1 ORB-SLAM2编译环境安装：
参考博客：https://blog.csdn.net/radiantjeral/article/details/82193370
按照链接安装编译环境基本没问题，只有一点需要注意：
打开ORB_SLAM2/Examples/ROS/ORB_SLAM2文件夹下的CMakeLists.txt，在set(LIBS ...)中同样添加上述的两个库文件。
/usr/lib/x86_64-linux-gnu/libboost_system.so
/usr/lib/x86_64-linux-gnu/libboost_filesystem.so
修改以上两条的原因是编译过程中可能会遇到cmake找不到libboost_system.so和libboost_filesystem.so的情况，也许你不会遇到。当然，上面两个库的路径要根据自己的实际情况进行适当修改，可以通过$ locate libboost_system.so来进行定位。
2 明确双目SLAM基本任务：
（1）提取特征点 （2）视觉里程计：根据特征点匹配结果进行跟，估计相机位移 （3）优化模块：需要一个后端进行全局的优化
3 ORB-SLAM2中双目任务部分代码：
在 http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets 下载一个序列 Vicon Room 102 大小1.2GB
./Examples/Stereo/stereo_euroc Vocabulary/ORBvoc.txt Examples/Stereo/EuRoC.yaml PATH_TO_SEQUENCE/cam0/data PATH_TO_SEQUENCE/cam1/data Examples/Stereo/EuRoC_TimeStamps/SEQUENCE.txtPATH_TO_SEQUENCE : 为数据集所在路径
例如我的代码输入为：
./Examples/Stereo/stereo_euroc Vocabulary/ORBvoc.txt Examples/Stereo/EuRoC.yaml ~/yu/ttee/mav0/cam0/data ~/yu/ttee/mav0/cam1/data Examples/Stereo/EuRoC_TimeStamps/V102.txt4 入口函数与各线程任务:
Converter.cc #opencv矩阵转化为g2o矩阵
Frame.cc #底层关键帧处理
FrameDrawer.cc #绘制函数 项目中可省略
Initializer.cc #初始化函数
#System入口&进入Track.cc线程
GrabImageStere(imRectLeft, imRectRight) #程序图片入口
mImGray #转化为灰度图像
Frame(mImGray, imGrayRight, mpORBextractorLeft) #构造Frame
StereInitialization（） #初始化函数
mbOnlyTracting() #相机位姿跟踪
UpdateLocalMap() #局部地图跟踪
SearchLocalPoints() #局部地图与当前帧的匹配
PoseOptimization() #最小化投影误差优化位姿
#是否生成关键帧
#生成关键帧
KeyFrame（mCurrentFrame, mpMap, mpKeyFrameDB）
#LocalMapping.cc线程
#mlNewKeyFrames关键帧进入
ProcessNewKeyFrame()#处理新关键帧
#剔除MapPoints
#生成MapPoints
#融合MapPoints
#和当前关键帧相连的关键帧以及MapPoints做局部优化
#剔除关键帧
#LocalClosing线程 闭环检测
#一系列关键帧检测与运算
#sim3计算
#Opyimazer.cc 优化线程
GlobalBundleAdjustment() #Map中所有的MapPoints和关键帧做优化
LocalBundleAdjustment() #会在线程处理完队列中最后一关键帧
PoseOptimization #位姿优化
OptimizeEssentialGraph #在成功进行闭环检测后全局，BA优化前进行
OptimizeSim3 #会在筛选闭环候选帧时用于位姿Sim3优化
5 各线程函数详细注释：
System.cc
/**
* 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 "System.h"
#include "Converter.h"
#include <thread>
#include <pangolin/pangolin.h>
#include <iomanip>
namespace ORB_SLAM2
{
System::System(const string &strVocFile, const string &strSettingsFile, const eSensor sensor,
const bool bUseViewer):mSensor(sensor), mpViewer(static_cast<Viewer*>(NULL)), mbReset(false),mbActivateLocalizationMode(false),
mbDeactivateLocalizationMode(false)
{
// Output welcome message
cout << endl <<
"ORB-SLAM2 Copyright (C) 2014-2016 Raul Mur-Artal, University of Zaragoza." << endl <<
"This program comes with ABSOLUTELY NO WARRANTY;" << endl <<
"This is free software, and you are welcome to redistribute it" << endl <<
"under certain conditions. See LICENSE.txt." << endl << endl;
cout << "Input sensor was set to: ";
if(mSensor==MONOCULAR)
cout << "Monocular" << endl;
else if(mSensor==STEREO)
cout << "Stereo" << endl;
else if(mSensor==RGBD)
cout << "RGB-D" << endl;
//Check settings file
cv::FileStorage fsSettings(strSettingsFile.c_str(), cv::FileStorage::READ);
if(!fsSettings.isOpened())
{
cerr << "Failed to open settings file at: " << strSettingsFile << endl;
exit(-1);
}
//Load ORB Vocabulary
cout << endl << "Loading ORB Vocabulary. This could take a while..." << endl;
mpVocabulary = new ORBVocabulary();
bool bVocLoad = mpVocabulary->loadFromTextFile(strVocFile);
if(!bVocLoad)
{
cerr << "Wrong path to vocabulary. " << endl;
cerr << "Falied to open at: " << strVocFile << endl;
exit(-1);
}
cout << "Vocabulary loaded!" << endl << endl;
//创建关键帧数据库
mpKeyFrameDatabase = new KeyFrameDatabase(*mpVocabulary);
//创建一个地图
mpMap = new Map();
//创建两个显示框
mpFrameDrawer = new FrameDrawer(mpMap);
mpMapDrawer = new MapDrawer(mpMap, strSettingsFile);
//进入四个主要线程
//Initialize the Tracking thread
//(it will live in the main thread of execution, the one that called this constructor)
mpTracker = new Tracking(this, mpVocabulary, mpFrameDrawer, mpMapDrawer,
mpMap, mpKeyFrameDatabase, strSettingsFile, mSensor);
//Initialize the Local Mapping thread and launch
mpLocalMapper = new LocalMapping(mpMap, mSensor==MONOCULAR);
mptLocalMapping = new thread(&ORB_SLAM2::LocalMapping::Run,mpLocalMapper);
//Initialize the Loop Closing thread and launch
mpLoopCloser = new LoopClosing(mpMap, mpKeyFrameDatabase, mpVocabulary, mSensor!=MONOCULAR);
mptLoopClosing = new thread(&ORB_SLAM2::LoopClosing::Run, mpLoopCloser);
//Initialize the Viewer thread and launch
if(bUseViewer)
{
mpViewer = new Viewer(this, mpFrameDrawer,mpMapDrawer,mpTracker,strSettingsFile);
mptViewer = new thread(&Viewer::Run, mpViewer);
mpTracker->SetViewer(mpViewer);
}
//设置各线程之间的关系（其实都是互相关）
//Set pointers between threads
mpTracker->SetLocalMapper(mpLocalMapper);
mpTracker->SetLoopClosing(mpLoopCloser);
mpLocalMapper->SetTracker(mpTracker);
mpLocalMapper->SetLoopCloser(mpLoopCloser);
mpLoopCloser->SetTracker(mpTracker);
mpLoopCloser->SetLocalMapper(mpLocalMapper);
}
//整个双目程序的入口
cv::Mat System::TrackStereo(const cv::Mat &imLeft, const cv::Mat &imRight, const double ×tamp)
{
if(mSensor!=STEREO)
{
cerr << "ERROR: you called TrackStereo but input sensor was not set to STEREO." << endl;
exit(-1);
}
// Check mode change
{
unique_lock<mutex> lock(mMutexMode);
if(mbActivateLocalizationMode)
{
mpLocalMapper->RequestStop();
// Wait until Local Mapping has effectively stopped
while(!mpLocalMapper->isStopped())
{
usleep(1000);
}
mpTracker->InformOnlyTracking(true);
mbActivateLocalizationMode = false;
}
if(mbDeactivateLocalizationMode)
{
mpTracker->InformOnlyTracking(false);
mpLocalMapper->Release();
mbDeactivateLocalizationMode = false;
}
}
// Check reset
{
unique_lock<mutex> lock(mMutexReset);
if(mbReset)
{
mpTracker->Reset();
mbReset = false;
}
}
cv::Mat Tcw = mpTracker->GrabImageStereo(imLeft,imRight,timestamp); //捕获一张图像 真正的程序入口
unique_lock<mutex> lock2(mMutexState);
mTrackingState = mpTracker->mState;
mTrackedMapPoints = mpTracker->mCurrentFrame.mvpMapPoints;
mTrackedKeyPointsUn = mpTracker->mCurrentFrame.mvKeysUn;
return Tcw;
}
//RGBD程序 省略注释
cv::Mat System::TrackRGBD(const cv::Mat &im, const cv::Mat &depthmap, const double ×tamp)
{
if(mSensor!=RGBD)
{
cerr << "ERROR: you called TrackRGBD but input sensor was not set to RGBD." << endl;
exit(-1);
}
// Check mode change
{
unique_lock<mutex> lock(mMutexMode);
if(mbActivateLocalizationMode)
{
mpLocalMapper->RequestStop();
// Wait until Local Mapping has effectively stopped
