,2);
o = o*(1.0f/nPoints);
const float f = 1.0f/sqrt(a*a+b*b+c*c);
// Compute XC just the first time
if(XC.empty())
{
cv::Mat Oc = -mTcw.colRange(0,3).rowRange(0,3).t()*mTcw.rowRange(0,3).col(3);
XC = Oc-o;
}
if((XC.at<float>(0)*a+XC.at<float>(1)*b+XC.at<float>(2)*c)>0)
{
a=-a;
b=-b;
c=-c;
}
const float nx = a*f;
const float ny = b*f;
const float nz = c*f;
n = (cv::Mat_<float>(3,1)<<nx,ny,nz);
cv::Mat up = (cv::Mat_<float>(3,1) << 0.0f, 1.0f, 0.0f);
cv::Mat v = up.cross(n);
const float sa = cv::norm(v);
const float ca = up.dot(n);
const float ang = atan2(sa,ca);
Tpw = cv::Mat::eye(4,4,CV_32F);
Tpw.rowRange(0,3).colRange(0,3) = ExpSO3(v*ang/sa)*ExpSO3(up*rang);
o.copyTo(Tpw.col(3).rowRange(0,3));
glTpw.m[0] = Tpw.at<float>(0,0);
glTpw.m[1] = Tpw.at<float>(1,0);
glTpw.m[2] = Tpw.at<float>(2,0);
glTpw.m[3] = 0.0;
glTpw.m[4] = Tpw.at<float>(0,1);
glTpw.m[5] = Tpw.at<float>(1,1);
glTpw.m[6] = Tpw.at<float>(2,1);
glTpw.m[7] = 0.0;
glTpw.m[8] = Tpw.at<float>(0,2);
glTpw.m[9] = Tpw.at<float>(1,2);
glTpw.m[10] = Tpw.at<float>(2,2);
glTpw.m[11] = 0.0;
glTpw.m[12] = Tpw.at<float>(0,3);
glTpw.m[13] = Tpw.at<float>(1,3);
glTpw.m[14] = Tpw.at<float>(2,3);
glTpw.m[15] = 1.0;
}
Plane::Plane(const float &nx, const float &ny, const float &nz, const float &ox, const float &oy, const float &oz)
{
n = (cv::Mat_<float>(3,1)<<nx,ny,nz);
o = (cv::Mat_<float>(3,1)<<ox,oy,oz);
cv::Mat up = (cv::Mat_<float>(3,1) << 0.0f, 1.0f, 0.0f);
cv::Mat v = up.cross(n);
const float s = cv::norm(v);
const float c = up.dot(n);
const float a = atan2(s,c);
Tpw = cv::Mat::eye(4,4,CV_32F);
const float rang = -3.14f/2+((float)rand()/RAND_MAX)*3.14f;
cout << rang;
Tpw.rowRange(0,3).colRange(0,3) = ExpSO3(v*a/s)*ExpSO3(up*rang);
o.copyTo(Tpw.col(3).rowRange(0,3));
glTpw.m[0] = Tpw.at<float>(0,0);
glTpw.m[1] = Tpw.at<float>(1,0);
glTpw.m[2] = Tpw.at<float>(2,0);
glTpw.m[3] = 0.0;
glTpw.m[4] = Tpw.at<float>(0,1);
glTpw.m[5] = Tpw.at<float>(1,1);
glTpw.m[6] = Tpw.at<float>(2,1);
glTpw.m[7] = 0.0;
glTpw.m[8] = Tpw.at<float>(0,2);
glTpw.m[9] = Tpw.at<float>(1,2);
glTpw.m[10] = Tpw.at<float>(2,2);
glTpw.m[11] = 0.0;
glTpw.m[12] = Tpw.at<float>(0,3);
glTpw.m[13] = Tpw.at<float>(1,3);
glTpw.m[14] = Tpw.at<float>(2,3);
glTpw.m[15] = 1.0;
}
}

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/**
* This file is part of ORB-SLAM2.
*
* Copyright (C) 2014-2016 Raúl Mur-Artal <raulmur at unizar dot es> (University of Zaragoza)
* For more information see <https://github.com/raulmur/ORB_SLAM2>
*
* ORB-SLAM2 is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM2 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM2. If not, see <http://www.gnu.org/licenses/>.
*/
#ifndef VIEWERAR_H
#define VIEWERAR_H
#include <mutex>
#include <opencv2/core/core.hpp>
#include <pangolin/pangolin.h>
#include <string>
#include"../../../include/System.h"
namespace ORB_SLAM2
{
class Plane
{
public:
Plane(const std::vector<MapPoint*> &vMPs, const cv::Mat &Tcw);
Plane(const float &nx, const float &ny, const float &nz, const float &ox, const float &oy, const float &oz);
void Recompute();
//normal
cv::Mat n;
//origin
cv::Mat o;
//arbitrary orientation along normal
float rang;
//transformation from world to the plane
cv::Mat Tpw;
pangolin::OpenGlMatrix glTpw;
//MapPoints that define the plane
std::vector<MapPoint*> mvMPs;
//camera pose when the plane was first observed (to compute normal direction)
cv::Mat mTcw, XC;
};
class ViewerAR
{
public:
ViewerAR();
void SetFPS(const float fps){
mFPS = fps;
mT=1e3/fps;
}
void SetSLAM(ORB_SLAM2::System* pSystem){
mpSystem = pSystem;
}
// Main thread function.
void Run();
void SetCameraCalibration(const float &fx_, const float &fy_, const float &cx_, const float &cy_){
fx = fx_; fy = fy_; cx = cx_; cy = cy_;
}
void SetImagePose(const cv::Mat &im, const cv::Mat &Tcw, const int &status,
const std::vector<cv::KeyPoint> &vKeys, const std::vector<MapPoint*> &vMPs);
void GetImagePose(cv::Mat &im, cv::Mat &Tcw, int &status,
std::vector<cv::KeyPoint> &vKeys, std::vector<MapPoint*> &vMPs);
private:
//SLAM
ORB_SLAM2::System* mpSystem;
void PrintStatus(const int &status, const bool &bLocMode, cv::Mat &im);
void AddTextToImage(const std::string &s, cv::Mat &im, const int r=0, const int g=0, const int b=0);
void LoadCameraPose(const cv::Mat &Tcw);
void DrawImageTexture(pangolin::GlTexture &imageTexture, cv::Mat &im);
void DrawCube(const float &size, const float x=0, const float y=0, const float z=0);
void DrawPlane(int ndivs, float ndivsize);
void DrawPlane(Plane* pPlane, int ndivs, float ndivsize);
void DrawTrackedPoints(const std::vector<cv::KeyPoint> &vKeys, const std::vector<MapPoint*> &vMPs, cv::Mat &im);
Plane* DetectPlane(const cv::Mat Tcw, const std::vector<MapPoint*> &vMPs, const int iterations=50);
// frame rate
float mFPS, mT;
float fx,fy,cx,cy;
// Last processed image and computed pose by the SLAM
std::mutex mMutexPoseImage;
cv::Mat mTcw;
cv::Mat mImage;
int mStatus;
std::vector<cv::KeyPoint> mvKeys;
std::vector<MapPoint*> mvMPs;
};
}
#endif // VIEWERAR_H

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/**
* This file is part of ORB-SLAM2.
*
* Copyright (C) 2014-2016 Raúl Mur-Artal <raulmur at unizar dot es> (University of Zaragoza)
* For more information see <https://github.com/raulmur/ORB_SLAM2>
*
* ORB-SLAM2 is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM2 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM2. If not, see <http://www.gnu.org/licenses/>.
*/
#include<iostream>
#include<algorithm>
#include<fstream>
#include<chrono>
#include<ros/ros.h>
#include <cv_bridge/cv_bridge.h>
#include<opencv2/core/core.hpp>
#include<opencv2/imgproc/imgproc.hpp>
#include"../../../include/System.h"
#include"ViewerAR.h"
using namespace std;
ORB_SLAM2::ViewerAR viewerAR;
bool bRGB = true;
cv::Mat K;
cv::Mat DistCoef;
class ImageGrabber
{
public:
ImageGrabber(ORB_SLAM2::System* pSLAM):mpSLAM(pSLAM){}
void GrabImage(const sensor_msgs::ImageConstPtr& msg);
ORB_SLAM2::System* mpSLAM;
};
int main(int argc, char **argv)
{
ros::init(argc, argv, "Mono");
ros::start();
if(argc != 3)
{
cerr << endl << "Usage: rosrun ORB_SLAM2 Mono path_to_vocabulary path_to_settings" << endl;
ros::shutdown();
return 1;
}
// Create SLAM system. It initializes all system threads and gets ready to process frames.
ORB_SLAM2::System SLAM(argv[1],argv[2],ORB_SLAM2::System::MONOCULAR,false);
cout << endl << endl;
cout << "-----------------------" << endl;
cout << "Augmented Reality Demo" << endl;
cout << "1) Translate the camera to initialize SLAM." << endl;
cout << "2) Look at a planar region and translate the camera." << endl;
cout << "3) Press Insert Cube to place a virtual cube in the plane. " << endl;
cout << endl;
cout << "You can place several cubes in different planes." << endl;
cout << "-----------------------" << endl;
cout << endl;
viewerAR.SetSLAM(&SLAM);
ImageGrabber igb(&SLAM);
ros::NodeHandle nodeHandler;
ros::Subscriber sub = nodeHandler.subscribe("/camera/image_raw", 1, &ImageGrabber::GrabImage,&igb);
cv::FileStorage fSettings(argv[2], cv::FileStorage::READ);
bRGB = static_cast<bool>((int)fSettings["Camera.RGB"]);
float fps = fSettings["Camera.fps"];
viewerAR.SetFPS(fps);
float fx = fSettings["Camera.fx"];
float fy = fSettings["Camera.fy"];
float cx = fSettings["Camera.cx"];
float cy = fSettings["Camera.cy"];
viewerAR.SetCameraCalibration(fx,fy,cx,cy);
K = cv::Mat::eye(3,3,CV_32F);
K.at<float>(0,0) = fx;
K.at<float>(1,1) = fy;
K.at<float>(0,2) = cx;
K.at<float>(1,2) = cy;
DistCoef = cv::Mat::zeros(4,1,CV_32F);
DistCoef.at<float>(0) = fSettings["Camera.k1"];
DistCoef.at<float>(1) = fSettings["Camera.k2"];
DistCoef.at<float>(2) = fSettings["Camera.p1"];
DistCoef.at<float>(3) = fSettings["Camera.p2"];
const float k3 = fSettings["Camera.k3"];
if(k3!=0)
{
DistCoef.resize(5);
DistCoef.at<float>(4) = k3;
}
thread tViewer = thread(&ORB_SLAM2::ViewerAR::Run,&viewerAR);
ros::spin();
// Stop all threads
SLAM.Shutdown();
// Save camera trajectory
SLAM.SaveKeyFrameTrajectoryTUM("KeyFrameTrajectory.txt");
ros::shutdown();
return 0;
}
void ImageGrabber::GrabImage(const sensor_msgs::ImageConstPtr& msg)
{
// Copy the ros image message to cv::Mat.
cv_bridge::CvImageConstPtr cv_ptr;
try
{
cv_ptr = cv_bridge::toCvShare(msg);
}
catch (cv_bridge::Exception& e)
{
ROS_ERROR("cv_bridge exception: %s", e.what());
return;
}
cv::Mat im = cv_ptr->image.clone();
cv::Mat imu;
cv::Mat Tcw = mpSLAM->TrackMonocular(cv_ptr->image,cv_ptr->header.stamp.toSec());
int state = mpSLAM->GetTrackingState();
vector<ORB_SLAM2::MapPoint*> vMPs = mpSLAM->GetTrackedMapPoints();
vector<cv::KeyPoint> vKeys = mpSLAM->GetTrackedKeyPointsUn();
cv::undistort(im,imu,K,DistCoef);
if(bRGB)
viewerAR.SetImagePose(imu,Tcw,state,vKeys,vMPs);
else
{
cv::cvtColor(imu,imu,CV_RGB2BGR);
viewerAR.SetImagePose(imu,Tcw,state,vKeys,vMPs);
}
}

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/**
* This file is part of ORB-SLAM2.
*
* Copyright (C) 2014-2016 Raúl Mur-Artal <raulmur at unizar dot es> (University of Zaragoza)
* For more information see <https://github.com/raulmur/ORB_SLAM2>
*
* ORB-SLAM2 is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM2 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM2. If not, see <http://www.gnu.org/licenses/>.
*/
#include<iostream>
#include<algorithm>
#include<fstream>
#include<chrono>
#include<ros/ros.h>
#include <cv_bridge/cv_bridge.h>
#include<opencv2/core/core.hpp>
#include"../../../include/System.h"
using namespace std;
class ImageGrabber
{
public:
ImageGrabber(ORB_SLAM2::System* pSLAM):mpSLAM(pSLAM){}
void GrabImage(const sensor_msgs::ImageConstPtr& msg);
ORB_SLAM2::System* mpSLAM;
};
int main(int argc, char **argv)
{
ros::init(argc, argv, "Mono");
ros::start();
if(argc != 3)
{
cerr << endl << "Usage: rosrun ORB_SLAM2 Mono path_to_vocabulary path_to_settings" << endl;
ros::shutdown();
return 1;
}
// Create SLAM system. It initializes all system threads and gets ready to process frames.
ORB_SLAM2::System SLAM(argv[1],argv[2],ORB_SLAM2::System::MONOCULAR,true);
ImageGrabber igb(&SLAM);
ros::NodeHandle nodeHandler;
ros::Subscriber sub = nodeHandler.subscribe("/camera/image_raw", 1, &ImageGrabber::GrabImage,&igb);
ros::spin();
// Stop all threads
SLAM.Shutdown();
// Save camera trajectory
SLAM.SaveKeyFrameTrajectoryTUM("KeyFrameTrajectory.txt");
ros::shutdown();
return 0;
}
void ImageGrabber::GrabImage(const sensor_msgs::ImageConstPtr& msg)
{
// Copy the ros image message to cv::Mat.
cv_bridge::CvImageConstPtr cv_ptr;
try
{
cv_ptr = cv_bridge::toCvShare(msg);
}
catch (cv_bridge::Exception& e)
{
ROS_ERROR("cv_bridge exception: %s", e.what());
return;
}
mpSLAM->TrackMonocular(cv_ptr->image,cv_ptr->header.stamp.toSec());
}

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/**
* This file is part of ORB-SLAM2.
*
* Copyright (C) 2014-2016 Raúl Mur-Artal <raulmur at unizar dot es> (University of Zaragoza)
* For more information see <https://github.com/raulmur/ORB_SLAM2>
*
* ORB-SLAM2 is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM2 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM2. If not, see <http://www.gnu.org/licenses/>.
*/
#include<iostream>
#include<algorithm>
#include<fstream>
#include<chrono>
#include<ros/ros.h>
#include <cv_bridge/cv_bridge.h>
#include <message_filters/subscriber.h>
#include <message_filters/time_synchronizer.h>
#include <message_filters/sync_policies/approximate_time.h>
#include<opencv2/core/core.hpp>
#include"../../../include/System.h"
using namespace std;
class ImageGrabber
{
public:
ImageGrabber(ORB_SLAM2::System* pSLAM):mpSLAM(pSLAM){}
void GrabRGBD(const sensor_msgs::ImageConstPtr& msgRGB,const sensor_msgs::ImageConstPtr& msgD);
ORB_SLAM2::System* mpSLAM;
};
int main(int argc, char **argv)
{
ros::init(argc, argv, "RGBD");
ros::start();
if(argc != 3)
{
cerr << endl << "Usage: rosrun ORB_SLAM2 RGBD path_to_vocabulary path_to_settings" << endl;
ros::shutdown();
return 1;
}
// Create SLAM system. It initializes all system threads and gets ready to process frames.
ORB_SLAM2::System SLAM(argv[1],argv[2],ORB_SLAM2::System::RGBD,true);
ImageGrabber igb(&SLAM);
ros::NodeHandle nh;
message_filters::Subscriber<sensor_msgs::Image> rgb_sub(nh, "/camera/rgb/image_raw", 1);
message_filters::Subscriber<sensor_msgs::Image> depth_sub(nh, "camera/depth_registered/image_raw", 1);
typedef message_filters::sync_policies::ApproximateTime<sensor_msgs::Image, sensor_msgs::Image> sync_pol;
message_filters::Synchronizer<sync_pol> sync(sync_pol(10), rgb_sub,depth_sub);
sync.registerCallback(boost::bind(&ImageGrabber::GrabRGBD,&igb,_1,_2));
ros::spin();
// Stop all threads
SLAM.Shutdown();
// Save camera trajectory
SLAM.SaveKeyFrameTrajectoryTUM("KeyFrameTrajectory.txt");
ros::shutdown();
return 0;
}
void ImageGrabber::GrabRGBD(const sensor_msgs::ImageConstPtr& msgRGB,const sensor_msgs::ImageConstPtr& msgD)
{
// Copy the ros image message to cv::Mat.
cv_bridge::CvImageConstPtr cv_ptrRGB;
try
{
cv_ptrRGB = cv_bridge::toCvShare(msgRGB);
}
catch (cv_bridge::Exception& e)
{
ROS_ERROR("cv_bridge exception: %s", e.what());
return;
}
cv_bridge::CvImageConstPtr cv_ptrD;
try
{
cv_ptrD = cv_bridge::toCvShare(msgD);
}
catch (cv_bridge::Exception& e)
{
ROS_ERROR("cv_bridge exception: %s", e.what());
return;
}
mpSLAM->TrackRGBD(cv_ptrRGB->image,cv_ptrD->image,cv_ptrRGB->header.stamp.toSec());
}

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/**
* This file is part of ORB-SLAM2.
*
* Copyright (C) 2014-2016 Raúl Mur-Artal <raulmur at unizar dot es> (University of Zaragoza)
* For more information see <https://github.com/raulmur/ORB_SLAM2>
*
* ORB-SLAM2 is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM2 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM2. If not, see <http://www.gnu.org/licenses/>.
*/
#include<iostream>
#include<algorithm>
#include<fstream>
#include<chrono>
#include<ros/ros.h>
#include <cv_bridge/cv_bridge.h>
#include <message_filters/subscriber.h>
#include <message_filters/time_synchronizer.h>
#include <message_filters/sync_policies/approximate_time.h>
#include<opencv2/core/core.hpp>
#include"../../../include/System.h"
using namespace std;
class ImageGrabber
{
public:
ImageGrabber(ORB_SLAM2::System* pSLAM):mpSLAM(pSLAM){}
void GrabStereo(const sensor_msgs::ImageConstPtr& msgLeft,const sensor_msgs::ImageConstPtr& msgRight);
ORB_SLAM2::System* mpSLAM;
bool do_rectify;
cv::Mat M1l,M2l,M1r,M2r;
};
int main(int argc, char **argv)
{
ros::init(argc, argv, "RGBD");
ros::start();
if(argc != 4)
{
cerr << endl << "Usage: rosrun ORB_SLAM2 Stereo path_to_vocabulary path_to_settings do_rectify" << endl;
ros::shutdown();
return 1;
}
// Create SLAM system. It initializes all system threads and gets ready to process frames.
ORB_SLAM2::System SLAM(argv[1],argv[2],ORB_SLAM2::System::STEREO,true);
ImageGrabber igb(&SLAM);
stringstream ss(argv[3]);
ss >> boolalpha >> igb.do_rectify;
if(igb.do_rectify)
{
// Load settings related to stereo calibration
cv::FileStorage fsSettings(argv[2], cv::FileStorage::READ);
if(!fsSettings.isOpened())
{
cerr << "ERROR: Wrong path to settings" << endl;
return -1;
}
cv::Mat K_l, K_r, P_l, P_r, R_l, R_r, D_l, D_r;
fsSettings["LEFT.K"] >> K_l;
fsSettings["RIGHT.K"] >> K_r;
fsSettings["LEFT.P"] >> P_l;
fsSettings["RIGHT.P"] >> P_r;
fsSettings["LEFT.R"] >> R_l;
fsSettings["RIGHT.R"] >> R_r;
fsSettings["LEFT.D"] >> D_l;
fsSettings["RIGHT.D"] >> D_r;
int rows_l = fsSettings["LEFT.height"];
int cols_l = fsSettings["LEFT.width"];
int rows_r = fsSettings["RIGHT.height"];
int cols_r = fsSettings["RIGHT.width"];
if(K_l.empty() || K_r.empty() || P_l.empty() || P_r.empty() || R_l.empty() || R_r.empty() || D_l.empty() || D_r.empty() ||
rows_l==0 || rows_r==0 || cols_l==0 || cols_r==0)
{
cerr << "ERROR: Calibration parameters to rectify stereo are missing!" << endl;
return -1;
}
cv::initUndistortRectifyMap(K_l,D_l,R_l,P_l.rowRange(0,3).colRange(0,3),cv::Size(cols_l,rows_l),CV_32F,igb.M1l,igb.M2l);
cv::initUndistortRectifyMap(K_r,D_r,R_r,P_r.rowRange(0,3).colRange(0,3),cv::Size(cols_r,rows_r),CV_32F,igb.M1r,igb.M2r);
}
ros::NodeHandle nh;
message_filters::Subscriber<sensor_msgs::Image> left_sub(nh, "/camera/left/image_raw", 1);
message_filters::Subscriber<sensor_msgs::Image> right_sub(nh, "camera/right/image_raw", 1);
typedef message_filters::sync_policies::ApproximateTime<sensor_msgs::Image, sensor_msgs::Image> sync_pol;
message_filters::Synchronizer<sync_pol> sync(sync_pol(10), left_sub,right_sub);
sync.registerCallback(boost::bind(&ImageGrabber::GrabStereo,&igb,_1,_2));
ros::spin();
// Stop all threads
SLAM.Shutdown();
// Save camera trajectory
SLAM.SaveKeyFrameTrajectoryTUM("KeyFrameTrajectory_TUM_Format.txt");
SLAM.SaveTrajectoryTUM("FrameTrajectory_TUM_Format.txt");
SLAM.SaveTrajectoryKITTI("FrameTrajectory_KITTI_Format.txt");
ros::shutdown();
return 0;
}
void ImageGrabber::GrabStereo(const sensor_msgs::ImageConstPtr& msgLeft,const sensor_msgs::ImageConstPtr& msgRight)
{
// Copy the ros image message to cv::Mat.
cv_bridge::CvImageConstPtr cv_ptrLeft;
try
{
cv_ptrLeft = cv_bridge::toCvShare(msgLeft);
}
catch (cv_bridge::Exception& e)
{
ROS_ERROR("cv_bridge exception: %s", e.what());
return;
}
cv_bridge::CvImageConstPtr cv_ptrRight;
try
{
cv_ptrRight = cv_bridge::toCvShare(msgRight);
}
catch (cv_bridge::Exception& e)
{
ROS_ERROR("cv_bridge exception: %s", e.what());
return;
}
if(do_rectify)
{
cv::Mat imLeft, imRight;
cv::remap(cv_ptrLeft->image,imLeft,M1l,M2l,cv::INTER_LINEAR);
cv::remap(cv_ptrRight->image,imRight,M1r,M2r,cv::INTER_LINEAR);
mpSLAM->TrackStereo(imLeft,imRight,cv_ptrLeft->header.stamp.toSec());
}
else
{
mpSLAM->TrackStereo(cv_ptrLeft->image,cv_ptrRight->image,cv_ptrLeft->header.stamp.toSec());
}
}

