添加主函数、tracking注释
cxl
parent
2fb9d9046b
commit
d90c628de4
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@ -38,6 +38,7 @@ void LoadIMU(const string &strImuPath, vector<double> &vTimeStamps, vector<cv::P
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double ttrack_tot = 0;
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int main(int argc, char **argv)
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{
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// 输出运行的序列数目
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const int num_seq = (argc-3)/3;
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cout << "num_seq = " << num_seq << endl;
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bool bFileName= ((argc % 3) == 1);
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@ -48,7 +49,7 @@ int main(int argc, char **argv)
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cout << "file name: " << file_name << endl;
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// 按照下面提示至少输入6个参数
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if(argc < 6)
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{
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cerr << endl << "Usage: ./mono_inertial_tum_vi path_to_vocabulary path_to_settings path_to_image_folder_1 path_to_times_file_1 path_to_imu_data_1 (path_to_image_folder_2 path_to_times_file_2 path_to_imu_data_2 ... path_to_image_folder_N path_to_times_file_N path_to_imu_data_N) (trajectory_file_name)" << endl;
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@ -57,11 +58,12 @@ int main(int argc, char **argv)
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// Load all sequences:
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// 准备加载所有序列的数据
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int seq;
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vector< vector<string> > vstrImageFilenames;
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vector< vector<double> > vTimestampsCam;
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vector< vector<cv::Point3f> > vAcc, vGyro;
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vector< vector<double> > vTimestampsImu;
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vector< vector<string> > vstrImageFilenames; //图像文件名
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vector< vector<double> > vTimestampsCam; //图像时间戳
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vector< vector<cv::Point3f> > vAcc, vGyro; //加速度计，陀螺仪
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vector< vector<double> > vTimestampsImu; //IMU时间戳
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vector<int> nImages;
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vector<int> nImu;
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vector<int> first_imu(num_seq,0);
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@ -75,12 +77,15 @@ int main(int argc, char **argv)
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nImu.resize(num_seq);
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int tot_images = 0;
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// 遍历每个序列
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for (seq = 0; seq<num_seq; seq++)
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{
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// Step 1 加载图像名和对应的图像时间戳
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cout << "Loading images for sequence " << seq << "...";
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LoadImages(string(argv[3*(seq+1)]), string(argv[3*(seq+1)+1]), vstrImageFilenames[seq], vTimestampsCam[seq]);
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cout << "LOADED!" << endl;
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// Step 2 加载IMU数据
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cout << "Loading IMU for sequence " << seq << "...";
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LoadIMU(string(argv[3*(seq+1)+2]), vTimestampsImu[seq], vAcc[seq], vGyro[seq]);
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cout << "LOADED!" << endl;
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@ -89,6 +94,7 @@ int main(int argc, char **argv)
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tot_images += nImages[seq];
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nImu[seq] = vTimestampsImu[seq].size();
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//检查是否存在有效数目的图像和imu数据
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if((nImages[seq]<=0)||(nImu[seq]<=0))
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{
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cerr << "ERROR: Failed to load images or IMU for sequence" << seq << endl;
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@ -96,9 +102,10 @@ int main(int argc, char **argv)
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}
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// Find first imu to be considered, supposing imu measurements start first
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// Step 3 默认IMU数据早于图像数据记录，找到和第一帧图像时间戳最接近的imu时间戳索引，记录在first_imu[seq]中
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while(vTimestampsImu[seq][first_imu[seq]]<=vTimestampsCam[seq][0])
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first_imu[seq]++;
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// 因为上面退出while循环时IMU时间戳刚刚超过图像时间戳，所以这里需要再减一个索引
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first_imu[seq]--; // first imu measurement to be considered
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}
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@ -115,12 +122,12 @@ int main(int argc, char **argv)
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cout << "IMU data in the sequence: " << nImu << endl << endl;*/
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// Create SLAM system. It initializes all system threads and gets ready to process frames.
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// Step 4 SLAM系统的初始化，包括读取配置文件、字典，创建跟踪、局部建图、闭环、显示线程
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ORB_SLAM3::System SLAM(argv[1],argv[2],ORB_SLAM3::System::IMU_MONOCULAR, true, 0, file_name);
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int proccIm = 0;
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for (seq = 0; seq<num_seq; seq++)
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{
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// Main loop
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cv::Mat im;
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vector<ORB_SLAM3::IMU::Point> vImuMeas;
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@ -128,15 +135,16 @@ int main(int argc, char **argv)
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cv::Ptr<cv::CLAHE> clahe = cv::createCLAHE(3.0, cv::Size(8, 8));
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for(int ni=0; ni<nImages[seq]; ni++, proccIm++)
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{
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// Read image from file
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// Step 5 读取每一帧图像并转换为灰度图存储在im
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im = cv::imread(vstrImageFilenames[seq][ni],cv::IMREAD_GRAYSCALE);
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// clahe
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// ?这里是什么作用？
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clahe->apply(im,im);
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// cout << "mat type: " << im.type() << endl;
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// 取出对应的图像时间戳
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double tframe = vTimestampsCam[seq][ni];
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if(im.empty())
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@ -148,12 +156,14 @@ int main(int argc, char **argv)
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// Load imu measurements from previous frame
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//清空imu测量
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vImuMeas.clear();
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if(ni>0)
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{
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// cout << "t_cam " << tframe << endl;
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// Step 6 把上一图像帧和当前图像帧之间的imu信息存储在vImuMeas里
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// 注意第一个图像帧没有对应的imu数据
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while(vTimestampsImu[seq][first_imu[seq]]<=vTimestampsCam[seq][ni])