while(!mpLocalMapper->isStopped())
{
usleep(1000);
}
mpTracker->InformOnlyTracking(true);
mbActivateLocalizationMode = false;
}
if(mbDeactivateLocalizationMode)
{
mpTracker->InformOnlyTracking(false);
mpLocalMapper->Release();
mbDeactivateLocalizationMode = false;
}
}
// Check reset
{
unique_lock<mutex> lock(mMutexReset);
if(mbReset)
{
mpTracker->Reset();
mbReset = false;
}
}
cv::Mat Tcw = mpTracker->GrabImageRGBD(im,depthmap,timestamp);
unique_lock<mutex> lock2(mMutexState);
mTrackingState = mpTracker->mState;
mTrackedMapPoints = mpTracker->mCurrentFrame.mvpMapPoints;
mTrackedKeyPointsUn = mpTracker->mCurrentFrame.mvKeysUn;
return Tcw;
}
//单目程序 省略注释
cv::Mat System::TrackMonocular(const cv::Mat &im, const double ×tamp)
{
if(mSensor!=MONOCULAR)
{
cerr << "ERROR: you called TrackMonocular but input sensor was not set to Monocular." << endl;
exit(-1);
}
// Check mode change
{
unique_lock<mutex> lock(mMutexMode);
if(mbActivateLocalizationMode)
{
mpLocalMapper->RequestStop();
// Wait until Local Mapping has effectively stopped
while(!mpLocalMapper->isStopped())
{
usleep(1000);
}
mpTracker->InformOnlyTracking(true);
mbActivateLocalizationMode = false;
}
if(mbDeactivateLocalizationMode)
{
mpTracker->InformOnlyTracking(false);
mpLocalMapper->Release();
mbDeactivateLocalizationMode = false;
}
}
// Check reset
{
unique_lock<mutex> lock(mMutexReset);
if(mbReset)
{
mpTracker->Reset();
mbReset = false;
}
}
cv::Mat Tcw = mpTracker->GrabImageMonocular(im,timestamp);
unique_lock<mutex> lock2(mMutexState);
mTrackingState = mpTracker->mState;
mTrackedMapPoints = mpTracker->mCurrentFrame.mvpMapPoints;
mTrackedKeyPointsUn = mpTracker->mCurrentFrame.mvKeysUn;
return Tcw;
}
//通过界面选择是否触发定位模式
void System::ActivateLocalizationMode()
{
unique_lock<mutex> lock(mMutexMode);
mbActivateLocalizationMode = true;
}
void System::DeactivateLocalizationMode()
{
unique_lock<mutex> lock(mMutexMode);
mbDeactivateLocalizationMode = true;
}
bool System::MapChanged()
{
static int n=0;
int curn = mpMap->GetLastBigChangeIdx();
if(n<curn)
{
n=curn;
return true;
}
else
return false;
}
void System::Reset()
{
unique_lock<mutex> lock(mMutexReset);
mbReset = true;
}
void System::Shutdown()
{
mpLocalMapper->RequestFinish();
mpLoopCloser->RequestFinish();
if(mpViewer)
{
mpViewer->RequestFinish();
while(!mpViewer->isFinished())
usleep(5000);
}
// Wait until all thread have effectively stopped
while(!mpLocalMapper->isFinished() || !mpLoopCloser->isFinished() || mpLoopCloser->isRunningGBA())
{
usleep(5000);
}
if(mpViewer)
pangolin::BindToContext("ORB-SLAM2: Map Viewer");
}
//保存路径函数
void System::SaveTrajectoryTUM(const string &filename)
{
cout << endl << "Saving camera trajectory to " << filename << " ..." << endl;
if(mSensor==MONOCULAR)
{
cerr << "ERROR: SaveTrajectoryTUM cannot be used for monocular." << endl;
return;
}
vector<KeyFrame*> vpKFs = mpMap->GetAllKeyFrames();
sort(vpKFs.begin(),vpKFs.end(),KeyFrame::lId);
// Transform all keyframes so that the first keyframe is at the origin.
// After a loop closure the first keyframe might not be at the origin.
cv::Mat Two = vpKFs[0]->GetPoseInverse();
ofstream f;
f.open(filename.c_str());
f << fixed;
// Frame pose is stored relative to its reference keyframe (which is optimized by BA and pose graph).
// We need to get first the keyframe pose and then concatenate the relative transformation.
// Frames not localized (tracking failure) are not saved.
// For each frame we have a reference keyframe (lRit), the timestamp (lT) and a flag
// which is true when tracking failed (lbL).
list<ORB_SLAM2::KeyFrame*>::iterator lRit = mpTracker->mlpReferences.begin();
list<double>::iterator lT = mpTracker->mlFrameTimes.begin();
list<bool>::iterator lbL = mpTracker->mlbLost.begin();
for(list<cv::Mat>::iterator lit=mpTracker->mlRelativeFramePoses.begin(),
lend=mpTracker->mlRelativeFramePoses.end();lit!=lend;lit++, lRit++, lT++, lbL++)
{
if(*lbL)
continue;
KeyFrame* pKF = *lRit;
cv::Mat Trw = cv::Mat::eye(4,4,CV_32F);
// If the reference keyframe was culled, traverse the spanning tree to get a suitable keyframe.
while(pKF->isBad())
{
Trw = Trw*pKF->mTcp;
pKF = pKF->GetParent();
}
Trw = Trw*pKF->GetPose()*Two;
cv::Mat Tcw = (*lit)*Trw;
cv::Mat Rwc = Tcw.rowRange(0,3).colRange(0,3).t();
cv::Mat twc = -Rwc*Tcw.rowRange(0,3).col(3);
vector<float> q = Converter::toQuaternion(Rwc);
f << setprecision(6) << *lT << " " << setprecision(9) << twc.at<float>(0) << " " << twc.at<float>(1) << " " << twc.at<float>(2) << " " << q[0] << " " << q[1] << " " << q[2] << " " << q[3] << endl;
}
f.close();
cout << endl << "trajectory saved!" << endl;
}
//保存关键帧
void System::SaveKeyFrameTrajectoryTUM(const string &filename)
{
cout << endl << "Saving keyframe trajectory to " << filename << " ..." << endl;
vector<KeyFrame*> vpKFs = mpMap->GetAllKeyFrames();
sort(vpKFs.begin(),vpKFs.end(),KeyFrame::lId);
// Transform all keyframes so that the first keyframe is at the origin.
// After a loop closure the first keyframe might not be at the origin.
//cv::Mat Two = vpKFs[0]->GetPoseInverse();
ofstream f;
f.open(filename.c_str());
f << fixed;
for(size_t i=0; i<vpKFs.size(); i++)
{
KeyFrame* pKF = vpKFs[i];
// pKF->SetPose(pKF->GetPose()*Two);
if(pKF->isBad())
continue;
cv::Mat R = pKF->GetRotation().t();
vector<float> q = Converter::toQuaternion(R);
cv::Mat t = pKF->GetCameraCenter();
f << setprecision(6) << pKF->mTimeStamp << setprecision(7) << " " << t.at<float>(0) << " " << t.at<float>(1) << " " << t.at<float>(2)
<< " " << q[0] << " " << q[1] << " " << q[2] << " " << q[3] << endl;
}
f.close();
cout << endl << "trajectory saved!" << endl;
}
//另外一种数据集的路径保存函数 省略注释
void System::SaveTrajectoryKITTI(const string &filename)
{
cout << endl << "Saving camera trajectory to " << filename << " ..." << endl;
if(mSensor==MONOCULAR)
{
cerr << "ERROR: SaveTrajectoryKITTI cannot be used for monocular." << endl;
return;
}
vector<KeyFrame*> vpKFs = mpMap->GetAllKeyFrames();
sort(vpKFs.begin(),vpKFs.end(),KeyFrame::lId);
// Transform all keyframes so that the first keyframe is at the origin.
// After a loop closure the first keyframe might not be at the origin.
cv::Mat Two = vpKFs[0]->GetPoseInverse();
ofstream f;
f.open(filename.c_str());
f << fixed;
// Frame pose is stored relative to its reference keyframe (which is optimized by BA and pose graph).