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%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 435.2046959714599
Camera.fy: 435.2046959714599
Camera.cx: 367.4517211914062
Camera.cy: 252.2008514404297
Camera.k1: 0.0
Camera.k2: 0.0
Camera.p1: 0.0
Camera.p2: 0.0
Camera.width: 752
Camera.height: 480
# Camera frames per second
Camera.fps: 20.0
# stereo baseline times fx
Camera.bf: 47.90639384423901
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
# Close/Far threshold. Baseline times.
ThDepth: 35
#--------------------------------------------------------------------------------------------
# Stereo Rectification. Only if you need to pre-rectify the images.
# Camera.fx, .fy, etc must be the same as in LEFT.P
#--------------------------------------------------------------------------------------------
LEFT.height: 480
LEFT.width: 752
LEFT.D: !!opencv-matrix
rows: 1
cols: 5
dt: d
data:[-0.28340811, 0.07395907, 0.00019359, 1.76187114e-05, 0.0]
LEFT.K: !!opencv-matrix
rows: 3
cols: 3
dt: d
data: [458.654, 0.0, 367.215, 0.0, 457.296, 248.375, 0.0, 0.0, 1.0]
LEFT.R: !!opencv-matrix
rows: 3
cols: 3
dt: d
data: [0.999966347530033, -0.001422739138722922, 0.008079580483432283, 0.001365741834644127, 0.9999741760894847, 0.007055629199258132, -0.008089410156878961, -0.007044357138835809, 0.9999424675829176]
LEFT.P: !!opencv-matrix
rows: 3
cols: 4
dt: d
data: [435.2046959714599, 0, 367.4517211914062, 0, 0, 435.2046959714599, 252.2008514404297, 0, 0, 0, 1, 0]
RIGHT.height: 480
RIGHT.width: 752
RIGHT.D: !!opencv-matrix
rows: 1
cols: 5
dt: d
data:[-0.28368365, 0.07451284, -0.00010473, -3.555907e-05, 0.0]
RIGHT.K: !!opencv-matrix
rows: 3
cols: 3
dt: d
data: [457.587, 0.0, 379.999, 0.0, 456.134, 255.238, 0.0, 0.0, 1]
RIGHT.R: !!opencv-matrix
rows: 3
cols: 3
dt: d
data: [0.9999633526194376, -0.003625811871560086, 0.007755443660172947, 0.003680398547259526, 0.9999684752771629, -0.007035845251224894, -0.007729688520722713, 0.007064130529506649, 0.999945173484644]
RIGHT.P: !!opencv-matrix
rows: 3
cols: 4
dt: d
data: [435.2046959714599, 0, 367.4517211914062, -47.90639384423901, 0, 435.2046959714599, 252.2008514404297, 0, 0, 0, 1, 0]
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 1200
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 20
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.05
Viewer.KeyFrameLineWidth: 1
Viewer.GraphLineWidth: 0.9
Viewer.PointSize:2
Viewer.CameraSize: 0.08
Viewer.CameraLineWidth: 3
Viewer.ViewpointX: 0
Viewer.ViewpointY: -0.7
Viewer.ViewpointZ: -1.8
Viewer.ViewpointF: 500

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%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 718.856
Camera.fy: 718.856
Camera.cx: 607.1928
Camera.cy: 185.2157
Camera.k1: 0.0
Camera.k2: 0.0
Camera.p1: 0.0
Camera.p2: 0.0
Camera.width: 1241
Camera.height: 376
# Camera frames per second
Camera.fps: 10.0
# stereo baseline times fx
Camera.bf: 386.1448
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
# Close/Far threshold. Baseline times.
ThDepth: 35
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 2000
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 20
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.6
Viewer.KeyFrameLineWidth: 2
Viewer.GraphLineWidth: 1
Viewer.PointSize:2
Viewer.CameraSize: 0.7
Viewer.CameraLineWidth: 3
Viewer.ViewpointX: 0
Viewer.ViewpointY: -100
Viewer.ViewpointZ: -0.1
Viewer.ViewpointF: 2000

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%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 721.5377
Camera.fy: 721.5377
Camera.cx: 609.5593
Camera.cy: 172.854
Camera.k1: 0.0
Camera.k2: 0.0
Camera.p1: 0.0
Camera.p2: 0.0
Camera.width: 1241
Camera.height: 376
# Camera frames per second
Camera.fps: 10.0
# stereo baseline times fx
Camera.bf: 387.5744
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
# Close/Far threshold. Baseline times.
ThDepth: 40
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 2000
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 20
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.6
Viewer.KeyFrameLineWidth: 2
Viewer.GraphLineWidth: 1
Viewer.PointSize:2
Viewer.CameraSize: 0.7
Viewer.CameraLineWidth: 3
Viewer.ViewpointX: 0
Viewer.ViewpointY: -100
Viewer.ViewpointZ: -0.1
Viewer.ViewpointF: 2000

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%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 707.0912
Camera.fy: 707.0912
Camera.cx: 601.8873
Camera.cy: 183.1104
Camera.k1: 0.0
Camera.k2: 0.0
Camera.p1: 0.0
Camera.p2: 0.0
Camera.width: 1241
Camera.height: 376
# Camera frames per second
Camera.fps: 10.0
# stereo baseline times fx
Camera.bf: 379.8145
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
# Close/Far threshold. Baseline times.
ThDepth: 40
#--------------------------------------------------------------------------------------------
# ORB Parameters
#--------------------------------------------------------------------------------------------
# ORB Extractor: Number of features per image
ORBextractor.nFeatures: 2000
# ORB Extractor: Scale factor between levels in the scale pyramid
ORBextractor.scaleFactor: 1.2
# ORB Extractor: Number of levels in the scale pyramid
ORBextractor.nLevels: 8
# ORB Extractor: Fast threshold
# Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
# Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
# You can lower these values if your images have low contrast
ORBextractor.iniThFAST: 12
ORBextractor.minThFAST: 7
#--------------------------------------------------------------------------------------------
# Viewer Parameters
#--------------------------------------------------------------------------------------------
Viewer.KeyFrameSize: 0.6
Viewer.KeyFrameLineWidth: 2
Viewer.GraphLineWidth: 1
Viewer.PointSize:2
Viewer.CameraSize: 0.7
Viewer.CameraLineWidth: 3
Viewer.ViewpointX: 0
Viewer.ViewpointY: -100
Viewer.ViewpointZ: -0.1
Viewer.ViewpointF: 2000

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/**
* This file is part of ORB-SLAM2.
*
* Copyright (C) 2014-2016 Raúl Mur-Artal <raulmur at unizar dot es> (University of Zaragoza)
* For more information see <https://github.com/raulmur/ORB_SLAM2>
*
* ORB-SLAM2 is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM2 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM2. If not, see <http://www.gnu.org/licenses/>.
*/
#include<iostream>
#include<algorithm>
#include<fstream>
#include<iomanip>
#include<chrono>
#include <unistd.h>
#include<opencv2/core/core.hpp>
#include<System.h>
using namespace std;
void LoadImages(const string &strPathLeft, const string &strPathRight, const string &strPathTimes,
vector<string> &vstrImageLeft, vector<string> &vstrImageRight, vector<double> &vTimeStamps);
int main(int argc, char **argv)
{
if(argc != 6)
{
cerr << endl << "Usage: ./stereo_euroc path_to_vocabulary path_to_settings path_to_left_folder path_to_right_folder path_to_times_file" << endl;
return 1;
}
// Retrieve paths to images
vector<string> vstrImageLeft;
vector<string> vstrImageRight;
vector<double> vTimeStamp;
LoadImages(string(argv[3]), string(argv[4]), string(argv[5]), vstrImageLeft, vstrImageRight, vTimeStamp);
if(vstrImageLeft.empty() || vstrImageRight.empty())
{
cerr << "ERROR: No images in provided path." << endl;
return 1;
}
if(vstrImageLeft.size()!=vstrImageRight.size())
{
cerr << "ERROR: Different number of left and right images." << endl;
return 1;
}
// Read rectification parameters
cv::FileStorage fsSettings(argv[2], cv::FileStorage::READ);
if(!fsSettings.isOpened())
{
cerr << "ERROR: Wrong path to settings" << endl;
return -1;
}
cv::Mat K_l, K_r, P_l, P_r, R_l, R_r, D_l, D_r;
fsSettings["LEFT.K"] >> K_l;
fsSettings["RIGHT.K"] >> K_r;
fsSettings["LEFT.P"] >> P_l;
fsSettings["RIGHT.P"] >> P_r;
fsSettings["LEFT.R"] >> R_l;
fsSettings["RIGHT.R"] >> R_r;
fsSettings["LEFT.D"] >> D_l;
fsSettings["RIGHT.D"] >> D_r;
int rows_l = fsSettings["LEFT.height"];
int cols_l = fsSettings["LEFT.width"];
int rows_r = fsSettings["RIGHT.height"];
int cols_r = fsSettings["RIGHT.width"];
if(K_l.empty() || K_r.empty() || P_l.empty() || P_r.empty() || R_l.empty() || R_r.empty() || D_l.empty() || D_r.empty() ||
rows_l==0 || rows_r==0 || cols_l==0 || cols_r==0)
{
cerr << "ERROR: Calibration parameters to rectify stereo are missing!" << endl;
return -1;
}
cv::Mat M1l,M2l,M1r,M2r;
cv::initUndistortRectifyMap(K_l,D_l,R_l,P_l.rowRange(0,3).colRange(0,3),cv::Size(cols_l,rows_l),CV_32F,M1l,M2l);
cv::initUndistortRectifyMap(K_r,D_r,R_r,P_r.rowRange(0,3).colRange(0,3),cv::Size(cols_r,rows_r),CV_32F,M1r,M2r);
const int nImages = vstrImageLeft.size();
// Create SLAM system. It initializes all system threads and gets ready to process frames.
ORB_SLAM2::System SLAM(argv[1],argv[2],ORB_SLAM2::System::STEREO,true);
// Vector for tracking time statistics
vector<float> vTimesTrack;
vTimesTrack.resize(nImages);
cout << endl << "-------" << endl;
cout << "Start processing sequence ..." << endl;
cout << "Images in the sequence: " << nImages << endl << endl;
// Main loop
cv::Mat imLeft, imRight, imLeftRect, imRightRect;
for(int ni=0; ni<nImages; ni++)
{
// Read left and right images from file
imLeft = cv::imread(vstrImageLeft[ni],CV_LOAD_IMAGE_UNCHANGED);
imRight = cv::imread(vstrImageRight[ni],CV_LOAD_IMAGE_UNCHANGED);
if(imLeft.empty())
{
cerr << endl << "Failed to load image at: "
<< string(vstrImageLeft[ni]) << endl;
return 1;
}
if(imRight.empty())
{
cerr << endl << "Failed to load image at: "
<< string(vstrImageRight[ni]) << endl;
return 1;
}
cv::remap(imLeft,imLeftRect,M1l,M2l,cv::INTER_LINEAR);
cv::remap(imRight,imRightRect,M1r,M2r,cv::INTER_LINEAR);
double tframe = vTimeStamp[ni];
#ifdef COMPILEDWITHC11
std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now();
#else
std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now();
#endif
// Pass the images to the SLAM system
SLAM.TrackStereo(imLeftRect,imRightRect,tframe);
#ifdef COMPILEDWITHC11
std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now();
#else
std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now();
#endif
double ttrack= std::chrono::duration_cast<std::chrono::duration<double> >(t2 - t1).count();
vTimesTrack[ni]=ttrack;
// Wait to load the next frame
double T=0;
if(ni<nImages-1)
T = vTimeStamp[ni+1]-tframe;
else if(ni>0)
T = tframe-vTimeStamp[ni-1];
if(ttrack<T)
usleep((T-ttrack)*1e6);
}
// Stop all threads
SLAM.Shutdown();
// Tracking time statistics
sort(vTimesTrack.begin(),vTimesTrack.end());
float totaltime = 0;
for(int ni=0; ni<nImages; ni++)
{
totaltime+=vTimesTrack[ni];
}
cout << "-------" << endl << endl;
cout << "median tracking time: " << vTimesTrack[nImages/2] << endl;
cout << "mean tracking time: " << totaltime/nImages << endl;
// Save camera trajectory
SLAM.SaveTrajectoryTUM("CameraTrajectory.txt");
return 0;
}
void LoadImages(const string &strPathLeft, const string &strPathRight, const string &strPathTimes,
vector<string> &vstrImageLeft, vector<string> &vstrImageRight, vector<double> &vTimeStamps)
{
ifstream fTimes;
fTimes.open(strPathTimes.c_str());
vTimeStamps.reserve(5000);
vstrImageLeft.reserve(5000);
vstrImageRight.reserve(5000);
while(!fTimes.eof())
{
string s;
getline(fTimes,s);
if(!s.empty())
{
stringstream ss;
ss << s;
vstrImageLeft.push_back(strPathLeft + "/" + ss.str() + ".png");
vstrImageRight.push_back(strPathRight + "/" + ss.str() + ".png");
double t;
ss >> t;
vTimeStamps.push_back(t/1e9);
}
}
}

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/**
* This file is part of ORB-SLAM2.
*
* Copyright (C) 2014-2016 Raúl Mur-Artal <raulmur at unizar dot es> (University of Zaragoza)
* For more information see <https://github.com/raulmur/ORB_SLAM2>
*
* ORB-SLAM2 is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM2 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM2. If not, see <http://www.gnu.org/licenses/>.
*/
#include<iostream>
#include<algorithm>
#include<fstream>
#include<iomanip>
#include<chrono>
#include <unistd.h>
#include<opencv2/core/core.hpp>
#include<System.h>
using namespace std;
void LoadImages(const string &strPathToSequence, vector<string> &vstrImageLeft,
vector<string> &vstrImageRight, vector<double> &vTimestamps);
int main(int argc, char **argv)
{
if(argc != 4)
{
cerr << endl << "Usage: ./stereo_kitti path_to_vocabulary path_to_settings path_to_sequence" << endl;
return 1;
}
// Retrieve paths to images
vector<string> vstrImageLeft;
vector<string> vstrImageRight;
vector<double> vTimestamps;
LoadImages(string(argv[3]), vstrImageLeft, vstrImageRight, vTimestamps);
const int nImages = vstrImageLeft.size();
// Create SLAM system. It initializes all system threads and gets ready to process frames.
ORB_SLAM2::System SLAM(argv[1],argv[2],ORB_SLAM2::System::STEREO,true);
// Vector for tracking time statistics
vector<float> vTimesTrack;
vTimesTrack.resize(nImages);
cout << endl << "-------" << endl;
cout << "Start processing sequence ..." << endl;
cout << "Images in the sequence: " << nImages << endl << endl;
// Main loop
cv::Mat imLeft, imRight;
for(int ni=0; ni<nImages; ni++)
{
// Read left and right images from file
imLeft = cv::imread(vstrImageLeft[ni],CV_LOAD_IMAGE_UNCHANGED);
imRight = cv::imread(vstrImageRight[ni],CV_LOAD_IMAGE_UNCHANGED);
double tframe = vTimestamps[ni];
if(imLeft.empty())
{
cerr << endl << "Failed to load image at: "
<< string(vstrImageLeft[ni]) << endl;
return 1;
}
#ifdef COMPILEDWITHC11
std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now();
#else
std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now();
#endif
// Pass the images to the SLAM system
SLAM.TrackStereo(imLeft,imRight,tframe);
#ifdef COMPILEDWITHC11
std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now();
#else
std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now();
#endif
double ttrack= std::chrono::duration_cast<std::chrono::duration<double> >(t2 - t1).count();
vTimesTrack[ni]=ttrack;
// Wait to load the next frame
double T=0;
if(ni<nImages-1)
T = vTimestamps[ni+1]-tframe;
else if(ni>0)
T = tframe-vTimestamps[ni-1];
if(ttrack<T)
usleep((T-ttrack)*1e6);
}
// Stop all threads
SLAM.Shutdown();
// Tracking time statistics
sort(vTimesTrack.begin(),vTimesTrack.end());
float totaltime = 0;
for(int ni=0; ni<nImages; ni++)
{
totaltime+=vTimesTrack[ni];
}
cout << "-------" << endl << endl;
cout << "median tracking time: " << vTimesTrack[nImages/2] << endl;
cout << "mean tracking time: " << totaltime/nImages << endl;
// Save camera trajectory
SLAM.SaveTrajectoryKITTI("CameraTrajectory.txt");
return 0;
}
void LoadImages(const string &strPathToSequence, vector<string> &vstrImageLeft,
vector<string> &vstrImageRight, vector<double> &vTimestamps)
{
ifstream fTimes;
string strPathTimeFile = strPathToSequence + "/times.txt";
fTimes.open(strPathTimeFile.c_str());
while(!fTimes.eof())
{
string s;
getline(fTimes,s);
if(!s.empty())
{
stringstream ss;
ss << s;
double t;
ss >> t;
vTimestamps.push_back(t);
}
}
string strPrefixLeft = strPathToSequence + "/image_0/";
string strPrefixRight = strPathToSequence + "/image_1/";
const int nTimes = vTimestamps.size();
vstrImageLeft.resize(nTimes);
vstrImageRight.resize(nTimes);
for(int i=0; i<nTimes; i++)
{
stringstream ss;
ss << setfill('0') << setw(6) << i;
vstrImageLeft[i] = strPrefixLeft + ss.str() + ".png";
vstrImageRight[i] = strPrefixRight + ss.str() + ".png";
}
}

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cmake_minimum_required(VERSION 2.8)
project(DBoW2)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wall -O3 -march=native ")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -O3 -march=native")
set(HDRS_DBOW2
DBoW2/BowVector.h
DBoW2/FORB.h
DBoW2/FClass.h
DBoW2/FeatureVector.h
DBoW2/ScoringObject.h
DBoW2/TemplatedVocabulary.h)
set(SRCS_DBOW2
DBoW2/BowVector.cpp
DBoW2/FORB.cpp
DBoW2/FeatureVector.cpp
DBoW2/ScoringObject.cpp)
set(HDRS_DUTILS
DUtils/Random.h
DUtils/Timestamp.h)
set(SRCS_DUTILS
DUtils/Random.cpp
DUtils/Timestamp.cpp)
find_package(OpenCV 3.0 QUIET)
if(NOT OpenCV_FOUND)
find_package(OpenCV 2.4.3 QUIET)
if(NOT OpenCV_FOUND)
message(FATAL_ERROR "OpenCV > 2.4.3 not found.")
endif()
endif()
set(LIBRARY_OUTPUT_PATH ${PROJECT_SOURCE_DIR}/lib)
include_directories(${OpenCV_INCLUDE_DIRS})
add_library(DBoW2 SHARED ${SRCS_DBOW2} ${SRCS_DUTILS})
target_link_libraries(DBoW2 ${OpenCV_LIBS})

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/**
* File: BowVector.cpp
* Date: March 2011
* Author: Dorian Galvez-Lopez
* Description: bag of words vector
* License: see the LICENSE.txt file
*
*/
#include <iostream>
#include <fstream>
#include <vector>
#include <algorithm>
#include <cmath>
#include "BowVector.h"
namespace DBoW2 {
// --------------------------------------------------------------------------
BowVector::BowVector(void)
{
}
// --------------------------------------------------------------------------
BowVector::~BowVector(void)
{
}
// --------------------------------------------------------------------------
void BowVector::addWeight(WordId id, WordValue v)
{
BowVector::iterator vit = this->lower_bound(id);
if(vit != this->end() && !(this->key_comp()(id, vit->first)))
{
vit->second += v;
}
else
{
this->insert(vit, BowVector::value_type(id, v));
}
}
// --------------------------------------------------------------------------
void BowVector::addIfNotExist(WordId id, WordValue v)
{
BowVector::iterator vit = this->lower_bound(id);
if(vit == this->end() || (this->key_comp()(id, vit->first)))
{
this->insert(vit, BowVector::value_type(id, v));
}
}
// --------------------------------------------------------------------------
void BowVector::normalize(LNorm norm_type)
{
double norm = 0.0;
BowVector::iterator it;
if(norm_type == DBoW2::L1)
{
for(it = begin(); it != end(); ++it)
norm += fabs(it->second);
}
else
{
for(it = begin(); it != end(); ++it)
norm += it->second * it->second;
norm = sqrt(norm);
}
if(norm > 0.0)
{
for(it = begin(); it != end(); ++it)
it->second /= norm;
}
}
// --------------------------------------------------------------------------
std::ostream& operator<< (std::ostream &out, const BowVector &v)
{
BowVector::const_iterator vit;
std::vector<unsigned int>::const_iterator iit;
unsigned int i = 0;
const unsigned int N = v.size();
for(vit = v.begin(); vit != v.end(); ++vit, ++i)
{
out << "<" << vit->first << ", " << vit->second << ">";
if(i < N-1) out << ", ";
}
return out;
}
// --------------------------------------------------------------------------
void BowVector::saveM(const std::string &filename, size_t W) const
{
std::fstream f(filename.c_str(), std::ios::out);
WordId last = 0;
BowVector::const_iterator bit;
for(bit = this->begin(); bit != this->end(); ++bit)
{
for(; last < bit->first; ++last)
{
f << "0 ";
}
f << bit->second << " ";
last = bit->first + 1;
}
for(; last < (WordId)W; ++last)
f << "0 ";
f.close();
}
// --------------------------------------------------------------------------
} // namespace DBoW2

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/**
* File: BowVector.h
* Date: March 2011
* Author: Dorian Galvez-Lopez
* Description: bag of words vector
* License: see the LICENSE.txt file
*
*/
#ifndef __D_T_BOW_VECTOR__
#define __D_T_BOW_VECTOR__
#include <iostream>
#include <map>
#include <vector>
namespace DBoW2 {
/// Id of words
typedef unsigned int WordId;
/// Value of a word
typedef double WordValue;
/// Id of nodes in the vocabulary treee
typedef unsigned int NodeId;
/// L-norms for normalization
enum LNorm
{
L1,
L2
};
/// Weighting type
enum WeightingType
{
TF_IDF,
TF,
IDF,
BINARY
};
/// Scoring type
enum ScoringType
{
L1_NORM,
L2_NORM,
CHI_SQUARE,
KL,
BHATTACHARYYA,
DOT_PRODUCT,
};
/// Vector of words to represent images
class BowVector:
public std::map<WordId, WordValue>
{
public:
/**
* Constructor
*/
BowVector(void);
/**
* Destructor
*/
~BowVector(void);
/**
* Adds a value to a word value existing in the vector, or creates a new
* word with the given value
* @param id word id to look for
* @param v value to create the word with, or to add to existing word
*/
void addWeight(WordId id, WordValue v);
/**
* Adds a word with a value to the vector only if this does not exist yet
* @param id word id to look for
* @param v value to give to the word if this does not exist
*/
void addIfNotExist(WordId id, WordValue v);
/**
* L1-Normalizes the values in the vector
* @param norm_type norm used
*/
void normalize(LNorm norm_type);
/**
* Prints the content of the bow vector
* @param out stream
* @param v
*/
friend std::ostream& operator<<(std::ostream &out, const BowVector &v);
/**
* Saves the bow vector as a vector in a matlab file
* @param filename
* @param W number of words in the vocabulary
*/
void saveM(const std::string &filename, size_t W) const;
};
} // namespace DBoW2
#endif

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/**
* File: FClass.h
* Date: November 2011
* Author: Dorian Galvez-Lopez
* Description: generic FClass to instantiate templated classes
* License: see the LICENSE.txt file
*
*/
#ifndef __D_T_FCLASS__
#define __D_T_FCLASS__
#include <opencv2/core/core.hpp>
#include <vector>
#include <string>
namespace DBoW2 {
/// Generic class to encapsulate functions to manage descriptors.
/**
* This class must be inherited. Derived classes can be used as the
* parameter F when creating Templated structures
* (TemplatedVocabulary, TemplatedDatabase, ...)
*/
class FClass
{
class TDescriptor;
typedef const TDescriptor *pDescriptor;
/**
* Calculates the mean value of a set of descriptors
* @param descriptors
* @param mean mean descriptor
*/
virtual void meanValue(const std::vector<pDescriptor> &descriptors,
TDescriptor &mean) = 0;
/**
* Calculates the distance between two descriptors
* @param a
* @param b
* @return distance
*/
static double distance(const TDescriptor &a, const TDescriptor &b);
/**
* Returns a string version of the descriptor
* @param a descriptor
* @return string version
*/
static std::string toString(const TDescriptor &a);
/**
* Returns a descriptor from a string
* @param a descriptor
* @param s string version
*/
static void fromString(TDescriptor &a, const std::string &s);
/**
* Returns a mat with the descriptors in float format
* @param descriptors
* @param mat (out) NxL 32F matrix
*/
static void toMat32F(const std::vector<TDescriptor> &descriptors,
cv::Mat &mat);
};
} // namespace DBoW2
#endif

