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

 root@song-Lenovo-G470:/home/song/ORB_SLAM2# ./Examples/Monocular/mono_tum Vocabulary/ORBvoc.txt Examples/Monocular/TUM1.yaml Examples/rgbd_dataset_freiburg1_xyz/

 ORB-SLAM2 Copyright (C) 2014-2016 Raul Mur-Artal, University of Zaragoza.

 This program comes with ABSOLUTELY NO WARRANTY;

 This is free software, and you are welcome to redistribute it

 under certain conditions. See LICENSE.txt.

 Input sensor was set to: Monocular

 Loading ORB Vocabulary. This could take a while...

 Vocabulary loaded!

 Camera Parameters:

 - fx: 517.306

 - fy: 516.469

 - cx: 318.643

 - cy: 255.314

 - k1: 0.262383

 - k2: -0.953104

 - k3: 1.16331

 - p1: -0.005358

 - p2: 0.002628

 - fps: 30

 - color order: RGB (ignored if grayscale)

 ORB Extractor Parameters:

 - Number of Features: 1000

 - Scale Levels: 8

 - Scale Factor: 1.2

 - Initial Fast Threshold: 20

 - Minimum Fast Threshold: 7

 -------

 Start processing sequence ...

 Images in the sequence: 798

 (ORB-SLAM2: Current Frame:6217): GLib-GIO-CRITICAL **: g_dbus_connection_register_object: assertion 'G_IS_DBUS_CONNECTION (connection)' failed

 (ORB-SLAM2: Current Frame:6217): GLib-GIO-CRITICAL **: g_dbus_connection_register_object: assertion 'G_IS_DBUS_CONNECTION (connection)' failed

 (ORB-SLAM2: Current Frame:6217): GLib-GIO-CRITICAL **: g_dbus_connection_get_unique_name: assertion 'G_IS_DBUS_CONNECTION (connection)' failed

 New Map created with 91 points

 Wrong initialization, reseting...

 System Reseting

 Reseting Local Mapper... done

 Reseting Loop Closing... done

 Reseting Database... done

 New Map created with 94 points

 Wrong initialization, reseting...

 System Reseting

 Reseting Local Mapper... done

 Reseting Loop Closing... done

 Reseting Database... done

 New Map created with 93 points

 Wrong initialization, reseting...

 System Reseting

 Reseting Local Mapper... done

 Reseting Loop Closing... done

 Reseting Database... done

 New Map created with 109 points

 -------

 median tracking time: 0.0856588

 mean tracking time: 0.0895457

 Saving keyframe trajectory to KeyFrameTrajectory.txt ...

 trajectory saved!

KeyFrameTrajectiory.txt:

 1305031108.743502 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 1.0000000

 1305031108.975268 0.0065185 0.0004955 -0.0036651 0.0103930 -0.0083979 0.0246982 0.9996057

 1305031109.011269 0.0064620 0.0005466 -0.0046352 0.0098092 -0.0126269 0.0230228 0.9996071

 1305031109.275308 -0.0469479 0.0049413 -0.0174485 -0.0103553 -0.0344164 0.0154323 0.9992348

 1305031109.443302 -0.0892515 0.0153173 -0.0280266 -0.0140529 -0.0712384 0.0042990 0.9973511

 1305031109.511273 -0.1050575 0.0111030 -0.0312677 -0.0181541 -0.0751181 -0.0034378 0.9970034

 1305031110.743249 -0.3816364 0.0458256 -0.0677707 -0.0680546 -0.1774805 -0.1051928 0.9761167

 1305031112.579252 -0.0067369 0.0014786 0.0044599 0.0209859 -0.0185699 0.0007425 0.9996070

 1305031114.443345 -0.3871921 0.0313219 -0.0518793 0.0027829 -0.1356153 -0.0941897 0.9862702

 1305031114.743200 -0.4173732 0.0278086 -0.0504622 -0.0148174 -0.1723966 -0.0957056 0.9802552

 1305031115.011300 -0.3965517 0.0403278 -0.0476828 -0.0166974 -0.1726241 -0.1066648 0.9790530

 1305031116.411333 -0.0315345 0.0211150 0.0542039 -0.0691888 0.0318968 0.0820211 0.9937143

 1305031116.611261 -0.0520643 0.0176461 0.0432618 -0.0689220 0.0308578 0.0769489 0.9941712

 1305031116.779298 -0.0941835 0.0238958 0.0310173 -0.0636232 0.0035438 0.0274622 0.9975898

 1305031120.115232 -0.2256815 0.1646495 -0.0312103 -0.0758519 -0.1084467 -0.0297357 0.9907581

 1305031121.614700 -0.2473447 -0.1002853 0.0445039 0.0032358 -0.1358825 -0.0292420 0.9902880

 1305031121.947288 -0.2520018 -0.1301537 0.0538647 -0.0119936 -0.1269417 -0.0187295 0.9916608

 1305031122.214959 -0.2473912 -0.0814654 0.0400963 -0.0012648 -0.1254375 -0.0465228 0.9910093