// We need to get first the keyframe pose and then concatenate the relative transformation.
// Frames not localized (tracking failure) are not saved.
// For each frame we have a reference keyframe (lRit), the timestamp (lT) and a flag
// which is true when tracking failed (lbL).
list<ORB_SLAM2::KeyFrame*>::iterator lRit = mpTracker->mlpReferences.begin();
list<double>::iterator lT = mpTracker->mlFrameTimes.begin();
for(list<cv::Mat>::iterator lit=mpTracker->mlRelativeFramePoses.begin(), lend=mpTracker->mlRelativeFramePoses.end();lit!=lend;lit++, lRit++, lT++)
{
ORB_SLAM2::KeyFrame* pKF = *lRit;
cv::Mat Trw = cv::Mat::eye(4,4,CV_32F);
while(pKF->isBad())
{
// cout << "bad parent" << endl;
Trw = Trw*pKF->mTcp;
pKF = pKF->GetParent();
}
Trw = Trw*pKF->GetPose()*Two;
cv::Mat Tcw = (*lit)*Trw;
cv::Mat Rwc = Tcw.rowRange(0,3).colRange(0,3).t();
cv::Mat twc = -Rwc*Tcw.rowRange(0,3).col(3);
f << setprecision(9) << Rwc.at<float>(0,0) << " " << Rwc.at<float>(0,1) << " " << Rwc.at<float>(0,2) << " " << twc.at<float>(0) << " " <<
Rwc.at<float>(1,0) << " " << Rwc.at<float>(1,1) << " " << Rwc.at<float>(1,2) << " " << twc.at<float>(1) << " " <<
Rwc.at<float>(2,0) << " " << Rwc.at<float>(2,1) << " " << Rwc.at<float>(2,2) << " " << twc.at<float>(2) << endl;
}
f.close();
cout << endl << "trajectory saved!" << endl;
}
int System::GetTrackingState()
{
unique_lock<mutex> lock(mMutexState);
return mTrackingState;
}
vector<MapPoint*> System::GetTrackedMapPoints()
{
unique_lock<mutex> lock(mMutexState);
return mTrackedMapPoints;
}
vector<cv::KeyPoint> System::GetTrackedKeyPointsUn()
{
unique_lock<mutex> lock(mMutexState);
return mTrackedKeyPointsUn;
}
} //namespace ORB_SLAM
Tracking.cc
/**
* 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 "Tracking.h"
#include<opencv2/core/core.hpp>
#include<opencv2/features2d/features2d.hpp>
#include"ORBmatcher.h"
#include"FrameDrawer.h"
#include"Converter.h"
#include"Map.h"
#include"Initializer.h"
#include"Optimizer.h"
#include"PnPsolver.h"
#include<iostream>
#include<mutex>
using namespace std;
namespace ORB_SLAM2
{
Tracking::Tracking(System *pSys, ORBVocabulary* pVoc, FrameDrawer *pFrameDrawer, MapDrawer *pMapDrawer, Map *pMap, KeyFrameDatabase* pKFDB, const string &strSettingPath, const int sensor):
mState(NO_IMAGES_YET), mSensor(sensor), mbOnlyTracking(false), mbVO(false), mpORBVocabulary(pVoc),
mpKeyFrameDB(pKFDB), mpInitializer(static_cast<Initializer*>(NULL)), mpSystem(pSys), mpViewer(NULL),
mpFrameDrawer(pFrameDrawer), mpMapDrawer(pMapDrawer), mpMap(pMap), mnLastRelocFrameId(0)
{
// Load camera parameters from settings file
cv::FileStorage fSettings(strSettingPath, cv::FileStorage::READ);
float fx = fSettings["Camera.fx"];
float fy = fSettings["Camera.fy"];
float cx = fSettings["Camera.cx"];
float cy = fSettings["Camera.cy"];
cv::Mat 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;
K.copyTo(mK);
cv::Mat DistCoef(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;
}
DistCoef.copyTo(mDistCoef);
mbf = fSettings["Camera.bf"];
float fps = fSettings["Camera.fps"];
if(fps==0)
fps=30;
// Max/Min Frames to insert keyframes and to check relocalisation
mMinFrames = 0;
mMaxFrames = fps;
cout << endl << "Camera Parameters: " << endl;
cout << "- fx: " << fx << endl;
cout << "- fy: " << fy << endl;
cout << "- cx: " << cx << endl;
cout << "- cy: " << cy << endl;
cout << "- k1: " << DistCoef.at<float>(0) << endl;
cout << "- k2: " << DistCoef.at<float>(1) << endl;
if(DistCoef.rows==5)
cout << "- k3: " << DistCoef.at<float>(4) << endl;
cout << "- p1: " << DistCoef.at<float>(2) << endl;
cout << "- p2: " << DistCoef.at<float>(3) << endl;
cout << "- fps: " << fps << endl;
int nRGB = fSettings["Camera.RGB"];
mbRGB = nRGB;
if(mbRGB)
cout << "- color order: RGB (ignored if grayscale)" << endl;
else
cout << "- color order: BGR (ignored if grayscale)" << endl;
// Load ORB parameters
int nFeatures = fSettings["ORBextractor.nFeatures"];
float fScaleFactor = fSettings["ORBextractor.scaleFactor"];
int nLevels = fSettings["ORBextractor.nLevels"];
int fIniThFAST = fSettings["ORBextractor.iniThFAST"];
int fMinThFAST = fSettings["ORBextractor.minThFAST"];
mpORBextractorLeft = new ORBextractor(nFeatures,fScaleFactor,nLevels,fIniThFAST,fMinThFAST);
if(sensor==System::STEREO)
mpORBextractorRight = new ORBextractor(nFeatures,fScaleFactor,nLevels,fIniThFAST,fMinThFAST);
if(sensor==System::MONOCULAR)
mpIniORBextractor = new ORBextractor(2*nFeatures,fScaleFactor,nLevels,fIniThFAST,fMinThFAST);
cout << endl << "ORB Extractor Parameters: " << endl;
cout << "- Number of Features: " << nFeatures << endl;
cout << "- Scale Levels: " << nLevels << endl;
cout << "- Scale Factor: " << fScaleFactor << endl;
cout << "- Initial Fast Threshold: " << fIniThFAST << endl;
cout << "- Minimum Fast Threshold: " << fMinThFAST << endl;
if(sensor==System::STEREO || sensor==System::RGBD)
{
mThDepth = mbf*(float)fSettings["ThDepth"]/fx;
cout << endl << "Depth Threshold (Close/Far Points): " << mThDepth << endl;
}
if(sensor==System::RGBD)
{
mDepthMapFactor = fSettings["DepthMapFactor"];
if(fabs(mDepthMapFactor)<1e-5)
mDepthMapFactor=1;
else
mDepthMapFactor = 1.0f/mDepthMapFactor;
}
}
void Tracking::SetLocalMapper(LocalMapping *pLocalMapper)
{
mpLocalMapper=pLocalMapper;
}
void Tracking::SetLoopClosing(LoopClosing *pLoopClosing)
{
mpLoopClosing=pLoopClosing;
}
void Tracking::SetViewer(Viewer *pViewer)
{
mpViewer=pViewer;
}
//程序入口
cv::Mat Tracking::GrabImageStereo(const cv::Mat &imRectLeft, const cv::Mat &imRectRight, const double ×tamp)
{
mImGray = imRectLeft;
cv::Mat imGrayRight = imRectRight;
//步骤1：将RGB图像转化为灰度图像
if(mImGray.channels()==3)
{
if(mbRGB)
{
cvtColor(mImGray,mImGray,CV_RGB2GRAY);
cvtColor(imGrayRight,imGrayRight,CV_RGB2GRAY);
}
else
{
cvtColor(mImGray,mImGray,CV_BGR2GRAY);
cvtColor(imGrayRight,imGrayRight,CV_BGR2GRAY);
}
}
else if(mImGray.channels()==4)
{
if(mbRGB)
{
cvtColor(mImGray,mImGray,CV_RGBA2GRAY);
cvtColor(imGrayRight,imGrayRight,CV_RGBA2GRAY);
}
else
{
cvtColor(mImGray,mImGray,CV_BGRA2GRAY);
cvtColor(imGrayRight,imGrayRight,CV_BGRA2GRAY);
}
}
//步骤2：构造Frame
//imGrayRight 捕获左目图像
//timestamp 时间戳
//mpORBextractorLeft,mpORBextractorRight 构造左右目ORB算子
