SLAM学习笔记 - ORB_SLAM2源码运行及分析
参考资料：
单目跑TUM数据集的运行和函数调用过程
跑数据集不需要ros和相机标定，进入ORB_SLAM目录，执行以下命令：
./Examples/Monocluar/mono_tum Vocabulary/ORBvoc.txt Examples/Monocular/TUM1.yaml Examples/rgbd_dataset_freiburg1_xyz
需要等一会，加载ORB词袋，然后会尝试初始化，再过一会儿就可以看到运行效果了，最后的轨迹会输出到KeyFrameTrajectory.txt
1 root@song-Lenovo-G470:/home/song/ORB_SLAM2# ./Examples/Monocular/mono_tum Vocabulary/ORBvoc.txt Examples/Monocular/TUM1.yaml Examples/rgbd_dataset_freiburg1_xyz/ 2 3 ORB-SLAM2 Copyright (C) 2014-2016 Raul Mur-Artal, University of Zaragoza. 4 This program comes with ABSOLUTELY NO WARRANTY; 5 This is free software, and you are welcome to redistribute it 6 under certain conditions. See LICENSE.txt. 7 8 Input sensor was set to: Monocular 9 10 Loading ORB Vocabulary. This could take a while... 11 Vocabulary loaded! 12 13 14 Camera Parameters: 15 - fx: 517.306 16 - fy: 516.469 17 - cx: 318.643 18 - cy: 255.314 19 - k1: 0.262383 20 - k2: -0.953104 21 - k3: 1.16331 22 - p1: -0.005358 23 - p2: 0.002628 24 - fps: 30 25 - color order: RGB (ignored if grayscale) 26 27 ORB Extractor Parameters: 28 - Number of Features: 1000 29 - Scale Levels: 8 30 - Scale Factor: 1.2 31 - Initial Fast Threshold: 20 32 - Minimum Fast Threshold: 7 33 34 ------- 35 Start processing sequence ... 36 Images in the sequence: 798 37 38 39 (ORB-SLAM2: Current Frame:6217): GLib-GIO-CRITICAL **: g_dbus_connection_register_object: assertion 'G_IS_DBUS_CONNECTION (connection)' failed 40 41 (ORB-SLAM2: Current Frame:6217): GLib-GIO-CRITICAL **: g_dbus_connection_register_object: assertion 'G_IS_DBUS_CONNECTION (connection)' failed 42 43 (ORB-SLAM2: Current Frame:6217): GLib-GIO-CRITICAL **: g_dbus_connection_get_unique_name: assertion 'G_IS_DBUS_CONNECTION (connection)' failed 44 New Map created with 91 points 45 Wrong initialization, reseting... 46 System Reseting 47 Reseting Local Mapper... done 48 Reseting Loop Closing... done 49 Reseting Database... done 50 New Map created with 94 points 51 Wrong initialization, reseting... 52 System Reseting 53 Reseting Local Mapper... done 54 Reseting Loop Closing... done 55 Reseting Database... done 56 New Map created with 93 points 57 Wrong initialization, reseting... 58 System Reseting 59 Reseting Local Mapper... done 60 Reseting Loop Closing... done 61 Reseting Database... done 62 New Map created with 109 points 63 ------- 64 65 median tracking time: 0.0856588 66 mean tracking time: 0.0895457 67 68 Saving keyframe trajectory to KeyFrameTrajectory.txt ... 69 70 trajectory saved!