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/**
* File: FORB.cpp
* Date: June 2012
* Author: Dorian Galvez-Lopez
* Description: functions for ORB descriptors
* License: see the LICENSE.txt file
*
* Distance function has been modified
*
*/
#include <vector>
#include <string>
#include <sstream>
#include <stdint-gcc.h>
#include "FORB.h"
using namespace std;
namespace DBoW2 {
// --------------------------------------------------------------------------
const int FORB::L=32;
void FORB::meanValue(const std::vector<FORB::pDescriptor> &descriptors,
FORB::TDescriptor &mean)
{
if(descriptors.empty())
{
mean.release();
return;
}
else if(descriptors.size() == 1)
{
mean = descriptors[0]->clone();
}
else
{
vector<int> sum(FORB::L * 8, 0);
for(size_t i = 0; i < descriptors.size(); ++i)
{
const cv::Mat &d = *descriptors[i];
const unsigned char *p = d.ptr<unsigned char>();
for(int j = 0; j < d.cols; ++j, ++p)
{
if(*p & (1 << 7)) ++sum[ j*8 ];
if(*p & (1 << 6)) ++sum[ j*8 + 1 ];
if(*p & (1 << 5)) ++sum[ j*8 + 2 ];
if(*p & (1 << 4)) ++sum[ j*8 + 3 ];
if(*p & (1 << 3)) ++sum[ j*8 + 4 ];
if(*p & (1 << 2)) ++sum[ j*8 + 5 ];
if(*p & (1 << 1)) ++sum[ j*8 + 6 ];
if(*p & (1)) ++sum[ j*8 + 7 ];
}
}
mean = cv::Mat::zeros(1, FORB::L, CV_8U);
unsigned char *p = mean.ptr<unsigned char>();
const int N2 = (int)descriptors.size() / 2 + descriptors.size() % 2;
for(size_t i = 0; i < sum.size(); ++i)
{
if(sum[i] >= N2)
{
// set bit
*p |= 1 << (7 - (i % 8));
}
if(i % 8 == 7) ++p;
}
}
}
// --------------------------------------------------------------------------
int FORB::distance(const FORB::TDescriptor &a,
const FORB::TDescriptor &b)
{
// Bit set count operation from
// http://graphics.stanford.edu/~seander/bithacks.html#CountBitsSetParallel
const int *pa = a.ptr<int32_t>();
const int *pb = b.ptr<int32_t>();
int dist=0;
for(int i=0; i<8; i++, pa++, pb++)
{
unsigned int v = *pa ^ *pb;
v = v - ((v >> 1) & 0x55555555);
v = (v & 0x33333333) + ((v >> 2) & 0x33333333);
dist += (((v + (v >> 4)) & 0xF0F0F0F) * 0x1010101) >> 24;
}
return dist;
}
// --------------------------------------------------------------------------
std::string FORB::toString(const FORB::TDescriptor &a)
{
stringstream ss;
const unsigned char *p = a.ptr<unsigned char>();
for(int i = 0; i < a.cols; ++i, ++p)
{
ss << (int)*p << " ";
}
return ss.str();
}
// --------------------------------------------------------------------------
void FORB::fromString(FORB::TDescriptor &a, const std::string &s)
{
a.create(1, FORB::L, CV_8U);
unsigned char *p = a.ptr<unsigned char>();
stringstream ss(s);
for(int i = 0; i < FORB::L; ++i, ++p)
{
int n;
ss >> n;
if(!ss.fail())
*p = (unsigned char)n;
}
}
// --------------------------------------------------------------------------
void FORB::toMat32F(const std::vector<TDescriptor> &descriptors,
cv::Mat &mat)
{
if(descriptors.empty())
{
mat.release();
return;
}
const size_t N = descriptors.size();
mat.create(N, FORB::L*8, CV_32F);
float *p = mat.ptr<float>();
for(size_t i = 0; i < N; ++i)
{
const int C = descriptors[i].cols;
const unsigned char *desc = descriptors[i].ptr<unsigned char>();
for(int j = 0; j < C; ++j, p += 8)
{
p[0] = (desc[j] & (1 << 7) ? 1 : 0);
p[1] = (desc[j] & (1 << 6) ? 1 : 0);
p[2] = (desc[j] & (1 << 5) ? 1 : 0);
p[3] = (desc[j] & (1 << 4) ? 1 : 0);
p[4] = (desc[j] & (1 << 3) ? 1 : 0);
p[5] = (desc[j] & (1 << 2) ? 1 : 0);
p[6] = (desc[j] & (1 << 1) ? 1 : 0);
p[7] = desc[j] & (1);
}
}
}
// --------------------------------------------------------------------------
void FORB::toMat8U(const std::vector<TDescriptor> &descriptors,
cv::Mat &mat)
{
mat.create(descriptors.size(), 32, CV_8U);
unsigned char *p = mat.ptr<unsigned char>();
for(size_t i = 0; i < descriptors.size(); ++i, p += 32)
{
const unsigned char *d = descriptors[i].ptr<unsigned char>();
std::copy(d, d+32, p);
}
}
// --------------------------------------------------------------------------
} // namespace DBoW2

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/**
* File: FORB.h
* Date: June 2012
* Author: Dorian Galvez-Lopez
* Description: functions for ORB descriptors
* License: see the LICENSE.txt file
*
*/
#ifndef __D_T_F_ORB__
#define __D_T_F_ORB__
#include <opencv2/core/core.hpp>
#include <vector>
#include <string>
#include "FClass.h"
namespace DBoW2 {
/// Functions to manipulate ORB descriptors
class FORB: protected FClass
{
public:
/// Descriptor type
typedef cv::Mat TDescriptor; // CV_8U
/// Pointer to a single descriptor
typedef const TDescriptor *pDescriptor;
/// Descriptor length (in bytes)
static const int L;
/**
* Calculates the mean value of a set of descriptors
* @param descriptors
* @param mean mean descriptor
*/
static void meanValue(const std::vector<pDescriptor> &descriptors,
TDescriptor &mean);
/**
* Calculates the distance between two descriptors
* @param a
* @param b
* @return distance
*/
static int distance(const TDescriptor &a, const TDescriptor &b);
/**
* Returns a string version of the descriptor
* @param a descriptor
* @return string version
*/
static std::string toString(const TDescriptor &a);
/**
* Returns a descriptor from a string
* @param a descriptor
* @param s string version
*/
static void fromString(TDescriptor &a, const std::string &s);
/**
* Returns a mat with the descriptors in float format
* @param descriptors
* @param mat (out) NxL 32F matrix
*/
static void toMat32F(const std::vector<TDescriptor> &descriptors,
cv::Mat &mat);
static void toMat8U(const std::vector<TDescriptor> &descriptors,
cv::Mat &mat);
};
} // namespace DBoW2
#endif

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/**
* File: FeatureVector.cpp
* Date: November 2011
* Author: Dorian Galvez-Lopez
* Description: feature vector
* License: see the LICENSE.txt file
*
*/
#include "FeatureVector.h"
#include <map>
#include <vector>
#include <iostream>
namespace DBoW2 {
// ---------------------------------------------------------------------------
FeatureVector::FeatureVector(void)
{
}
// ---------------------------------------------------------------------------
FeatureVector::~FeatureVector(void)
{
}
// ---------------------------------------------------------------------------
void FeatureVector::addFeature(NodeId id, unsigned int i_feature)
{
FeatureVector::iterator vit = this->lower_bound(id);
if(vit != this->end() && vit->first == id)
{
vit->second.push_back(i_feature);
}
else
{
vit = this->insert(vit, FeatureVector::value_type(id,
std::vector<unsigned int>() ));
vit->second.push_back(i_feature);
}
}
// ---------------------------------------------------------------------------
std::ostream& operator<<(std::ostream &out,
const FeatureVector &v)
{
if(!v.empty())
{
FeatureVector::const_iterator vit = v.begin();
const std::vector<unsigned int>* f = &vit->second;
out << "<" << vit->first << ": [";
if(!f->empty()) out << (*f)[0];
for(unsigned int i = 1; i < f->size(); ++i)
{
out << ", " << (*f)[i];
}
out << "]>";
for(++vit; vit != v.end(); ++vit)
{
f = &vit->second;
out << ", <" << vit->first << ": [";
if(!f->empty()) out << (*f)[0];
for(unsigned int i = 1; i < f->size(); ++i)
{
out << ", " << (*f)[i];
}
out << "]>";
}
}
return out;
}
// ---------------------------------------------------------------------------
} // namespace DBoW2

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/**
* File: FeatureVector.h
* Date: November 2011
* Author: Dorian Galvez-Lopez
* Description: feature vector
* License: see the LICENSE.txt file
*
*/
#ifndef __D_T_FEATURE_VECTOR__
#define __D_T_FEATURE_VECTOR__
#include "BowVector.h"
#include <map>
#include <vector>
#include <iostream>
namespace DBoW2 {
/// Vector of nodes with indexes of local features
class FeatureVector:
public std::map<NodeId, std::vector<unsigned int> >
{
public:
/**
* Constructor
*/
FeatureVector(void);
/**
* Destructor
*/
~FeatureVector(void);
/**
* Adds a feature to an existing node, or adds a new node with an initial
* feature
* @param id node id to add or to modify
* @param i_feature index of feature to add to the given node
*/
void addFeature(NodeId id, unsigned int i_feature);
/**
* Sends a string versions of the feature vector through the stream
* @param out stream
* @param v feature vector
*/
friend std::ostream& operator<<(std::ostream &out, const FeatureVector &v);
};
} // namespace DBoW2
#endif

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/**
* File: ScoringObject.cpp
* Date: November 2011
* Author: Dorian Galvez-Lopez
* Description: functions to compute bow scores
* License: see the LICENSE.txt file
*
*/
#include <cfloat>
#include "TemplatedVocabulary.h"
#include "BowVector.h"
using namespace DBoW2;
// If you change the type of WordValue, make sure you change also the
// epsilon value (this is needed by the KL method)
const double GeneralScoring::LOG_EPS = log(DBL_EPSILON); // FLT_EPSILON
// ---------------------------------------------------------------------------
// ---------------------------------------------------------------------------
double L1Scoring::score(const BowVector &v1, const BowVector &v2) const
{
BowVector::const_iterator v1_it, v2_it;
const BowVector::const_iterator v1_end = v1.end();
const BowVector::const_iterator v2_end = v2.end();
v1_it = v1.begin();
v2_it = v2.begin();
double score = 0;
while(v1_it != v1_end && v2_it != v2_end)
{
const WordValue& vi = v1_it->second;
const WordValue& wi = v2_it->second;
if(v1_it->first == v2_it->first)
{
score += fabs(vi - wi) - fabs(vi) - fabs(wi);
// move v1 and v2 forward
++v1_it;
++v2_it;
}
else if(v1_it->first < v2_it->first)
{
// move v1 forward
v1_it = v1.lower_bound(v2_it->first);
// v1_it = (first element >= v2_it.id)
}
else
{
// move v2 forward
v2_it = v2.lower_bound(v1_it->first);
// v2_it = (first element >= v1_it.id)
}
}
// ||v - w||_{L1} = 2 + Sum(|v_i - w_i| - |v_i| - |w_i|)
// for all i | v_i != 0 and w_i != 0
// (Nister, 2006)
// scaled_||v - w||_{L1} = 1 - 0.5 * ||v - w||_{L1}
score = -score/2.0;
return score; // [0..1]
}
// ---------------------------------------------------------------------------
// ---------------------------------------------------------------------------
double L2Scoring::score(const BowVector &v1, const BowVector &v2) const
{
BowVector::const_iterator v1_it, v2_it;
const BowVector::const_iterator v1_end = v1.end();
const BowVector::const_iterator v2_end = v2.end();
v1_it = v1.begin();
v2_it = v2.begin();
double score = 0;
while(v1_it != v1_end && v2_it != v2_end)
{
const WordValue& vi = v1_it->second;
const WordValue& wi = v2_it->second;
if(v1_it->first == v2_it->first)
{
score += vi * wi;
// move v1 and v2 forward
++v1_it;
++v2_it;
}
else if(v1_it->first < v2_it->first)
{
// move v1 forward
v1_it = v1.lower_bound(v2_it->first);
// v1_it = (first element >= v2_it.id)
}
else
{
// move v2 forward
v2_it = v2.lower_bound(v1_it->first);
// v2_it = (first element >= v1_it.id)
}
}
// ||v - w||_{L2} = sqrt( 2 - 2 * Sum(v_i * w_i) )
// for all i | v_i != 0 and w_i != 0 )
// (Nister, 2006)
if(score >= 1) // rounding errors
score = 1.0;
else
score = 1.0 - sqrt(1.0 - score); // [0..1]
return score;
}
// ---------------------------------------------------------------------------
// ---------------------------------------------------------------------------
double ChiSquareScoring::score(const BowVector &v1, const BowVector &v2)
const
{
BowVector::const_iterator v1_it, v2_it;
const BowVector::const_iterator v1_end = v1.end();
const BowVector::const_iterator v2_end = v2.end();
v1_it = v1.begin();
v2_it = v2.begin();
double score = 0;
// all the items are taken into account
while(v1_it != v1_end && v2_it != v2_end)
{
const WordValue& vi = v1_it->second;
const WordValue& wi = v2_it->second;
if(v1_it->first == v2_it->first)
{
// (v-w)^2/(v+w) - v - w = -4 vw/(v+w)
// we move the -4 out
if(vi + wi != 0.0) score += vi * wi / (vi + wi);
// move v1 and v2 forward
++v1_it;
++v2_it;
}
else if(v1_it->first < v2_it->first)
{
// move v1 forward
v1_it = v1.lower_bound(v2_it->first);
}
else
{
// move v2 forward
v2_it = v2.lower_bound(v1_it->first);
}
}
// this takes the -4 into account
score = 2. * score; // [0..1]
return score;
}
// ---------------------------------------------------------------------------
// ---------------------------------------------------------------------------
double KLScoring::score(const BowVector &v1, const BowVector &v2) const
{
BowVector::const_iterator v1_it, v2_it;
const BowVector::const_iterator v1_end = v1.end();
const BowVector::const_iterator v2_end = v2.end();
v1_it = v1.begin();
v2_it = v2.begin();
double score = 0;
// all the items or v are taken into account
while(v1_it != v1_end && v2_it != v2_end)
{
const WordValue& vi = v1_it->second;
const WordValue& wi = v2_it->second;
if(v1_it->first == v2_it->first)
{
if(vi != 0 && wi != 0) score += vi * log(vi/wi);
// move v1 and v2 forward
++v1_it;
++v2_it;
}
else if(v1_it->first < v2_it->first)
{
// move v1 forward
score += vi * (log(vi) - LOG_EPS);
++v1_it;
}
else
{
// move v2_it forward, do not add any score
v2_it = v2.lower_bound(v1_it->first);
// v2_it = (first element >= v1_it.id)
}
}
// sum rest of items of v
for(; v1_it != v1_end; ++v1_it)
if(v1_it->second != 0)
score += v1_it->second * (log(v1_it->second) - LOG_EPS);
return score; // cannot be scaled
}
// ---------------------------------------------------------------------------
// ---------------------------------------------------------------------------
double BhattacharyyaScoring::score(const BowVector &v1,
const BowVector &v2) const
{
BowVector::const_iterator v1_it, v2_it;
const BowVector::const_iterator v1_end = v1.end();
const BowVector::const_iterator v2_end = v2.end();
v1_it = v1.begin();
v2_it = v2.begin();
double score = 0;
while(v1_it != v1_end && v2_it != v2_end)
{
const WordValue& vi = v1_it->second;
const WordValue& wi = v2_it->second;
if(v1_it->first == v2_it->first)
{
score += sqrt(vi * wi);
// move v1 and v2 forward
++v1_it;
++v2_it;
}
else if(v1_it->first < v2_it->first)
{
// move v1 forward
v1_it = v1.lower_bound(v2_it->first);
// v1_it = (first element >= v2_it.id)
}
else
{
// move v2 forward
v2_it = v2.lower_bound(v1_it->first);
// v2_it = (first element >= v1_it.id)
}
}
return score; // already scaled
}
// ---------------------------------------------------------------------------
// ---------------------------------------------------------------------------
double DotProductScoring::score(const BowVector &v1,
const BowVector &v2) const
{
BowVector::const_iterator v1_it, v2_it;
const BowVector::const_iterator v1_end = v1.end();
const BowVector::const_iterator v2_end = v2.end();
v1_it = v1.begin();
v2_it = v2.begin();
double score = 0;
while(v1_it != v1_end && v2_it != v2_end)
{
const WordValue& vi = v1_it->second;
const WordValue& wi = v2_it->second;
if(v1_it->first == v2_it->first)
{
score += vi * wi;
// move v1 and v2 forward
++v1_it;
++v2_it;
}
else if(v1_it->first < v2_it->first)
{
// move v1 forward
v1_it = v1.lower_bound(v2_it->first);
// v1_it = (first element >= v2_it.id)
}
else
{
// move v2 forward
v2_it = v2.lower_bound(v1_it->first);
// v2_it = (first element >= v1_it.id)
}
}
return score; // cannot scale
}
// ---------------------------------------------------------------------------
// ---------------------------------------------------------------------------

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/**
* File: ScoringObject.h
* Date: November 2011
* Author: Dorian Galvez-Lopez
* Description: functions to compute bow scores
* License: see the LICENSE.txt file
*
*/
#ifndef __D_T_SCORING_OBJECT__
#define __D_T_SCORING_OBJECT__
#include "BowVector.h"
namespace DBoW2 {
/// Base class of scoring functions
class GeneralScoring
{
public:
/**
* Computes the score between two vectors. Vectors must be sorted and
* normalized if necessary
* @param v (in/out)
* @param w (in/out)
* @return score
*/
virtual double score(const BowVector &v, const BowVector &w) const = 0;
/**
* Returns whether a vector must be normalized before scoring according
* to the scoring scheme
* @param norm norm to use
* @return true iff must normalize
*/
virtual bool mustNormalize(LNorm &norm) const = 0;
/// Log of epsilon
static const double LOG_EPS;
// If you change the type of WordValue, make sure you change also the
// epsilon value (this is needed by the KL method)
virtual ~GeneralScoring() {} //!< Required for virtual base classes
};
/**
* Macro for defining Scoring classes
* @param NAME name of class
* @param MUSTNORMALIZE if vectors must be normalized to compute the score
* @param NORM type of norm to use when MUSTNORMALIZE
*/
#define __SCORING_CLASS(NAME, MUSTNORMALIZE, NORM) \
NAME: public GeneralScoring \
{ public: \
/** \
* Computes score between two vectors \
* @param v \
* @param w \
* @return score between v and w \
*/ \
virtual double score(const BowVector &v, const BowVector &w) const; \
\
/** \
* Says if a vector must be normalized according to the scoring function \
* @param norm (out) if true, norm to use
* @return true iff vectors must be normalized \
*/ \
virtual inline bool mustNormalize(LNorm &norm) const \
{ norm = NORM; return MUSTNORMALIZE; } \
}
/// L1 Scoring object
class __SCORING_CLASS(L1Scoring, true, L1);
/// L2 Scoring object
class __SCORING_CLASS(L2Scoring, true, L2);
/// Chi square Scoring object
class __SCORING_CLASS(ChiSquareScoring, true, L1);
/// KL divergence Scoring object
class __SCORING_CLASS(KLScoring, true, L1);
/// Bhattacharyya Scoring object
class __SCORING_CLASS(BhattacharyyaScoring, true, L1);
/// Dot product Scoring object
class __SCORING_CLASS(DotProductScoring, false, L1);
#undef __SCORING_CLASS
} // namespace DBoW2
#endif

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/*
* File: Random.cpp
* Project: DUtils library
* Author: Dorian Galvez-Lopez
* Date: April 2010
* Description: manages pseudo-random numbers
* License: see the LICENSE.txt file
*
*/
#include "Random.h"
#include "Timestamp.h"
#include <cstdlib>
using namespace std;
bool DUtils::Random::m_already_seeded = false;
void DUtils::Random::SeedRand(){
Timestamp time;
time.setToCurrentTime();
srand((unsigned)time.getFloatTime());
}
void DUtils::Random::SeedRandOnce()
{
if(!m_already_seeded)
{
DUtils::Random::SeedRand();
m_already_seeded = true;
}
}
void DUtils::Random::SeedRand(int seed)
{
srand(seed);
}
void DUtils::Random::SeedRandOnce(int seed)
{
if(!m_already_seeded)
{
DUtils::Random::SeedRand(seed);
m_already_seeded = true;
}
}
int DUtils::Random::RandomInt(int min, int max){
int d = max - min + 1;
return int(((double)rand()/((double)RAND_MAX + 1.0)) * d) + min;
}
// ---------------------------------------------------------------------------
// ---------------------------------------------------------------------------
DUtils::Random::UnrepeatedRandomizer::UnrepeatedRandomizer(int min, int max)
{
if(min <= max)
{
m_min = min;
m_max = max;
}
else
{
m_min = max;
m_max = min;
}
createValues();
}
// ---------------------------------------------------------------------------
DUtils::Random::UnrepeatedRandomizer::UnrepeatedRandomizer
(const DUtils::Random::UnrepeatedRandomizer& rnd)
{
*this = rnd;
}
// ---------------------------------------------------------------------------
int DUtils::Random::UnrepeatedRandomizer::get()
{
if(empty()) createValues();
DUtils::Random::SeedRandOnce();
int k = DUtils::Random::RandomInt(0, m_values.size()-1);
int ret = m_values[k];
m_values[k] = m_values.back();
m_values.pop_back();
return ret;
}
// ---------------------------------------------------------------------------
void DUtils::Random::UnrepeatedRandomizer::createValues()
{
int n = m_max - m_min + 1;
m_values.resize(n);
for(int i = 0; i < n; ++i) m_values[i] = m_min + i;
}
// ---------------------------------------------------------------------------
void DUtils::Random::UnrepeatedRandomizer::reset()
{
if((int)m_values.size() != m_max - m_min + 1) createValues();
}
// ---------------------------------------------------------------------------
DUtils::Random::UnrepeatedRandomizer&
DUtils::Random::UnrepeatedRandomizer::operator=
(const DUtils::Random::UnrepeatedRandomizer& rnd)
{
if(this != &rnd)
{
this->m_min = rnd.m_min;
this->m_max = rnd.m_max;
this->m_values = rnd.m_values;
}
return *this;
}
// ---------------------------------------------------------------------------

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/*
* File: Random.h
* Project: DUtils library
* Author: Dorian Galvez-Lopez
* Date: April 2010, November 2011
* Description: manages pseudo-random numbers
* License: see the LICENSE.txt file
*
*/
#pragma once
#ifndef __D_RANDOM__
#define __D_RANDOM__
#include <cstdlib>
#include <vector>
namespace DUtils {
/// Functions to generate pseudo-random numbers
class Random
{
public:
class UnrepeatedRandomizer;
public:
/**
* Sets the random number seed to the current time
*/
static void SeedRand();
/**
* Sets the random number seed to the current time only the first
* time this function is called
*/
static void SeedRandOnce();
/**
* Sets the given random number seed
* @param seed
*/
static void SeedRand(int seed);
/**
* Sets the given random number seed only the first time this function
* is called
* @param seed
*/
static void SeedRandOnce(int seed);
/**
* Returns a random number in the range [0..1]
* @return random T number in [0..1]
*/
template <class T>
static T RandomValue(){
return (T)rand()/(T)RAND_MAX;
}
/**
* Returns a random number in the range [min..max]
* @param min
* @param max
* @return random T number in [min..max]
*/
template <class T>
static T RandomValue(T min, T max){
return Random::RandomValue<T>() * (max - min) + min;
}
/**
* Returns a random int in the range [min..max]
* @param min
* @param max
* @return random int in [min..max]
*/
static int RandomInt(int min, int max);
/**
* Returns a random number from a gaussian distribution
* @param mean
* @param sigma standard deviation
*/
template <class T>
static T RandomGaussianValue(T mean, T sigma)
{
// Box-Muller transformation
T x1, x2, w, y1;
do {
x1 = (T)2. * RandomValue<T>() - (T)1.;
x2 = (T)2. * RandomValue<T>() - (T)1.;
w = x1 * x1 + x2 * x2;
} while ( w >= (T)1. || w == (T)0. );
w = sqrt( ((T)-2.0 * log( w ) ) / w );
y1 = x1 * w;
return( mean + y1 * sigma );
}
private:
/// If SeedRandOnce() or SeedRandOnce(int) have already been called
static bool m_already_seeded;
};
// ---------------------------------------------------------------------------
/// Provides pseudo-random numbers with no repetitions
class Random::UnrepeatedRandomizer
{
public:
/**
* Creates a randomizer that returns numbers in the range [min, max]
* @param min
* @param max
*/
UnrepeatedRandomizer(int min, int max);
~UnrepeatedRandomizer(){}
/**
* Copies a randomizer
* @param rnd
*/
UnrepeatedRandomizer(const UnrepeatedRandomizer& rnd);
/**
* Copies a randomizer
* @param rnd
*/
UnrepeatedRandomizer& operator=(const UnrepeatedRandomizer& rnd);
/**
* Returns a random number not given before. If all the possible values
* were already given, the process starts again
* @return unrepeated random number
*/
int get();
/**
* Returns whether all the possible values between min and max were
* already given. If get() is called when empty() is true, the behaviour
* is the same than after creating the randomizer
* @return true iff all the values were returned
*/
inline bool empty() const { return m_values.empty(); }
/**
* Returns the number of values still to be returned
* @return amount of values to return
*/
inline unsigned int left() const { return m_values.size(); }
/**
* Resets the randomizer as it were just created
*/
void reset();
protected:
/**
* Creates the vector with available values
*/
void createValues();
protected:
/// Min of range of values
int m_min;
/// Max of range of values
int m_max;
/// Available values
std::vector<int> m_values;
};
}
#endif