mCurrentFrame = Frame(mImGray,imGrayRight,timestamp,mpORBextractorLeft,mpORBextractorRight,mpORBVocabulary,mK,mDistCoef,mbf,mThDepth);
//步骤3：跟踪
Track();
return mCurrentFrame.mTcw.clone();
}
//RGBD程序 省略注释
cv::Mat Tracking::GrabImageRGBD(const cv::Mat &imRGB,const cv::Mat &imD, const double ×tamp)
{
mImGray = imRGB;
cv::Mat imDepth = imD;
if(mImGray.channels()==3)
{
if(mbRGB)
cvtColor(mImGray,mImGray,CV_RGB2GRAY);
else
cvtColor(mImGray,mImGray,CV_BGR2GRAY);
}
else if(mImGray.channels()==4)
{
if(mbRGB)
cvtColor(mImGray,mImGray,CV_RGBA2GRAY);
else
cvtColor(mImGray,mImGray,CV_BGRA2GRAY);
}
if((fabs(mDepthMapFactor-1.0f)>1e-5) || imDepth.type()!=CV_32F)
imDepth.convertTo(imDepth,CV_32F,mDepthMapFactor);
mCurrentFrame = Frame(mImGray,imDepth,timestamp,mpORBextractorLeft,mpORBVocabulary,mK,mDistCoef,mbf,mThDepth);
Track();
return mCurrentFrame.mTcw.clone();
}
//单目程序 省略注释
cv::Mat Tracking::GrabImageMonocular(const cv::Mat &im, const double ×tamp)
{
mImGray = im;
if(mImGray.channels()==3)
{
if(mbRGB)
cvtColor(mImGray,mImGray,CV_RGB2GRAY);
else
cvtColor(mImGray,mImGray,CV_BGR2GRAY);
}
else if(mImGray.channels()==4)
{
if(mbRGB)
cvtColor(mImGray,mImGray,CV_RGBA2GRAY);
else
cvtColor(mImGray,mImGray,CV_BGRA2GRAY);
}
if(mState==NOT_INITIALIZED || mState==NO_IMAGES_YET)
mCurrentFrame = Frame(mImGray,timestamp,mpIniORBextractor,mpORBVocabulary,mK,mDistCoef,mbf,mThDepth);
else
mCurrentFrame = Frame(mImGray,timestamp,mpORBextractorLeft,mpORBVocabulary,mK,mDistCoef,mbf,mThDepth);
Track();
return mCurrentFrame.mTcw.clone();
}
//Trick主线程
void Tracking::Track()
{
//track包括两部分：估计运动 跟踪局部地图
//如果图像复位，或者第一次运行，则为 NO_IMAGES_YET
if(mState==NO_IMAGES_YET)
{
mState = NOT_INITIALIZED;
}
mLastProcessedState=mState;
// Get Map Mutex -> Map cannot be changed
unique_lock<mutex> lock(mpMap->mMutexMapUpdate);
//步骤1：初始化
if(mState==NOT_INITIALIZED)
{
if(mSensor==System::STEREO || mSensor==System::RGBD)
StereoInitialization();
else
MonocularInitialization();
mpFrameDrawer->Update(this);
if(mState!=OK)
return;
}
else //步骤2：跟踪
{
// System is initialized. Track Frame.
bool bOK; //bOK为临时变量，用于表示每个函数是否运行成功
// Initial camera pose estimation using motion model or relocalization (if tracking is lost)
// 在viewer中有个开关，menuLocalizationMode，由它控制是否ActivateLocalizationMode，并最终管控mbOnlyTracting
// mbOnlyTracking等于false表示VD模式（有地图更新）mbOnlyTracking等于true表示用户手动选择定位模式
if(!mbOnlyTracking)
{
// Local Mapping is activated. This is the normal behaviour, unless
// you explicitly activate the "only tracking" mode.
//正式初始化成功
if(mState==OK)
{
// Local Mapping might have changed some MapPoints tracked in last frame
// 检查幷更新上一帧的MapPoints
// 对于双目和RGBD，UpdateLastFrame函数会为上一帧添加MapPoints
// 上一帧的MapPoints主要用来SearchByProjection时加速匹配
CheckReplacedInLastFrame();
// 步骤2.1：跟踪上一帧或者参考帧或者重定位
// 运动模式时空的或者刚完成重定位
// mCurrentFrame.mnIdmnLastRelocFrame+2这个函数判断不应该有
// 应该只要mVelocity不为空，就优先选择TrackwithMotionModel
// mnLastRelocFrameId上一次重定位的那一帧
if(mVelocity.empty() || mCurrentFrame.mnId<mnLastRelocFrameId+2)
{
bOK = TrackReferenceKeyFrame();
}
else
{
bOK = TrackWithMotionModel();
if(!bOK)
bOK = TrackReferenceKeyFrame();
}
}
else
{
bOK = Relocalization();
}
}
else
{
// Localization Mode: Local Mapping is deactivated
if(mState==LOST)
{
bOK = Relocalization();
}
else
{
if(!mbVO)
{
// In last frame we tracked enough MapPoints in the map
if(!mVelocity.empty())
{
bOK = TrackWithMotionModel();
}
else
{
bOK = TrackReferenceKeyFrame();
}
}
else
{
// In last frame we tracked mainly "visual odometry" points.
// We compute two camera poses, one from motion model and one doing relocalization.
// If relocalization is sucessfull we choose that solution, otherwise we retain
// the "visual odometry" solution.
bool bOKMM = false;
bool bOKReloc = false;
vector<MapPoint*> vpMPsMM;
vector<bool> vbOutMM;
cv::Mat TcwMM;
if(!mVelocity.empty())
{
bOKMM = TrackWithMotionModel();
vpMPsMM = mCurrentFrame.mvpMapPoints;
vbOutMM = mCurrentFrame.mvbOutlier;
TcwMM = mCurrentFrame.mTcw.clone();
}
bOKReloc = Relocalization();
if(bOKMM && !bOKReloc)
{
mCurrentFrame.SetPose(TcwMM);
mCurrentFrame.mvpMapPoints = vpMPsMM;
mCurrentFrame.mvbOutlier = vbOutMM;
if(mbVO)
{
for(int i =0; i<mCurrentFrame.N; i++)
{
if(mCurrentFrame.mvpMapPoints[i] && !mCurrentFrame.mvbOutlier[i])
{
mCurrentFrame.mvpMapPoints[i]->IncreaseFound();
}
}
}
}
else if(bOKReloc)
{
mbVO = false;
}
bOK = bOKReloc || bOKMM;
}
}
}
mCurrentFrame.mpReferenceKF = mpReferenceKF;
// If we have an initial estimation of the camera pose and matching. Track the local map.
if(!mbOnlyTracking)
{
if(bOK)
bOK = TrackLocalMap();
}
else
{
// mbVO true means that there are few matches to MapPoints in the map. We cannot retrieve
// a local map and therefore we do not perform TrackLocalMap(). Once the system relocalizes
// the camera we will use the local map again.
if(bOK && !mbVO)
bOK = TrackLocalMap();
}
if(bOK)
mState = OK;
else
mState=LOST;
// Update drawer
mpFrameDrawer->Update(this);
// If tracking were good, check if we insert a keyframe
if(bOK)
{
// Update motion model
if(!mLastFrame.mTcw.empty())
{
cv::Mat LastTwc = cv::Mat::eye(4,4,CV_32F);
mLastFrame.GetRotationInverse().copyTo(LastTwc.rowRange(0,3).colRange(0,3));
mLastFrame.GetCameraCenter().copyTo(LastTwc.rowRange(0,3).col(3));
mVelocity = mCurrentFrame.mTcw*LastTwc;
}
else
mVelocity = cv::Mat();
mpMapDrawer->SetCurrentCameraPose(mCurrentFrame.mTcw);
// Clean VO matches
for(int i=0; i<mCurrentFrame.N; i++)
{
MapPoint* pMP = mCurrentFrame.mvpMapPoints[i];
if(pMP)
if(pMP->Observations()<1)
{
mCurrentFrame.mvbOutlier[i] = false;
mCurrentFrame.mvpMapPoints[i]=static_cast<MapPoint*>(NULL);
}
}
// Delete temporal MapPoints
for(list<MapPoint*>::iterator lit = mlpTemporalPoints.begin(), lend = mlpTemporalPoints.end(); lit!=lend; lit++)
{
MapPoint* pMP = *lit;
delete pMP;
}
mlpTemporalPoints.clear();
// Check if we need to insert a new keyframe
if(NeedNewKeyFrame())
CreateNewKeyFrame();
// We allow points with high innovation (considererd outliers by the Huber Function)
// pass to the new keyframe, so that bundle adjustment will finally decide
// if they are outliers or not. We don't want next frame to estimate its position
// with those points so we discard them in the frame.