KeyFrameTrajectiory.txt:
1 1305031108.743502 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 1.0000000 2 1305031108.975268 0.0065185 0.0004955 -0.0036651 0.0103930 -0.0083979 0.0246982 0.9996057 3 1305031109.011269 0.0064620 0.0005466 -0.0046352 0.0098092 -0.0126269 0.0230228 0.9996071 4 1305031109.275308 -0.0469479 0.0049413 -0.0174485 -0.0103553 -0.0344164 0.0154323 0.9992348 5 1305031109.443302 -0.0892515 0.0153173 -0.0280266 -0.0140529 -0.0712384 0.0042990 0.9973511 6 1305031109.511273 -0.1050575 0.0111030 -0.0312677 -0.0181541 -0.0751181 -0.0034378 0.9970034 7 1305031110.743249 -0.3816364 0.0458256 -0.0677707 -0.0680546 -0.1774805 -0.1051928 0.9761167 8 1305031112.579252 -0.0067369 0.0014786 0.0044599 0.0209859 -0.0185699 0.0007425 0.9996070 9 1305031114.443345 -0.3871921 0.0313219 -0.0518793 0.0027829 -0.1356153 -0.0941897 0.9862702 10 1305031114.743200 -0.4173732 0.0278086 -0.0504622 -0.0148174 -0.1723966 -0.0957056 0.9802552 11 1305031115.011300 -0.3965517 0.0403278 -0.0476828 -0.0166974 -0.1726241 -0.1066648 0.9790530 12 1305031116.411333 -0.0315345 0.0211150 0.0542039 -0.0691888 0.0318968 0.0820211 0.9937143 13 1305031116.611261 -0.0520643 0.0176461 0.0432618 -0.0689220 0.0308578 0.0769489 0.9941712 14 1305031116.779298 -0.0941835 0.0238958 0.0310173 -0.0636232 0.0035438 0.0274622 0.9975898 15 1305031120.115232 -0.2256815 0.1646495 -0.0312103 -0.0758519 -0.1084467 -0.0297357 0.9907581 16 1305031121.614700 -0.2473447 -0.1002853 0.0445039 0.0032358 -0.1358825 -0.0292420 0.9902880 17 1305031121.947288 -0.2520018 -0.1301537 0.0538647 -0.0119936 -0.1269417 -0.0187295 0.9916608 18 1305031122.214959 -0.2473912 -0.0814654 0.0400963 -0.0012648 -0.1254375 -0.0465228 0.9910093 19 1305031123.483594 -0.2247328 0.1646738 -0.0226976 -0.0903746 -0.1040182 -0.0355113 0.9898240 20 1305031123.883786 -0.2164652 0.1992804 -0.0265738 -0.1179534 -0.0926862 -0.0172076 0.9885343 21 1305031124.011302 -0.2176097 0.1940599 -0.0240379 -0.1085390 -0.1011495 -0.0143865 0.9888281 22 1305031124.182694 -0.2220777 0.1754000 -0.0158519 -0.0885530 -0.1114043 -0.0177032 0.9896636 23 1305031125.650575 -0.2531929 -0.1005618 0.0571583 -0.0092919 -0.1137758 -0.0495259 0.9922277 24 1305031125.947343 -0.2492735 -0.0554180 0.0344920 -0.0047475 -0.1333339 -0.0485214 0.9898713 25 1305031127.047297 -0.2140729 0.1238928 0.0036196 -0.0799393 -0.1149576 -0.0228445 0.9898852 26 1305031127.247374 -0.2147132 0.1147137 0.0066310 -0.0767380 -0.1260436 -0.0226731 0.9887923 27 1305031127.811829 -0.2220709 0.0967660 0.0158834 -0.0659883 -0.1373733 -0.0272094 0.9879442 28 1305031128.679282 -0.2259168 0.0828904 0.0226595 -0.0682258 -0.1419439 -0.0312618 0.9870258
上述命令会调用Monocluar目录mono_tum.cc的main函数，有四个参数，argv[1]是词袋；argv[2]是参数，相机内参和ORB特征提取器参数；argv[3]是相机数据
rgbd_dataset_freiburg1_xyz事先下载好的数据集，目录结构长这样：