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{
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vImuMeas.push_back(ORB_SLAM3::IMU::Point(vAcc[seq][first_imu[seq]].x,vAcc[seq][first_imu[seq]].y,vAcc[seq][first_imu[seq]].z,
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@ -175,6 +185,7 @@ int main(int argc, char **argv)
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// Pass the image to the SLAM system
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// cout << "tframe = " << tframe << endl;
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// Step 7 跟踪线程作为主线程运行
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SLAM.TrackMonocular(im,tframe,vImuMeas); // TODO change to monocular_inertial
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#ifdef COMPILEDWITHC11
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@ -190,6 +201,7 @@ int main(int argc, char **argv)
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vTimesTrack[ni]=ttrack;
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// Wait to load the next frame
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// 等待读取下一帧
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double T=0;
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if(ni<nImages[seq]-1)
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T = vTimestampsCam[seq][ni+1]-tframe;
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@ -203,7 +215,7 @@ int main(int argc, char **argv)
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if(seq < num_seq - 1)
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{
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cout << "Changing the dataset" << endl;
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// Step 8 更换数据集
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SLAM.ChangeDataset();
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}
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@ -211,13 +223,14 @@ int main(int argc, char **argv)
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// cout << "ttrack_tot = " << ttrack_tot << std::endl;
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// Stop all threads
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// Step 9 关闭SLAM中所有线程
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SLAM.Shutdown();
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// Tracking time statistics
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// Save camera trajectory
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// Step 10 保存相机位姿（轨迹）
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if (bFileName)
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{
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const string kf_file = "kf_" + string(argv[argc-1]) + ".txt";
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@ -250,6 +263,14 @@ int main(int argc, char **argv)
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return 0;
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}
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/**
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* @brief 加载图像及其时间戳
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*
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* @param[in] strImagePath 图像路径名称
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* @param[in] strPathTimes 图像时间戳文件名
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* @param[in] vstrImages 图像路径及名称
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* @param[in] vTimeStamps 图像时间戳和vstrImages一一对应
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*/
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void LoadImages(const string &strImagePath, const string &strPathTimes,
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vector<string> &vstrImages, vector<double> &vTimeStamps)
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{
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@ -276,10 +297,19 @@ void LoadImages(const string &strImagePath, const string &strPathTimes,
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}
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}
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/**
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* @brief 加载IMU数据
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*
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* @param[in] strImuPath IMU文件路径
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* @param[in] vTimeStamps IMU时间戳
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* @param[in] vAcc 加速度计数据
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* @param[in] vGyro 陀螺仪数据
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*/
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void LoadIMU(const string &strImuPath, vector<double> &vTimeStamps, vector<cv::Point3f> &vAcc, vector<cv::Point3f> &vGyro)
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{
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ifstream fImu;
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fImu.open(strImuPath.c_str());
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// 预申请大小为5000的vector，不够可以再扩展
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vTimeStamps.reserve(5000);
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vAcc.reserve(5000);
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vGyro.reserve(5000);
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@ -288,6 +318,7 @@ void LoadIMU(const string &strImuPath, vector<double> &vTimeStamps, vector<cv::P
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{
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string s;
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getline(fImu,s);
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// 跳过注释或无效数据
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if (s[0] == '#')
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continue;
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@ -305,6 +336,7 @@ void LoadIMU(const string &strImuPath, vector<double> &vTimeStamps, vector<cv::P
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item = s.substr(0, pos);
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data[6] = stod(item);
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// 注意这里的时间戳除以10的9次方转化为秒单位
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vTimeStamps.push_back(data[0]/1e9);
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vAcc.push_back(cv::Point3f(data[4],data[5],data[6]));
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vGyro.push_back(cv::Point3f(data[1],data[2],data[3]));
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@ -1333,7 +1333,13 @@ int TemplatedVocabulary<TDescriptor,F>::stopWords(double minWeight)
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}
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// --------------------------------------------------------------------------
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/**
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* @brief 读取TXT格式的预训练好的ORB字典
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*
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* @param[in] filename 文件名
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* @return true 读取成功
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* @return false 读取失败
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*/
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template<class TDescriptor, class F>
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bool TemplatedVocabulary<TDescriptor,F>::loadFromTextFile(const std::string &filename)
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{
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@ -1341,7 +1347,7 @@ bool TemplatedVocabulary<TDescriptor,F>::loadFromTextFile(const std::string &fil
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f.open(filename.c_str());
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if(f.eof())
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return false;
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return false;
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m_words.clear();
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m_nodes.clear();
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@ -1350,8 +1356,8 @@ bool TemplatedVocabulary<TDescriptor,F>::loadFromTextFile(const std::string &fil
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getline(f,s);
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stringstream ss;
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ss << s;