for(int i=0; i<mCurrentFrame.N;i++)
{
if(mCurrentFrame.mvpMapPoints[i] && mCurrentFrame.mvbOutlier[i])
mCurrentFrame.mvpMapPoints[i]=static_cast<MapPoint*>(NULL);
}
}
// Reset if the camera get lost soon after initialization
if(mState==LOST)
{
if(mpMap->KeyFramesInMap()<=5)
{
cout << "Track lost soon after initialisation, reseting..." << endl;
mpSystem->Reset();
return;
}
}
if(!mCurrentFrame.mpReferenceKF)
mCurrentFrame.mpReferenceKF = mpReferenceKF;
mLastFrame = Frame(mCurrentFrame);
}
// Store frame pose information to retrieve the complete camera trajectory afterwards.
if(!mCurrentFrame.mTcw.empty())
{
cv::Mat Tcr = mCurrentFrame.mTcw*mCurrentFrame.mpReferenceKF->GetPoseInverse();
mlRelativeFramePoses.push_back(Tcr);
mlpReferences.push_back(mpReferenceKF);
mlFrameTimes.push_back(mCurrentFrame.mTimeStamp);
mlbLost.push_back(mState==LOST);
}
else
{
// This can happen if tracking is lost
mlRelativeFramePoses.push_back(mlRelativeFramePoses.back());
mlpReferences.push_back(mlpReferences.back());
mlFrameTimes.push_back(mlFrameTimes.back());
mlbLost.push_back(mState==LOST);
}
}
void Tracking::StereoInitialization()
{
if(mCurrentFrame.N>500)
{
// Set Frame pose to the origin
mCurrentFrame.SetPose(cv::Mat::eye(4,4,CV_32F));
// Create KeyFrame
KeyFrame* pKFini = new KeyFrame(mCurrentFrame,mpMap,mpKeyFrameDB);
// Insert KeyFrame in the map
mpMap->AddKeyFrame(pKFini);
// Create MapPoints and asscoiate to KeyFrame
for(int i=0; i<mCurrentFrame.N;i++)
{
float z = mCurrentFrame.mvDepth[i];
if(z>0)
{
cv::Mat x3D = mCurrentFrame.UnprojectStereo(i);
MapPoint* pNewMP = new MapPoint(x3D,pKFini,mpMap);
pNewMP->AddObservation(pKFini,i);
pKFini->AddMapPoint(pNewMP,i);
pNewMP->ComputeDistinctiveDescriptors();
pNewMP->UpdateNormalAndDepth();
mpMap->AddMapPoint(pNewMP);
mCurrentFrame.mvpMapPoints[i]=pNewMP;
}
}
cout << "New map created with " << mpMap->MapPointsInMap() << " points" << endl;
mpLocalMapper->InsertKeyFrame(pKFini);
mLastFrame = Frame(mCurrentFrame);
mnLastKeyFrameId=mCurrentFrame.mnId;
mpLastKeyFrame = pKFini;
mvpLocalKeyFrames.push_back(pKFini);
mvpLocalMapPoints=mpMap->GetAllMapPoints();
mpReferenceKF = pKFini;
mCurrentFrame.mpReferenceKF = pKFini;
mpMap->SetReferenceMapPoints(mvpLocalMapPoints);
mpMap->mvpKeyFrameOrigins.push_back(pKFini);
mpMapDrawer->SetCurrentCameraPose(mCurrentFrame.mTcw);
mState=OK;
}
}
void Tracking::MonocularInitialization()
{
if(!mpInitializer)
{
// Set Reference Frame
if(mCurrentFrame.mvKeys.size()>100)
{
mInitialFrame = Frame(mCurrentFrame);
mLastFrame = Frame(mCurrentFrame);
mvbPrevMatched.resize(mCurrentFrame.mvKeysUn.size());
for(size_t i=0; i<mCurrentFrame.mvKeysUn.size(); i++)
mvbPrevMatched[i]=mCurrentFrame.mvKeysUn[i].pt;
if(mpInitializer)
delete mpInitializer;
mpInitializer = new Initializer(mCurrentFrame,1.0,200);
fill(mvIniMatches.begin(),mvIniMatches.end(),-1);
return;
}
}
else
{
// Try to initialize
if((int)mCurrentFrame.mvKeys.size()<=100)
{
delete mpInitializer;
mpInitializer = static_cast<Initializer*>(NULL);
fill(mvIniMatches.begin(),mvIniMatches.end(),-1);
return;
}
// Find correspondences
ORBmatcher matcher(0.9,true);
int nmatches = matcher.SearchForInitialization(mInitialFrame,mCurrentFrame,mvbPrevMatched,mvIniMatches,100);
// Check if there are enough correspondences
if(nmatches<100)
{
delete mpInitializer;
mpInitializer = static_cast<Initializer*>(NULL);
return;
}
cv::Mat Rcw; // Current Camera Rotation
cv::Mat tcw; // Current Camera Translation
vector<bool> vbTriangulated; // Triangulated Correspondences (mvIniMatches)
if(mpInitializer->Initialize(mCurrentFrame, mvIniMatches, Rcw, tcw, mvIniP3D, vbTriangulated))
{
for(size_t i=0, iend=mvIniMatches.size(); i<iend;i++)
{
if(mvIniMatches[i]>=0 && !vbTriangulated[i])
{
mvIniMatches[i]=-1;
nmatches--;
}
}
// Set Frame Poses
mInitialFrame.SetPose(cv::Mat::eye(4,4,CV_32F));
cv::Mat Tcw = cv::Mat::eye(4,4,CV_32F);
Rcw.copyTo(Tcw.rowRange(0,3).colRange(0,3));
tcw.copyTo(Tcw.rowRange(0,3).col(3));
mCurrentFrame.SetPose(Tcw);
CreateInitialMapMonocular();
}
}
}
void Tracking::CreateInitialMapMonocular()
{
// Create KeyFrames
KeyFrame* pKFini = new KeyFrame(mInitialFrame,mpMap,mpKeyFrameDB);
KeyFrame* pKFcur = new KeyFrame(mCurrentFrame,mpMap,mpKeyFrameDB);
pKFini->ComputeBoW();
pKFcur->ComputeBoW();
// Insert KFs in the map
mpMap->AddKeyFrame(pKFini);
mpMap->AddKeyFrame(pKFcur);
// Create MapPoints and asscoiate to keyframes
for(size_t i=0; i<mvIniMatches.size();i++)
{
if(mvIniMatches[i]<0)
continue;
//Create MapPoint.