rgb和depth目录下分别保存彩色图和深度图，rgb.txt里保存了rgb目录下的图片名和对应的时间戳，这里使用单目，所以只使用rgb数据。
rgb.txt:
1 # color images 2 # file: 'rgbd_dataset_freiburg1_xyz.bag' 3 # timestamp filename 4 1305031102.175304 rgb/1305031102.175304.png 5 1305031102.211214 rgb/1305031102.211214.png 6 1305031102.243211 rgb/1305031102.243211.png 7 1305031102.275326 rgb/1305031102.275326.png 8 1305031102.311267 rgb/1305031102.311267.png 9 1305031102.343233 rgb/1305031102.343233.png 10 1305031102.375329 rgb/1305031102.375329.png 11 1305031102.411258 rgb/1305031102.411258.png 12 1305031102.443271 rgb/1305031102.443271.png 13 1305031102.475318 rgb/1305031102.475318.png 14 1305031102.511219 rgb/1305031102.511219.png 15 1305031102.543220 rgb/1305031102.543220.png 16 1305031102.575286 rgb/1305031102.575286.png 17 1305031102.611233 rgb/1305031102.611233.png 18 1305031102.643265 rgb/1305031102.643265.png 19 1305031102.675285 rgb/1305031102.675285.png 20 1305031102.711263 rgb/1305031102.711263.png 21 1305031102.743234 rgb/1305031102.743234.png 22 1305031102.775472 rgb/1305031102.775472.png 23 1305031102.811232 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mono_tum.cc里的main函数：
int main(int argc, char **argv) { // Retrieve paths to images vector<string> vstrImageFilenames; vector<double> vTimestamps; string strFile = string(argv[3])+"/rgb.txt"; LoadImages(strFile, vstrImageFilenames, vTimestamps); //以上根据参数读入图片目录和时间戳（数据帧信息） int nImages = vstrImageFilenames.size();
//在这里调用src/system.cc里定义的system构造函数，进行初始化 // 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);// 根据图片个数，逐帧计算 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]; // Pass the image to the SLAM system SLAM.TrackMonocular(im,tframe); //system类成员函数，用来追踪单目图像（system还有用来追踪双目或rgbd的成员函数），返回当前帧相机位姿（Tcw） usleep();// 为了抓主线，这里前前后后删掉了很多代码，总之就是如果计算时间比帧率快就sleep一会儿 } // Stop all threads SLAM.Shutdown(); SLAM.SaveKeyFrameTrajectoryTUM("KeyFrameTrajectory.txt"); return 0; }
所以main函数只做五件事：读入数据、调用system(初始化、开启ORB_SLAM里的三个线程）、调用system::TrackMonocular处理每帧图像、调用shutdown(关闭打开的线程）、保存路径。
再看system.cc里的system函数：
1 System::System(const string &strVocFile, const string &strSettingsFile, const eSensor sensor, const bool bUseViewer) : 2 mSensor(sensor), mpViewer(static_cast<Viewer*>(NULL)), mbReset(false),
3 mbActivateLocalizationMode(false), mbDeactivateLocalizationMode(false) 4 { 21 //前边很多行用来打印信息，检查路径，删掉 30 //根据参数2加载ORB词袋，这里用了第三方库DBow2,也是同一个团队出品 33 mpVocabulary = new ORBVocabulary(); 34 bool bVocLoad = mpVocabulary->loadFromTextFile(strVocFile); 35 43 //Create KeyFrame Database 44 mpKeyFrameDatabase = new KeyFrameDatabase(*mpVocabulary); 45 46 //Create the Map 47 mpMap = new Map(); 48 49 //Create Drawers. These are used by the Viewer 50 mpFrameDrawer = new FrameDrawer(mpMap); 51 mpMapDrawer = new MapDrawer(mpMap, strSettingsFile); 52 53 //Initialize the Tracking