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ss >> m_k;
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ss >> m_L;
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ss >> m_k; // 树的分支数目
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ss >> m_L; // 树的深度
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int n1, n2;
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ss >> n1;
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ss >> n2;
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@ -1359,20 +1365,22 @@ bool TemplatedVocabulary<TDescriptor,F>::loadFromTextFile(const std::string &fil
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if(m_k<0 || m_k>20 || m_L<1 || m_L>10 || n1<0 || n1>5 || n2<0 || n2>3)
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{
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std::cerr << "Vocabulary loading failure: This is not a correct text file!" << endl;
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return false;
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return false;
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}
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m_scoring = (ScoringType)n1;
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m_weighting = (WeightingType)n2;
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m_scoring = (ScoringType)n1; // 评分类型
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m_weighting = (WeightingType)n2; // 权重类型
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createScoringObject();
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// nodes
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// 总共节点（nodes）数，是一个等比数列求和
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//! bug 没有包含最后叶子节点数，应该改为 ((pow((double)m_k, (double)m_L + 2) - 1)/(m_k - 1))
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//! 但是没有影响，因为这里只是reserve，实际存储是一步步resize实现
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int expected_nodes =
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(int)((pow((double)m_k, (double)m_L + 1) - 1)/(m_k - 1));
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m_nodes.reserve(expected_nodes);
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// 预分配空间给 单词（叶子）数
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m_words.reserve(pow((double)m_k, (double)m_L + 1));
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// 第一个节点是根节点，id设为0
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m_nodes.resize(1);
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m_nodes[0].id = 0;
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while(!f.eof())
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@ -1381,40 +1389,53 @@ bool TemplatedVocabulary<TDescriptor,F>::loadFromTextFile(const std::string &fil
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getline(f,snode);
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stringstream ssnode;
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ssnode << snode;
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// nid 表示当前节点id，实际是读取顺序，从0开始
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int nid = m_nodes.size();
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// 节点size 加1
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m_nodes.resize(m_nodes.size()+1);
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m_nodes[nid].id = nid;
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m_nodes[nid].id = nid;
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// 读每行的第1个数字，表示父节点id
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int pid ;
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ssnode >> pid;
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// 记录节点id的相互父子关系
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m_nodes[nid].parent = pid;
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m_nodes[pid].children.push_back(nid);
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// 读取第2个数字，表示是否是叶子（Word）
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int nIsLeaf;
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ssnode >> nIsLeaf;
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// 每个特征点描述子是256 bit，一个字节对应8 bit，所以一个特征点需要32个字节存储。
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// 这里 F::L=32，也就是读取32个字节，最后以字符串形式存储在ssd
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stringstream ssd;
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for(int iD=0;iD<F::L;iD++)
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{
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string sElement;
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ssnode >> sElement;
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ssd << sElement << " ";
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}
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}
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// 将ssd存储在该节点的描述子
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F::fromString(m_nodes[nid].descriptor, ssd.str());
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// 读取最后一个数字：节点的权重（Word才有）
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ssnode >> m_nodes[nid].weight;
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if(nIsLeaf>0)
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{
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// 如果是叶子（Word），存储到m_words
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int wid = m_words.size();
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m_words.resize(wid+1);
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//存储Word的id，具有唯一性
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m_nodes[nid].word_id = wid;
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//构建 vector<Node*> m_words，存储word所在node的指针
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m_words[wid] = &m_nodes[nid];
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}
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else
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{
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//非叶子节点，直接分配 m_k个分支
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m_nodes[nid].children.reserve(m_k);
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}
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}
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@ -103,13 +103,14 @@ public:
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public:
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// Tracking states
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// 跟踪状态
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enum eTrackingState{
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SYSTEM_NOT_READY=-1,
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NO_IMAGES_YET=0,
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NOT_INITIALIZED=1,
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OK=2,
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RECENTLY_LOST=3,
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LOST=4,
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SYSTEM_NOT_READY=-1, //系统没有准备好的状态,一般就是在启动后加载配置文件和词典文件时候的状态
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NO_IMAGES_YET=0, //当前无图像
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NOT_INITIALIZED=1, //有图像但是没有完成初始化
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OK=2, //正常跟踪状态
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RECENTLY_LOST=3, //IMU模式：当前地图中的KF>10,且丢失时间<5秒。纯视觉模式没有该状态
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LOST=4, //IMU模式：当前帧跟丢超过5s。纯视觉模式：重定位失败
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OK_KLT=5
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};
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@ -275,6 +276,7 @@ protected:
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int mMaxFrames;
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int mnFirstImuFrameId;
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// 经过多少帧后可以重置IMU，一般设置为和帧率相同，对应的时间是1s
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int mnFramesToResetIMU;
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// Threshold close/far points
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@ -52,7 +52,7 @@ System::System(const string &strVocFile, const string &strSettingsFile, const eS
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"under certain conditions. See LICENSE.txt." << endl << endl;
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cout << "Input sensor was set to: ";
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// Step 1 输出当前传感器类型