cv::Mat worldPos(mvIniP3D[i]);
MapPoint* pMP = new MapPoint(worldPos,pKFcur,mpMap);
pKFini->AddMapPoint(pMP,i);
pKFcur->AddMapPoint(pMP,mvIniMatches[i]);
pMP->AddObservation(pKFini,i);
pMP->AddObservation(pKFcur,mvIniMatches[i]);
pMP->ComputeDistinctiveDescriptors();
pMP->UpdateNormalAndDepth();
//Fill Current Frame structure
mCurrentFrame.mvpMapPoints[mvIniMatches[i]] = pMP;
mCurrentFrame.mvbOutlier[mvIniMatches[i]] = false;
//Add to Map
mpMap->AddMapPoint(pMP);
}
// Update Connections
pKFini->UpdateConnections();
pKFcur->UpdateConnections();
// Bundle Adjustment
cout << "New Map created with " << mpMap->MapPointsInMap() << " points" << endl;
Optimizer::GlobalBundleAdjustemnt(mpMap,20);
// Set median depth to 1
float medianDepth = pKFini->ComputeSceneMedianDepth(2);
float invMedianDepth = 1.0f/medianDepth;
if(medianDepth<0 || pKFcur->TrackedMapPoints(1)<100)
{
cout << "Wrong initialization, reseting..." << endl;
Reset();
return;
}
// Scale initial baseline
cv::Mat Tc2w = pKFcur->GetPose();
Tc2w.col(3).rowRange(0,3) = Tc2w.col(3).rowRange(0,3)*invMedianDepth;
pKFcur->SetPose(Tc2w);
// Scale points
vector<MapPoint*> vpAllMapPoints = pKFini->GetMapPointMatches();
for(size_t iMP=0; iMP<vpAllMapPoints.size(); iMP++)
{
if(vpAllMapPoints[iMP])
{
MapPoint* pMP = vpAllMapPoints[iMP];
pMP->SetWorldPos(pMP->GetWorldPos()*invMedianDepth);
}
}
mpLocalMapper->InsertKeyFrame(pKFini);
mpLocalMapper->InsertKeyFrame(pKFcur);
mCurrentFrame.SetPose(pKFcur->GetPose());
mnLastKeyFrameId=mCurrentFrame.mnId;
mpLastKeyFrame = pKFcur;
mvpLocalKeyFrames.push_back(pKFcur);
mvpLocalKeyFrames.push_back(pKFini);
mvpLocalMapPoints=mpMap->GetAllMapPoints();
mpReferenceKF = pKFcur;
mCurrentFrame.mpReferenceKF = pKFcur;
mLastFrame = Frame(mCurrentFrame);
mpMap->SetReferenceMapPoints(mvpLocalMapPoints);
mpMapDrawer->SetCurrentCameraPose(pKFcur->GetPose());
mpMap->mvpKeyFrameOrigins.push_back(pKFini);
mState=OK;
}
void Tracking::CheckReplacedInLastFrame()
{
for(int i =0; i<mLastFrame.N; i++)
{
MapPoint* pMP = mLastFrame.mvpMapPoints[i];
if(pMP)
{
MapPoint* pRep = pMP->GetReplaced();
if(pRep)
{
mLastFrame.mvpMapPoints[i] = pRep;
}
}
}
}
bool Tracking::TrackReferenceKeyFrame()
{
// Compute Bag of Words vector
mCurrentFrame.ComputeBoW();
// We perform first an ORB matching with the reference keyframe
// If enough matches are found we setup a PnP solver
ORBmatcher matcher(0.7,true);
vector<MapPoint*> vpMapPointMatches;
int nmatches = matcher.SearchByBoW(mpReferenceKF,mCurrentFrame,vpMapPointMatches);
if(nmatches<15)
return false;
mCurrentFrame.mvpMapPoints = vpMapPointMatches;
mCurrentFrame.SetPose(mLastFrame.mTcw);
Optimizer::PoseOptimization(&mCurrentFrame);
// Discard outliers
int nmatchesMap = 0;
for(int i =0; i<mCurrentFrame.N; i++)
{
if(mCurrentFrame.mvpMapPoints[i])
{
if(mCurrentFrame.mvbOutlier[i])
{
MapPoint* pMP = mCurrentFrame.mvpMapPoints[i];
mCurrentFrame.mvpMapPoints[i]=static_cast<MapPoint*>(NULL);
mCurrentFrame.mvbOutlier[i]=false;
pMP->mbTrackInView = false;
pMP->mnLastFrameSeen = mCurrentFrame.mnId;
nmatches--;
}
else if(mCurrentFrame.mvpMapPoints[i]->Observations()>0)
nmatchesMap++;
}
}
return nmatchesMap>=10;
}
void Tracking::UpdateLastFrame()
{
// Update pose according to reference keyframe
KeyFrame* pRef = mLastFrame.mpReferenceKF;
cv::Mat Tlr = mlRelativeFramePoses.back();
mLastFrame.SetPose(Tlr*pRef->GetPose());
if(mnLastKeyFrameId==mLastFrame.mnId || mSensor==System::MONOCULAR || !mbOnlyTracking)
return;
// Create "visual odometry" MapPoints
// We sort points according to their measured depth by the stereo/RGB-D sensor
vector<pair<float,int> > vDepthIdx;
vDepthIdx.reserve(mLastFrame.N);
for(int i=0; i<mLastFrame.N;i++)
{
float z = mLastFrame.mvDepth[i];
if(z>0)
{
vDepthIdx.push_back(make_pair(z,i));
}
}
if(vDepthIdx.empty())
return;
sort(vDepthIdx.begin(),vDepthIdx.end());
// We insert all close points (depth<mThDepth)
// If less than 100 close points, we insert the 100 closest ones.
int nPoints = 0;
for(size_t j=0; j<vDepthIdx.size();j++)
{
int i = vDepthIdx[j].second;
bool bCreateNew = false;
MapPoint* pMP = mLastFrame.mvpMapPoints[i];
if(!pMP)
bCreateNew = true;
else if(pMP->Observations()<1)
{
bCreateNew = true;
}
if(bCreateNew)
{
cv::Mat x3D = mLastFrame.UnprojectStereo(i);
MapPoint* pNewMP = new MapPoint(x3D,mpMap,&mLastFrame,i);
mLastFrame.mvpMapPoints[i]=pNewMP;
mlpTemporalPoints.push_back(pNewMP);
nPoints++;
}
else
{
nPoints++;
}
if(vDepthIdx[j].first>mThDepth && nPoints>100)
break;
}
}
bool Tracking::TrackWithMotionModel()
{
ORBmatcher matcher(0.9,true);
// Update last frame pose according to its reference keyframe
// Create "visual odometry" points if in Localization Mode
UpdateLastFrame();
mCurrentFrame.SetPose(mVelocity*mLastFrame.mTcw);
fill(mCurrentFrame.mvpMapPoints.begin(),mCurrentFrame.mvpMapPoints.end(),static_cast<MapPoint*>(NULL));
// Project points seen in previous frame
int th;
if(mSensor!=System::STEREO)
th=15;
else
th=7;
int nmatches = matcher.SearchByProjection(mCurrentFrame,mLastFrame,th,mSensor==System::MONOCULAR);
// If few matches, uses a wider window search
if(nmatches<20)
{
fill(mCurrentFrame.mvpMapPoints.begin(),mCurrentFrame.mvpMapPoints.end(),static_cast<MapPoint*>(NULL));
nmatches = matcher.SearchByProjection(mCurrentFrame,mLastFrame,2*th,mSensor==System::MONOCULAR);
}
if(nmatches<20)
return false;
// Optimize frame pose with all matches
Optimizer::PoseOptimization(&mCurrentFrame);
// Discard outliers
int nmatchesMap = 0;
for(int i =0; i<mCurrentFrame.N; i++)
{
if(mCurrentFrame.mvpMapPoints[i])
{
if(mCurrentFrame.mvbOutlier[i])
{
MapPoint* pMP = mCurrentFrame.mvpMapPoints[i];
mCurrentFrame.mvpMapPoints[i]=static_cast<MapPoint*>(NULL);
mCurrentFrame.mvbOutlier[i]=false;
pMP->mbTrackInView = false;
pMP->mnLastFrameSeen = mCurrentFrame.mnId;
nmatches--;
}
else if(mCurrentFrame.mvpMapPoints[i]->Observations()>0)
nmatchesMap++;
}
}
if(mbOnlyTracking)
{
mbVO = nmatchesMap<10;
return nmatches>20;
}
return nmatchesMap>=10;
}
bool Tracking::TrackLocalMap()
{
// We have an estimation of the camera pose and some map points tracked in the frame.
// We retrieve the local map and try to find matches to points in the local map.
UpdateLocalMap();
SearchLocalPoints();
// Optimize Pose
Optimizer::PoseOptimization(&mCurrentFrame);
mnMatchesInliers = 0;
// Update MapPoints Statistics
for(int i=0; i<mCurrentFrame.N; i++)
{
if(mCurrentFrame.mvpMapPoints[i])
{
if(!mCurrentFrame.mvbOutlier[i])
{
mCurrentFrame.mvpMapPoints[i]->IncreaseFound();
if(!mbOnlyTracking)
{
if(mCurrentFrame.mvpMapPoints[i]->Observations()>0)
mnMatchesInliers++;
}
else
mnMatchesInliers++;
}
else if(mSensor==System::STEREO)
mCurrentFrame.mvpMapPoints[i] = static_cast<MapPoint*>(NULL);
}
}
// Decide if the tracking was succesful
// More restrictive if there was a relocalization recently
if(mCurrentFrame.mnId<mnLastRelocFrameId+mMaxFrames && mnMatchesInliers<50)
return false;
if(mnMatchesInliers<30)
return false;
else
return true;
}
bool Tracking::NeedNewKeyFrame()
{
if(mbOnlyTracking)
return false;
// If Local Mapping is freezed by a Loop Closure do not insert keyframes
if(mpLocalMapper->isStopped() || mpLocalMapper->stopRequested())
return false;
const int nKFs = mpMap->KeyFramesInMap();
// Do not insert keyframes if not enough frames have passed from last relocalisation
if(mCurrentFrame.mnId<mnLastRelocFrameId+mMaxFrames && nKFs>mMaxFrames)
return false;
// Tracked MapPoints in the reference keyframe
int nMinObs = 3;
if(nKFs<=2)
nMinObs=2;
int nRefMatches = mpReferenceKF->TrackedMapPoints(nMinObs);
// Local Mapping accept keyframes?