thread跟踪线程，会根据strSettingFile读入相机参数 54 //(it will live in the main thread of execution, the one that called this constructor) 55 mpTracker = new Tracking(this, mpVocabulary, mpFrameDrawer, mpMapDrawer, 56 mpMap, mpKeyFrameDatabase, strSettingsFile, mSensor); 57 58 //Initialize the Local Mapping thread and launch局部优化线程（BA） 59 mpLocalMapper = new LocalMapping(mpMap, mSensor==MONOCULAR); 60 mptLocalMapping = new thread(&ORB_SLAM2::LocalMapping::Run,mpLocalMapper); 61 62 //Initialize the Loop Closing thread and launch闭环线程（全局优化） 63 mpLoopCloser = new LoopClosing(mpMap, mpKeyFrameDatabase, mpVocabulary, mSensor!=MONOCULAR); 64 mptLoopClosing = new thread(&ORB_SLAM2::LoopClosing::Run, mpLoopCloser);65 66 74 //Set pointers between threads 75 mpTracker->SetLocalMapper(mpLocalMapper); 76 mpTracker->SetLoopClosing(mpLoopCloser); 77 78 mpLocalMapper->SetTracker(mpTracker); 79 mpLocalMapper->SetLoopCloser(mpLoopCloser); 80 81 mpLoopCloser->SetTracker(mpTracker); 82 mpLoopCloser->SetLocalMapper(mpLocalMapper); 83 }
实例化了mpVocabulary、mpKeyFrameDatabasempMap、mpFrameDrawer、mpMapDrawer、mpTracker、mpLocalMapper、mpLoopCloser类。
mpVocabulary： 词袋管理，这里要加载一个一百多兆的词袋；
mpKeyFrameDatabasempMap：关键帧数据管理；
mpFrameDrawer：ORBSLAM跑起来以后维护右边那个图，图下面显示关键帧信息；
mpMapDrawer：ORBSLAM跑起来以后维护左边那个点云和关键帧位置的图；
mpTracker、mpLocalMapper、mpLoopCloser：跟踪、局部图优化、闭环。
所以system只做两件事：加载词袋、建立论文里所描述的那三个线程（这才是ORB_SLAM的核心）。
三个线程分别在Tracking.cc、LocalMapping.cc、LoopClosing.cc里，关键帧数据管理在KeyFrameDatabase.cc里，以后慢慢看。
15年发的paper里有对这三个线程的详细描述。
system里调用的Tracking()并没有真正跟踪相机位姿，只是根据参数对系统进行了初始化，真正的跟踪是在Track()里，main函数的for循环里调用：
SLAM.TrackMonocular(im,tframe); -> GrabImageMonocular(im,timestamp); -> mCurrentFrame(计算ORB特征）-> Track();
Track()函数中，如果没有初始化调用 MonocularInitialization();初始化相机位姿，这里会抽取ORB特征、匹配、计算单应矩阵和基本矩阵。
初始化完成以后，跟踪：
1 if(mState==OK) 2 { 3 // Local Mapping might have changed some MapPoints tracked in last frame 4 CheckReplacedInLastFrame(); 5 6 if(mVelocity.empty() || mCurrentFrame.mnId<mnLastRelocFrameId+2) 7 { 8 bOK = TrackReferenceKeyFrame(); 9 } 10 else 11 { 12 bOK = TrackWithMotionModel(); 13 if(!bOK) 14 bOK = TrackReferenceKeyFrame(); 15 } 16 } 17 else 18 { 19 bOK = Relocalization(); 20 }
得到匹配(当前帧、关键帧）以后,如果有局部图优化（mbOnlyTracking=0)，调用TrackLocalMap()
cv::Mat Rcw; // Current Camera Rotation cv::Mat tcw; // Current Camera Translation vector<bool> vbTriangulated; // Triangulated Correspondences (mvIniMatches)
LocalMapping线程:
1 void LocalMapping::Run() 2 { 4 mbFinished = false; 5 6 while(1) 7 { 8 // Tracking will see that Local Mapping is busy 9 SetAcceptKeyFrames(false); 10 if(CheckNewKeyFrames()) 11 { 12 //如果有新的关键帧，处理 13 } 14 else if(Stop()) //LocalMaping类成员函数 bool Stop() 15 { 16 // Safe area to stop 17 while(isStopped() && !CheckFinish()) 18 { 19 usleep(3000); 20 } 21 if(CheckFinish()) 22 break; 23 } 25 ResetIfRequested(); 27 // Tracking will see that Local Mapping is busy 28 SetAcceptKeyFrames(true); 29 30 if(CheckFinish()) 31 break; 32 33 usleep(3000); 34 } 36 SetFinish(); 37 }