 1305031123.483594 -0.2247328 0.1646738 -0.0226976 -0.0903746 -0.1040182 -0.0355113 0.9898240

 1305031123.883786 -0.2164652 0.1992804 -0.0265738 -0.1179534 -0.0926862 -0.0172076 0.9885343

 1305031124.011302 -0.2176097 0.1940599 -0.0240379 -0.1085390 -0.1011495 -0.0143865 0.9888281

 1305031124.182694 -0.2220777 0.1754000 -0.0158519 -0.0885530 -0.1114043 -0.0177032 0.9896636

 1305031125.650575 -0.2531929 -0.1005618 0.0571583 -0.0092919 -0.1137758 -0.0495259 0.9922277

 1305031125.947343 -0.2492735 -0.0554180 0.0344920 -0.0047475 -0.1333339 -0.0485214 0.9898713

 1305031127.047297 -0.2140729 0.1238928 0.0036196 -0.0799393 -0.1149576 -0.0228445 0.9898852

 1305031127.247374 -0.2147132 0.1147137 0.0066310 -0.0767380 -0.1260436 -0.0226731 0.9887923

 1305031127.811829 -0.2220709 0.0967660 0.0158834 -0.0659883 -0.1373733 -0.0272094 0.9879442

 1305031128.679282 -0.2259168 0.0828904 0.0226595 -0.0682258 -0.1419439 -0.0312618 0.9870258

SLAM学习笔记 - ORB_SLAM2源码运行及分析

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：

SLAM学习笔记 - ORB_SLAM2源码运行及分析

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:

 # color images

 # file: 'rgbd_dataset_freiburg1_xyz.bag'

 # timestamp filename

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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[])+"/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[],argv[],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=; ni<nImages; ni++)

    {

        // Read image from file

        im = cv::imread(string(argv[])+"/"+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 ;

}

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：

 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)

 {

     //前边很多行用来打印信息，检查路径，删掉

     //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

     mpVocabulary = new ORBVocabulary();

     bool bVocLoad = mpVocabulary->loadFromTextFile(strVocFile);

     //Create KeyFrame Database

     mpKeyFrameDatabase = new KeyFrameDatabase(*mpVocabulary);

     //Create the Map

     mpMap = new Map();

     //Create Drawers. These are used by the Viewer

     mpFrameDrawer = new FrameDrawer(mpMap);

     mpMapDrawer = new MapDrawer(mpMap, strSettingsFile);

     //Initialize the Tracking thread跟踪线程，会根据strSettingFile读入相机参数

     //(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局部优化线程（BA）

     mpLocalMapper = new LocalMapping(mpMap, mSensor==MONOCULAR);

     mptLocalMapping = new thread(&ORB_SLAM2::LocalMapping::Run,mpLocalMapper);

     //Initialize the Loop Closing thread and launch闭环线程（全局优化）

     mpLoopCloser = new LoopClosing(mpMap, mpKeyFrameDatabase, mpVocabulary, mSensor!=MONOCULAR);

     mptLoopClosing = new thread(&ORB_SLAM2::LoopClosing::Run, mpLoopCloser); 

     //Set pointers between threads

     mpTracker->SetLocalMapper(mpLocalMapper);

     mpTracker->SetLoopClosing(mpLoopCloser);

     mpLocalMapper->SetTracker(mpTracker);

     mpLocalMapper->SetLoopCloser(mpLoopCloser);

     mpLoopCloser->SetTracker(mpTracker);

     mpLoopCloser->SetLocalMapper(mpLocalMapper);

 }

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。

SLAM学习笔记 - ORB_SLAM2源码运行及分析

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：

             if(mState==OK)

             {

                 // Local Mapping might have changed some MapPoints tracked in last frame

                 CheckReplacedInLastFrame();

                 if(mVelocity.empty() || mCurrentFrame.mnId<mnLastRelocFrameId+)

                 {

                     bOK = TrackReferenceKeyFrame();

                 }

                 else

                 {

                     bOK = TrackWithMotionModel();

                     if(!bOK)

                         bOK = TrackReferenceKeyFrame();

                 }

             }

             else

             {

                 bOK = Relocalization();

             }

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:

 void LocalMapping::Run()

 {

     mbFinished = false;

     while()

     {

         // Tracking will see that Local Mapping is busy

         SetAcceptKeyFrames(false);

         if(CheckNewKeyFrames())

         {

              //如果有新的关键帧，处理

         }

         else if(Stop())　　//LocalMaping类成员函数  bool Stop()

         {

             // Safe area to stop

             while(isStopped() && !CheckFinish())

             {

                 usleep();

             }

             if(CheckFinish())

                 break;

         }

         ResetIfRequested();

         // Tracking will see that Local Mapping is busy

         SetAcceptKeyFrames(true);

         if(CheckFinish())

             break;

         usleep();

     }

     SetFinish();

 }

LocalMapping主要工作：

SLAM学习笔记 - ORB_SLAM2源码运行及分析

关于优化：

1. ORB_SLAM里的BA

SLAM学习笔记 - ORB_SLAM2源码运行及分析

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