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if(mSensor==MONOCULAR)
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cout << "Monocular" << endl;
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else if(mSensor==STEREO)
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@ -65,9 +65,10 @@ System::System(const string &strVocFile, const string &strSettingsFile, const eS
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cout << "Stereo-Inertial" << endl;
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//Check settings file
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// Step 2 读取配置文件
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cv::FileStorage fsSettings(strSettingsFile.c_str(), //将配置文件名转换成为字符串
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cv::FileStorage::READ); //只读
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//如果打开失败，就输出调试信息
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//如果打开失败，就输出错误信息
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if(!fsSettings.isOpened())
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{
|
||||
cerr << "Failed to open settings file at: " << strSettingsFile << endl;
|
||||
|
|
@ -78,13 +79,14 @@ System::System(const string &strVocFile, const string &strSettingsFile, const eS
|
|||
|
||||
//----
|
||||
//Load ORB Vocabulary
|
||||
// Step 3 加载ORB字典
|
||||
cout << endl << "Loading ORB Vocabulary. This could take a while..." << endl;
|
||||
|
||||
//建立一个新的ORB字典
|
||||
mpVocabulary = new ORBVocabulary();
|
||||
//获取字典加载状态
|
||||
//读取预训练好的ORB字典并返回成功/失败标志
|
||||
bool bVocLoad = mpVocabulary->loadFromTextFile(strVocFile);
|
||||
//如果加载失败，就输出调试信息
|
||||
//如果加载失败，就输出错误信息
|
||||
if(!bVocLoad)
|
||||
{
|
||||
cerr << "Wrong path to vocabulary. " << endl;
|
||||
|
|
@ -94,13 +96,14 @@ System::System(const string &strVocFile, const string &strSettingsFile, const eS
|
|||
cout << "Vocabulary loaded!" << endl << endl;
|
||||
|
||||
//Create KeyFrame Database
|
||||
// Step 4 创建关键帧数据库
|
||||
mpKeyFrameDatabase = new KeyFrameDatabase(*mpVocabulary);
|
||||
|
||||
//Create the Atlas
|
||||
//mpMap = new Map();
|
||||
// Step 5 创建多地图，参数0表示初始化关键帧id为0
|
||||
mpAtlas = new Atlas(0);
|
||||
//----
|
||||
|
||||
// 下面注释看起来作者貌似想加地图重载功能，期待期待
|
||||
/*if(strLoadingFile.empty())
|
||||
{
|
||||
//Load ORB Vocabulary
|
||||
|
|
@ -167,24 +170,31 @@ System::System(const string &strVocFile, const string &strSettingsFile, const eS
|
|||
//usleep(10*1000*1000);
|
||||
}*/
|
||||
|
||||
|
||||
// 设置Atlas中的传感器类型
|
||||
if (mSensor==IMU_STEREO || mSensor==IMU_MONOCULAR)
|
||||
mpAtlas->SetInertialSensor();
|
||||
|
||||
// Step 6 依次创建跟踪、局部建图、闭环、显示线程
|
||||
//Create Drawers. These are used by the Viewer
|
||||
// 创建用于显示帧和地图的类，由Viewer调用
|
||||
mpFrameDrawer = new FrameDrawer(mpAtlas);
|
||||
mpMapDrawer = new MapDrawer(mpAtlas, strSettingsFile);
|
||||
|
||||
//Initialize the Tracking thread
|
||||
//(it will live in the main thread of execution, the one that called this constructor)
|
||||
// 创建跟踪线程（主线程）
|
||||
cout << "Seq. Name: " << strSequence << endl;
|
||||
mpTracker = new Tracking(this, mpVocabulary, mpFrameDrawer, mpMapDrawer,
|
||||
mpAtlas, mpKeyFrameDatabase, strSettingsFile, mSensor, strSequence);
|
||||
|
||||
//Initialize the Local Mapping thread and launch
|
||||
//创建并开启local mapping线程
|
||||
mpLocalMapper = new LocalMapping(this, mpAtlas, mSensor==MONOCULAR || mSensor==IMU_MONOCULAR, mSensor==IMU_MONOCULAR || mSensor==IMU_STEREO, strSequence);
|
||||
mptLocalMapping = new thread(&ORB_SLAM3::LocalMapping::Run,mpLocalMapper);
|
||||
|
||||
//initFr表示初始化帧的id，代码里设置为0
|
||||
mpLocalMapper->mInitFr = initFr;
|
||||
//设置最远3D地图点的深度值，如果超过阈值，说明可能三角化不太准确，丢弃
|
||||
mpLocalMapper->mThFarPoints = fsSettings["thFarPoints"];
|
||||
if(mpLocalMapper->mThFarPoints!=0)
|
||||
{
|
||||
|
|
@ -196,10 +206,12 @@ System::System(const string &strVocFile, const string &strSettingsFile, const eS
|
|||
|
||||
//Initialize the Loop Closing thread and launch
|
||||
// mSensor!=MONOCULAR && mSensor!=IMU_MONOCULAR
|
||||
// 创建并开启闭环线程
|
||||
mpLoopCloser = new LoopClosing(mpAtlas, mpKeyFrameDatabase, mpVocabulary, mSensor!=MONOCULAR); // mSensor!=MONOCULAR);
|
||||
mptLoopClosing = new thread(&ORB_SLAM3::LoopClosing::Run, mpLoopCloser);
|
||||
|
||||
//Initialize the Viewer thread and launch
|
||||
// 创建并开启显示线程
|
||||
if(bUseViewer)
|
||||
{
|
||||
mpViewer = new Viewer(this, mpFrameDrawer,mpMapDrawer,mpTracker,strSettingsFile);
|
||||
|
|
@ -210,6 +222,7 @@ System::System(const string &strVocFile, const string &strSettingsFile, const eS
|
|||
}
|
||||
|
||||
//Set pointers between threads
|
||||
// 设置线程间的指针
|
||||
mpTracker->SetLocalMapper(mpLocalMapper);
|
||||
mpTracker->SetLoopClosing(mpLoopCloser);
|
||||
|
||||
|
|
@ -348,8 +361,18 @@ cv::Mat System::TrackRGBD(const cv::Mat &im, const cv::Mat &depthmap, const doub
|
|||
return Tcw;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief 单目/单目VIO跟踪
|
||||
*
|
||||
* @param[in] im 灰度图像
|
||||
* @param[in] timestamp 图像时间戳
|
||||
* @param[in] vImuMeas 上一帧到当前帧图像之间的IMU测量值
|
||||
* @param[in] filename 调试用的文件名
|
||||
* @return cv::Mat 当前帧位姿Tcw
|
||||
*/
|
||||
cv::Mat System::TrackMonocular(const cv::Mat &im, const double ×tamp, const vector<IMU::Point>& vImuMeas, string filename)
|
||||
{
|
||||
// 确保是单目或单目VIO模式
|
||||
if(mSensor!=MONOCULAR && mSensor!=IMU_MONOCULAR)
|
||||
{
|
||||
cerr << "ERROR: you called TrackMonocular but input sensor was not set to Monocular nor Monocular-Inertial." << endl;
|
||||
|
|
@ -359,6 +382,7 @@ cv::Mat System::TrackMonocular(const cv::Mat &im, const double ×tamp, const
|
|||
// Check mode change
|
||||
{
|
||||
unique_lock<mutex> lock(mMutexMode);
|
||||
// mbActivateLocalizationMode为true会关闭局部地图线程，仅跟踪模式
|
||||
if(mbActivateLocalizationMode)
|
||||
{
|
||||
mpLocalMapper->RequestStop();
|
||||
|
|
@ -368,7 +392,7 @@ cv::Mat System::TrackMonocular(const cv::Mat &im, const double ×tamp, const
|
|||
{
|
||||
usleep(1000);
|
||||
}
|
||||
|
||||
// 局部地图关闭以后，只进行追踪的线程，只计算相机的位姿，没有对局部地图进行更新
|
||||
mpTracker->InformOnlyTracking(true);
|
||||
mbActivateLocalizationMode = false;
|
||||
}
|
||||
|
|
@ -396,11 +420,12 @@ cv::Mat System::TrackMonocular(const cv::Mat &im, const double ×tamp, const
|
|||
mbResetActiveMap = false;
|
||||
}
|
||||
}
|
||||
|
||||
// 如果是单目VIO模式，把IMU数据存储到mlQueueImuData
|
||||
if (mSensor == System::IMU_MONOCULAR)
|
||||
for(size_t i_imu = 0; i_imu < vImuMeas.size(); i_imu++)
|
||||
mpTracker->GrabImuData(vImuMeas[i_imu]);
|
||||
|
||||
// 计算相机位姿
|
||||
cv::Mat Tcw = mpTracker->GrabImageMonocular(im,timestamp,filename);
|
||||
|
||||
unique_lock<mutex> lock2(mMutexState);
|
||||
|
|
|
|||
175
src/Tracking.cc
175
src/Tracking.cc
|
|
@ -1034,11 +1034,20 @@ cv::Mat Tracking::GrabImageRGBD(const cv::Mat &imRGB,const cv::Mat &imD, const d
|
|||
return mCurrentFrame.mTcw.clone();
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* @brief 输入单目图像，输出世界坐标系到该帧相机坐标系的变换矩阵
|
||||
*
|
||||
* @param[in] im 单目图像
|
||||
* @param[in] timestamp 时间戳
|
||||
* @param[in] filename 文件名（调试用）
|
||||
* @return cv::Mat 世界坐标系到相机坐标系的变换矩阵
|
||||
*/
|
||||
cv::Mat Tracking::GrabImageMonocular(const cv::Mat &im, const double ×tamp, string filename)
|
||||
{
|
||||
mImGray = im;
|
||||
|
||||
// Step 1 ：将彩色图像转为灰度图像
|
||||
//若图片是3、4通道的，还需要转化成单通道灰度图
|
||||
if(mImGray.channels()==3)
|
||||
{
|
||||
if(mbRGB)
|
||||
|
|
@ -1054,6 +1063,7 @@ cv::Mat Tracking::GrabImageMonocular(const cv::Mat &im, const double ×tamp,
|
|||
cvtColor(mImGray,mImGray,CV_BGRA2GRAY);
|
||||
}
|
||||
|
||||
// Step 2 ：构造Frame
|
||||
if (mSensor == System::MONOCULAR)
|
||||
{
|
||||
if(mState==NOT_INITIALIZED || mState==NO_IMAGES_YET ||(lastID - initID) < mMaxFrames)
|
||||
|
|
@ -1071,6 +1081,7 @@ cv::Mat Tracking::GrabImageMonocular(const cv::Mat &im, const double ×tamp,
|
|||
mCurrentFrame = Frame(mImGray,timestamp,mpORBextractorLeft,mpORBVocabulary,mpCamera,mDistCoef,mbf,mThDepth,&mLastFrame,*mpImuCalib);
|
||||
}
|
||||
|
||||
// t0存储未初始化时的第1帧时间戳
|
||||
if (mState==NO_IMAGES_YET)
|
||||
t0=timestamp;
|
||||
|
||||
|
|
@ -1080,6 +1091,8 @@ cv::Mat Tracking::GrabImageMonocular(const cv::Mat &im, const double ×tamp,
|
|||
mCurrentFrame.mnDataset = mnNumDataset;
|
||||
|
||||
lastID = mCurrentFrame.mnId;
|
||||
|
||||
// Step 3 ：跟踪
|
||||
Track();
|
||||
|
||||
std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now();
|
||||
|
|
@ -1101,6 +1114,7 @@ cv::Mat Tracking::GrabImageMonocular(const cv::Mat &im, const double ×tamp,
|
|||
f_track_times << t_track << endl;
|
||||
#endif
|
||||
|
||||
//返回当前帧的位姿
|
||||
return mCurrentFrame.mTcw.clone();
|
||||
}
|
||||
|
||||
|
|
@ -1383,7 +1397,10 @@ void Tracking::ResetFrameIMU()
|
|||
// TODO To implement...