LocalMapping主要工作：
关于优化：
1. ORB_SLAM里的BA
ORB_SLAM里用了三种BA：
full BA - LoopClosing线程用，除了第一帧，图里所有的关键帧和所有的MapPoint共同参与；
local BA - LocalMapping线程用，局部区域内优化；
motion-only BA - Tracking线程用，只使用当前帧，用来优化相机位姿。
附：之前用ROS跑笔记本自带摄像头的安装和调试步骤，怕丢，存在这里
http://www.cnblogs.com/huicanlin/p/5265477.html 基本完全按照这个安装 只有./build.sh执行之前需要先打开编辑最后一行: make -j改成make -j是不限制同时执行的线程数，可能电脑比较差，没改之前每次build的时候都会死。 安装ROS http://wiki.ros.org/indigo/Installation/Ubuntu#indigo.2BAC8-Installation.2BAC8-Sources.Setup_your_sources.list 第一步ubuntu软件中心配置不用设置，默认已经允许。 开始的时候按照ros官方安装，没成功，在apt-get update那一步时会报错说packages.ros.org无法解析域名，但在浏览器里可以打开，各种搜索，包括修改用代理下载，参考：http://www.cnblogs.com/nowornever-L/p/5743648.html，未果。 之后照这个 http://blog.exbot.net/archives/1270 包括密钥命令都输入一模一样，可以下载了 摄像头驱动安装：参考http://www.liuxiao.org/2015/10/ros-%E5%AE%89%E8%A3%85-usb-camera-%E9%A9%B1%E5%8A%A8%E5%B9%B6%E8%B0%83%E7%94%A8/ 执行roscd usb_cam时总是说找不到这个文件夹 输入命令：source ~/catkin_ws/usb_cam/devel/setup.bash 执行usb_cam下的setup.bash通过。 摄像头驱动安装好以后 先打开一个控制台，执行roscore 再开一个控制台，依次执行： source ~/catkin_ws/usb_cam/devel/setup.bash roslaunch usb_cam usb_cam.launch 可看到笔记本摄像头采集到图像并显示 或： 先打开一个控制台，执行roscore 再开一个控制台，依次执行： source ~/catkin_ws/usb_cam/devel/setup.bash roscd usb_cam rosrun usb_cam usb_cam_node 此时打印一堆信息，不管他，再打开一个控制台 rosrun image_view image_view image:=/usb_cam/image_raw 也可看到笔记本摄像头采集到事实图像并显示。 可看到笔记本摄像头采集到图像并显示 运行ros+ORB_SLAM2： 打开控制台，执行 roscore 再开控制台，执行 source ~/catkin_ws/usb_cam/devel/setup.bash roslaunch usb_cam usb_cam-test.launch 再开控制台，执行 rosrun ORB_SLAM2 Mono /home/song/ORB_SLAM2/Vocabulary/ORBvoc.txt /home/song/ORB_SLAM2/Examples/Monocular/TUM1.YAML 如果报错说找不到package ORB_SLAM2，需要进入/home/song/ORB_SLAM2/Example/ROS/ORB_SLAM2/build重新make一下，再重新打开控制台从新roscore,再rosrun就好了。 此时curretn frame会是黑屏，显示WAITING FOR IMAGES 参考《在ROS中使用USB摄像头跑ORB SLAM2》里，修改/ORB_SLAM2/Example/ROS/ORB_SLAM2/src/ros_mono.cc里的： ros::Subscriber sub = nodeHandler.subscribe("/usb_cam/image_raw", 1, &ImageGrabber::GrabImage,&igb); 重新build一下，可以了。 上述步骤成功以后再次打开： 1. roscore 2. 另外一个控制台： source ~/catkin_ws/usb_cam/devel/setup.bash roslaunch usb_cam usb_cam-test.launch ctrl+c关闭打开的图形窗口和launch界面 roscd usb_cam rosrun usb_cam usb_cam_node 3. 再打开一个控制台 rosrun ORB_SLAM2 Mono /home/song/ORB_SLAM2/Vocabulary/ORBvoc.txt /home/song/ORB_SLAM2/Examples/Monocular/TUM1.ymal [ERROR] [1496303422.284785359]: [registerPublisher] Failed to contact master at [localhost:11311]. Retrying... 如果出现这个错误，需要执行roscore
新电脑编译另外一个版本SLAM时报错没有boost,参考这里安装，其实就是apt-get
sudo apt-get install libboost-dev
附：对第一版ORB_SLAM的理解
初步看一下，只有一个main函数，是基于ROS调度的，也有三个线程，闭环线程LoopClosing.run()除了ROS调度部分和ORB_SLAM2里长的一样。
ORB_SLAM2比ORB_SLAM多了三个.cc：
猜测system.cc功能类似ROS，viewer、MapDraw用于建图
附： ORB特征
这里讲的很清楚，特征点的匹配通过汉明距离计算。