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/*
* File: Timestamp.cpp
* Author: Dorian Galvez-Lopez
* Date: March 2009
* Description: timestamping functions
*
* Note: in windows, this class has a 1ms resolution
*
* License: see the LICENSE.txt file
*
*/
#include <cstdio>
#include <cstdlib>
#include <ctime>
#include <cmath>
#include <sstream>
#include <iomanip>
#ifdef _WIN32
#ifndef WIN32
#define WIN32
#endif
#endif
#ifdef WIN32
#include <sys/timeb.h>
#define sprintf sprintf_s
#else
#include <sys/time.h>
#endif
#include "Timestamp.h"
using namespace std;
using namespace DUtils;
Timestamp::Timestamp(Timestamp::tOptions option)
{
if(option & CURRENT_TIME)
setToCurrentTime();
else if(option & ZERO)
setTime(0.);
}
Timestamp::~Timestamp(void)
{
}
bool Timestamp::empty() const
{
return m_secs == 0 && m_usecs == 0;
}
void Timestamp::setToCurrentTime(){
#ifdef WIN32
struct __timeb32 timebuffer;
_ftime32_s( &timebuffer ); // C4996
// Note: _ftime is deprecated; consider using _ftime_s instead
m_secs = timebuffer.time;
m_usecs = timebuffer.millitm * 1000;
#else
struct timeval now;
gettimeofday(&now, NULL);
m_secs = now.tv_sec;
m_usecs = now.tv_usec;
#endif
}
void Timestamp::setTime(const string &stime){
string::size_type p = stime.find('.');
if(p == string::npos){
m_secs = atol(stime.c_str());
m_usecs = 0;
}else{
m_secs = atol(stime.substr(0, p).c_str());
string s_usecs = stime.substr(p+1, 6);
m_usecs = atol(stime.substr(p+1).c_str());
m_usecs *= (unsigned long)pow(10.0, double(6 - s_usecs.length()));
}
}
void Timestamp::setTime(double s)
{
m_secs = (unsigned long)s;
m_usecs = (s - (double)m_secs) * 1e6;
}
double Timestamp::getFloatTime() const {
return double(m_secs) + double(m_usecs)/1000000.0;
}
string Timestamp::getStringTime() const {
char buf[32];
sprintf(buf, "%.6lf", this->getFloatTime());
return string(buf);
}
double Timestamp::operator- (const Timestamp &t) const {
return this->getFloatTime() - t.getFloatTime();
}
Timestamp& Timestamp::operator+= (double s)
{
*this = *this + s;
return *this;
}
Timestamp& Timestamp::operator-= (double s)
{
*this = *this - s;
return *this;
}
Timestamp Timestamp::operator+ (double s) const
{
unsigned long secs = (long)floor(s);
unsigned long usecs = (long)((s - (double)secs) * 1e6);
return this->plus(secs, usecs);
}
Timestamp Timestamp::plus(unsigned long secs, unsigned long usecs) const
{
Timestamp t;
const unsigned long max = 1000000ul;
if(m_usecs + usecs >= max)
t.setTime(m_secs + secs + 1, m_usecs + usecs - max);
else
t.setTime(m_secs + secs, m_usecs + usecs);
return t;
}
Timestamp Timestamp::operator- (double s) const
{
unsigned long secs = (long)floor(s);
unsigned long usecs = (long)((s - (double)secs) * 1e6);
return this->minus(secs, usecs);
}
Timestamp Timestamp::minus(unsigned long secs, unsigned long usecs) const
{
Timestamp t;
const unsigned long max = 1000000ul;
if(m_usecs < usecs)
t.setTime(m_secs - secs - 1, max - (usecs - m_usecs));
else
t.setTime(m_secs - secs, m_usecs - usecs);
return t;
}
bool Timestamp::operator> (const Timestamp &t) const
{
if(m_secs > t.m_secs) return true;
else if(m_secs == t.m_secs) return m_usecs > t.m_usecs;
else return false;
}
bool Timestamp::operator>= (const Timestamp &t) const
{
if(m_secs > t.m_secs) return true;
else if(m_secs == t.m_secs) return m_usecs >= t.m_usecs;
else return false;
}
bool Timestamp::operator< (const Timestamp &t) const
{
if(m_secs < t.m_secs) return true;
else if(m_secs == t.m_secs) return m_usecs < t.m_usecs;
else return false;
}
bool Timestamp::operator<= (const Timestamp &t) const
{
if(m_secs < t.m_secs) return true;
else if(m_secs == t.m_secs) return m_usecs <= t.m_usecs;
else return false;
}
bool Timestamp::operator== (const Timestamp &t) const
{
return(m_secs == t.m_secs && m_usecs == t.m_usecs);
}
string Timestamp::Format(bool machine_friendly) const
{
struct tm tm_time;
time_t t = (time_t)getFloatTime();
#ifdef WIN32
localtime_s(&tm_time, &t);
#else
localtime_r(&t, &tm_time);
#endif
char buffer[128];
if(machine_friendly)
{
strftime(buffer, 128, "%Y%m%d_%H%M%S", &tm_time);
}
else
{
strftime(buffer, 128, "%c", &tm_time); // Thu Aug 23 14:55:02 2001
}
return string(buffer);
}
string Timestamp::Format(double s) {
int days = int(s / (24. * 3600.0));
s -= days * (24. * 3600.0);
int hours = int(s / 3600.0);
s -= hours * 3600;
int minutes = int(s / 60.0);
s -= minutes * 60;
int seconds = int(s);
int ms = int((s - seconds)*1e6);
stringstream ss;
ss.fill('0');
bool b;
if((b = (days > 0))) ss << days << "d ";
if((b = (b || hours > 0))) ss << setw(2) << hours << ":";
if((b = (b || minutes > 0))) ss << setw(2) << minutes << ":";
if(b) ss << setw(2);
ss << seconds;
if(!b) ss << "." << setw(6) << ms;
return ss.str();
}

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/*
* File: Timestamp.h
* Author: Dorian Galvez-Lopez
* Date: March 2009
* Description: timestamping functions
* License: see the LICENSE.txt file
*
*/
#ifndef __D_TIMESTAMP__
#define __D_TIMESTAMP__
#include <iostream>
using namespace std;
namespace DUtils {
/// Timestamp
class Timestamp
{
public:
/// Options to initiate a timestamp
enum tOptions
{
NONE = 0,
CURRENT_TIME = 0x1,
ZERO = 0x2
};
public:
/**
* Creates a timestamp
* @param option option to set the initial time stamp
*/
Timestamp(Timestamp::tOptions option = NONE);
/**
* Destructor
*/
virtual ~Timestamp(void);
/**
* Says if the timestamp is "empty": seconds and usecs are both 0, as
* when initiated with the ZERO flag
* @return true iif secs == usecs == 0
*/
bool empty() const;
/**
* Sets this instance to the current time
*/
void setToCurrentTime();
/**
* Sets the timestamp from seconds and microseconds
* @param secs: seconds
* @param usecs: microseconds
*/
inline void setTime(unsigned long secs, unsigned long usecs){
m_secs = secs;
m_usecs = usecs;
}
/**
* Returns the timestamp in seconds and microseconds
* @param secs seconds
* @param usecs microseconds
*/
inline void getTime(unsigned long &secs, unsigned long &usecs) const
{
secs = m_secs;
usecs = m_usecs;
}
/**
* Sets the timestamp from a string with the time in seconds
* @param stime: string such as "1235603336.036609"
*/
void setTime(const string &stime);
/**
* Sets the timestamp from a number of seconds from the epoch
* @param s seconds from the epoch
*/
void setTime(double s);
/**
* Returns this timestamp as the number of seconds in (long) float format
*/
double getFloatTime() const;
/**
* Returns this timestamp as the number of seconds in fixed length string format
*/
string getStringTime() const;
/**
* Returns the difference in seconds between this timestamp (greater) and t (smaller)
* If the order is swapped, a negative number is returned
* @param t: timestamp to subtract from this timestamp
* @return difference in seconds
*/
double operator- (const Timestamp &t) const;
/**
* Returns a copy of this timestamp + s seconds + us microseconds
* @param s seconds
* @param us microseconds
*/
Timestamp plus(unsigned long s, unsigned long us) const;
/**
* Returns a copy of this timestamp - s seconds - us microseconds
* @param s seconds
* @param us microseconds
*/
Timestamp minus(unsigned long s, unsigned long us) const;
/**
* Adds s seconds to this timestamp and returns a reference to itself
* @param s seconds
* @return reference to this timestamp
*/
Timestamp& operator+= (double s);
/**
* Substracts s seconds to this timestamp and returns a reference to itself
* @param s seconds
* @return reference to this timestamp
*/
Timestamp& operator-= (double s);
/**
* Returns a copy of this timestamp + s seconds
* @param s: seconds
*/
Timestamp operator+ (double s) const;
/**
* Returns a copy of this timestamp - s seconds
* @param s: seconds
*/
Timestamp operator- (double s) const;
/**
* Returns whether this timestamp is at the future of t
* @param t
*/
bool operator> (const Timestamp &t) const;
/**
* Returns whether this timestamp is at the future of (or is the same as) t
* @param t
*/
bool operator>= (const Timestamp &t) const;
/**
* Returns whether this timestamp and t represent the same instant
* @param t
*/
bool operator== (const Timestamp &t) const;
/**
* Returns whether this timestamp is at the past of t
* @param t
*/
bool operator< (const Timestamp &t) const;
/**
* Returns whether this timestamp is at the past of (or is the same as) t
* @param t
*/
bool operator<= (const Timestamp &t) const;
/**
* Returns the timestamp in a human-readable string
* @param machine_friendly if true, the returned string is formatted
* to yyyymmdd_hhmmss, without weekday or spaces
* @note This has not been tested under Windows
* @note The timestamp is truncated to seconds
*/
string Format(bool machine_friendly = false) const;
/**
* Returns a string version of the elapsed time in seconds, with the format
* xd hh:mm:ss, hh:mm:ss, mm:ss or s.us
* @param s: elapsed seconds (given by getFloatTime) to format
*/
static string Format(double s);
protected:
/// Seconds
unsigned long m_secs; // seconds
/// Microseconds
unsigned long m_usecs; // microseconds
};
}
#endif

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DBoW2: bag-of-words library for C++ with generic descriptors
Copyright (c) 2015 Dorian Galvez-Lopez <http://doriangalvez.com> (Universidad de Zaragoza)
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
1. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
3. Neither the name of copyright holders nor the names of its
contributors may be used to endorse or promote products derived
from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
''AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL COPYRIGHT HOLDERS OR CONTRIBUTORS
BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
POSSIBILITY OF SUCH DAMAGE.
If you use it in an academic work, please cite:
@ARTICLE{GalvezTRO12,
author={G\'alvez-L\'opez, Dorian and Tard\'os, J. D.},
journal={IEEE Transactions on Robotics},
title={Bags of Binary Words for Fast Place Recognition in Image Sequences},
year={2012},
month={October},
volume={28},
number={5},
pages={1188--1197},
doi={10.1109/TRO.2012.2197158},
ISSN={1552-3098}
}

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You should have received this DBoW2 version along with ORB-SLAM2 (https://github.com/raulmur/ORB_SLAM2).
See the original DBoW2 library at: https://github.com/dorian3d/DBoW2
All files included in this version are BSD, see LICENSE.txt
We also use Random.h, Random.cpp, Timestamp.pp and Timestamp.h from DLib/DUtils.
See the original DLib library at: https://github.com/dorian3d/DLib
All files included in this version are BSD, see LICENSE.txt

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CMAKE_MINIMUM_REQUIRED(VERSION 2.6)
SET(CMAKE_LEGACY_CYGWIN_WIN32 0)
PROJECT(g2o)
SET(g2o_C_FLAGS)
SET(g2o_CXX_FLAGS)
# default built type
IF(NOT CMAKE_BUILD_TYPE)
SET(CMAKE_BUILD_TYPE Release)
ENDIF()
MESSAGE(STATUS "BUILD TYPE:" ${CMAKE_BUILD_TYPE})
SET (G2O_LIB_TYPE SHARED)
# There seems to be an issue with MSVC8
# see http://eigen.tuxfamily.org/bz/show_bug.cgi?id=83
if(MSVC90)
add_definitions(-DEIGEN_DONT_ALIGN_STATICALLY=1)
message(STATUS "Disabling memory alignment for MSVC8")
endif(MSVC90)
# Set the output directory for the build executables and libraries
IF(WIN32)
SET(g2o_LIBRARY_OUTPUT_DIRECTORY ${g2o_SOURCE_DIR}/bin CACHE PATH "Target for the libraries")
ELSE(WIN32)
SET(g2o_LIBRARY_OUTPUT_DIRECTORY ${g2o_SOURCE_DIR}/lib CACHE PATH "Target for the libraries")
ENDIF(WIN32)
SET(CMAKE_ARCHIVE_OUTPUT_DIRECTORY ${g2o_LIBRARY_OUTPUT_DIRECTORY})
SET(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${g2o_LIBRARY_OUTPUT_DIRECTORY})
SET(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${g2o_RUNTIME_OUTPUT_DIRECTORY})
# Set search directory for looking for our custom CMake scripts to
# look for SuiteSparse, QGLViewer, and Eigen3.
LIST(APPEND CMAKE_MODULE_PATH ${g2o_SOURCE_DIR}/cmake_modules)
# Detect OS and define macros appropriately
IF(UNIX)
ADD_DEFINITIONS(-DUNIX)
MESSAGE(STATUS "Compiling on Unix")
ENDIF(UNIX)
# Eigen library parallelise itself, though, presumably due to performance issues
# OPENMP is experimental. We experienced some slowdown with it
FIND_PACKAGE(OpenMP)
SET(G2O_USE_OPENMP OFF CACHE BOOL "Build g2o with OpenMP support (EXPERIMENTAL)")
IF(OPENMP_FOUND AND G2O_USE_OPENMP)
SET (G2O_OPENMP 1)
SET(g2o_C_FLAGS "${g2o_C_FLAGS} ${OpenMP_C_FLAGS}")
SET(g2o_CXX_FLAGS "${g2o_CXX_FLAGS} -DEIGEN_DONT_PARALLELIZE ${OpenMP_CXX_FLAGS}")
MESSAGE(STATUS "Compiling with OpenMP support")
ENDIF(OPENMP_FOUND AND G2O_USE_OPENMP)
# Compiler specific options for gcc
SET(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -O3 -march=native")
SET(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} -O3 -march=native")
# activate warnings !!!
SET(g2o_C_FLAGS "${g2o_C_FLAGS} -Wall -W")
SET(g2o_CXX_FLAGS "${g2o_CXX_FLAGS} -Wall -W")
# specifying compiler flags
SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${g2o_CXX_FLAGS}")
SET(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${g2o_C_FLAGS}")
# Find Eigen3
SET(EIGEN3_INCLUDE_DIR ${G2O_EIGEN3_INCLUDE})
FIND_PACKAGE(Eigen3 3.1.0 REQUIRED)
IF(EIGEN3_FOUND)
SET(G2O_EIGEN3_INCLUDE ${EIGEN3_INCLUDE_DIR} CACHE PATH "Directory of Eigen3")
ELSE(EIGEN3_FOUND)
SET(G2O_EIGEN3_INCLUDE "" CACHE PATH "Directory of Eigen3")
ENDIF(EIGEN3_FOUND)
# Generate config.h
SET(G2O_CXX_COMPILER "${CMAKE_CXX_COMPILER_ID} ${CMAKE_CXX_COMPILER}")
configure_file(config.h.in ${g2o_SOURCE_DIR}/config.h)
# Set up the top-level include directories
INCLUDE_DIRECTORIES(
${g2o_SOURCE_DIR}/core
${g2o_SOURCE_DIR}/types
${g2o_SOURCE_DIR}/stuff
${G2O_EIGEN3_INCLUDE})
# Include the subdirectories
ADD_LIBRARY(g2o ${G2O_LIB_TYPE}
#types
g2o/types/types_sba.h
g2o/types/types_six_dof_expmap.h
g2o/types/types_sba.cpp
g2o/types/types_six_dof_expmap.cpp
g2o/types/types_seven_dof_expmap.cpp
g2o/types/types_seven_dof_expmap.h
g2o/types/se3quat.h
g2o/types/se3_ops.h
g2o/types/se3_ops.hpp
#core
g2o/core/base_edge.h
g2o/core/base_binary_edge.h
g2o/core/hyper_graph_action.cpp
g2o/core/base_binary_edge.hpp
g2o/core/hyper_graph_action.h
g2o/core/base_multi_edge.h
g2o/core/hyper_graph.cpp
g2o/core/base_multi_edge.hpp
g2o/core/hyper_graph.h
g2o/core/base_unary_edge.h
g2o/core/linear_solver.h
g2o/core/base_unary_edge.hpp
g2o/core/marginal_covariance_cholesky.cpp
g2o/core/base_vertex.h
g2o/core/marginal_covariance_cholesky.h
g2o/core/base_vertex.hpp
g2o/core/matrix_structure.cpp
g2o/core/batch_stats.cpp
g2o/core/matrix_structure.h
g2o/core/batch_stats.h
g2o/core/openmp_mutex.h
g2o/core/block_solver.h
g2o/core/block_solver.hpp
g2o/core/parameter.cpp
g2o/core/parameter.h
g2o/core/cache.cpp
g2o/core/cache.h
g2o/core/optimizable_graph.cpp
g2o/core/optimizable_graph.h
g2o/core/solver.cpp
g2o/core/solver.h
g2o/core/creators.h
g2o/core/optimization_algorithm_factory.cpp
g2o/core/estimate_propagator.cpp
g2o/core/optimization_algorithm_factory.h
g2o/core/estimate_propagator.h
g2o/core/factory.cpp
g2o/core/optimization_algorithm_property.h
g2o/core/factory.h
g2o/core/sparse_block_matrix.h
g2o/core/sparse_optimizer.cpp
g2o/core/sparse_block_matrix.hpp
g2o/core/sparse_optimizer.h
g2o/core/hyper_dijkstra.cpp
g2o/core/hyper_dijkstra.h
g2o/core/parameter_container.cpp
g2o/core/parameter_container.h
g2o/core/optimization_algorithm.cpp
g2o/core/optimization_algorithm.h
g2o/core/optimization_algorithm_with_hessian.cpp
g2o/core/optimization_algorithm_with_hessian.h
g2o/core/optimization_algorithm_levenberg.cpp
g2o/core/optimization_algorithm_levenberg.h
g2o/core/jacobian_workspace.cpp
g2o/core/jacobian_workspace.h
g2o/core/robust_kernel.cpp
g2o/core/robust_kernel.h
g2o/core/robust_kernel_factory.cpp
g2o/core/robust_kernel_factory.h
g2o/core/robust_kernel_impl.cpp
g2o/core/robust_kernel_impl.h
#stuff
g2o/stuff/string_tools.h
g2o/stuff/color_macros.h
g2o/stuff/macros.h
g2o/stuff/timeutil.cpp
g2o/stuff/misc.h
g2o/stuff/timeutil.h
g2o/stuff/os_specific.c
g2o/stuff/os_specific.h
g2o/stuff/string_tools.cpp
g2o/stuff/property.cpp
g2o/stuff/property.h
)

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You should have received this g2o version along with ORB-SLAM2 (https://github.com/raulmur/ORB_SLAM2).
See the original g2o library at: https://github.com/RainerKuemmerle/g2o
All files included in this g2o version are BSD, see license-bsd.txt