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* @brief 跟踪过程，包括恒速模型跟踪、参考关键帧跟踪、局部地图跟踪
|
||||
*
|
||||
*/
|
||||
void Tracking::Track()
|
||||
{
|
||||
#ifdef SAVE_TIMES
|
||||
|
|
@ -1400,6 +1417,7 @@ void Tracking::Track()
|
|||
mbStep = false;
|
||||
}
|
||||
|
||||
// Step 1 如局部建图里认为IMU有问题，重置地图
|
||||
if(mpLocalMapper->mbBadImu)
|
||||
{
|
||||
cout << "TRACK: Reset map because local mapper set the bad imu flag " << endl;
|
||||
|
|
@ -1407,12 +1425,16 @@ void Tracking::Track()
|
|||
return;
|
||||
}
|
||||
|
||||
// 从Atlas中取出当前active的地图
|
||||
Map* pCurrentMap = mpAtlas->GetCurrentMap();
|
||||
|
||||
// Step 2 处理时间戳异常的情况
|
||||
if(mState!=NO_IMAGES_YET)
|
||||
{
|
||||
// 进入以下两个if语句都是不正常的情况，不进行跟踪直接返回
|
||||
if(mLastFrame.mTimeStamp>mCurrentFrame.mTimeStamp)
|
||||
{
|
||||
// 如果当前图像时间戳比前一帧图像时间戳小，说明出错了，清除imu数据，创建新的子地图
|
||||
cerr << "ERROR: Frame with a timestamp older than previous frame detected!" << endl;
|
||||
unique_lock<mutex> lock(mMutexImuQueue);
|
||||
mlQueueImuData.clear();
|
||||
|
|
@ -1421,34 +1443,38 @@ void Tracking::Track()
|
|||
}
|
||||
else if(mCurrentFrame.mTimeStamp>mLastFrame.mTimeStamp+1.0)
|
||||
{
|
||||
// 如果当前图像时间戳和前一帧图像时间戳大于1s，说明时间戳明显跳变了，重置地图后直接返回
|
||||
cout << "id last: " << mLastFrame.mnId << " id curr: " << mCurrentFrame.mnId << endl;
|
||||
if(mpAtlas->isInertial())
|
||||
{
|
||||
|
||||
// 如果当前地图imu成功初始化
|
||||
if(mpAtlas->isImuInitialized())
|
||||
{
|
||||
cout << "Timestamp jump detected. State set to LOST. Reseting IMU integration..." << endl;
|
||||
if(!pCurrentMap->GetIniertialBA2())
|
||||
{
|
||||
// 如果当前子图中imu没有经过BA2，重置active地图
|
||||
mpSystem->ResetActiveMap();
|
||||
}
|
||||
else
|
||||
{
|
||||
// 如果当前子图中imu进行了BA2，重新创建新的子图
|
||||
CreateMapInAtlas();
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
// 如果当前子图中imu没有初始化，重置active地图
|
||||
cout << "Timestamp jump detected, before IMU initialization. Reseting..." << endl;
|
||||
mpSystem->ResetActiveMap();
|
||||
}
|
||||
}
|
||||
|
||||
// 不跟踪直接返回
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
// Step 3 IMU模式下设置IMU的Bias参数
|
||||
if ((mSensor == System::IMU_MONOCULAR || mSensor == System::IMU_STEREO) && mpLastKeyFrame)
|
||||
mCurrentFrame.SetNewBias(mpLastKeyFrame->GetImuBias());
|
||||
|
||||
|
|
@ -1458,12 +1484,13 @@ void Tracking::Track()
|
|||
}
|
||||
|
||||
mLastProcessedState=mState;
|
||||
|
||||
// Step 4 IMU模式下对IMU数据进行预积分
|
||||
if ((mSensor == System::IMU_MONOCULAR || mSensor == System::IMU_STEREO) && !mbCreatedMap)
|
||||
{
|
||||
#ifdef SAVE_TIMES
|
||||
std::chrono::steady_clock::time_point t0 = std::chrono::steady_clock::now();
|
||||
#endif
|
||||
// IMU数据进行预积分
|
||||
PreintegrateIMU();
|
||||
#ifdef SAVE_TIMES
|
||||
std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now();
|
||||
|
|
@ -1476,10 +1503,14 @@ void Tracking::Track()
|
|||
mbCreatedMap = false;
|
||||
|
||||
// Get Map Mutex -> Map cannot be changed
|
||||
// 地图更新时加锁。保证地图不会发生变化
|
||||
// 疑问:这样子会不会影响地图的实时更新?
|
||||
// 回答：主要耗时在构造帧中特征点的提取和匹配部分,在那个时候地图是没有被上锁的,有足够的时间更新地图
|
||||
unique_lock<mutex> lock(pCurrentMap->mMutexMapUpdate);
|
||||
|
||||
mbMapUpdated = false;
|
||||
|
||||
// 判断地图id是否更新了
|
||||
int nCurMapChangeIndex = pCurrentMap->GetMapChangeIndex();
|
||||
int nMapChangeIndex = pCurrentMap->GetLastMapChange();
|
||||
if(nCurMapChangeIndex>nMapChangeIndex)
|
||||
|
|
@ -1492,10 +1523,15 @@ void Tracking::Track()
|
|||
|
||||
if(mState==NOT_INITIALIZED)
|
||||
{
|
||||
// Step 5 初始化
|
||||
if(mSensor==System::STEREO || mSensor==System::RGBD || mSensor==System::IMU_STEREO)
|
||||
{
|
||||
//双目RGBD相机的初始化共用一个函数
|
||||
StereoInitialization();
|
||||
}
|
||||
else
|
||||
{
|
||||
//单目初始化
|
||||
MonocularInitialization();
|
||||
}
|
||||
|
||||
|
|
@ -1503,21 +1539,26 @@ void Tracking::Track()
|
|||
|
||||
if(mState!=OK) // If rightly initialized, mState=OK
|
||||
{
|
||||
// 如果没有成功初始化，直接返回
|
||||
mLastFrame = Frame(mCurrentFrame);
|
||||
return;
|
||||
}
|
||||
|
||||
if(mpAtlas->GetAllMaps().size() == 1)
|
||||
{
|
||||
// 如果当前地图是第一个地图，记录当前帧id为第一帧
|
||||
mnFirstFrameId = mCurrentFrame.mnId;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
// System is initialized. Track Frame.
|
||||
// Step 6 系统成功初始化，下面是具体跟踪过程
|
||||
bool bOK;
|
||||
|
||||
// Initial camera pose estimation using motion model or relocalization (if tracking is lost)
|
||||
// mbOnlyTracking等于false表示正常SLAM模式（定位+地图更新），mbOnlyTracking等于true表示仅定位模式
|
||||
// tracking 类构造时默认为false。在viewer中有个开关ActivateLocalizationMode，可以控制是否开启mbOnlyTracking
|
||||
if(!mbOnlyTracking)
|
||||
{
|
||||
#ifdef SAVE_TIMES
|
||||
|
|
@ -1527,12 +1568,22 @@ void Tracking::Track()
|
|||
// State OK
|
||||
// Local Mapping is activated. This is the normal behaviour, unless
|
||||
// you explicitly activate the "only tracking" mode.