bool bLocalMappingIdle = mpLocalMapper->AcceptKeyFrames();
// Check how many "close" points are being tracked and how many could be potentially created.
int nNonTrackedClose = 0;
int nTrackedClose= 0;
if(mSensor!=System::MONOCULAR)
{
for(int i =0; i<mCurrentFrame.N; i++)
{
if(mCurrentFrame.mvDepth[i]>0 && mCurrentFrame.mvDepth[i]<mThDepth)
{
if(mCurrentFrame.mvpMapPoints[i] && !mCurrentFrame.mvbOutlier[i])
nTrackedClose++;
else
nNonTrackedClose++;
}
}
}
bool bNeedToInsertClose = (nTrackedClose<100) && (nNonTrackedClose>70);
// Thresholds
float thRefRatio = 0.75f;
if(nKFs<2)
thRefRatio = 0.4f;
if(mSensor==System::MONOCULAR)
thRefRatio = 0.9f;
// Condition 1a: More than "MaxFrames" have passed from last keyframe insertion
const bool c1a = mCurrentFrame.mnId>=mnLastKeyFrameId+mMaxFrames;
// Condition 1b: More than "MinFrames" have passed and Local Mapping is idle
const bool c1b = (mCurrentFrame.mnId>=mnLastKeyFrameId+mMinFrames && bLocalMappingIdle);
//Condition 1c: tracking is weak
const bool c1c = mSensor!=System::MONOCULAR && (mnMatchesInliers<nRefMatches*0.25 || bNeedToInsertClose) ;
// Condition 2: Few tracked points compared to reference keyframe. Lots of visual odometry compared to map matches.
const bool c2 = ((mnMatchesInliers<nRefMatches*thRefRatio|| bNeedToInsertClose) && mnMatchesInliers>15);
if((c1a||c1b||c1c)&&c2)
{
// If the mapping accepts keyframes, insert keyframe.
// Otherwise send a signal to interrupt BA
if(bLocalMappingIdle)
{
return true;
}
else
{
mpLocalMapper->InterruptBA();
if(mSensor!=System::MONOCULAR)
{
if(mpLocalMapper->KeyframesInQueue()<3)
return true;
else
return false;
}
else
return false;
}
}
else
return false;
}
void Tracking::CreateNewKeyFrame()
{
if(!mpLocalMapper->SetNotStop(true))
return;
KeyFrame* pKF = new KeyFrame(mCurrentFrame,mpMap,mpKeyFrameDB);
mpReferenceKF = pKF;
mCurrentFrame.mpReferenceKF = pKF;
if(mSensor!=System::MONOCULAR)
{
mCurrentFrame.UpdatePoseMatrices();
// We sort points by the measured depth by the stereo/RGBD sensor.
// We create all those MapPoints whose depth < mThDepth.
// If there are less than 100 close points we create the 100 closest.
vector<pair<float,int> > vDepthIdx;
vDepthIdx.reserve(mCurrentFrame.N);
for(int i=0; i<mCurrentFrame.N; i++)
{
float z = mCurrentFrame.mvDepth[i];
if(z>0)
{
vDepthIdx.push_back(make_pair(z,i));
}
}
if(!vDepthIdx.empty())
{
sort(vDepthIdx.begin(),vDepthIdx.end());
int nPoints = 0;
for(size_t j=0; j<vDepthIdx.size();j++)
{
int i = vDepthIdx[j].second;
bool bCreateNew = false;
MapPoint* pMP = mCurrentFrame.mvpMapPoints[i];
if(!pMP)
bCreateNew = true;
else if(pMP->Observations()<1)
{
bCreateNew = true;
mCurrentFrame.mvpMapPoints[i] = static_cast<MapPoint*>(NULL);
}
if(bCreateNew)
{
cv::Mat x3D = mCurrentFrame.UnprojectStereo(i);
MapPoint* pNewMP = new MapPoint(x3D,pKF,mpMap);
pNewMP->AddObservation(pKF,i);
pKF->AddMapPoint(pNewMP,i);
pNewMP->ComputeDistinctiveDescriptors();
pNewMP->UpdateNormalAndDepth();
mpMap->AddMapPoint(pNewMP);
mCurrentFrame.mvpMapPoints[i]=pNewMP;
nPoints++;
}
else
{
nPoints++;
}
if(vDepthIdx[j].first>mThDepth && nPoints>100)
break;
}
}
}
mpLocalMapper->InsertKeyFrame(pKF);
mpLocalMapper->SetNotStop(false);
mnLastKeyFrameId = mCurrentFrame.mnId;
mpLastKeyFrame = pKF;
}
void Tracking::SearchLocalPoints()
{
// Do not search map points already matched
for(vector<MapPoint*>::iterator vit=mCurrentFrame.mvpMapPoints.begin(), vend=mCurrentFrame.mvpMapPoints.end(); vit!=vend; vit++)
{
MapPoint* pMP = *vit;
if(pMP)
{
if(pMP->isBad())
{
*vit = static_cast<MapPoint*>(NULL);
}
else
{
pMP->IncreaseVisible();
pMP->mnLastFrameSeen = mCurrentFrame.mnId;
pMP->mbTrackInView = false;
}
}
}
int nToMatch=0;
// Project points in frame and check its visibility
for(vector<MapPoint*>::iterator vit=mvpLocalMapPoints.begin(), vend=mvpLocalMapPoints.end(); vit!=vend; vit++)
{
MapPoint* pMP = *vit;
if(pMP->mnLastFrameSeen == mCurrentFrame.mnId)
continue;
if(pMP->isBad())
continue;
// Project (this fills MapPoint variables for matching)
if(mCurrentFrame.isInFrustum(pMP,0.5))
{
pMP->IncreaseVisible();
nToMatch++;
}
}
if(nToMatch>0)
{
ORBmatcher matcher(0.8);
int th = 1;
if(mSensor==System::RGBD)
th=3;
// If the camera has been relocalised recently, perform a coarser search
if(mCurrentFrame.mnId<mnLastRelocFrameId+2)
th=5;
matcher.SearchByProjection(mCurrentFrame,mvpLocalMapPoints,th);
}
}
void Tracking::UpdateLocalMap()
{
// This is for visualization
mpMap->SetReferenceMapPoints(mvpLocalMapPoints);
// Update
UpdateLocalKeyFrames();
UpdateLocalPoints();
}
void Tracking::UpdateLocalPoints()
{
mvpLocalMapPoints.clear();
for(vector<KeyFrame*>::const_iterator itKF=mvpLocalKeyFrames.begin(), itEndKF=mvpLocalKeyFrames.end(); itKF!=itEndKF; itKF++)
{
KeyFrame* pKF = *itKF;
const vector<MapPoint*> vpMPs = pKF->GetMapPointMatches();
for(vector<MapPoint*>::const_iterator itMP=vpMPs.begin(), itEndMP=vpMPs.end(); itMP!=itEndMP; itMP++)
{
MapPoint* pMP = *itMP;
if(!pMP)
continue;
if(pMP->mnTrackReferenceForFrame==mCurrentFrame.mnId)
continue;
if(!pMP->isBad())
{
mvpLocalMapPoints.push_back(pMP);
pMP->mnTrackReferenceForFrame=mCurrentFrame.mnId;
}
}
}
}
void Tracking::UpdateLocalKeyFrames()
{
// Each map point vote for the keyframes in which it has been observed
map<KeyFrame*,int> keyframeCounter;
for(int i=0; i<mCurrentFrame.N; i++)
{
if(mCurrentFrame.mvpMapPoints[i])
{
MapPoint* pMP = mCurrentFrame.mvpMapPoints[i];
if(!pMP->isBad())
{
const map<KeyFrame*,size_t> observations = pMP->GetObservations();
for(map<KeyFrame*,size_t>::const_iterator it=observations.begin(), itend=observations.end(); it!=itend; it++)
keyframeCounter[it->first]++;
}
else
{
mCurrentFrame.mvpMapPoints[i]=NULL;
}
}
}
if(keyframeCounter.empty())
return;
int max=0;
KeyFrame* pKFmax= static_cast<KeyFrame*>(NULL);
mvpLocalKeyFrames.clear();
mvpLocalKeyFrames.reserve(3*keyframeCounter.size());
// All keyframes that observe a map point are included in the local map. Also check which keyframe shares most points
for(map<KeyFrame*,int>::const_iterator it=keyframeCounter.begin(), itEnd=keyframeCounter.end(); it!=itEnd; it++)
{
KeyFrame* pKF = it->first;
if(pKF->isBad())
continue;
if(it->second>max)
{
max=it->second;
pKFmax=pKF;
}
mvpLocalKeyFrames.push_back(it->first);
pKF->mnTrackReferenceForFrame = mCurrentFrame.mnId;
