系統入口是feature_tracker_node.cpp文件中的main函數
1. 首先創建feature_tracker節點，從配置文件中讀取信息（parameters.cpp），包括：
- ROS中發布訂閱的話題名稱；
- 圖像尺寸；
- 特征跟蹤參數；
- 是否需要加上魚眼mask來去除邊緣噪點；
%YAML:1.0 #common parameters imu_topic: "/imu0" image_topic: "/cam0/image_raw" #camera calibration model_type: PINHOLE camera_name: camera image_width: 752 image_height: 480 distortion_parameters: k1: -2.917e-01 k2: 8.228e-02 p1: 5.333e-05 p2: -1.578e-04 projection_parameters: fx: 4.616e+02 fy: 4.603e+02 cx: 3.630e+02 cy: 2.481e+02 # Extrinsic parameter between IMU and Camera. estimate_extrinsic: 1 # 0 Have an accurate extrinsic parameters. We will trust the following imu^R_cam, imu^T_cam, don't change it. # 1 Have an initial guess about extrinsic parameters. We will optimize around your initial guess. # 2 Don't know anything about extrinsic parameters. You don't need to give R,T. We will try to calibrate it. Do some rotation movement at beginning. ex_calib_result_path: "/config/euroc/ex_calib_result.yaml" # If you choose 1 or 2, the extrinsic calibration result will be written vins_folder_path + ex_calib_result_path. #If you choose 0 or 1, you should write down the following matrix. #Rotation from camera frame to imu frame, imu^R_cam extrinsicRotation: !!opencv-matrix rows: 3 cols: 3 dt: d data: [0, -1, 0, 1, 0, 0, 0, 0, 1] #Translation from camera frame to imu frame, imu^T_cam extrinsicTranslation: !!opencv-matrix rows: 3 cols: 1 dt: d data: [-0.02,-0.06, 0.01] #feature traker paprameters max_cnt: 150 # max feature number in feature tracking min_dist: 30 # min distance between two features freq: 10 # frequence (Hz) of publish tracking result. At least 10Hz for good estimation. If set 0, the frequence will be same as raw image F_threshold: 1.0 # ransac threshold (pixel) show_track: 1 # publish tracking image as topic equalize: 1 # if image is too dark or light, trun on equalize to find enough features fisheye: 0 # if using fisheye, trun on it. A circle mask will be loaded to remove edge noisy points #optimization parameters max_solver_time: 0.04 # max solver itration time (ms), to guarantee real time max_num_iterations: 8 # max solver itrations, to guarantee real time keyframe_parallax: 10.0 # keyframe selection threshold (pixel) #imu parameters The more accurate parameters you provide, the better performance acc_n: 0.2 # accelerometer measurement noise standard deviation. #0.2 gyr_n: 0.02 # gyroscope measurement noise standard deviation. #0.05 acc_w: 0.0002 # accelerometer bias random work noise standard deviation. #0.02 gyr_w: 2.0e-5 # gyroscope bias random work noise standard deviation. #4.0e-5 g_norm: 9.81007 # gravity magnitude #loop closure parameters loop_closure: 1 #if you want to use loop closure to minimize the drift, set loop_closure true and give your brief pattern file path and vocabulary file path accordingly; #also give the camera calibration file same as feature_tracker node pattern_file: "/support_files/brief_pattern.yml" voc_file: "/support_files/brief_k10L6.bin" min_loop_num: 25
該config.yaml文件中的其他參數在vins_estimator_node中被讀取，屬於融合算法的參數。
- 優化參數（最大求解時間以保證實時性，不卡頓；最大迭代次數，避免冗余計算；視差閾值，用於選取sliding window中的關鍵幀）；
- imu參數，包括加速度計陀螺儀的測量噪聲標准差、零偏隨機游走噪聲標准差，重力值（imu放火星上需要改變）；
- imu和camera之間的外參R，t；可選（0）已知精確的外參，運行中無需改變,（1）已知外參初值，運行中優化，（2）什么都不知道，在線初始化中標定
- 閉環參數，包括brief描述子的pattern文件（前端視覺使用光流跟蹤，不需要計算描述子），針對場景訓練好的DBow二進制字典文件；
2. 監聽IMAGE_TOPIC, 有圖像信息發布到IMAGE_TOPIC上時，執行回調函數：
ros::Subscriber sub_img = n.subscribe(IMAGE_TOPIC, 100, img_callback);
3. img_callback()
前端視覺的算法基本在這個回調函數中，步驟為：
1. 頻率控制，保證每秒鍾處理的image不多於FREQ；
2. 對於單目：
1）. readImage;
2）. showUndistortion(可選);
3）. 將特征點矯正（相機模型camodocal）后歸一化平面的3D點（此時沒有尺度信息，3D點p.z=1），像素2D點，以及特征的id，封裝成ros的sensor_msgs::PointCloud消息類型;
3. 將處理完的圖像信息用PointCloud和Image的消息類型，發布到"feature"和"feature_img"的topic：
pub_img = n.advertise<sensor_msgs::PointCloud>("feature", 1000); pub_match = n.advertise<sensor_msgs::Image>("feature_img",1000);
4. 包含的視覺算法：
1. CLAHE(Contrast Limited Adaptive Histogram Equalization)
cv::Ptr<cv::CLAHE> clahe = cv::createCLAHE(3.0, cv::Size(8, 8));
2. Optical Flow(光流追蹤)
cv::calcOpticalFlowPyrLK(cur_img, forw_img, cur_pts, forw_pts, status, err, cv::Size(21, 21), 3);
3. 根據匹配點計算Fundamental Matrix， 然后用Ransac剔除不符合Fundamental Matrix的外點
cv::findFundamentalMat(un_prev_pts, un_forw_pts, cv::FM_RANSAC, F_THRESHOLD, 0.99, status);
4. 特征點檢測：goodFeaturesToTrack， 使用Shi-Tomasi的改進版Harris corner
cv::goodFeaturesToTrack(forw_img, n_pts, MAX_CNT - forw_pts.size(), 0.1, MIN_DIST, mask);
特征點之間保證了最小距離30個像素，跟蹤成功的特征點需要經過rotation-compensated旋轉補償的視差計算，視差在30個像素以上的特征點才會去參與三角化和后續的優化，保證了所有的特征點質量都是比較高的，同時降低了計算量。