|
||||
// 跟踪进入正常SLAM模式，有地图更新
|
||||
|
||||
// 如果正常跟踪
|
||||
if(mState==OK)
|
||||
{
|
||||
|
||||
// Local Mapping might have changed some MapPoints tracked in last frame
|
||||
// Step 6.1 检查并更新上一帧被替换的MapPoints
|
||||
// 局部建图线程则可能会对原有的地图点进行替换.在这里进行检查
|
||||
CheckReplacedInLastFrame();
|
||||
|
||||
|
||||
// Step 6.2 运动模型是空的并且imu未初始化或刚完成重定位，跟踪参考关键帧；否则恒速模型跟踪
|
||||
// 第一个条件,如果运动模型为空并且imu未初始化,说明是刚开始第一帧跟踪，或者已经跟丢了。
|
||||
// 第二个条件,如果当前帧紧紧地跟着在重定位的帧的后面，我们用重定位帧来恢复位姿
|
||||
// mnLastRelocFrameId 上一次重定位的那一帧
|
||||
if((mVelocity.empty() && !pCurrentMap->isImuInitialized()) || mCurrentFrame.mnId<mnLastRelocFrameId+2)
|
||||
{
|
||||
//Verbose::PrintMess("TRACK: Track with respect to the reference KF ", Verbose::VERBOSITY_DEBUG);
|
||||
|
|
@ -1541,14 +1592,21 @@ void Tracking::Track()
|
|||
else
|
||||
{
|
||||
//Verbose::PrintMess("TRACK: Track with motion model", Verbose::VERBOSITY_DEBUG);
|
||||
// 用恒速模型跟踪。所谓的恒速就是假设上上帧到上一帧的位姿=上一帧的位姿到当前帧位姿
|
||||
// 根据恒速模型设定当前帧的初始位姿，用最近的普通帧来跟踪当前的普通帧
|
||||
// 通过投影的方式在参考帧中找当前帧特征点的匹配点，优化每个特征点所对应3D点的投影误差即可得到位姿
|
||||
bOK = TrackWithMotionModel();
|
||||
if(!bOK)
|
||||
//根据恒速模型失败了，只能根据参考关键帧来跟踪
|
||||
bOK = TrackReferenceKeyFrame();
|
||||
}
|
||||
|
||||
|
||||
// Step 6.3 如果经过跟踪参考关键帧、恒速模型跟踪都失败的话，并满足一定条件就要标记为LOST了
|
||||
if (!bOK)
|
||||
{
|
||||
// 条件1：如果当前帧距离上次重定位帧不久（时间在1s内）
|
||||
// mnFramesToResetIMU 表示经过多少帧后可以重置IMU，一般设置为和帧率相同，对应的时间是1s
|
||||
// 条件2：单目+IMU 或者 双目+IMU模式
|
||||
if ( mCurrentFrame.mnId<=(mnLastRelocFrameId+mnFramesToResetIMU) &&
|
||||
(mSensor==System::IMU_MONOCULAR || mSensor==System::IMU_STEREO))
|
||||
{
|
||||
|
|
@ -1556,6 +1614,8 @@ void Tracking::Track()
|
|||
}
|
||||
else if(pCurrentMap->KeyFramesInMap()>10)
|
||||
{
|
||||
// 当前地图中关键帧数目大于10 并且 当前帧距离上次重定位帧超过1s（隐藏条件）
|
||||
// 状态标记为RECENTLY_LOST
|
||||
cout << "KF in map: " << pCurrentMap->KeyFramesInMap() << endl;
|
||||
mState = RECENTLY_LOST;
|
||||
mTimeStampLost = mCurrentFrame.mTimeStamp;
|
||||
|
|
@ -1567,21 +1627,26 @@ void Tracking::Track()
|
|||
}
|
||||
}
|
||||
}
|
||||
else
|
||||
else //跟踪不正常按照下面处理
|
||||
{
|
||||
|
||||
if (mState == RECENTLY_LOST)
|
||||
{
|
||||
// 如果是RECENTLY_LOST状态（仅存在IMU模式下，纯视觉模式无此状态）
|
||||
Verbose::PrintMess("Lost for a short time", Verbose::VERBOSITY_NORMAL);
|
||||
|
||||
// bOK先置为true
|
||||
bOK = true;
|
||||
if((mSensor == System::IMU_MONOCULAR || mSensor == System::IMU_STEREO))
|
||||
|
||||
if((mSensor == System::IMU_MONOCULAR || mSensor == System::IMU_STEREO)) // IMU模式
|
||||
{
|
||||
// Step 6.4 如果当前地图中IMU已经成功初始化，就用IMU数据预测位姿
|
||||
if(pCurrentMap->isImuInitialized())
|
||||
PredictStateIMU();
|
||||
else
|
||||
bOK = false;
|
||||
|
||||
// 如果当前帧距离跟丢帧已经超过了5s（time_recently_lost默认为5）
|
||||
// 将RECENTLY_LOST状态改为LOST
|
||||
if (mCurrentFrame.mTimeStamp-mTimeStampLost>time_recently_lost)
|
||||
{
|
||||
mState = LOST;
|
||||
|
|
@ -1591,10 +1656,12 @@ void Tracking::Track()
|
|||
}
|
||||
else
|
||||
{
|
||||
// Step 6.5 纯视觉模式则进行重定位。主要是BOW搜索，EPnP求解位姿
|
||||
// TODO fix relocalization
|
||||
bOK = Relocalization();
|
||||
if(!bOK)
|
||||
{
|
||||
// 纯视觉模式下重定位失败，状态为LOST
|
||||
mState = LOST;
|
||||
Verbose::PrintMess("Track Lost...", Verbose::VERBOSITY_NORMAL);
|
||||
bOK=false;
|
||||
|
|
@ -1603,14 +1670,16 @@ void Tracking::Track()
|
|||
}
|
||||
else if (mState == LOST)
|
||||
{
|
||||
|
||||
// Step 6.6 如果是LOST状态
|
||||
Verbose::PrintMess("A new map is started...", Verbose::VERBOSITY_NORMAL);
|
||||
|
||||
if (pCurrentMap->KeyFramesInMap()<10)
|
||||
{
|
||||
//当前地图中关键帧数目小于10，重置当前地图
|
||||
mpSystem->ResetActiveMap();
|
||||
cout << "Reseting current map..." << endl;
|
||||
}else
|
||||
//当前地图中关键帧数目超过10，创建新地图
|
||||
CreateMapInAtlas();
|
||||
|
||||
if(mpLastKeyFrame)
|
||||
|
|
@ -1633,23 +1702,31 @@ void Tracking::Track()
|
|||
else
|
||||
{
|
||||
// Localization Mode: Local Mapping is deactivated (TODO Not available in inertial mode)
|
||||
// 只进行跟踪tracking，局部地图不工作
|
||||
if(mState==LOST)
|
||||
{
|
||||
if(mSensor == System::IMU_MONOCULAR || mSensor == System::IMU_STEREO)
|
||||
Verbose::PrintMess("IMU. State LOST", Verbose::VERBOSITY_NORMAL);
|
||||
// Step 6.1 LOST状态进行重定位
|
||||
bOK = Relocalization();
|
||||
}
|
||||
else
|
||||
{
|
||||
// mbVO是mbOnlyTracking为true时的才有的一个变量
|
||||
// mbVO为false表示此帧匹配了很多的MapPoints，跟踪很正常 (注意有点反直觉)
|
||||
// mbVO为true表明此帧匹配了很少的MapPoints，少于10个，要跟丢
|
||||
if(!mbVO)
|
||||
{
|
||||
// In last frame we tracked enough MapPoints in the map
|
||||
// Step 6.2 如果跟踪状态正常，使用恒速模型或参考关键帧跟踪
|
||||
if(!mVelocity.empty())
|
||||
{
|
||||
// 优先使用恒速模型跟踪
|
||||
bOK = TrackWithMotionModel();
|
||||
}
|
||||
else
|
||||
{
|
||||
// 如果恒速模型不被满足,那么就只能够通过参考关键帧来跟踪
|
||||
bOK = TrackReferenceKeyFrame();
|
||||
}
|
||||
}
|
||||
|
|
@ -1661,32 +1738,49 @@ void Tracking::Track()
|
|||
// If relocalization is sucessfull we choose that solution, otherwise we retain
|
||||
// the "visual odometry" solution.