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# Find BLAS library
#
# This module finds an installed library that implements the BLAS
# linear-algebra interface (see http://www.netlib.org/blas/).
# The list of libraries searched for is mainly taken
# from the autoconf macro file, acx_blas.m4 (distributed at
# http://ac-archive.sourceforge.net/ac-archive/acx_blas.html).
#
# This module sets the following variables:
# BLAS_FOUND - set to true if a library implementing the BLAS interface
# is found
# BLAS_INCLUDE_DIR - Directories containing the BLAS header files
# BLAS_DEFINITIONS - Compilation options to use BLAS
# BLAS_LINKER_FLAGS - Linker flags to use BLAS (excluding -l
# and -L).
# BLAS_LIBRARIES_DIR - Directories containing the BLAS libraries.
# May be null if BLAS_LIBRARIES contains libraries name using full path.
# BLAS_LIBRARIES - List of libraries to link against BLAS interface.
# May be null if the compiler supports auto-link (e.g. VC++).
# BLAS_USE_FILE - The name of the cmake module to include to compile
# applications or libraries using BLAS.
#
# This module was modified by CGAL team:
# - find libraries for a C++ compiler, instead of Fortran
# - added BLAS_INCLUDE_DIR, BLAS_DEFINITIONS and BLAS_LIBRARIES_DIR
# - removed BLAS95_LIBRARIES
include(CheckFunctionExists)
# This macro checks for the existence of the combination of fortran libraries
# given by _list. If the combination is found, this macro checks (using the
# check_function_exists macro) whether can link against that library
# combination using the name of a routine given by _name using the linker
# flags given by _flags. If the combination of libraries is found and passes
# the link test, LIBRARIES is set to the list of complete library paths that
# have been found and DEFINITIONS to the required definitions.
# Otherwise, LIBRARIES is set to FALSE.
# N.B. _prefix is the prefix applied to the names of all cached variables that
# are generated internally and marked advanced by this macro.
macro(check_fortran_libraries DEFINITIONS LIBRARIES _prefix _name _flags _list _path)
#message("DEBUG: check_fortran_libraries(${_list} in ${_path})")
# Check for the existence of the libraries given by _list
set(_libraries_found TRUE)
set(_libraries_work FALSE)
set(${DEFINITIONS} "")
set(${LIBRARIES} "")
set(_combined_name)
foreach(_library ${_list})
set(_combined_name ${_combined_name}_${_library})
if(_libraries_found)
# search first in ${_path}
find_library(${_prefix}_${_library}_LIBRARY
NAMES ${_library}
PATHS ${_path} NO_DEFAULT_PATH
)
# if not found, search in environment variables and system
if ( WIN32 )
find_library(${_prefix}_${_library}_LIBRARY
NAMES ${_library}
PATHS ENV LIB
)
elseif ( APPLE )
find_library(${_prefix}_${_library}_LIBRARY
NAMES ${_library}
PATHS /usr/local/lib /usr/lib /usr/local/lib64 /usr/lib64 ENV DYLD_LIBRARY_PATH
)
else ()
find_library(${_prefix}_${_library}_LIBRARY
NAMES ${_library}
PATHS /usr/local/lib /usr/lib /usr/local/lib64 /usr/lib64 ENV LD_LIBRARY_PATH
)
endif()
mark_as_advanced(${_prefix}_${_library}_LIBRARY)
set(${LIBRARIES} ${${LIBRARIES}} ${${_prefix}_${_library}_LIBRARY})
set(_libraries_found ${${_prefix}_${_library}_LIBRARY})
endif(_libraries_found)
endforeach(_library ${_list})
if(_libraries_found)
set(_libraries_found ${${LIBRARIES}})
endif()
# Test this combination of libraries with the Fortran/f2c interface.
# We test the Fortran interface first as it is well standardized.
if(_libraries_found AND NOT _libraries_work)
set(${DEFINITIONS} "-D${_prefix}_USE_F2C")
set(${LIBRARIES} ${_libraries_found})
# Some C++ linkers require the f2c library to link with Fortran libraries.
# I do not know which ones, thus I just add the f2c library if it is available.
find_package( F2C QUIET )
if ( F2C_FOUND )
set(${DEFINITIONS} ${${DEFINITIONS}} ${F2C_DEFINITIONS})
set(${LIBRARIES} ${${LIBRARIES}} ${F2C_LIBRARIES})
endif()
set(CMAKE_REQUIRED_DEFINITIONS ${${DEFINITIONS}})
set(CMAKE_REQUIRED_LIBRARIES ${_flags} ${${LIBRARIES}})
#message("DEBUG: CMAKE_REQUIRED_DEFINITIONS = ${CMAKE_REQUIRED_DEFINITIONS}")
#message("DEBUG: CMAKE_REQUIRED_LIBRARIES = ${CMAKE_REQUIRED_LIBRARIES}")
# Check if function exists with f2c calling convention (ie a trailing underscore)
check_function_exists(${_name}_ ${_prefix}_${_name}_${_combined_name}_f2c_WORKS)
set(CMAKE_REQUIRED_DEFINITIONS} "")
set(CMAKE_REQUIRED_LIBRARIES "")
mark_as_advanced(${_prefix}_${_name}_${_combined_name}_f2c_WORKS)
set(_libraries_work ${${_prefix}_${_name}_${_combined_name}_f2c_WORKS})
endif(_libraries_found AND NOT _libraries_work)
# If not found, test this combination of libraries with a C interface.
# A few implementations (ie ACML) provide a C interface. Unfortunately, there is no standard.
if(_libraries_found AND NOT _libraries_work)
set(${DEFINITIONS} "")
set(${LIBRARIES} ${_libraries_found})
set(CMAKE_REQUIRED_DEFINITIONS "")
set(CMAKE_REQUIRED_LIBRARIES ${_flags} ${${LIBRARIES}})
#message("DEBUG: CMAKE_REQUIRED_LIBRARIES = ${CMAKE_REQUIRED_LIBRARIES}")
check_function_exists(${_name} ${_prefix}_${_name}${_combined_name}_WORKS)
set(CMAKE_REQUIRED_LIBRARIES "")
mark_as_advanced(${_prefix}_${_name}${_combined_name}_WORKS)
set(_libraries_work ${${_prefix}_${_name}${_combined_name}_WORKS})
endif(_libraries_found AND NOT _libraries_work)
# on failure
if(NOT _libraries_work)
set(${DEFINITIONS} "")
set(${LIBRARIES} FALSE)
endif()
#message("DEBUG: ${DEFINITIONS} = ${${DEFINITIONS}}")
#message("DEBUG: ${LIBRARIES} = ${${LIBRARIES}}")
endmacro(check_fortran_libraries)
#
# main
#
# Is it already configured?
if (BLAS_LIBRARIES_DIR OR BLAS_LIBRARIES)
set(BLAS_FOUND TRUE)
else()
# reset variables
set( BLAS_INCLUDE_DIR "" )
set( BLAS_DEFINITIONS "" )
set( BLAS_LINKER_FLAGS "" )
set( BLAS_LIBRARIES "" )
set( BLAS_LIBRARIES_DIR "" )
#
# If Unix, search for BLAS function in possible libraries
#
# BLAS in ATLAS library? (http://math-atlas.sourceforge.net/)
if(NOT BLAS_LIBRARIES)
check_fortran_libraries(
BLAS_DEFINITIONS
BLAS_LIBRARIES
BLAS
sgemm
""
"cblas;f77blas;atlas"
"${CGAL_TAUCS_LIBRARIES_DIR} ENV BLAS_LIB_DIR"
)
endif()
# BLAS in PhiPACK libraries? (requires generic BLAS lib, too)
if(NOT BLAS_LIBRARIES)
check_fortran_libraries(
BLAS_DEFINITIONS
BLAS_LIBRARIES
BLAS
sgemm
""
"sgemm;dgemm;blas"
"${CGAL_TAUCS_LIBRARIES_DIR} ENV BLAS_LIB_DIR"
)
endif()
# BLAS in Alpha CXML library?
if(NOT BLAS_LIBRARIES)
check_fortran_libraries(
BLAS_DEFINITIONS
BLAS_LIBRARIES
BLAS
sgemm
""
"cxml"
"${CGAL_TAUCS_LIBRARIES_DIR} ENV BLAS_LIB_DIR"
)
endif()
# BLAS in Alpha DXML library? (now called CXML, see above)
if(NOT BLAS_LIBRARIES)
check_fortran_libraries(
BLAS_DEFINITIONS
BLAS_LIBRARIES
BLAS
sgemm
""
"dxml"
"${CGAL_TAUCS_LIBRARIES_DIR} ENV BLAS_LIB_DIR"
)
endif()
# BLAS in Sun Performance library?
if(NOT BLAS_LIBRARIES)
check_fortran_libraries(
BLAS_DEFINITIONS
BLAS_LIBRARIES
BLAS
sgemm
"-xlic_lib=sunperf"
"sunperf;sunmath"
"${CGAL_TAUCS_LIBRARIES_DIR} ENV BLAS_LIB_DIR"
)
if(BLAS_LIBRARIES)
# Extra linker flag
set(BLAS_LINKER_FLAGS "-xlic_lib=sunperf")
endif()
endif()
# BLAS in SCSL library? (SGI/Cray Scientific Library)
if(NOT BLAS_LIBRARIES)
check_fortran_libraries(
BLAS_DEFINITIONS
BLAS_LIBRARIES
BLAS
sgemm
""
"scsl"
"${CGAL_TAUCS_LIBRARIES_DIR} ENV BLAS_LIB_DIR"
)
endif()
# BLAS in SGIMATH library?
if(NOT BLAS_LIBRARIES)
check_fortran_libraries(
BLAS_DEFINITIONS
BLAS_LIBRARIES
BLAS
sgemm
""
"complib.sgimath"
"${CGAL_TAUCS_LIBRARIES_DIR} ENV BLAS_LIB_DIR"
)
endif()
# BLAS in IBM ESSL library? (requires generic BLAS lib, too)
if(NOT BLAS_LIBRARIES)
check_fortran_libraries(
BLAS_DEFINITIONS
BLAS_LIBRARIES
BLAS
sgemm
""
"essl;blas"
"${CGAL_TAUCS_LIBRARIES_DIR} ENV BLAS_LIB_DIR"
)
endif()
#BLAS in intel mkl 10 library? (em64t 64bit)
if(NOT BLAS_LIBRARIES)
check_fortran_libraries(
BLAS_DEFINITIONS
BLAS_LIBRARIES
BLAS
sgemm
""
"mkl_intel_lp64;mkl_intel_thread;mkl_core;guide;pthread"
"${CGAL_TAUCS_LIBRARIES_DIR} ENV BLAS_LIB_DIR"
)
endif()
### windows version of intel mkl 10?
if(NOT BLAS_LIBRARIES)
check_fortran_libraries(
BLAS_DEFINITIONS
BLAS_LIBRARIES
BLAS
SGEMM
""
"mkl_c_dll;mkl_intel_thread_dll;mkl_core_dll;libguide40"
"${CGAL_TAUCS_LIBRARIES_DIR} ENV BLAS_LIB_DIR"
)
endif()
#older versions of intel mkl libs
# BLAS in intel mkl library? (shared)
if(NOT BLAS_LIBRARIES)
check_fortran_libraries(
BLAS_DEFINITIONS
BLAS_LIBRARIES
BLAS
sgemm
""
"mkl;guide;pthread"
"${CGAL_TAUCS_LIBRARIES_DIR} ENV BLAS_LIB_DIR"
)
endif()
#BLAS in intel mkl library? (static, 32bit)
if(NOT BLAS_LIBRARIES)
check_fortran_libraries(
BLAS_DEFINITIONS
BLAS_LIBRARIES
BLAS
sgemm
""
"mkl_ia32;guide;pthread"
"${CGAL_TAUCS_LIBRARIES_DIR} ENV BLAS_LIB_DIR"
)
endif()
#BLAS in intel mkl library? (static, em64t 64bit)
if(NOT BLAS_LIBRARIES)
check_fortran_libraries(
BLAS_DEFINITIONS
BLAS_LIBRARIES
BLAS
sgemm
""
"mkl_em64t;guide;pthread"
"${CGAL_TAUCS_LIBRARIES_DIR} ENV BLAS_LIB_DIR"
)
endif()
#BLAS in acml library?
if(NOT BLAS_LIBRARIES)
check_fortran_libraries(
BLAS_DEFINITIONS
BLAS_LIBRARIES
BLAS
sgemm
""
"acml"
"${CGAL_TAUCS_LIBRARIES_DIR} ENV BLAS_LIB_DIR"
)
endif()
# Apple BLAS library?
if(NOT BLAS_LIBRARIES)
check_fortran_libraries(
BLAS_DEFINITIONS
BLAS_LIBRARIES
BLAS
sgemm
""
"Accelerate"
"${CGAL_TAUCS_LIBRARIES_DIR} ENV BLAS_LIB_DIR"
)
endif()
if ( NOT BLAS_LIBRARIES )
check_fortran_libraries(
BLAS_DEFINITIONS
BLAS_LIBRARIES
BLAS
sgemm
""
"vecLib"
"${CGAL_TAUCS_LIBRARIES_DIR} ENV BLAS_LIB_DIR"
)
endif ( NOT BLAS_LIBRARIES )
# Generic BLAS library?
# This configuration *must* be the last try as this library is notably slow.
if ( NOT BLAS_LIBRARIES )
check_fortran_libraries(
BLAS_DEFINITIONS
BLAS_LIBRARIES
BLAS
sgemm
""
"blas"
"${CGAL_TAUCS_LIBRARIES_DIR} ENV BLAS_LIB_DIR"
)
endif()
if(BLAS_LIBRARIES_DIR OR BLAS_LIBRARIES)
set(BLAS_FOUND TRUE)
else()
set(BLAS_FOUND FALSE)
endif()
if(NOT BLAS_FIND_QUIETLY)
if(BLAS_FOUND)
message(STATUS "A library with BLAS API found.")
else(BLAS_FOUND)
if(BLAS_FIND_REQUIRED)
message(FATAL_ERROR "A required library with BLAS API not found. Please specify library location.")
else()
message(STATUS "A library with BLAS API not found. Please specify library location.")
endif()
endif(BLAS_FOUND)
endif(NOT BLAS_FIND_QUIETLY)
# Add variables to cache
set( BLAS_INCLUDE_DIR "${BLAS_INCLUDE_DIR}"
CACHE PATH "Directories containing the BLAS header files" FORCE )
set( BLAS_DEFINITIONS "${BLAS_DEFINITIONS}"
CACHE STRING "Compilation options to use BLAS" FORCE )
set( BLAS_LINKER_FLAGS "${BLAS_LINKER_FLAGS}"
CACHE STRING "Linker flags to use BLAS" FORCE )
set( BLAS_LIBRARIES "${BLAS_LIBRARIES}"
CACHE FILEPATH "BLAS libraries name" FORCE )
set( BLAS_LIBRARIES_DIR "${BLAS_LIBRARIES_DIR}"
CACHE PATH "Directories containing the BLAS libraries" FORCE )
#message("DEBUG: BLAS_INCLUDE_DIR = ${BLAS_INCLUDE_DIR}")
#message("DEBUG: BLAS_DEFINITIONS = ${BLAS_DEFINITIONS}")
#message("DEBUG: BLAS_LINKER_FLAGS = ${BLAS_LINKER_FLAGS}")
#message("DEBUG: BLAS_LIBRARIES = ${BLAS_LIBRARIES}")
#message("DEBUG: BLAS_LIBRARIES_DIR = ${BLAS_LIBRARIES_DIR}")
#message("DEBUG: BLAS_FOUND = ${BLAS_FOUND}")
endif(BLAS_LIBRARIES_DIR OR BLAS_LIBRARIES)

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# - Try to find Eigen3 lib
#
# This module supports requiring a minimum version, e.g. you can do
# find_package(Eigen3 3.1.2)
# to require version 3.1.2 or newer of Eigen3.
#
# Once done this will define
#
# EIGEN3_FOUND - system has eigen lib with correct version
# EIGEN3_INCLUDE_DIR - the eigen include directory
# EIGEN3_VERSION - eigen version
# Copyright (c) 2006, 2007 Montel Laurent, <montel@kde.org>
# Copyright (c) 2008, 2009 Gael Guennebaud, <g.gael@free.fr>
# Copyright (c) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
# Redistribution and use is allowed according to the terms of the 2-clause BSD license.
if(NOT Eigen3_FIND_VERSION)
if(NOT Eigen3_FIND_VERSION_MAJOR)
set(Eigen3_FIND_VERSION_MAJOR 2)
endif(NOT Eigen3_FIND_VERSION_MAJOR)
if(NOT Eigen3_FIND_VERSION_MINOR)
set(Eigen3_FIND_VERSION_MINOR 91)
endif(NOT Eigen3_FIND_VERSION_MINOR)
if(NOT Eigen3_FIND_VERSION_PATCH)
set(Eigen3_FIND_VERSION_PATCH 0)
endif(NOT Eigen3_FIND_VERSION_PATCH)
set(Eigen3_FIND_VERSION "${Eigen3_FIND_VERSION_MAJOR}.${Eigen3_FIND_VERSION_MINOR}.${Eigen3_FIND_VERSION_PATCH}")
endif(NOT Eigen3_FIND_VERSION)
macro(_eigen3_check_version)
file(READ "${EIGEN3_INCLUDE_DIR}/Eigen/src/Core/util/Macros.h" _eigen3_version_header)
string(REGEX MATCH "define[ \t]+EIGEN_WORLD_VERSION[ \t]+([0-9]+)" _eigen3_world_version_match "${_eigen3_version_header}")
set(EIGEN3_WORLD_VERSION "${CMAKE_MATCH_1}")
string(REGEX MATCH "define[ \t]+EIGEN_MAJOR_VERSION[ \t]+([0-9]+)" _eigen3_major_version_match "${_eigen3_version_header}")
set(EIGEN3_MAJOR_VERSION "${CMAKE_MATCH_1}")
string(REGEX MATCH "define[ \t]+EIGEN_MINOR_VERSION[ \t]+([0-9]+)" _eigen3_minor_version_match "${_eigen3_version_header}")
set(EIGEN3_MINOR_VERSION "${CMAKE_MATCH_1}")
set(EIGEN3_VERSION ${EIGEN3_WORLD_VERSION}.${EIGEN3_MAJOR_VERSION}.${EIGEN3_MINOR_VERSION})
if(${EIGEN3_VERSION} VERSION_LESS ${Eigen3_FIND_VERSION})
set(EIGEN3_VERSION_OK FALSE)
else(${EIGEN3_VERSION} VERSION_LESS ${Eigen3_FIND_VERSION})
set(EIGEN3_VERSION_OK TRUE)
endif(${EIGEN3_VERSION} VERSION_LESS ${Eigen3_FIND_VERSION})
if(NOT EIGEN3_VERSION_OK)
message(STATUS "Eigen3 version ${EIGEN3_VERSION} found in ${EIGEN3_INCLUDE_DIR}, "
"but at least version ${Eigen3_FIND_VERSION} is required")
endif(NOT EIGEN3_VERSION_OK)
endmacro(_eigen3_check_version)
if (EIGEN3_INCLUDE_DIR)
# in cache already
_eigen3_check_version()
set(EIGEN3_FOUND ${EIGEN3_VERSION_OK})
else (EIGEN3_INCLUDE_DIR)
# specific additional paths for some OS
if (WIN32)
set(EIGEN_ADDITIONAL_SEARCH_PATHS ${EIGEN_ADDITIONAL_SEARCH_PATHS} "C:/Program Files/Eigen/include" "C:/Program Files (x86)/Eigen/include")
endif(WIN32)
find_path(EIGEN3_INCLUDE_DIR NAMES signature_of_eigen3_matrix_library
PATHS
${CMAKE_INSTALL_PREFIX}/include
${EIGEN_ADDITIONAL_SEARCH_PATHS}
${KDE4_INCLUDE_DIR}
PATH_SUFFIXES eigen3 eigen
)
if(EIGEN3_INCLUDE_DIR)
_eigen3_check_version()
endif(EIGEN3_INCLUDE_DIR)
include(FindPackageHandleStandardArgs)
find_package_handle_standard_args(Eigen3 DEFAULT_MSG EIGEN3_INCLUDE_DIR EIGEN3_VERSION_OK)
mark_as_advanced(EIGEN3_INCLUDE_DIR)
endif(EIGEN3_INCLUDE_DIR)

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# Find LAPACK library
#
# This module finds an installed library that implements the LAPACK
# linear-algebra interface (see http://www.netlib.org/lapack/).
# The approach follows mostly that taken for the autoconf macro file, acx_lapack.m4
# (distributed at http://ac-archive.sourceforge.net/ac-archive/acx_lapack.html).
#
# This module sets the following variables:
# LAPACK_FOUND - set to true if a library implementing the LAPACK interface
# is found
# LAPACK_INCLUDE_DIR - Directories containing the LAPACK header files
# LAPACK_DEFINITIONS - Compilation options to use LAPACK
# LAPACK_LINKER_FLAGS - Linker flags to use LAPACK (excluding -l
# and -L).
# LAPACK_LIBRARIES_DIR - Directories containing the LAPACK libraries.
# May be null if LAPACK_LIBRARIES contains libraries name using full path.
# LAPACK_LIBRARIES - List of libraries to link against LAPACK interface.
# May be null if the compiler supports auto-link (e.g. VC++).
# LAPACK_USE_FILE - The name of the cmake module to include to compile
# applications or libraries using LAPACK.
#
# This module was modified by CGAL team:
# - find libraries for a C++ compiler, instead of Fortran
# - added LAPACK_INCLUDE_DIR, LAPACK_DEFINITIONS and LAPACK_LIBRARIES_DIR
# - removed LAPACK95_LIBRARIES
include(CheckFunctionExists)
# This macro checks for the existence of the combination of fortran libraries
# given by _list. If the combination is found, this macro checks (using the
# check_function_exists macro) whether can link against that library
# combination using the name of a routine given by _name using the linker
# flags given by _flags. If the combination of libraries is found and passes
# the link test, LIBRARIES is set to the list of complete library paths that
# have been found and DEFINITIONS to the required definitions.
# Otherwise, LIBRARIES is set to FALSE.
# N.B. _prefix is the prefix applied to the names of all cached variables that
# are generated internally and marked advanced by this macro.
macro(check_lapack_libraries DEFINITIONS LIBRARIES _prefix _name _flags _list _blas _path)
#message("DEBUG: check_lapack_libraries(${_list} in ${_path} with ${_blas})")
# Check for the existence of the libraries given by _list
set(_libraries_found TRUE)
set(_libraries_work FALSE)
set(${DEFINITIONS} "")
set(${LIBRARIES} "")
set(_combined_name)
foreach(_library ${_list})
set(_combined_name ${_combined_name}_${_library})
if(_libraries_found)
# search first in ${_path}
find_library(${_prefix}_${_library}_LIBRARY
NAMES ${_library}
PATHS ${_path} NO_DEFAULT_PATH
)
# if not found, search in environment variables and system
if ( WIN32 )
find_library(${_prefix}_${_library}_LIBRARY
NAMES ${_library}
PATHS ENV LIB
)
elseif ( APPLE )
find_library(${_prefix}_${_library}_LIBRARY
NAMES ${_library}
PATHS /usr/local/lib /usr/lib /usr/local/lib64 /usr/lib64 ENV DYLD_LIBRARY_PATH
)
else ()
find_library(${_prefix}_${_library}_LIBRARY
NAMES ${_library}
PATHS /usr/local/lib /usr/lib /usr/local/lib64 /usr/lib64 ENV LD_LIBRARY_PATH
)
endif()
mark_as_advanced(${_prefix}_${_library}_LIBRARY)
set(${LIBRARIES} ${${LIBRARIES}} ${${_prefix}_${_library}_LIBRARY})
set(_libraries_found ${${_prefix}_${_library}_LIBRARY})
endif(_libraries_found)
endforeach(_library ${_list})
if(_libraries_found)
set(_libraries_found ${${LIBRARIES}})
endif()
# Test this combination of libraries with the Fortran/f2c interface.
# We test the Fortran interface first as it is well standardized.
if(_libraries_found AND NOT _libraries_work)
set(${DEFINITIONS} "-D${_prefix}_USE_F2C")
set(${LIBRARIES} ${_libraries_found})
# Some C++ linkers require the f2c library to link with Fortran libraries.
# I do not know which ones, thus I just add the f2c library if it is available.
find_package( F2C QUIET )
if ( F2C_FOUND )
set(${DEFINITIONS} ${${DEFINITIONS}} ${F2C_DEFINITIONS})
set(${LIBRARIES} ${${LIBRARIES}} ${F2C_LIBRARIES})
endif()
set(CMAKE_REQUIRED_DEFINITIONS ${${DEFINITIONS}})
set(CMAKE_REQUIRED_LIBRARIES ${_flags} ${${LIBRARIES}} ${_blas})
#message("DEBUG: CMAKE_REQUIRED_DEFINITIONS = ${CMAKE_REQUIRED_DEFINITIONS}")
#message("DEBUG: CMAKE_REQUIRED_LIBRARIES = ${CMAKE_REQUIRED_LIBRARIES}")
# Check if function exists with f2c calling convention (ie a trailing underscore)
check_function_exists(${_name}_ ${_prefix}_${_name}_${_combined_name}_f2c_WORKS)
set(CMAKE_REQUIRED_DEFINITIONS} "")
set(CMAKE_REQUIRED_LIBRARIES "")
mark_as_advanced(${_prefix}_${_name}_${_combined_name}_f2c_WORKS)
set(_libraries_work ${${_prefix}_${_name}_${_combined_name}_f2c_WORKS})
endif(_libraries_found AND NOT _libraries_work)
# If not found, test this combination of libraries with a C interface.
# A few implementations (ie ACML) provide a C interface. Unfortunately, there is no standard.
if(_libraries_found AND NOT _libraries_work)
set(${DEFINITIONS} "")
set(${LIBRARIES} ${_libraries_found})
set(CMAKE_REQUIRED_DEFINITIONS "")
set(CMAKE_REQUIRED_LIBRARIES ${_flags} ${${LIBRARIES}} ${_blas})
#message("DEBUG: CMAKE_REQUIRED_LIBRARIES = ${CMAKE_REQUIRED_LIBRARIES}")
check_function_exists(${_name} ${_prefix}_${_name}${_combined_name}_WORKS)
set(CMAKE_REQUIRED_LIBRARIES "")
mark_as_advanced(${_prefix}_${_name}${_combined_name}_WORKS)
set(_libraries_work ${${_prefix}_${_name}${_combined_name}_WORKS})
endif(_libraries_found AND NOT _libraries_work)
# on failure
if(NOT _libraries_work)
set(${DEFINITIONS} "")
set(${LIBRARIES} FALSE)
endif()
#message("DEBUG: ${DEFINITIONS} = ${${DEFINITIONS}}")
#message("DEBUG: ${LIBRARIES} = ${${LIBRARIES}}")
endmacro(check_lapack_libraries)
#
# main
#
# LAPACK requires BLAS
if(LAPACK_FIND_QUIETLY OR NOT LAPACK_FIND_REQUIRED)
find_package(BLAS)
else()
find_package(BLAS REQUIRED)
endif()
if (NOT BLAS_FOUND)
message(STATUS "LAPACK requires BLAS.")
set(LAPACK_FOUND FALSE)
# Is it already configured?
elseif (LAPACK_LIBRARIES_DIR OR LAPACK_LIBRARIES)
set(LAPACK_FOUND TRUE)
else()
# reset variables
set( LAPACK_INCLUDE_DIR "" )
set( LAPACK_DEFINITIONS "" )
set( LAPACK_LINKER_FLAGS "" ) # unused (yet)
set( LAPACK_LIBRARIES "" )
set( LAPACK_LIBRARIES_DIR "" )
#
# If Unix, search for LAPACK function in possible libraries
#
#intel mkl lapack?
if(NOT LAPACK_LIBRARIES)
check_lapack_libraries(
LAPACK_DEFINITIONS
LAPACK_LIBRARIES
LAPACK
cheev
""
"mkl_lapack"
"${BLAS_LIBRARIES}"
"${CGAL_TAUCS_LIBRARIES_DIR} ENV LAPACK_LIB_DIR"
)
endif()
#acml lapack?
if(NOT LAPACK_LIBRARIES)
check_lapack_libraries(
LAPACK_DEFINITIONS
LAPACK_LIBRARIES
LAPACK
cheev
""
"acml"
"${BLAS_LIBRARIES}"
"${CGAL_TAUCS_LIBRARIES_DIR} ENV LAPACK_LIB_DIR"
)
endif()
# Apple LAPACK library?
if(NOT LAPACK_LIBRARIES)
check_lapack_libraries(
LAPACK_DEFINITIONS
LAPACK_LIBRARIES
LAPACK
cheev
""
"Accelerate"
"${BLAS_LIBRARIES}"
"${CGAL_TAUCS_LIBRARIES_DIR} ENV LAPACK_LIB_DIR"
)
endif()
if ( NOT LAPACK_LIBRARIES )
check_lapack_libraries(
LAPACK_DEFINITIONS
LAPACK_LIBRARIES
LAPACK
cheev
""
"vecLib"
"${BLAS_LIBRARIES}"
"${CGAL_TAUCS_LIBRARIES_DIR} ENV LAPACK_LIB_DIR"
)
endif ( NOT LAPACK_LIBRARIES )
# Generic LAPACK library?
# This configuration *must* be the last try as this library is notably slow.
if ( NOT LAPACK_LIBRARIES )
check_lapack_libraries(
LAPACK_DEFINITIONS
LAPACK_LIBRARIES
LAPACK
cheev
""
"lapack"
"${BLAS_LIBRARIES}"
"${CGAL_TAUCS_LIBRARIES_DIR} ENV LAPACK_LIB_DIR"
)
endif()
if(LAPACK_LIBRARIES_DIR OR LAPACK_LIBRARIES)
set(LAPACK_FOUND TRUE)
else()
set(LAPACK_FOUND FALSE)
endif()
if(NOT LAPACK_FIND_QUIETLY)
if(LAPACK_FOUND)
message(STATUS "A library with LAPACK API found.")
else(LAPACK_FOUND)
if(LAPACK_FIND_REQUIRED)
message(FATAL_ERROR "A required library with LAPACK API not found. Please specify library location.")
else()
message(STATUS "A library with LAPACK API not found. Please specify library location.")
endif()
endif(LAPACK_FOUND)
endif(NOT LAPACK_FIND_QUIETLY)
# Add variables to cache
set( LAPACK_INCLUDE_DIR "${LAPACK_INCLUDE_DIR}"
CACHE PATH "Directories containing the LAPACK header files" FORCE )
set( LAPACK_DEFINITIONS "${LAPACK_DEFINITIONS}"
CACHE STRING "Compilation options to use LAPACK" FORCE )
set( LAPACK_LINKER_FLAGS "${LAPACK_LINKER_FLAGS}"
CACHE STRING "Linker flags to use LAPACK" FORCE )
set( LAPACK_LIBRARIES "${LAPACK_LIBRARIES}"
CACHE FILEPATH "LAPACK libraries name" FORCE )
set( LAPACK_LIBRARIES_DIR "${LAPACK_LIBRARIES_DIR}"
CACHE PATH "Directories containing the LAPACK libraries" FORCE )
#message("DEBUG: LAPACK_INCLUDE_DIR = ${LAPACK_INCLUDE_DIR}")
#message("DEBUG: LAPACK_DEFINITIONS = ${LAPACK_DEFINITIONS}")
#message("DEBUG: LAPACK_LINKER_FLAGS = ${LAPACK_LINKER_FLAGS}")
#message("DEBUG: LAPACK_LIBRARIES = ${LAPACK_LIBRARIES}")
#message("DEBUG: LAPACK_LIBRARIES_DIR = ${LAPACK_LIBRARIES_DIR}")
#message("DEBUG: LAPACK_FOUND = ${LAPACK_FOUND}")
endif(NOT BLAS_FOUND)

