TUM数据集测试ORB_SLAM2的单目效果和源码解读

编译调试

cd ORB_SLAM2
./Examples/Monocular/mono_tum Vocabulary/ORBvoc.txt Examples/Monocular/TUM1.yaml /home/q/projects/ORB_SLAM2/TUM_Dataset/rgbd_dataset_freiburg1_xyz

测试效果视频
ORB_SLAM2|mono|tum
https://www.bilibili.com/video/BV1Ei4y1G7xa/

算法解析

感觉单目mono和RGBD差不多，也许是数据集合的原因，后面补充。

源码解读

#include
#include
#include
#include
#include
#include

using namespace std;

/**
 * @brief 导入图片
 *
 * @param[in] strFile                   读入的文件名称
 * @param[in&out] vstrImageFilenames    彩色图片名称
 * @param[in&out] vTimestamps           记录时间戳
 */
void LoadImages(const string &strFile, vector<string> &vstrImageFilenames, vector<double> &vTimestamps)
{
    ifstream f;
    f.open(strFile.c_str());

    // skip first three lines
    // 前三行是注释，跳过
    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);
        }
    }
}


/**
 * @brief
 * @param argc
 * @param argv
 * @return
 * ./Examples/Monocular/mono_tum 可执行程序
 * Vocabulary/ORBvoc.txt 字典
 * Examples/Monocular/TUM1.yaml 数据集参数
 * /home/q/projects/ORB_SLAM2/TUM_Dataset/rgbd_dataset_freiburg1_xyz 数据集
 */
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;
}