|
||||
|
||||
// mbVO为true，表明此帧匹配了很少（小于10）的地图点，要跟丢的节奏，既做跟踪又做重定位
|
||||
|
||||
// MM=Motion Model,通过运动模型进行跟踪的结果
|
||||
bool bOKMM = false;
|
||||
// 通过重定位方法来跟踪的结果
|
||||
bool bOKReloc = false;
|
||||
// 运动模型中构造的地图点
|
||||
vector<MapPoint*> vpMPsMM;
|
||||
// 在追踪运动模型后发现的外点
|
||||
vector<bool> vbOutMM;
|
||||
// 运动模型得到的位姿
|
||||
cv::Mat TcwMM;
|
||||
// Step 6.3 当运动模型有效的时候,根据运动模型计算位姿
|
||||
if(!mVelocity.empty())
|
||||
{
|
||||
bOKMM = TrackWithMotionModel();
|
||||
// 将恒速模型跟踪结果暂存到这几个变量中，因为后面重定位会改变这些变量
|
||||
vpMPsMM = mCurrentFrame.mvpMapPoints;
|
||||
vbOutMM = mCurrentFrame.mvbOutlier;
|
||||
TcwMM = mCurrentFrame.mTcw.clone();
|
||||
}
|
||||
// Step 6.4 使用重定位的方法来得到当前帧的位姿
|
||||
bOKReloc = Relocalization();
|
||||
|
||||
// Step 6.5 根据前面的恒速模型、重定位结果来更新状态
|
||||
if(bOKMM && !bOKReloc)
|
||||
{
|
||||
// 恒速模型成功、重定位失败，重新使用之前暂存的恒速模型结果
|
||||
mCurrentFrame.SetPose(TcwMM);
|
||||
mCurrentFrame.mvpMapPoints = vpMPsMM;
|
||||
mCurrentFrame.mvbOutlier = vbOutMM;
|
||||
|
||||
//? 疑似bug！这段代码是不是重复增加了观测次数？后面 TrackLocalMap 函数中会有这些操作
|
||||
// 如果当前帧匹配的3D点很少，增加当前可视地图点的被观测次数
|
||||
if(mbVO)
|
||||
{
|
||||
// 更新当前帧的地图点被观测次数
|
||||
for(int i =0; i<mCurrentFrame.N; i++)
|
||||
{
|
||||
// 如果这个特征点形成了地图点,并且也不是外点的时候
|
||||
if(mCurrentFrame.mvpMapPoints[i] && !mCurrentFrame.mvbOutlier[i])
|
||||
{
|
||||
// 增加能观测到该地图点的帧数
|
||||
mCurrentFrame.mvpMapPoints[i]->IncreaseFound();
|
||||
}
|
||||
}
|
||||
|
|
@ -1694,18 +1788,21 @@ void Tracking::Track()
|
|||
}
|
||||
else if(bOKReloc)
|
||||
{
|
||||
// 只要重定位成功整个跟踪过程正常进行（重定位与跟踪，更相信重定位）
|
||||
mbVO = false;
|
||||
}
|
||||
|
||||
// 有一个成功我们就认为执行成功了
|
||||
bOK = bOKReloc || bOKMM;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// 将最新的关键帧作为当前帧的参考关键帧
|
||||
if(!mCurrentFrame.mpReferenceKF)
|
||||
mCurrentFrame.mpReferenceKF = mpReferenceKF;
|
||||
|
||||
// If we have an initial estimation of the camera pose and matching. Track the local map.
|
||||
// Step 7 在跟踪得到当前帧初始姿态后，现在对local map进行跟踪得到更多的匹配，并优化当前位姿
|
||||
// 前面只是跟踪一帧得到初始位姿，这里搜索局部关键帧、局部地图点，和当前帧进行投影匹配，得到更多匹配的MapPoints后进行Pose优化
|
||||
if(!mbOnlyTracking)
|
||||
{
|
||||
if(bOK)
|
||||
|
|
@ -1713,6 +1810,7 @@ void Tracking::Track()
|
|||
#ifdef SAVE_TIMES
|
||||
std::chrono::steady_clock::time_point time_StartTrackLocalMap = std::chrono::steady_clock::now();
|
||||
#endif
|
||||
// 局部地图跟踪
|
||||
bOK = TrackLocalMap();
|
||||
#ifdef SAVE_TIMES
|
||||
std::chrono::steady_clock::time_point time_EndTrackLocalMap = std::chrono::steady_clock::now();
|
||||
|
|
@ -1734,7 +1832,9 @@ void Tracking::Track()
|
|||
bOK = TrackLocalMap();
|
||||
}
|
||||
|
||||
// Step 8 根据上面的操作来判断是否追踪成功
|
||||
if(bOK)
|
||||
// 此时还OK才说明跟踪成功了
|
||||
mState = OK;
|
||||
else if (mState == OK)
|
||||
{
|
||||
|
|
@ -1743,6 +1843,7 @@ void Tracking::Track()
|
|||
Verbose::PrintMess("Track lost for less than one second...", Verbose::VERBOSITY_NORMAL);
|
||||
if(!pCurrentMap->isImuInitialized() || !pCurrentMap->GetIniertialBA2())
|
||||
{
|
||||
// IMU模式下IMU没有成功初始化或者没有完成IMU BA，则重置当前地图
|
||||
cout << "IMU is not or recently initialized. Reseting active map..." << endl;
|
||||
mpSystem->ResetActiveMap();
|
||||
}
|
||||
|
|
@ -1752,6 +1853,7 @@ void Tracking::Track()
|
|||
else
|
||||
mState=LOST; // visual to lost
|
||||
|
||||
// 如果当前帧距离上次重定位帧超过1s，用当前帧时间戳更新lost帧时间戳
|
||||
if(mCurrentFrame.mnId>mnLastRelocFrameId+mMaxFrames)
|
||||
{
|
||||
mTimeStampLost = mCurrentFrame.mTimeStamp;
|
||||
|
|
@ -1759,7 +1861,10 @@ void Tracking::Track()
|
|||
}
|
||||
|
||||
// Save frame if recent relocalization, since they are used for IMU reset (as we are making copy, it shluld be once mCurrFrame is completely modified)
|
||||
if((mCurrentFrame.mnId<(mnLastRelocFrameId+mnFramesToResetIMU)) && (mCurrentFrame.mnId > mnFramesToResetIMU) && ((mSensor == System::IMU_MONOCULAR) || (mSensor == System::IMU_STEREO)) && pCurrentMap->isImuInitialized())
|
||||
if((mCurrentFrame.mnId<(mnLastRelocFrameId+mnFramesToResetIMU)) && // 当前帧距离上次重定位帧小于1s
|
||||
(mCurrentFrame.mnId > mnFramesToResetIMU) && // 当前帧已经运行了超过1s
|
||||
((mSensor == System::IMU_MONOCULAR) || (mSensor == System::IMU_STEREO)) && // IMU模式
|
||||
pCurrentMap->isImuInitialized()) // IMU已经成功初始化
|
||||
{
|
||||
// TODO check this situation
|
||||
Verbose::PrintMess("Saving pointer to frame. imu needs reset...", Verbose::VERBOSITY_NORMAL);
|
||||
|
|
@ -1770,10 +1875,11 @@ void Tracking::Track()
|
|||
pF->mpImuPreintegratedFrame = new IMU::Preintegrated(mCurrentFrame.mpImuPreintegratedFrame);
|
||||
}
|
||||
|
||||
if(pCurrentMap->isImuInitialized())
|
||||
if(pCurrentMap->isImuInitialized()) // IMU成功初始化
|
||||
{
|
||||
if(bOK)
|
||||
if(bOK) // 跟踪成功
|
||||
{
|
||||
// 当前帧距离上次重定位帧刚好等于1s，重置IMU
|
||||
if(mCurrentFrame.mnId==(mnLastRelocFrameId+mnFramesToResetIMU))
|
||||
{
|
||||
cout << "RESETING FRAME!!!" << endl;
|
||||
|
|
@ -1785,27 +1891,33 @@ void Tracking::Track()