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#ifndef G2O_CONFIG_H
#define G2O_CONFIG_H
#cmakedefine G2O_OPENMP 1
#cmakedefine G2O_SHARED_LIBS 1
// give a warning if Eigen defaults to row-major matrices.
// We internally assume column-major matrices throughout the code.
#ifdef EIGEN_DEFAULT_TO_ROW_MAJOR
# error "g2o requires column major Eigen matrices (see http://eigen.tuxfamily.org/bz/show_bug.cgi?id=422)"
#endif
#endif

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// g2o - General Graph Optimization
// Copyright (C) 2011 R. Kuemmerle, G. Grisetti, H. Strasdat, W. Burgard
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
// IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
// TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
// PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
// TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#ifndef G2O_BASE_BINARY_EDGE_H
#define G2O_BASE_BINARY_EDGE_H
#include <iostream>
#include <limits>
#include "base_edge.h"
#include "robust_kernel.h"
#include "../../config.h"
namespace g2o {
using namespace Eigen;
template <int D, typename E, typename VertexXi, typename VertexXj>
class BaseBinaryEdge : public BaseEdge<D, E>
{
public:
typedef VertexXi VertexXiType;
typedef VertexXj VertexXjType;
static const int Di = VertexXiType::Dimension;
static const int Dj = VertexXjType::Dimension;
static const int Dimension = BaseEdge<D, E>::Dimension;
typedef typename BaseEdge<D,E>::Measurement Measurement;
typedef typename Matrix<double, D, Di>::AlignedMapType JacobianXiOplusType;
typedef typename Matrix<double, D, Dj>::AlignedMapType JacobianXjOplusType;
typedef typename BaseEdge<D,E>::ErrorVector ErrorVector;
typedef typename BaseEdge<D,E>::InformationType InformationType;
typedef Eigen::Map<Matrix<double, Di, Dj>, Matrix<double, Di, Dj>::Flags & AlignedBit ? Aligned : Unaligned > HessianBlockType;
typedef Eigen::Map<Matrix<double, Dj, Di>, Matrix<double, Dj, Di>::Flags & AlignedBit ? Aligned : Unaligned > HessianBlockTransposedType;
BaseBinaryEdge() : BaseEdge<D,E>(),
_hessianRowMajor(false),
_hessian(0, VertexXiType::Dimension, VertexXjType::Dimension), // HACK we map to the null pointer for initializing the Maps
_hessianTransposed(0, VertexXjType::Dimension, VertexXiType::Dimension),
_jacobianOplusXi(0, D, Di), _jacobianOplusXj(0, D, Dj)
{
_vertices.resize(2);
}
virtual OptimizableGraph::Vertex* createFrom();
virtual OptimizableGraph::Vertex* createTo();
virtual void resize(size_t size);
virtual bool allVerticesFixed() const;
virtual void linearizeOplus(JacobianWorkspace& jacobianWorkspace);
/**
* Linearizes the oplus operator in the vertex, and stores
* the result in temporary variables _jacobianOplusXi and _jacobianOplusXj
*/
virtual void linearizeOplus();
//! returns the result of the linearization in the manifold space for the node xi
const JacobianXiOplusType& jacobianOplusXi() const { return _jacobianOplusXi;}
//! returns the result of the linearization in the manifold space for the node xj
const JacobianXjOplusType& jacobianOplusXj() const { return _jacobianOplusXj;}
virtual void constructQuadraticForm() ;
virtual void mapHessianMemory(double* d, int i, int j, bool rowMajor);
using BaseEdge<D,E>::resize;
using BaseEdge<D,E>::computeError;
protected:
using BaseEdge<D,E>::_measurement;
using BaseEdge<D,E>::_information;
using BaseEdge<D,E>::_error;
using BaseEdge<D,E>::_vertices;
using BaseEdge<D,E>::_dimension;
bool _hessianRowMajor;
HessianBlockType _hessian;
HessianBlockTransposedType _hessianTransposed;
JacobianXiOplusType _jacobianOplusXi;
JacobianXjOplusType _jacobianOplusXj;
public:
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
};
#include "base_binary_edge.hpp"
} // end namespace g2o
#endif

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// g2o - General Graph Optimization
// Copyright (C) 2011 R. Kuemmerle, G. Grisetti, H. Strasdat, W. Burgard
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
// IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
// TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
// PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
// TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
template <int D, typename E, typename VertexXiType, typename VertexXjType>
OptimizableGraph::Vertex* BaseBinaryEdge<D, E, VertexXiType, VertexXjType>::createFrom(){
return new VertexXiType();
}
template <int D, typename E, typename VertexXiType, typename VertexXjType>
OptimizableGraph::Vertex* BaseBinaryEdge<D, E, VertexXiType, VertexXjType>::createTo(){
return new VertexXjType();
}
template <int D, typename E, typename VertexXiType, typename VertexXjType>
void BaseBinaryEdge<D, E, VertexXiType, VertexXjType>::resize(size_t size)
{
if (size != 2) {
std::cerr << "WARNING, attempting to resize binary edge " << BaseEdge<D, E>::id() << " to " << size << std::endl;
}
BaseEdge<D, E>::resize(size);
}
template <int D, typename E, typename VertexXiType, typename VertexXjType>
bool BaseBinaryEdge<D, E, VertexXiType, VertexXjType>::allVerticesFixed() const
{
return (static_cast<const VertexXiType*> (_vertices[0])->fixed() &&
static_cast<const VertexXjType*> (_vertices[1])->fixed());
}
template <int D, typename E, typename VertexXiType, typename VertexXjType>
void BaseBinaryEdge<D, E, VertexXiType, VertexXjType>::constructQuadraticForm()
{
VertexXiType* from = static_cast<VertexXiType*>(_vertices[0]);
VertexXjType* to = static_cast<VertexXjType*>(_vertices[1]);
// get the Jacobian of the nodes in the manifold domain
const JacobianXiOplusType& A = jacobianOplusXi();
const JacobianXjOplusType& B = jacobianOplusXj();
bool fromNotFixed = !(from->fixed());
bool toNotFixed = !(to->fixed());
if (fromNotFixed || toNotFixed) {
#ifdef G2O_OPENMP
from->lockQuadraticForm();
to->lockQuadraticForm();
#endif
const InformationType& omega = _information;
Matrix<double, D, 1> omega_r = - omega * _error;
if (this->robustKernel() == 0) {
if (fromNotFixed) {
Matrix<double, VertexXiType::Dimension, D> AtO = A.transpose() * omega;
from->b().noalias() += A.transpose() * omega_r;
from->A().noalias() += AtO*A;
if (toNotFixed ) {
if (_hessianRowMajor) // we have to write to the block as transposed
_hessianTransposed.noalias() += B.transpose() * AtO.transpose();
else
_hessian.noalias() += AtO * B;
}
}
if (toNotFixed) {
to->b().noalias() += B.transpose() * omega_r;
to->A().noalias() += B.transpose() * omega * B;
}
} else { // robust (weighted) error according to some kernel
double error = this->chi2();
Eigen::Vector3d rho;
this->robustKernel()->robustify(error, rho);
InformationType weightedOmega = this->robustInformation(rho);
//std::cout << PVAR(rho.transpose()) << std::endl;
//std::cout << PVAR(weightedOmega) << std::endl;
omega_r *= rho[1];
if (fromNotFixed) {
from->b().noalias() += A.transpose() * omega_r;
from->A().noalias() += A.transpose() * weightedOmega * A;
if (toNotFixed ) {
if (_hessianRowMajor) // we have to write to the block as transposed
_hessianTransposed.noalias() += B.transpose() * weightedOmega * A;
else
_hessian.noalias() += A.transpose() * weightedOmega * B;
}
}
if (toNotFixed) {
to->b().noalias() += B.transpose() * omega_r;
to->A().noalias() += B.transpose() * weightedOmega * B;
}
}
#ifdef G2O_OPENMP
to->unlockQuadraticForm();
from->unlockQuadraticForm();
#endif
}
}
template <int D, typename E, typename VertexXiType, typename VertexXjType>
void BaseBinaryEdge<D, E, VertexXiType, VertexXjType>::linearizeOplus(JacobianWorkspace& jacobianWorkspace)
{
new (&_jacobianOplusXi) JacobianXiOplusType(jacobianWorkspace.workspaceForVertex(0), D, Di);
new (&_jacobianOplusXj) JacobianXjOplusType(jacobianWorkspace.workspaceForVertex(1), D, Dj);
linearizeOplus();
}
template <int D, typename E, typename VertexXiType, typename VertexXjType>
void BaseBinaryEdge<D, E, VertexXiType, VertexXjType>::linearizeOplus()
{
VertexXiType* vi = static_cast<VertexXiType*>(_vertices[0]);
VertexXjType* vj = static_cast<VertexXjType*>(_vertices[1]);
bool iNotFixed = !(vi->fixed());
bool jNotFixed = !(vj->fixed());
if (!iNotFixed && !jNotFixed)
return;
#ifdef G2O_OPENMP
vi->lockQuadraticForm();
vj->lockQuadraticForm();
#endif
const double delta = 1e-9;
const double scalar = 1.0 / (2*delta);
ErrorVector errorBak;
ErrorVector errorBeforeNumeric = _error;
if (iNotFixed) {
//Xi - estimate the jacobian numerically
double add_vi[VertexXiType::Dimension];
std::fill(add_vi, add_vi + VertexXiType::Dimension, 0.0);
// add small step along the unit vector in each dimension
for (int d = 0; d < VertexXiType::Dimension; ++d) {
vi->push();
add_vi[d] = delta;
vi->oplus(add_vi);
computeError();
errorBak = _error;
vi->pop();
vi->push();
add_vi[d] = -delta;
vi->oplus(add_vi);
computeError();
errorBak -= _error;
vi->pop();
add_vi[d] = 0.0;
_jacobianOplusXi.col(d) = scalar * errorBak;
} // end dimension
}
if (jNotFixed) {
//Xj - estimate the jacobian numerically
double add_vj[VertexXjType::Dimension];
std::fill(add_vj, add_vj + VertexXjType::Dimension, 0.0);
// add small step along the unit vector in each dimension
for (int d = 0; d < VertexXjType::Dimension; ++d) {
vj->push();
add_vj[d] = delta;
vj->oplus(add_vj);
computeError();
errorBak = _error;
vj->pop();
vj->push();
add_vj[d] = -delta;
vj->oplus(add_vj);
computeError();
errorBak -= _error;
vj->pop();
add_vj[d] = 0.0;
_jacobianOplusXj.col(d) = scalar * errorBak;
}
} // end dimension
_error = errorBeforeNumeric;
#ifdef G2O_OPENMP
vj->unlockQuadraticForm();
vi->unlockQuadraticForm();
#endif
}
template <int D, typename E, typename VertexXiType, typename VertexXjType>
void BaseBinaryEdge<D, E, VertexXiType, VertexXjType>::mapHessianMemory(double* d, int i, int j, bool rowMajor)
{
(void) i; (void) j;
//assert(i == 0 && j == 1);
if (rowMajor) {
new (&_hessianTransposed) HessianBlockTransposedType(d, VertexXjType::Dimension, VertexXiType::Dimension);
} else {
new (&_hessian) HessianBlockType(d, VertexXiType::Dimension, VertexXjType::Dimension);
}
_hessianRowMajor = rowMajor;
}

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// g2o - General Graph Optimization
// Copyright (C) 2011 R. Kuemmerle, G. Grisetti, H. Strasdat, W. Burgard
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
// IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
// TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
// PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
// TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#ifndef G2O_BASE_EDGE_H
#define G2O_BASE_EDGE_H
#include <iostream>
#include <limits>
#include <Eigen/Core>
#include "optimizable_graph.h"
namespace g2o {
using namespace Eigen;
template <int D, typename E>
class BaseEdge : public OptimizableGraph::Edge
{
public:
static const int Dimension = D;
typedef E Measurement;
typedef Matrix<double, D, 1> ErrorVector;
typedef Matrix<double, D, D> InformationType;
BaseEdge() : OptimizableGraph::Edge()
{
_dimension = D;
}
virtual ~BaseEdge() {}
virtual double chi2() const
{
return _error.dot(information()*_error);
}
virtual const double* errorData() const { return _error.data();}
virtual double* errorData() { return _error.data();}
const ErrorVector& error() const { return _error;}
ErrorVector& error() { return _error;}
//! information matrix of the constraint
const InformationType& information() const { return _information;}
InformationType& information() { return _information;}
void setInformation(const InformationType& information) { _information = information;}
virtual const double* informationData() const { return _information.data();}
virtual double* informationData() { return _information.data();}
//! accessor functions for the measurement represented by the edge
const Measurement& measurement() const { return _measurement;}
virtual void setMeasurement(const Measurement& m) { _measurement = m;}
virtual int rank() const {return _dimension;}
virtual void initialEstimate(const OptimizableGraph::VertexSet&, OptimizableGraph::Vertex*)
{
std::cerr << "inititialEstimate() is not implemented, please give implementation in your derived class" << std::endl;
}
protected:
Measurement _measurement;
InformationType _information;
ErrorVector _error;
/**
* calculate the robust information matrix by updating the information matrix of the error
*/
InformationType robustInformation(const Eigen::Vector3d& rho)
{
InformationType result = rho[1] * _information;
//ErrorVector weightedErrror = _information * _error;
//result.noalias() += 2 * rho[2] * (weightedErrror * weightedErrror.transpose());
return result;
}
public:
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
};
} // end namespace g2o
#endif

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// g2o - General Graph Optimization
// Copyright (C) 2011 R. Kuemmerle, G. Grisetti, H. Strasdat, W. Burgard
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
// IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
// TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
// PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
// TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#ifndef G2O_BASE_MULTI_EDGE_H
#define G2O_BASE_MULTI_EDGE_H
#include <iostream>
#include <iomanip>
#include <limits>
#include <Eigen/StdVector>
#include "base_edge.h"
#include "robust_kernel.h"
#include "../../config.h"
namespace g2o {
using namespace Eigen;
/**
* \brief base class to represent an edge connecting an arbitrary number of nodes
*
* D - Dimension of the measurement
* E - type to represent the measurement
*/
template <int D, typename E>
class BaseMultiEdge : public BaseEdge<D,E>
{
public:
/**
* \brief helper for mapping the Hessian memory of the upper triangular block
*/
struct HessianHelper {
Eigen::Map<MatrixXd> matrix; ///< the mapped memory
bool transposed; ///< the block has to be transposed
HessianHelper() : matrix(0, 0, 0), transposed(false) {}
};
public:
static const int Dimension = BaseEdge<D,E>::Dimension;
typedef typename BaseEdge<D,E>::Measurement Measurement;
typedef MatrixXd::MapType JacobianType;
typedef typename BaseEdge<D,E>::ErrorVector ErrorVector;
typedef typename BaseEdge<D,E>::InformationType InformationType;
typedef Eigen::Map<MatrixXd, MatrixXd::Flags & AlignedBit ? Aligned : Unaligned > HessianBlockType;
BaseMultiEdge() : BaseEdge<D,E>()
{
}
virtual void linearizeOplus(JacobianWorkspace& jacobianWorkspace);
/**
* Linearizes the oplus operator in the vertex, and stores
* the result in temporary variable vector _jacobianOplus
*/
virtual void linearizeOplus();
virtual void resize(size_t size);
virtual bool allVerticesFixed() const;
virtual void constructQuadraticForm() ;
virtual void mapHessianMemory(double* d, int i, int j, bool rowMajor);
using BaseEdge<D,E>::computeError;
protected:
using BaseEdge<D,E>::_measurement;
using BaseEdge<D,E>::_information;
using BaseEdge<D,E>::_error;
using BaseEdge<D,E>::_vertices;
using BaseEdge<D,E>::_dimension;
std::vector<HessianHelper> _hessian;
std::vector<JacobianType, aligned_allocator<JacobianType> > _jacobianOplus; ///< jacobians of the edge (w.r.t. oplus)
void computeQuadraticForm(const InformationType& omega, const ErrorVector& weightedError);
public:
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
};
#include "base_multi_edge.hpp"
} // end namespace g2o
#endif

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// g2o - General Graph Optimization
// Copyright (C) 2011 R. Kuemmerle, G. Grisetti, H. Strasdat, W. Burgard
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
// IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
// TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
// PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
// TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
namespace internal {
inline int computeUpperTriangleIndex(int i, int j)
{
int elemsUpToCol = ((j-1) * j) / 2;
return elemsUpToCol + i;
}
}
template <int D, typename E>
void BaseMultiEdge<D, E>::constructQuadraticForm()
{
if (this->robustKernel()) {
double error = this->chi2();
Eigen::Vector3d rho;
this->robustKernel()->robustify(error, rho);
Matrix<double, D, 1> omega_r = - _information * _error;
omega_r *= rho[1];
computeQuadraticForm(this->robustInformation(rho), omega_r);
} else {
computeQuadraticForm(_information, - _information * _error);
}
}
template <int D, typename E>
void BaseMultiEdge<D, E>::linearizeOplus(JacobianWorkspace& jacobianWorkspace)
{
for (size_t i = 0; i < _vertices.size(); ++i) {
OptimizableGraph::Vertex* v = static_cast<OptimizableGraph::Vertex*>(_vertices[i]);
assert(v->dimension() >= 0);
new (&_jacobianOplus[i]) JacobianType(jacobianWorkspace.workspaceForVertex(i), D, v->dimension());
}
linearizeOplus();
}
template <int D, typename E>
void BaseMultiEdge<D, E>::linearizeOplus()
{
#ifdef G2O_OPENMP
for (size_t i = 0; i < _vertices.size(); ++i) {
OptimizableGraph::Vertex* v = static_cast<OptimizableGraph::Vertex*>(_vertices[i]);
v->lockQuadraticForm();
}
#endif
const double delta = 1e-9;
const double scalar = 1.0 / (2*delta);
ErrorVector errorBak;
ErrorVector errorBeforeNumeric = _error;
for (size_t i = 0; i < _vertices.size(); ++i) {
//Xi - estimate the jacobian numerically
OptimizableGraph::Vertex* vi = static_cast<OptimizableGraph::Vertex*>(_vertices[i]);
if (vi->fixed())
continue;
const int vi_dim = vi->dimension();
assert(vi_dim >= 0);
#ifdef _MSC_VER
double* add_vi = new double[vi_dim];
#else
double add_vi[vi_dim];
#endif
std::fill(add_vi, add_vi + vi_dim, 0.0);
assert(_dimension >= 0);
assert(_jacobianOplus[i].rows() == _dimension && _jacobianOplus[i].cols() == vi_dim && "jacobian cache dimension does not match");
_jacobianOplus[i].resize(_dimension, vi_dim);
// add small step along the unit vector in each dimension
for (int d = 0; d < vi_dim; ++d) {
vi->push();
add_vi[d] = delta;
vi->oplus(add_vi);
computeError();
errorBak = _error;
vi->pop();
vi->push();
add_vi[d] = -delta;
vi->oplus(add_vi);
computeError();
errorBak -= _error;
vi->pop();
add_vi[d] = 0.0;
_jacobianOplus[i].col(d) = scalar * errorBak;
} // end dimension
#ifdef _MSC_VER
delete[] add_vi;
#endif
}
_error = errorBeforeNumeric;
#ifdef G2O_OPENMP
for (int i = (int)(_vertices.size()) - 1; i >= 0; --i) {
OptimizableGraph::Vertex* v = static_cast<OptimizableGraph::Vertex*>(_vertices[i]);
v->unlockQuadraticForm();
}
#endif
}
template <int D, typename E>
void BaseMultiEdge<D, E>::mapHessianMemory(double* d, int i, int j, bool rowMajor)
{
int idx = internal::computeUpperTriangleIndex(i, j);
assert(idx < (int)_hessian.size());
OptimizableGraph::Vertex* vi = static_cast<OptimizableGraph::Vertex*>(HyperGraph::Edge::vertex(i));
OptimizableGraph::Vertex* vj = static_cast<OptimizableGraph::Vertex*>(HyperGraph::Edge::vertex(j));
assert(vi->dimension() >= 0);
assert(vj->dimension() >= 0);
HessianHelper& h = _hessian[idx];
if (rowMajor) {
if (h.matrix.data() != d || h.transposed != rowMajor)
new (&h.matrix) HessianBlockType(d, vj->dimension(), vi->dimension());
} else {
if (h.matrix.data() != d || h.transposed != rowMajor)
new (&h.matrix) HessianBlockType(d, vi->dimension(), vj->dimension());
}
h.transposed = rowMajor;
}
template <int D, typename E>
void BaseMultiEdge<D, E>::resize(size_t size)
{
BaseEdge<D,E>::resize(size);
int n = (int)_vertices.size();
int maxIdx = (n * (n-1))/2;
assert(maxIdx >= 0);
_hessian.resize(maxIdx);
_jacobianOplus.resize(size, JacobianType(0,0,0));
}
template <int D, typename E>
bool BaseMultiEdge<D, E>::allVerticesFixed() const
{
for (size_t i = 0; i < _vertices.size(); ++i) {
if (!static_cast<const OptimizableGraph::Vertex*> (_vertices[i])->fixed()) {
return false;
}
}
return true;
}
template <int D, typename E>
void BaseMultiEdge<D, E>::computeQuadraticForm(const InformationType& omega, const ErrorVector& weightedError)
{
for (size_t i = 0; i < _vertices.size(); ++i) {
OptimizableGraph::Vertex* from = static_cast<OptimizableGraph::Vertex*>(_vertices[i]);
bool istatus = !(from->fixed());
if (istatus) {
const MatrixXd& A = _jacobianOplus[i];
MatrixXd AtO = A.transpose() * omega;
int fromDim = from->dimension();
assert(fromDim >= 0);
Eigen::Map<MatrixXd> fromMap(from->hessianData(), fromDim, fromDim);
Eigen::Map<VectorXd> fromB(from->bData(), fromDim);
// ii block in the hessian
#ifdef G2O_OPENMP
from->lockQuadraticForm();
#endif
fromMap.noalias() += AtO * A;
fromB.noalias() += A.transpose() * weightedError;
// compute the off-diagonal blocks ij for all j
for (size_t j = i+1; j < _vertices.size(); ++j) {
OptimizableGraph::Vertex* to = static_cast<OptimizableGraph::Vertex*>(_vertices[j]);
#ifdef G2O_OPENMP
to->lockQuadraticForm();
#endif
bool jstatus = !(to->fixed());
if (jstatus) {
const MatrixXd& B = _jacobianOplus[j];
int idx = internal::computeUpperTriangleIndex(i, j);
assert(idx < (int)_hessian.size());
HessianHelper& hhelper = _hessian[idx];
if (hhelper.transposed) { // we have to write to the block as transposed
hhelper.matrix.noalias() += B.transpose() * AtO.transpose();
} else {
hhelper.matrix.noalias() += AtO * B;
}
}
#ifdef G2O_OPENMP
to->unlockQuadraticForm();
#endif
}
#ifdef G2O_OPENMP
from->unlockQuadraticForm();
#endif
}
}
}