}
// Include also some not-already-included keyframes that are neighbors to already-included keyframes
for(vector<KeyFrame*>::const_iterator itKF=mvpLocalKeyFrames.begin(), itEndKF=mvpLocalKeyFrames.end(); itKF!=itEndKF; itKF++)
{
// Limit the number of keyframes
if(mvpLocalKeyFrames.size()>80)
break;
KeyFrame* pKF = *itKF;
const vector<KeyFrame*> vNeighs = pKF->GetBestCovisibilityKeyFrames(10);
for(vector<KeyFrame*>::const_iterator itNeighKF=vNeighs.begin(), itEndNeighKF=vNeighs.end(); itNeighKF!=itEndNeighKF; itNeighKF++)
{
KeyFrame* pNeighKF = *itNeighKF;
if(!pNeighKF->isBad())
{
if(pNeighKF->mnTrackReferenceForFrame!=mCurrentFrame.mnId)
{
mvpLocalKeyFrames.push_back(pNeighKF);
pNeighKF->mnTrackReferenceForFrame=mCurrentFrame.mnId;
break;
}
}
}
const set<KeyFrame*> spChilds = pKF->GetChilds();
for(set<KeyFrame*>::const_iterator sit=spChilds.begin(), send=spChilds.end(); sit!=send; sit++)
{
KeyFrame* pChildKF = *sit;
if(!pChildKF->isBad())
{
if(pChildKF->mnTrackReferenceForFrame!=mCurrentFrame.mnId)
{
mvpLocalKeyFrames.push_back(pChildKF);
pChildKF->mnTrackReferenceForFrame=mCurrentFrame.mnId;
break;
}
}
}
KeyFrame* pParent = pKF->GetParent();
if(pParent)
{
if(pParent->mnTrackReferenceForFrame!=mCurrentFrame.mnId)
{
mvpLocalKeyFrames.push_back(pParent);
pParent->mnTrackReferenceForFrame=mCurrentFrame.mnId;
break;
}
}
}
if(pKFmax)
{
mpReferenceKF = pKFmax;
mCurrentFrame.mpReferenceKF = mpReferenceKF;
}
}
bool Tracking::Relocalization()
{
// Compute Bag of Words Vector
mCurrentFrame.ComputeBoW();
// Relocalization is performed when tracking is lost
// Track Lost: Query KeyFrame Database for keyframe candidates for relocalisation
vector<KeyFrame*> vpCandidateKFs = mpKeyFrameDB->DetectRelocalizationCandidates(&mCurrentFrame);
if(vpCandidateKFs.empty())
return false;
const int nKFs = vpCandidateKFs.size();
// We perform first an ORB matching with each candidate
// If enough matches are found we setup a PnP solver
ORBmatcher matcher(0.75,true);
vector<PnPsolver*> vpPnPsolvers;
vpPnPsolvers.resize(nKFs);
vector<vector<MapPoint*> > vvpMapPointMatches;
vvpMapPointMatches.resize(nKFs);
vector<bool> vbDiscarded;
vbDiscarded.resize(nKFs);
int nCandidates=0;
for(int i=0; i<nKFs; i++)
{
KeyFrame* pKF = vpCandidateKFs[i];
if(pKF->isBad())
vbDiscarded[i] = true;
else
{
int nmatches = matcher.SearchByBoW(pKF,mCurrentFrame,vvpMapPointMatches[i]);
if(nmatches<15)
{
vbDiscarded[i] = true;
continue;
}
else
{
PnPsolver* pSolver = new PnPsolver(mCurrentFrame,vvpMapPointMatches[i]);
pSolver->SetRansacParameters(0.99,10,300,4,0.5,5.991);
vpPnPsolvers[i] = pSolver;
nCandidates++;
}
}
}
// Alternatively perform some iterations of P4P RANSAC
// Until we found a camera pose supported by enough inliers
bool bMatch = false;
ORBmatcher matcher2(0.9,true);
while(nCandidates>0 && !bMatch)
{
for(int i=0; i<nKFs; i++)
{
if(vbDiscarded[i])
continue;
// Perform 5 Ransac Iterations
vector<bool> vbInliers;
int nInliers;
bool bNoMore;
PnPsolver* pSolver = vpPnPsolvers[i];
cv::Mat Tcw = pSolver->iterate(5,bNoMore,vbInliers,nInliers);
// If Ransac reachs max. iterations discard keyframe
if(bNoMore)
{
vbDiscarded[i]=true;
nCandidates--;
}
// If a Camera Pose is computed, optimize
if(!Tcw.empty())
{
Tcw.copyTo(mCurrentFrame.mTcw);
set<MapPoint*> sFound;
const int np = vbInliers.size();
for(int j=0; j<np; j++)
{
if(vbInliers[j])
{
mCurrentFrame.mvpMapPoints[j]=vvpMapPointMatches[i][j];
sFound.insert(vvpMapPointMatches[i][j]);
}
else
mCurrentFrame.mvpMapPoints[j]=NULL;
}
int nGood = Optimizer::PoseOptimization(&mCurrentFrame);
if(nGood<10)
continue;
for(int io =0; io<mCurrentFrame.N; io++)
if(mCurrentFrame.mvbOutlier[io])
mCurrentFrame.mvpMapPoints[io]=static_cast<MapPoint*>(NULL);
// If few inliers, search by projection in a coarse window and optimize again
if(nGood<50)
{
int nadditional =matcher2.SearchByProjection(mCurrentFrame,vpCandidateKFs[i],sFound,10,100);
if(nadditional+nGood>=50)
{
nGood = Optimizer::PoseOptimization(&mCurrentFrame);
// If many inliers but still not enough, search by projection again in a narrower window
// the camera has been already optimized with many points
if(nGood>30 && nGood<50)
{
sFound.clear();
for(int ip =0; ip<mCurrentFrame.N; ip++)
if(mCurrentFrame.mvpMapPoints[ip])
sFound.insert(mCurrentFrame.mvpMapPoints[ip]);
nadditional =matcher2.SearchByProjection(mCurrentFrame,vpCandidateKFs[i],sFound,3,64);
// Final optimization
if(nGood+nadditional>=50)
{
nGood = Optimizer::PoseOptimization(&mCurrentFrame);
for(int io =0; io<mCurrentFrame.N; io++)
if(mCurrentFrame.mvbOutlier[io])
mCurrentFrame.mvpMapPoints[io]=NULL;
}
}
}
}
// If the pose is supported by enough inliers stop ransacs and continue
if(nGood>=50)
{
bMatch = true;
break;
}
}
}
}
if(!bMatch)
{
return false;
}
else
{
mnLastRelocFrameId = mCurrentFrame.mnId;
return true;
}
}
void Tracking::Reset()
{
cout << "System Reseting" << endl;
if(mpViewer)
{
mpViewer->RequestStop();
while(!mpViewer->isStopped())
usleep(3000);
}
// Reset Local Mapping
cout << "Reseting Local Mapper...";
mpLocalMapper->RequestReset();
cout << " done" << endl;
// Reset Loop Closing
cout << "Reseting Loop Closing...";
mpLoopClosing->RequestReset();
cout << " done" << endl;
// Clear BoW Database
cout << "Reseting Database...";
mpKeyFrameDB->clear();
cout << " done" << endl;
// Clear Map (this erase MapPoints and KeyFrames)
mpMap->clear();
KeyFrame::nNextId = 0;
Frame::nNextId = 0;
mState = NO_IMAGES_YET;
if(mpInitializer)
{
delete mpInitializer;
mpInitializer = static_cast<Initializer*>(NULL);
}
mlRelativeFramePoses.clear();
mlpReferences.clear();
mlFrameTimes.clear();
mlbLost.clear();
if(mpViewer)
mpViewer->Release();
}
void Tracking::ChangeCalibration(const string &strSettingPath)
{
cv::FileStorage fSettings(strSettingPath, cv::FileStorage::READ);
float fx = fSettings["Camera.fx"];
float fy = fSettings["Camera.fy"];
float cx = fSettings["Camera.cx"];
float cy = fSettings["Camera.cy"];
cv::Mat 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;
K.copyTo(mK);
cv::Mat DistCoef(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;
}
DistCoef.copyTo(mDistCoef);
mbf = fSettings["Camera.bf"];
Frame::mbInitialComputations = true;
}
void Tracking::InformOnlyTracking(const bool &flag)
{
mbOnlyTracking = flag;
}
} //namespace ORB_SLAM
