系統入口是feature_tracker_node.cpp文件中的main函數

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；
• 優化參數（最大求解時間以保證實時性，不卡頓；對大叠代次數，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準差、零偏隨機遊走噪聲標準差，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）什麽都不知道，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；

imu_topic: "/imu0"
image_topic: "/cam0/image_raw"

image_width: 752
image_height: 480

#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

# 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. 

#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

VINS（二）Feature Detection and Tracking