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// g2o - General Graph Optimization
// Copyright (C) 2011 R. Kuemmerle, G. Grisetti, H. Strasdat, W. Burgard
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
// IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
// TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
// PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
// TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#ifndef G2O_BASE_UNARY_EDGE_H
#define G2O_BASE_UNARY_EDGE_H
#include <iostream>
#include <cassert>
#include <limits>
#include "base_edge.h"
#include "robust_kernel.h"
#include "../../config.h"
namespace g2o {
using namespace Eigen;
template <int D, typename E, typename VertexXi>
class BaseUnaryEdge : public BaseEdge<D,E>
{
public:
static const int Dimension = BaseEdge<D, E>::Dimension;
typedef typename BaseEdge<D,E>::Measurement Measurement;
typedef VertexXi VertexXiType;
typedef typename Matrix<double, D, VertexXiType::Dimension>::AlignedMapType JacobianXiOplusType;
typedef typename BaseEdge<D,E>::ErrorVector ErrorVector;
typedef typename BaseEdge<D,E>::InformationType InformationType;
BaseUnaryEdge() : BaseEdge<D,E>(),
_jacobianOplusXi(0, D, VertexXiType::Dimension)
{
_vertices.resize(1);
}
virtual void resize(size_t size);
virtual bool allVerticesFixed() const;
virtual void linearizeOplus(JacobianWorkspace& jacobianWorkspace);
/**
* Linearizes the oplus operator in the vertex, and stores
* the result in temporary variables _jacobianOplusXi and _jacobianOplusXj
*/
virtual void linearizeOplus();
//! returns the result of the linearization in the manifold space for the node xi
const JacobianXiOplusType& jacobianOplusXi() const { return _jacobianOplusXi;}
virtual void constructQuadraticForm();
virtual void initialEstimate(const OptimizableGraph::VertexSet& from, OptimizableGraph::Vertex* to);
virtual void mapHessianMemory(double*, int, int, bool) {assert(0 && "BaseUnaryEdge does not map memory of the Hessian");}
using BaseEdge<D,E>::resize;
using BaseEdge<D,E>::computeError;
protected:
using BaseEdge<D,E>::_measurement;
using BaseEdge<D,E>::_information;
using BaseEdge<D,E>::_error;
using BaseEdge<D,E>::_vertices;
using BaseEdge<D,E>::_dimension;
JacobianXiOplusType _jacobianOplusXi;
public:
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
};
#include "base_unary_edge.hpp"
} // end namespace g2o
#endif

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// g2o - General Graph Optimization
// Copyright (C) 2011 R. Kuemmerle, G. Grisetti, H. Strasdat, W. Burgard
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
// IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
// TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
// PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
// TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
template <int D, typename E, typename VertexXiType>
void BaseUnaryEdge<D, E, VertexXiType>::resize(size_t size)
{
if (size != 1) {
std::cerr << "WARNING, attempting to resize unary edge " << BaseEdge<D, E>::id() << " to " << size << std::endl;
}
BaseEdge<D, E>::resize(size);
}
template <int D, typename E, typename VertexXiType>
bool BaseUnaryEdge<D, E, VertexXiType>::allVerticesFixed() const
{
return static_cast<const VertexXiType*> (_vertices[0])->fixed();
}
template <int D, typename E, typename VertexXiType>
void BaseUnaryEdge<D, E, VertexXiType>::constructQuadraticForm()
{
VertexXiType* from=static_cast<VertexXiType*>(_vertices[0]);
// chain rule to get the Jacobian of the nodes in the manifold domain
const JacobianXiOplusType& A = jacobianOplusXi();
const InformationType& omega = _information;
bool istatus = !from->fixed();
if (istatus) {
#ifdef G2O_OPENMP
from->lockQuadraticForm();
#endif
if (this->robustKernel()) {
double error = this->chi2();
Eigen::Vector3d rho;
this->robustKernel()->robustify(error, rho);
InformationType weightedOmega = this->robustInformation(rho);
from->b().noalias() -= rho[1] * A.transpose() * omega * _error;
from->A().noalias() += A.transpose() * weightedOmega * A;
} else {
from->b().noalias() -= A.transpose() * omega * _error;
from->A().noalias() += A.transpose() * omega * A;
}
#ifdef G2O_OPENMP
from->unlockQuadraticForm();
#endif
}
}
template <int D, typename E, typename VertexXiType>
void BaseUnaryEdge<D, E, VertexXiType>::linearizeOplus(JacobianWorkspace& jacobianWorkspace)
{
new (&_jacobianOplusXi) JacobianXiOplusType(jacobianWorkspace.workspaceForVertex(0), D, VertexXiType::Dimension);
linearizeOplus();
}
template <int D, typename E, typename VertexXiType>
void BaseUnaryEdge<D, E, VertexXiType>::linearizeOplus()
{
//Xi - estimate the jacobian numerically
VertexXiType* vi = static_cast<VertexXiType*>(_vertices[0]);
if (vi->fixed())
return;
#ifdef G2O_OPENMP
vi->lockQuadraticForm();
#endif
const double delta = 1e-9;
const double scalar = 1.0 / (2*delta);
ErrorVector error1;
ErrorVector errorBeforeNumeric = _error;
double add_vi[VertexXiType::Dimension];
std::fill(add_vi, add_vi + VertexXiType::Dimension, 0.0);
// add small step along the unit vector in each dimension
for (int d = 0; d < VertexXiType::Dimension; ++d) {
vi->push();
add_vi[d] = delta;
vi->oplus(add_vi);
computeError();
error1 = _error;
vi->pop();
vi->push();
add_vi[d] = -delta;
vi->oplus(add_vi);
computeError();
vi->pop();
add_vi[d] = 0.0;
_jacobianOplusXi.col(d) = scalar * (error1 - _error);
} // end dimension
_error = errorBeforeNumeric;
#ifdef G2O_OPENMP
vi->unlockQuadraticForm();
#endif
}
template <int D, typename E, typename VertexXiType>
void BaseUnaryEdge<D, E, VertexXiType>::initialEstimate(const OptimizableGraph::VertexSet&, OptimizableGraph::Vertex*)
{
std::cerr << __PRETTY_FUNCTION__ << " is not implemented, please give implementation in your derived class" << std::endl;
}

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// g2o - General Graph Optimization
// Copyright (C) 2011 R. Kuemmerle, G. Grisetti, W. Burgard
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
// IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
// TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
// PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
// TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#ifndef G2O_BASE_VERTEX_H
#define G2O_BASE_VERTEX_H
#include "optimizable_graph.h"
#include "creators.h"
#include "../stuff/macros.h"
#include <Eigen/Core>
#include <Eigen/Dense>
#include <Eigen/Cholesky>
#include <Eigen/StdVector>
#include <stack>
namespace g2o {
using namespace Eigen;
/**
* \brief Templatized BaseVertex
*
* Templatized BaseVertex
* D : minimal dimension of the vertex, e.g., 3 for rotation in 3D
* T : internal type to represent the estimate, e.g., Quaternion for rotation in 3D
*/
template <int D, typename T>
class BaseVertex : public OptimizableGraph::Vertex {
public:
typedef T EstimateType;
typedef std::stack<EstimateType,
std::vector<EstimateType, Eigen::aligned_allocator<EstimateType> > >
BackupStackType;
static const int Dimension = D; ///< dimension of the estimate (minimal) in the manifold space
typedef Eigen::Map<Matrix<double, D, D>, Matrix<double,D,D>::Flags & AlignedBit ? Aligned : Unaligned > HessianBlockType;
public:
BaseVertex();
virtual const double& hessian(int i, int j) const { assert(i<D && j<D); return _hessian(i,j);}
virtual double& hessian(int i, int j) { assert(i<D && j<D); return _hessian(i,j);}
virtual double hessianDeterminant() const {return _hessian.determinant();}
virtual double* hessianData() { return const_cast<double*>(_hessian.data());}
virtual void mapHessianMemory(double* d);
virtual int copyB(double* b_) const {
memcpy(b_, _b.data(), Dimension * sizeof(double));
return Dimension;
}
virtual const double& b(int i) const { assert(i < D); return _b(i);}
virtual double& b(int i) { assert(i < D); return _b(i);}
virtual double* bData() { return _b.data();}
virtual void clearQuadraticForm();
//! updates the current vertex with the direct solution x += H_ii\b_ii
//! @returns the determinant of the inverted hessian
virtual double solveDirect(double lambda=0);
//! return right hand side b of the constructed linear system
Matrix<double, D, 1>& b() { return _b;}
const Matrix<double, D, 1>& b() const { return _b;}
//! return the hessian block associated with the vertex
HessianBlockType& A() { return _hessian;}
const HessianBlockType& A() const { return _hessian;}
virtual void push() { _backup.push(_estimate);}
virtual void pop() { assert(!_backup.empty()); _estimate = _backup.top(); _backup.pop(); updateCache();}
virtual void discardTop() { assert(!_backup.empty()); _backup.pop();}
virtual int stackSize() const {return _backup.size();}
//! return the current estimate of the vertex
const EstimateType& estimate() const { return _estimate;}
//! set the estimate for the vertex also calls updateCache()
void setEstimate(const EstimateType& et) { _estimate = et; updateCache();}
protected:
HessianBlockType _hessian;
Matrix<double, D, 1> _b;
EstimateType _estimate;
BackupStackType _backup;
public:
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
};
#include "base_vertex.hpp"
} // end namespace g2o
#endif

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// g2o - General Graph Optimization
// Copyright (C) 2011 R. Kuemmerle, G. Grisetti, W. Burgard
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
// IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
// TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
// PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
// TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
template <int D, typename T>
BaseVertex<D, T>::BaseVertex() :
OptimizableGraph::Vertex(),
_hessian(0, D, D)
{
_dimension = D;
}
template <int D, typename T>
double BaseVertex<D, T>::solveDirect(double lambda) {
Matrix <double, D, D> tempA=_hessian + Matrix <double, D, D>::Identity()*lambda;
double det=tempA.determinant();
if (g2o_isnan(det) || det < std::numeric_limits<double>::epsilon())
return det;
Matrix <double, D, 1> dx=tempA.llt().solve(_b);
oplus(&dx[0]);
return det;
}
template <int D, typename T>
void BaseVertex<D, T>::clearQuadraticForm() {
_b.setZero();
}
template <int D, typename T>
void BaseVertex<D, T>::mapHessianMemory(double* d)
{
new (&_hessian) HessianBlockType(d, D, D);
}

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// g2o - General Graph Optimization
// Copyright (C) 2011 R. Kuemmerle, G. Grisetti, W. Burgard
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
// IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
// TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
// PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
// TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#include "batch_stats.h"
#include <cstring>
namespace g2o {
using namespace std;
G2OBatchStatistics* G2OBatchStatistics::_globalStats=0;
#ifndef PTHING
#define PTHING(s) \
#s << "= " << (st.s) << "\t "
#endif
G2OBatchStatistics::G2OBatchStatistics(){
// zero all.
memset (this, 0, sizeof(G2OBatchStatistics));
// set the iteration to -1 to show that it isn't valid
iteration = -1;
}
std::ostream& operator << (std::ostream& os , const G2OBatchStatistics& st)
{
os << PTHING(iteration);
os << PTHING( numVertices ); // how many vertices are involved
os << PTHING( numEdges ); // hoe many edges
os << PTHING( chi2 ); // total chi2
/** timings **/
// nonlinear part
os << PTHING( timeResiduals );
os << PTHING( timeLinearize ); // jacobians
os << PTHING( timeQuadraticForm ); // construct the quadratic form in the graph
// block_solver (constructs Ax=b, plus maybe schur);
os << PTHING( timeSchurComplement ); // compute schur complement (0 if not done);
// linear solver (computes Ax=b); );
os << PTHING( timeSymbolicDecomposition ); // symbolic decomposition (0 if not done);
os << PTHING( timeNumericDecomposition ); // numeric decomposition (0 if not done);
os << PTHING( timeLinearSolution ); // total time for solving Ax=b
os << PTHING( iterationsLinearSolver ); // iterations of PCG
os << PTHING( timeUpdate ); // oplus
os << PTHING( timeIteration ); // total time );
os << PTHING( levenbergIterations );
os << PTHING( timeLinearSolver);
os << PTHING(hessianDimension);
os << PTHING(hessianPoseDimension);
os << PTHING(hessianLandmarkDimension);
os << PTHING(choleskyNNZ);
os << PTHING(timeMarginals);
return os;
};
void G2OBatchStatistics::setGlobalStats(G2OBatchStatistics* b)
{
_globalStats = b;
}
} // end namespace

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// g2o - General Graph Optimization
// Copyright (C) 2011 R. Kuemmerle, G. Grisetti, W. Burgard
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
// IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
// TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
// PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
// TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#ifndef G2O_BATCH_STATS_H_
#define G2O_BATCH_STATS_H_
#include <iostream>
#include <vector>
namespace g2o {
/**
* \brief statistics about the optimization
*/
struct G2OBatchStatistics {
G2OBatchStatistics();
int iteration; ///< which iteration
int numVertices; ///< how many vertices are involved
int numEdges; ///< how many edges
double chi2; ///< total chi2
/** timings **/
// nonlinear part
double timeResiduals; ///< residuals
double timeLinearize; ///< jacobians
double timeQuadraticForm; ///< construct the quadratic form in the graph
int levenbergIterations; ///< number of iterations performed by LM
// block_solver (constructs Ax=b, plus maybe schur)
double timeSchurComplement; ///< compute schur complement (0 if not done)
// linear solver (computes Ax=b);
double timeSymbolicDecomposition; ///< symbolic decomposition (0 if not done)
double timeNumericDecomposition; ///< numeric decomposition (0 if not done)
double timeLinearSolution; ///< total time for solving Ax=b (including detup for schur)
double timeLinearSolver; ///< time for solving, excluding Schur setup
int iterationsLinearSolver; ///< iterations of PCG, (0 if not used, i.e., Cholesky)
double timeUpdate; ///< time to apply the update
double timeIteration; ///< total time;
double timeMarginals; ///< computing the inverse elements (solve blocks) and thus the marginal covariances
// information about the Hessian matrix
size_t hessianDimension; ///< rows / cols of the Hessian
size_t hessianPoseDimension; ///< dimension of the pose matrix in Schur
size_t hessianLandmarkDimension; ///< dimension of the landmark matrix in Schur
size_t choleskyNNZ; ///< number of non-zeros in the cholesky factor
static G2OBatchStatistics* globalStats() {return _globalStats;}
static void setGlobalStats(G2OBatchStatistics* b);
protected:
static G2OBatchStatistics* _globalStats;
};
std::ostream& operator<<(std::ostream&, const G2OBatchStatistics&);
typedef std::vector<G2OBatchStatistics> BatchStatisticsContainer;
}
#endif

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// g2o - General Graph Optimization
// Copyright (C) 2011 R. Kuemmerle, G. Grisetti, W. Burgard
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
// IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
// TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
// PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
// TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#ifndef G2O_BLOCK_SOLVER_H
#define G2O_BLOCK_SOLVER_H
#include <Eigen/Core>
#include "solver.h"
#include "linear_solver.h"
#include "sparse_block_matrix.h"
#include "sparse_block_matrix_diagonal.h"
#include "openmp_mutex.h"
#include "../../config.h"
namespace g2o {
using namespace Eigen;
/**
* \brief traits to summarize the properties of the fixed size optimization problem
*/
template <int _PoseDim, int _LandmarkDim>
struct BlockSolverTraits
{
static const int PoseDim = _PoseDim;
static const int LandmarkDim = _LandmarkDim;
typedef Matrix<double, PoseDim, PoseDim> PoseMatrixType;
typedef Matrix<double, LandmarkDim, LandmarkDim> LandmarkMatrixType;
typedef Matrix<double, PoseDim, LandmarkDim> PoseLandmarkMatrixType;
typedef Matrix<double, PoseDim, 1> PoseVectorType;
typedef Matrix<double, LandmarkDim, 1> LandmarkVectorType;
typedef SparseBlockMatrix<PoseMatrixType> PoseHessianType;
typedef SparseBlockMatrix<LandmarkMatrixType> LandmarkHessianType;
typedef SparseBlockMatrix<PoseLandmarkMatrixType> PoseLandmarkHessianType;
typedef LinearSolver<PoseMatrixType> LinearSolverType;
};
/**
* \brief traits to summarize the properties of the dynamic size optimization problem
*/
template <>
struct BlockSolverTraits<Eigen::Dynamic, Eigen::Dynamic>
{
static const int PoseDim = Eigen::Dynamic;
static const int LandmarkDim = Eigen::Dynamic;
typedef MatrixXd PoseMatrixType;
typedef MatrixXd LandmarkMatrixType;
typedef MatrixXd PoseLandmarkMatrixType;
typedef VectorXd PoseVectorType;
typedef VectorXd LandmarkVectorType;
typedef SparseBlockMatrix<PoseMatrixType> PoseHessianType;
typedef SparseBlockMatrix<LandmarkMatrixType> LandmarkHessianType;
typedef SparseBlockMatrix<PoseLandmarkMatrixType> PoseLandmarkHessianType;
typedef LinearSolver<PoseMatrixType> LinearSolverType;
};
/**
* \brief base for the block solvers with some basic function interfaces
*/
class BlockSolverBase : public Solver
{
public:
virtual ~BlockSolverBase() {}
/**
* compute dest = H * src
*/
virtual void multiplyHessian(double* dest, const double* src) const = 0;
};
/**
* \brief Implementation of a solver operating on the blocks of the Hessian
*/
template <typename Traits>
class BlockSolver: public BlockSolverBase {
public:
static const int PoseDim = Traits::PoseDim;
static const int LandmarkDim = Traits::LandmarkDim;
typedef typename Traits::PoseMatrixType PoseMatrixType;
typedef typename Traits::LandmarkMatrixType LandmarkMatrixType;
typedef typename Traits::PoseLandmarkMatrixType PoseLandmarkMatrixType;
typedef typename Traits::PoseVectorType PoseVectorType;
typedef typename Traits::LandmarkVectorType LandmarkVectorType;
typedef typename Traits::PoseHessianType PoseHessianType;
typedef typename Traits::LandmarkHessianType LandmarkHessianType;
typedef typename Traits::PoseLandmarkHessianType PoseLandmarkHessianType;
typedef typename Traits::LinearSolverType LinearSolverType;
public:
/**
* allocate a block solver ontop of the underlying linear solver.
* NOTE: The BlockSolver assumes exclusive access to the linear solver and will therefore free the pointer
* in its destructor.
*/
BlockSolver(LinearSolverType* linearSolver);
~BlockSolver();
virtual bool init(SparseOptimizer* optmizer, bool online = false);
virtual bool buildStructure(bool zeroBlocks = false);
virtual bool updateStructure(const std::vector<HyperGraph::Vertex*>& vset, const HyperGraph::EdgeSet& edges);
virtual bool buildSystem();
virtual bool solve();
virtual bool computeMarginals(SparseBlockMatrix<MatrixXd>& spinv, const std::vector<std::pair<int, int> >& blockIndices);
virtual bool setLambda(double lambda, bool backup = false);
virtual void restoreDiagonal();
virtual bool supportsSchur() {return true;}
virtual bool schur() { return _doSchur;}
virtual void setSchur(bool s) { _doSchur = s;}
LinearSolver<PoseMatrixType>* linearSolver() const { return _linearSolver;}
virtual void setWriteDebug(bool writeDebug);
virtual bool writeDebug() const {return _linearSolver->writeDebug();}
virtual bool saveHessian(const std::string& fileName) const;
virtual void multiplyHessian(double* dest, const double* src) const { _Hpp->multiplySymmetricUpperTriangle(dest, src);}
protected:
void resize(int* blockPoseIndices, int numPoseBlocks,
int* blockLandmarkIndices, int numLandmarkBlocks, int totalDim);
void deallocate();
SparseBlockMatrix<PoseMatrixType>* _Hpp;
SparseBlockMatrix<LandmarkMatrixType>* _Hll;
SparseBlockMatrix<PoseLandmarkMatrixType>* _Hpl;
SparseBlockMatrix<PoseMatrixType>* _Hschur;
SparseBlockMatrixDiagonal<LandmarkMatrixType>* _DInvSchur;
SparseBlockMatrixCCS<PoseLandmarkMatrixType>* _HplCCS;
SparseBlockMatrixCCS<PoseMatrixType>* _HschurTransposedCCS;
LinearSolver<PoseMatrixType>* _linearSolver;
std::vector<PoseVectorType, Eigen::aligned_allocator<PoseVectorType> > _diagonalBackupPose;
std::vector<LandmarkVectorType, Eigen::aligned_allocator<LandmarkVectorType> > _diagonalBackupLandmark;
# ifdef G2O_OPENMP
std::vector<OpenMPMutex> _coefficientsMutex;
# endif
bool _doSchur;
double* _coefficients;
double* _bschur;
int _numPoses, _numLandmarks;
int _sizePoses, _sizeLandmarks;
};
//variable size solver
typedef BlockSolver< BlockSolverTraits<Eigen::Dynamic, Eigen::Dynamic> > BlockSolverX;
// solver for BA/3D SLAM
typedef BlockSolver< BlockSolverTraits<6, 3> > BlockSolver_6_3;
// solver fo BA with scale
typedef BlockSolver< BlockSolverTraits<7, 3> > BlockSolver_7_3;
// 2Dof landmarks 3Dof poses
typedef BlockSolver< BlockSolverTraits<3, 2> > BlockSolver_3_2;
} // end namespace
#include "block_solver.hpp"
#endif