|
|||
}
|
||||
|
||||
// Update drawer
|
||||
// 更新显示线程中的图像、特征点、地图点等信息
|
||||
mpFrameDrawer->Update(this);
|
||||
if(!mCurrentFrame.mTcw.empty())
|
||||
mpMapDrawer->SetCurrentCameraPose(mCurrentFrame.mTcw);
|
||||
|
||||
// Step 9 如果跟踪成功 或 最近刚刚跟丢，更新速度，清除无效地图点，按需创建关键帧
|
||||
if(bOK || mState==RECENTLY_LOST)
|
||||
{
|
||||
// Update motion model
|
||||
// Step 9.1 更新恒速运动模型 TrackWithMotionModel 中的mVelocity
|
||||
if(!mLastFrame.mTcw.empty() && !mCurrentFrame.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 = Tcl = Tcw * Twl,表示上一帧到当前帧的变换， 其中 Twl = LastTwc
|
||||
mVelocity = mCurrentFrame.mTcw*LastTwc;
|
||||
}
|
||||
else
|
||||
//否则速度为空
|
||||
mVelocity = cv::Mat();
|
||||
|
||||
if(mSensor == System::IMU_MONOCULAR || mSensor == System::IMU_STEREO)
|
||||
mpMapDrawer->SetCurrentCameraPose(mCurrentFrame.mTcw);
|
||||
|
||||
// Clean VO matches
|
||||
// Step 9.2 清除观测不到的地图点
|
||||
for(int i=0; i<mCurrentFrame.N; i++)
|
||||
{
|
||||
MapPoint* pMP = mCurrentFrame.mvpMapPoints[i];
|
||||
|
|
@ -1818,16 +1930,22 @@ void Tracking::Track()
|
|||
}
|
||||
|
||||
// Delete temporal MapPoints
|
||||
// Step 9.3 清除恒速模型跟踪中 UpdateLastFrame中为当前帧临时添加的MapPoints（仅双目和rgbd）
|
||||
// 上个步骤中只是在当前帧中将这些MapPoints剔除，这里从MapPoints数据库中删除
|
||||
// 临时地图点仅仅是为了提高双目或rgbd摄像头的帧间跟踪效果，用完以后就扔了，没有添加到地图中
|
||||
for(list<MapPoint*>::iterator lit = mlpTemporalPoints.begin(), lend = mlpTemporalPoints.end(); lit!=lend; lit++)
|
||||
{
|
||||
MapPoint* pMP = *lit;
|
||||
delete pMP;
|
||||
}
|
||||
// 这里不仅仅是清除mlpTemporalPoints，通过delete pMP还删除了指针指向的MapPoint
|
||||
// 不能够直接执行这个是因为其中存储的都是指针,之前的操作都是为了避免内存泄露
|
||||
mlpTemporalPoints.clear();
|
||||
|
||||
#ifdef SAVE_TIMES
|
||||
std::chrono::steady_clock::time_point timeStartNewKF = std::chrono::steady_clock::now();
|
||||
#endif
|
||||
// 判断是否需要插入关键帧
|
||||
bool bNeedKF = NeedNewKeyFrame();
|
||||
#ifdef SAVE_TIMES
|
||||
std::chrono::steady_clock::time_point timeEndNewKF = std::chrono::steady_clock::now();
|
||||
|
|
@ -1838,23 +1956,35 @@ void Tracking::Track()
|
|||
|
||||
|
||||
// Check if we need to insert a new keyframe
|
||||
// Step 9.4 根据条件来判断是否插入关键帧
|
||||
// 需要同时满足下面条件1和2
|
||||
// 条件1：bNeedKF=true，需要插入关键帧
|
||||
// 条件2：bOK=true跟踪成功 或 IMU模式下的RECENTLY_LOST模式
|
||||
if(bNeedKF && (bOK|| (mState==RECENTLY_LOST && (mSensor == System::IMU_MONOCULAR || mSensor == System::IMU_STEREO))))
|
||||
// 创建关键帧，对于双目或RGB-D会产生新的地图点
|
||||
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. Only has effect if lastframe is tracked
|
||||
// 作者这里说允许在BA中被Huber核函数判断为外点的传入新的关键帧中，让后续的BA来审判他们是不是真正的外点
|
||||
// 但是估计下一帧位姿的时候我们不想用这些外点，所以删掉
|
||||
|
||||
// Step 9.5 删除那些在BA中检测为外点的地图点
|
||||
for(int i=0; i<mCurrentFrame.N;i++)
|
||||
{
|
||||
// 这里第一个条件还要执行判断是因为, 前面的操作中可能删除了其中的地图点
|
||||
if(mCurrentFrame.mvpMapPoints[i] && mCurrentFrame.mvbOutlier[i])
|
||||
mCurrentFrame.mvpMapPoints[i]=static_cast<MapPoint*>(NULL);
|
||||
}
|
||||
}
|
||||
|
||||
// Reset if the camera get lost soon after initialization
|
||||
// Step 10 如果跟踪失败，复位
|
||||
if(mState==LOST)
|
||||
{
|
||||
// 如果地图中关键帧小于5，重置当前地图，退出当前跟踪
|
||||
if(pCurrentMap->KeyFramesInMap()<=5)
|
||||
{
|
||||
mpSystem->ResetActiveMap();
|
||||
|
|
@ -1863,28 +1993,28 @@ void Tracking::Track()
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|||
if ((mSensor == System::IMU_MONOCULAR) || (mSensor == System::IMU_STEREO))
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||||
if (!pCurrentMap->isImuInitialized())
|
||||
{
|
||||
// 如果是IMU模式并且还未进行IMU初始化，重置当前地图，退出当前跟踪
|
||||
Verbose::PrintMess("Track lost before IMU initialisation, reseting...", Verbose::VERBOSITY_QUIET);
|
||||
mpSystem->ResetActiveMap();
|
||||
return;
|
||||
}
|
||||
|
||||
// 如果地图中关键帧超过5 并且 纯视觉模式 或 虽然是IMU模式但是已经完成IMU初始化了，创建新的地图
|
||||
CreateMapInAtlas();
|
||||
}
|
||||
|
||||
//确保已经设置了参考关键帧
|
||||
if(!mCurrentFrame.mpReferenceKF)
|
||||
mCurrentFrame.mpReferenceKF = mpReferenceKF;
|
||||
|
||||
// 保存上一帧的数据,当前帧变上一帧
|
||||
mLastFrame = Frame(mCurrentFrame);
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
// Step 11 记录位姿信息，用于最后保存所有的轨迹
|
||||
if(mState==OK || mState==RECENTLY_LOST)
|
||||
{
|
||||
// Store frame pose information to retrieve the complete camera trajectory afterwards.
|
||||
if(!mCurrentFrame.mTcw.empty())
|
||||
{
|
||||
// 计算相对姿态Tcr = Tcw * Twr, Twr = Trw^-1
|
||||
cv::Mat Tcr = mCurrentFrame.mTcw*mCurrentFrame.mpReferenceKF->GetPoseInverse();
|
||||
mlRelativeFramePoses.push_back(Tcr);
|
||||
mlpReferences.push_back(mCurrentFrame.mpReferenceKF);
|
||||
|
|
@ -1894,6 +2024,7 @@ void Tracking::Track()
|
|||
else
|
||||
{
|
||||
// This can happen if tracking is lost
|
||||
// 如果跟踪失败，则相对位姿使用上一次值
|
||||
mlRelativeFramePoses.push_back(mlRelativeFramePoses.back());
|
||||
mlpReferences.push_back(mlpReferences.back());
|
||||
mlFrameTimes.push_back(mlFrameTimes.back());
|
||||
|
|
|
|||
Loading…
Reference in New Issue