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// g2o - General Graph Optimization
// Copyright (C) 2011 R. Kuemmerle, G. Grisetti, W. Burgard
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
// IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
// TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
// PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
// TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#include "sparse_optimizer.h"
#include <Eigen/LU>
#include <fstream>
#include <iomanip>
#include "../stuff/timeutil.h"
#include "../stuff/macros.h"
#include "../stuff/misc.h"
namespace g2o {
using namespace std;
using namespace Eigen;
template <typename Traits>
BlockSolver<Traits>::BlockSolver(LinearSolverType* linearSolver) :
BlockSolverBase(),
_linearSolver(linearSolver)
{
// workspace
_Hpp=0;
_Hll=0;
_Hpl=0;
_HplCCS = 0;
_HschurTransposedCCS = 0;
_Hschur=0;
_DInvSchur=0;
_coefficients=0;
_bschur = 0;
_xSize=0;
_numPoses=0;
_numLandmarks=0;
_sizePoses=0;
_sizeLandmarks=0;
_doSchur=true;
}
template <typename Traits>
void BlockSolver<Traits>::resize(int* blockPoseIndices, int numPoseBlocks,
int* blockLandmarkIndices, int numLandmarkBlocks,
int s)
{
deallocate();
resizeVector(s);
if (_doSchur) {
// the following two are only used in schur
assert(_sizePoses > 0 && "allocating with wrong size");
_coefficients = new double [s];
_bschur = new double[_sizePoses];
}
_Hpp=new PoseHessianType(blockPoseIndices, blockPoseIndices, numPoseBlocks, numPoseBlocks);
if (_doSchur) {
_Hschur=new PoseHessianType(blockPoseIndices, blockPoseIndices, numPoseBlocks, numPoseBlocks);
_Hll=new LandmarkHessianType(blockLandmarkIndices, blockLandmarkIndices, numLandmarkBlocks, numLandmarkBlocks);
_DInvSchur = new SparseBlockMatrixDiagonal<LandmarkMatrixType>(_Hll->colBlockIndices());
_Hpl=new PoseLandmarkHessianType(blockPoseIndices, blockLandmarkIndices, numPoseBlocks, numLandmarkBlocks);
_HplCCS = new SparseBlockMatrixCCS<PoseLandmarkMatrixType>(_Hpl->rowBlockIndices(), _Hpl->colBlockIndices());
_HschurTransposedCCS = new SparseBlockMatrixCCS<PoseMatrixType>(_Hschur->colBlockIndices(), _Hschur->rowBlockIndices());
#ifdef G2O_OPENMP
_coefficientsMutex.resize(numPoseBlocks);
#endif
}
}
template <typename Traits>
void BlockSolver<Traits>::deallocate()
{
if (_Hpp){
delete _Hpp;
_Hpp=0;
}
if (_Hll){
delete _Hll;
_Hll=0;
}
if (_Hpl){
delete _Hpl;
_Hpl = 0;
}
if (_Hschur){
delete _Hschur;
_Hschur=0;
}
if (_DInvSchur){
delete _DInvSchur;
_DInvSchur=0;
}
if (_coefficients) {
delete[] _coefficients;
_coefficients = 0;
}
if (_bschur) {
delete[] _bschur;
_bschur = 0;
}
if (_HplCCS) {
delete _HplCCS;
_HplCCS = 0;
}
if (_HschurTransposedCCS) {
delete _HschurTransposedCCS;
_HschurTransposedCCS = 0;
}
}
template <typename Traits>
BlockSolver<Traits>::~BlockSolver()
{
delete _linearSolver;
deallocate();
}
template <typename Traits>
bool BlockSolver<Traits>::buildStructure(bool zeroBlocks)
{
assert(_optimizer);
size_t sparseDim = 0;
_numPoses=0;
_numLandmarks=0;
_sizePoses=0;
_sizeLandmarks=0;
int* blockPoseIndices = new int[_optimizer->indexMapping().size()];
int* blockLandmarkIndices = new int[_optimizer->indexMapping().size()];
for (size_t i = 0; i < _optimizer->indexMapping().size(); ++i) {
OptimizableGraph::Vertex* v = _optimizer->indexMapping()[i];
int dim = v->dimension();
if (! v->marginalized()){
v->setColInHessian(_sizePoses);
_sizePoses+=dim;
blockPoseIndices[_numPoses]=_sizePoses;
++_numPoses;
} else {
v->setColInHessian(_sizeLandmarks);
_sizeLandmarks+=dim;
blockLandmarkIndices[_numLandmarks]=_sizeLandmarks;
++_numLandmarks;
}
sparseDim += dim;
}
resize(blockPoseIndices, _numPoses, blockLandmarkIndices, _numLandmarks, sparseDim);
delete[] blockLandmarkIndices;
delete[] blockPoseIndices;
// allocate the diagonal on Hpp and Hll
int poseIdx = 0;
int landmarkIdx = 0;
for (size_t i = 0; i < _optimizer->indexMapping().size(); ++i) {
OptimizableGraph::Vertex* v = _optimizer->indexMapping()[i];
if (! v->marginalized()){
//assert(poseIdx == v->hessianIndex());
PoseMatrixType* m = _Hpp->block(poseIdx, poseIdx, true);
if (zeroBlocks)
m->setZero();
v->mapHessianMemory(m->data());
++poseIdx;
} else {
LandmarkMatrixType* m = _Hll->block(landmarkIdx, landmarkIdx, true);
if (zeroBlocks)
m->setZero();
v->mapHessianMemory(m->data());
++landmarkIdx;
}
}
assert(poseIdx == _numPoses && landmarkIdx == _numLandmarks);
// temporary structures for building the pattern of the Schur complement
SparseBlockMatrixHashMap<PoseMatrixType>* schurMatrixLookup = 0;
if (_doSchur) {
schurMatrixLookup = new SparseBlockMatrixHashMap<PoseMatrixType>(_Hschur->rowBlockIndices(), _Hschur->colBlockIndices());
schurMatrixLookup->blockCols().resize(_Hschur->blockCols().size());
}
// here we assume that the landmark indices start after the pose ones
// create the structure in Hpp, Hll and in Hpl
for (SparseOptimizer::EdgeContainer::const_iterator it=_optimizer->activeEdges().begin(); it!=_optimizer->activeEdges().end(); ++it){
OptimizableGraph::Edge* e = *it;
for (size_t viIdx = 0; viIdx < e->vertices().size(); ++viIdx) {
OptimizableGraph::Vertex* v1 = (OptimizableGraph::Vertex*) e->vertex(viIdx);
int ind1 = v1->hessianIndex();
if (ind1 == -1)
continue;
int indexV1Bak = ind1;
for (size_t vjIdx = viIdx + 1; vjIdx < e->vertices().size(); ++vjIdx) {
OptimizableGraph::Vertex* v2 = (OptimizableGraph::Vertex*) e->vertex(vjIdx);
int ind2 = v2->hessianIndex();
if (ind2 == -1)
continue;
ind1 = indexV1Bak;
bool transposedBlock = ind1 > ind2;
if (transposedBlock){ // make sure, we allocate the upper triangle block
swap(ind1, ind2);
}
if (! v1->marginalized() && !v2->marginalized()){
PoseMatrixType* m = _Hpp->block(ind1, ind2, true);
if (zeroBlocks)
m->setZero();
e->mapHessianMemory(m->data(), viIdx, vjIdx, transposedBlock);
if (_Hschur) {// assume this is only needed in case we solve with the schur complement
schurMatrixLookup->addBlock(ind1, ind2);
}
} else if (v1->marginalized() && v2->marginalized()){
// RAINER hmm.... should we ever reach this here????
LandmarkMatrixType* m = _Hll->block(ind1-_numPoses, ind2-_numPoses, true);
if (zeroBlocks)
m->setZero();
e->mapHessianMemory(m->data(), viIdx, vjIdx, false);
} else {
if (v1->marginalized()){
PoseLandmarkMatrixType* m = _Hpl->block(v2->hessianIndex(),v1->hessianIndex()-_numPoses, true);
if (zeroBlocks)
m->setZero();
e->mapHessianMemory(m->data(), viIdx, vjIdx, true); // transpose the block before writing to it
} else {
PoseLandmarkMatrixType* m = _Hpl->block(v1->hessianIndex(),v2->hessianIndex()-_numPoses, true);
if (zeroBlocks)
m->setZero();
e->mapHessianMemory(m->data(), viIdx, vjIdx, false); // directly the block
}
}
}
}
}
if (! _doSchur)
return true;
_DInvSchur->diagonal().resize(landmarkIdx);
_Hpl->fillSparseBlockMatrixCCS(*_HplCCS);
for (size_t i = 0; i < _optimizer->indexMapping().size(); ++i) {
OptimizableGraph::Vertex* v = _optimizer->indexMapping()[i];
if (v->marginalized()){
const HyperGraph::EdgeSet& vedges=v->edges();
for (HyperGraph::EdgeSet::const_iterator it1=vedges.begin(); it1!=vedges.end(); ++it1){
for (size_t i=0; i<(*it1)->vertices().size(); ++i)
{
OptimizableGraph::Vertex* v1= (OptimizableGraph::Vertex*) (*it1)->vertex(i);
if (v1->hessianIndex()==-1 || v1==v)
continue;
for (HyperGraph::EdgeSet::const_iterator it2=vedges.begin(); it2!=vedges.end(); ++it2){
for (size_t j=0; j<(*it2)->vertices().size(); ++j)
{
OptimizableGraph::Vertex* v2= (OptimizableGraph::Vertex*) (*it2)->vertex(j);
if (v2->hessianIndex()==-1 || v2==v)
continue;
int i1=v1->hessianIndex();
int i2=v2->hessianIndex();
if (i1<=i2) {
schurMatrixLookup->addBlock(i1, i2);
}
}
}
}
}
}
}
_Hschur->takePatternFromHash(*schurMatrixLookup);
delete schurMatrixLookup;
_Hschur->fillSparseBlockMatrixCCSTransposed(*_HschurTransposedCCS);
return true;
}
template <typename Traits>
bool BlockSolver<Traits>::updateStructure(const std::vector<HyperGraph::Vertex*>& vset, const HyperGraph::EdgeSet& edges)
{
for (std::vector<HyperGraph::Vertex*>::const_iterator vit = vset.begin(); vit != vset.end(); ++vit) {
OptimizableGraph::Vertex* v = static_cast<OptimizableGraph::Vertex*>(*vit);
int dim = v->dimension();
if (! v->marginalized()){
v->setColInHessian(_sizePoses);
_sizePoses+=dim;
_Hpp->rowBlockIndices().push_back(_sizePoses);
_Hpp->colBlockIndices().push_back(_sizePoses);
_Hpp->blockCols().push_back(typename SparseBlockMatrix<PoseMatrixType>::IntBlockMap());
++_numPoses;
int ind = v->hessianIndex();
PoseMatrixType* m = _Hpp->block(ind, ind, true);
v->mapHessianMemory(m->data());
} else {
std::cerr << "updateStructure(): Schur not supported" << std::endl;
abort();
}
}
resizeVector(_sizePoses + _sizeLandmarks);
for (HyperGraph::EdgeSet::const_iterator it = edges.begin(); it != edges.end(); ++it) {
OptimizableGraph::Edge* e = static_cast<OptimizableGraph::Edge*>(*it);
for (size_t viIdx = 0; viIdx < e->vertices().size(); ++viIdx) {
OptimizableGraph::Vertex* v1 = (OptimizableGraph::Vertex*) e->vertex(viIdx);
int ind1 = v1->hessianIndex();
int indexV1Bak = ind1;
if (ind1 == -1)
continue;
for (size_t vjIdx = viIdx + 1; vjIdx < e->vertices().size(); ++vjIdx) {
OptimizableGraph::Vertex* v2 = (OptimizableGraph::Vertex*) e->vertex(vjIdx);
int ind2 = v2->hessianIndex();
if (ind2 == -1)
continue;
ind1 = indexV1Bak;
bool transposedBlock = ind1 > ind2;
if (transposedBlock) // make sure, we allocate the upper triangular block
swap(ind1, ind2);
if (! v1->marginalized() && !v2->marginalized()) {
PoseMatrixType* m = _Hpp->block(ind1, ind2, true);
e->mapHessianMemory(m->data(), viIdx, vjIdx, transposedBlock);
} else {
std::cerr << __PRETTY_FUNCTION__ << ": not supported" << std::endl;
}
}
}
}
return true;
}
template <typename Traits>
bool BlockSolver<Traits>::solve(){
//cerr << __PRETTY_FUNCTION__ << endl;
if (! _doSchur){
double t=get_monotonic_time();
bool ok = _linearSolver->solve(*_Hpp, _x, _b);
G2OBatchStatistics* globalStats = G2OBatchStatistics::globalStats();
if (globalStats) {
globalStats->timeLinearSolver = get_monotonic_time() - t;
globalStats->hessianDimension = globalStats->hessianPoseDimension = _Hpp->cols();
}
return ok;
}
// schur thing
// backup the coefficient matrix
double t=get_monotonic_time();
// _Hschur = _Hpp, but keeping the pattern of _Hschur
_Hschur->clear();
_Hpp->add(_Hschur);
//_DInvSchur->clear();
memset (_coefficients, 0, _sizePoses*sizeof(double));
# ifdef G2O_OPENMP
# pragma omp parallel for default (shared) schedule(dynamic, 10)
# endif
for (int landmarkIndex = 0; landmarkIndex < static_cast<int>(_Hll->blockCols().size()); ++landmarkIndex) {
const typename SparseBlockMatrix<LandmarkMatrixType>::IntBlockMap& marginalizeColumn = _Hll->blockCols()[landmarkIndex];
assert(marginalizeColumn.size() == 1 && "more than one block in _Hll column");
// calculate inverse block for the landmark
const LandmarkMatrixType * D = marginalizeColumn.begin()->second;
assert (D && D->rows()==D->cols() && "Error in landmark matrix");
LandmarkMatrixType& Dinv = _DInvSchur->diagonal()[landmarkIndex];
Dinv = D->inverse();
LandmarkVectorType db(D->rows());
for (int j=0; j<D->rows(); ++j) {
db[j]=_b[_Hll->rowBaseOfBlock(landmarkIndex) + _sizePoses + j];
}
db=Dinv*db;
assert((size_t)landmarkIndex < _HplCCS->blockCols().size() && "Index out of bounds");
const typename SparseBlockMatrixCCS<PoseLandmarkMatrixType>::SparseColumn& landmarkColumn = _HplCCS->blockCols()[landmarkIndex];
for (typename SparseBlockMatrixCCS<PoseLandmarkMatrixType>::SparseColumn::const_iterator it_outer = landmarkColumn.begin();
it_outer != landmarkColumn.end(); ++it_outer) {
int i1 = it_outer->row;
const PoseLandmarkMatrixType* Bi = it_outer->block;
assert(Bi);
PoseLandmarkMatrixType BDinv = (*Bi)*(Dinv);
assert(_HplCCS->rowBaseOfBlock(i1) < _sizePoses && "Index out of bounds");
typename PoseVectorType::MapType Bb(&_coefficients[_HplCCS->rowBaseOfBlock(i1)], Bi->rows());
# ifdef G2O_OPENMP
ScopedOpenMPMutex mutexLock(&_coefficientsMutex[i1]);
# endif
Bb.noalias() += (*Bi)*db;
assert(i1 >= 0 && i1 < static_cast<int>(_HschurTransposedCCS->blockCols().size()) && "Index out of bounds");
typename SparseBlockMatrixCCS<PoseMatrixType>::SparseColumn::iterator targetColumnIt = _HschurTransposedCCS->blockCols()[i1].begin();
typename SparseBlockMatrixCCS<PoseLandmarkMatrixType>::RowBlock aux(i1, 0);
typename SparseBlockMatrixCCS<PoseLandmarkMatrixType>::SparseColumn::const_iterator it_inner = lower_bound(landmarkColumn.begin(), landmarkColumn.end(), aux);
for (; it_inner != landmarkColumn.end(); ++it_inner) {
int i2 = it_inner->row;
const PoseLandmarkMatrixType* Bj = it_inner->block;
assert(Bj);
while (targetColumnIt->row < i2 /*&& targetColumnIt != _HschurTransposedCCS->blockCols()[i1].end()*/)
++targetColumnIt;
assert(targetColumnIt != _HschurTransposedCCS->blockCols()[i1].end() && targetColumnIt->row == i2 && "invalid iterator, something wrong with the matrix structure");
PoseMatrixType* Hi1i2 = targetColumnIt->block;//_Hschur->block(i1,i2);
assert(Hi1i2);
(*Hi1i2).noalias() -= BDinv*Bj->transpose();
}
}
}
//cerr << "Solve [marginalize] = " << get_monotonic_time()-t << endl;
// _bschur = _b for calling solver, and not touching _b
memcpy(_bschur, _b, _sizePoses * sizeof(double));
for (int i=0; i<_sizePoses; ++i){
_bschur[i]-=_coefficients[i];
}
G2OBatchStatistics* globalStats = G2OBatchStatistics::globalStats();
if (globalStats){
globalStats->timeSchurComplement = get_monotonic_time() - t;
}
t=get_monotonic_time();
bool solvedPoses = _linearSolver->solve(*_Hschur, _x, _bschur);
if (globalStats) {
globalStats->timeLinearSolver = get_monotonic_time() - t;
globalStats->hessianPoseDimension = _Hpp->cols();
globalStats->hessianLandmarkDimension = _Hll->cols();
globalStats->hessianDimension = globalStats->hessianPoseDimension + globalStats->hessianLandmarkDimension;
}
//cerr << "Solve [decompose and solve] = " << get_monotonic_time()-t << endl;
if (! solvedPoses)
return false;
// _x contains the solution for the poses, now applying it to the landmarks to get the new part of the
// solution;
double* xp = _x;
double* cp = _coefficients;
double* xl=_x+_sizePoses;
double* cl=_coefficients + _sizePoses;
double* bl=_b+_sizePoses;
// cp = -xp
for (int i=0; i<_sizePoses; ++i)
cp[i]=-xp[i];
// cl = bl
memcpy(cl,bl,_sizeLandmarks*sizeof(double));
// cl = bl - Bt * xp
//Bt->multiply(cl, cp);
_HplCCS->rightMultiply(cl, cp);
// xl = Dinv * cl
memset(xl,0, _sizeLandmarks*sizeof(double));
_DInvSchur->multiply(xl,cl);
//_DInvSchur->rightMultiply(xl,cl);
//cerr << "Solve [landmark delta] = " << get_monotonic_time()-t << endl;
return true;
}
template <typename Traits>
bool BlockSolver<Traits>::computeMarginals(SparseBlockMatrix<MatrixXd>& spinv, const std::vector<std::pair<int, int> >& blockIndices)
{
double t = get_monotonic_time();
bool ok = _linearSolver->solvePattern(spinv, blockIndices, *_Hpp);
G2OBatchStatistics* globalStats = G2OBatchStatistics::globalStats();
if (globalStats) {
globalStats->timeMarginals = get_monotonic_time() - t;
}
return ok;
}
template <typename Traits>
bool BlockSolver<Traits>::buildSystem()
{
// clear b vector
# ifdef G2O_OPENMP
# pragma omp parallel for default (shared) if (_optimizer->indexMapping().size() > 1000)
# endif
for (int i = 0; i < static_cast<int>(_optimizer->indexMapping().size()); ++i) {
OptimizableGraph::Vertex* v=_optimizer->indexMapping()[i];
assert(v);
v->clearQuadraticForm();
}
_Hpp->clear();
if (_doSchur) {
_Hll->clear();
_Hpl->clear();
}
// resetting the terms for the pairwise constraints
// built up the current system by storing the Hessian blocks in the edges and vertices
# ifndef G2O_OPENMP
// no threading, we do not need to copy the workspace
JacobianWorkspace& jacobianWorkspace = _optimizer->jacobianWorkspace();
# else
// if running with threads need to produce copies of the workspace for each thread
JacobianWorkspace jacobianWorkspace = _optimizer->jacobianWorkspace();
# pragma omp parallel for default (shared) firstprivate(jacobianWorkspace) if (_optimizer->activeEdges().size() > 100)
# endif
for (int k = 0; k < static_cast<int>(_optimizer->activeEdges().size()); ++k) {
OptimizableGraph::Edge* e = _optimizer->activeEdges()[k];
e->linearizeOplus(jacobianWorkspace); // jacobian of the nodes' oplus (manifold)
e->constructQuadraticForm();
# ifndef NDEBUG
for (size_t i = 0; i < e->vertices().size(); ++i) {
const OptimizableGraph::Vertex* v = static_cast<const OptimizableGraph::Vertex*>(e->vertex(i));
if (! v->fixed()) {
bool hasANan = arrayHasNaN(jacobianWorkspace.workspaceForVertex(i), e->dimension() * v->dimension());
if (hasANan) {
cerr << "buildSystem(): NaN within Jacobian for edge " << e << " for vertex " << i << endl;
break;
}
}
}
# endif
}
// flush the current system in a sparse block matrix
# ifdef G2O_OPENMP
# pragma omp parallel for default (shared) if (_optimizer->indexMapping().size() > 1000)
# endif
for (int i = 0; i < static_cast<int>(_optimizer->indexMapping().size()); ++i) {
OptimizableGraph::Vertex* v=_optimizer->indexMapping()[i];
int iBase = v->colInHessian();
if (v->marginalized())
iBase+=_sizePoses;
v->copyB(_b+iBase);
}
return 0;
}
template <typename Traits>
bool BlockSolver<Traits>::setLambda(double lambda, bool backup)
{
if (backup) {
_diagonalBackupPose.resize(_numPoses);
_diagonalBackupLandmark.resize(_numLandmarks);
}
# ifdef G2O_OPENMP
# pragma omp parallel for default (shared) if (_numPoses > 100)
# endif
for (int i = 0; i < _numPoses; ++i) {
PoseMatrixType *b=_Hpp->block(i,i);
if (backup)
_diagonalBackupPose[i] = b->diagonal();
b->diagonal().array() += lambda;
}
# ifdef G2O_OPENMP
# pragma omp parallel for default (shared) if (_numLandmarks > 100)
# endif
for (int i = 0; i < _numLandmarks; ++i) {
LandmarkMatrixType *b=_Hll->block(i,i);
if (backup)
_diagonalBackupLandmark[i] = b->diagonal();
b->diagonal().array() += lambda;
}
return true;
}
template <typename Traits>
void BlockSolver<Traits>::restoreDiagonal()
{
assert((int) _diagonalBackupPose.size() == _numPoses && "Mismatch in dimensions");
assert((int) _diagonalBackupLandmark.size() == _numLandmarks && "Mismatch in dimensions");
for (int i = 0; i < _numPoses; ++i) {
PoseMatrixType *b=_Hpp->block(i,i);
b->diagonal() = _diagonalBackupPose[i];
}
for (int i = 0; i < _numLandmarks; ++i) {
LandmarkMatrixType *b=_Hll->block(i,i);
b->diagonal() = _diagonalBackupLandmark[i];
}
}
template <typename Traits>
bool BlockSolver<Traits>::init(SparseOptimizer* optimizer, bool online)
{
_optimizer = optimizer;
if (! online) {
if (_Hpp)
_Hpp->clear();
if (_Hpl)
_Hpl->clear();
if (_Hll)
_Hll->clear();
}
_linearSolver->init();
return true;
}
template <typename Traits>
void BlockSolver<Traits>::setWriteDebug(bool writeDebug)
{
_linearSolver->setWriteDebug(writeDebug);
}
template <typename Traits>
bool BlockSolver<Traits>::saveHessian(const std::string& fileName) const
{
return _Hpp->writeOctave(fileName.c_str(), true);
}
} // end namespace

183
Thirdparty/g2o/g2o/core/cache.cpp vendored Normal file
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@ -0,0 +1,183 @@
// g2o - General Graph Optimization
// Copyright (C) 2011 G. Grisetti, R. Kuemmerle, W. Burgard
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
// IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
// TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
// PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
// TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#include "cache.h"
#include "optimizable_graph.h"
#include "factory.h"
#include <iostream>
namespace g2o {
using namespace std;
Cache::CacheKey::CacheKey() :
_type(), _parameters()
{
}
Cache::CacheKey::CacheKey(const std::string& type_, const ParameterVector& parameters_) :
_type(type_), _parameters(parameters_)
{
}
Cache::Cache(CacheContainer* container_, const ParameterVector& parameters_) :
_updateNeeded(true), _parameters(parameters_), _container(container_)
{
}
bool Cache::CacheKey::operator<(const Cache::CacheKey& c) const{
if (_type < c._type)
return true;
return std::lexicographical_compare (_parameters.begin( ), _parameters.end( ),
c._parameters.begin( ), c._parameters.end( ) );
}
OptimizableGraph::Vertex* Cache::vertex() {
if (container() )
return container()->vertex();
return 0;
}
OptimizableGraph* Cache::graph() {
if (container())
return container()->graph();
return 0;
}
CacheContainer* Cache::container() {
return _container;
}
ParameterVector& Cache::parameters() {
return _parameters;
}
Cache::CacheKey Cache::key() const {
Factory* factory=Factory::instance();
return CacheKey(factory->tag(this), _parameters);
};
void Cache::update(){
if (! _updateNeeded)
return;
for(std::vector<Cache*>::iterator it=_parentCaches.begin(); it!=_parentCaches.end(); it++){
(*it)->update();
}
updateImpl();
_updateNeeded=false;
}
Cache* Cache::installDependency(const std::string& type_, const std::vector<int>& parameterIndices){
ParameterVector pv(parameterIndices.size());
for (size_t i=0; i<parameterIndices.size(); i++){
if (parameterIndices[i]<0 || parameterIndices[i] >=(int)_parameters.size())
return 0;
pv[i]=_parameters[ parameterIndices[i] ];
}
CacheKey k(type_, pv);
if (!container())
return 0;
Cache* c=container()->findCache(k);
if (!c) {
c = container()->createCache(k);
}
if (c)
_parentCaches.push_back(c);
return c;
}
bool Cache::resolveDependancies(){
return true;
}
CacheContainer::CacheContainer(OptimizableGraph::Vertex* vertex_) {
_vertex = vertex_;
}
Cache* CacheContainer::findCache(const Cache::CacheKey& key) {
iterator it=find(key);
if (it==end())
return 0;
return it->second;
}
Cache* CacheContainer::createCache(const Cache::CacheKey& key){
Factory* f = Factory::instance();
HyperGraph::HyperGraphElement* e = f->construct(key.type());
if (!e) {
cerr << __PRETTY_FUNCTION__ << endl;
cerr << "fatal error in creating cache of type " << key.type() << endl;
return 0;
}
Cache* c = dynamic_cast<Cache*>(e);
if (! c){
cerr << __PRETTY_FUNCTION__ << endl;
cerr << "fatal error in creating cache of type " << key.type() << endl;
return 0;
}
c->_container = this;
c->_parameters = key._parameters;
if (c->resolveDependancies()){
insert(make_pair(key,c));
c->update();
return c;
}
return 0;
}
OptimizableGraph::Vertex* CacheContainer::vertex() {
return _vertex;
}
OptimizableGraph* CacheContainer::graph(){
if (_vertex)
return _vertex->graph();
return 0;
}
void CacheContainer::update() {
for (iterator it=begin(); it!=end(); it++){
(it->second)->update();
}
_updateNeeded=false;
}
void CacheContainer::setUpdateNeeded(bool needUpdate) {
_updateNeeded=needUpdate;
for (iterator it=begin(); it!=end(); ++it){
(it->second)->_updateNeeded = needUpdate;
}
}
CacheContainer::~CacheContainer(){
for (iterator it=begin(); it!=end(); ++it){
delete (it->second);
}
}
} // end namespace

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