源码地址

源码链接：https://github.com/HKUST-Aerial-Robotics/VINS-Mono.git

电脑配置

Ubuntu 18.04 + ROS Melodic + GTSAM 4.0.2 + CERES 1.14.0
pcl1.8+vtk8.2.0+opencv3.2.0

环境配置

之前已经配置过LVI-SAM的环境，所以没有什么额外需要配置的（可参考之前的博客）

编译

 cd ~/catkin_ws/src
 git clone https://github.com/HKUST-Aerial-Robotics/VINS-Mono.git
 cd ..
 catkin_make -j2

注：直接catkin_make会死机

适配Kitti数据集

在config文件夹下新建kitti文件夹
新建kitti_config.yaml文件
(具体参数设置的方式，可以参考之前LVI-SAM博客)

%YAML:1.0

#common parameters
imu_topic: "/imu_raw"   #"/kitti/oxts/imu"
image_topic: "/kitti/camera_gray_left/image_raw"
output_path: "/home/nssc/sbk/outputs/map/vinsmoon/"

#camera calibration 
model_type: PINHOLE
camera_name: camera
#10_03
# image_width: 1241
# image_height: 376
# 09_30
image_width: 1226
image_height: 370
distortion_parameters:
  k1: 0.0
  k2: 0.0
  p1: 0.0
  p2: 0.0
projection_parameters:
# 10_03
#   fx: 7.188560e+02
#   fy: 7.188560e+02
#   cx: 6.071928e+02
#   cy: 1.852157e+02
  # 09_30
  fx: 7.070912e+02
  fy: 7.070912e+02
  cx: 6.018873e+02
  cy: 1.831104e+02

# Extrinsic parameter between IMU and Camera.
estimate_extrinsic: 0   # 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.                        
#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
  #  10_03
   # data: [0.00875116, -0.00479609,  0.99995027, -0.99986428, -0.01400249,  0.00868325, 0.01396015, -0.99989044, -0.00491798]
  #  09_30
   data: [0.00781298, -0.0042792,  0.99996033,-0.99985947, -0.01486805,  0.00774856, 0.0148343 , -0.99988023, -0.00439476]     

#Translation from camera frame to imu frame, imu^T_cam
extrinsicTranslation: !!opencv-matrix
   rows: 3
   cols: 1
   dt: d
  #  10_03
   # data: [1.10224312,-0.31907194,  0.74606588]
#09_30 
   data: [1.14389871,-0.31271847,  0.72654605]

#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: 0.08          # accelerometer measurement noise standard deviation. #0.2   0.04
gyr_n: 0.004         # gyroscope measurement noise standard deviation.     #0.05  0.004
acc_w: 0.00004         # accelerometer bias random work noise standard deviation.  #0.02
gyr_w: 2.0e-6       # gyroscope bias random work noise standard deviation.     #4.0e-5
g_norm: 9.81007     # gravity magnitude

#loop closure parameters
loop_closure: 1                    # start loop closure
load_previous_pose_graph: 0        # load and reuse previous pose graph; load from 'pose_graph_save_path'
fast_relocalization: 0             # useful in real-time and large project
pose_graph_save_path: "/home/nssc/sbk/outputs/map/vinsmoon/pose_graph/" # save and load path

#unsynchronization parameters
estimate_td: 0                      # online estimate time offset between camera and imu
td: 0.0                             # initial value of time offset. unit: s. readed image clock + td = real image clock (IMU clock)

#rolling shutter parameters
rolling_shutter: 0                  # 0: global shutter camera, 1: rolling shutter camera
rolling_shutter_tr: 0               # unit: s. rolling shutter read out time per frame (from data sheet). 

#visualization parameters
save_image: 1                   # save image in pose graph for visualization prupose; you can close this function by setting 0 
visualize_imu_forward: 0        # output imu forward propogation to achieve low latency and high frequence results
visualize_camera_size: 0.4      # size of camera marker in RVIZ

在vins_estimator/launch/文件夹下新建文件kitti.launch
（主要修改一下config_path的路径）

<launch>
    <arg name="config_path" default = "$(find feature_tracker)/../config/kitti/kitti_config.yaml" />
	  <arg name="vins_path" default = "$(find feature_tracker)/../config/../" />
    
    <node name="feature_tracker" pkg="feature_tracker" type="feature_tracker" output="log">
        <param name="config_file" type="string" value="$(arg config_path)" />
        <param name="vins_folder" type="string" value="$(arg vins_path)" />
    </node>

    <node name="vins_estimator" pkg="vins_estimator" type="vins_estimator" output="screen">
       <param name="config_file" type="string" value="$(arg config_path)" />
       <param name="vins_folder" type="string" value="$(arg vins_path)" />
    </node>

    <node name="pose_graph" pkg="pose_graph" type="pose_graph" output="screen">
        <param name="config_file" type="string" value="$(arg config_path)" />
        <param name="visualization_shift_x" type="int" value="0" />
        <param name="visualization_shift_y" type="int" value="0" />
        <param name="skip_cnt" type="int" value="0" />
        <param name="skip_dis" type="double" value="0" />
    </node>

</launch>

运行结果

Euroc数据集

 roslaunch vins_estimator euroc.launch 
 roslaunch vins_estimator vins_rviz.launch
 rosbag play YOUR_PATH_TO_DATASET/MH_01_easy.bag 

同时看到groundtrue:

roslaunch benchmark_publisher publish.launch sequence_name:=MH_01_easy

kitti数据集

有关kitti数据集生成bag包的方式，可参考之前生成LVI-SAM适配数据的博客

 roslaunch vins_estimator kitti.launch 
 roslaunch vins_estimator vins_rviz.launch
 rosbag play YOUR_PATH_TO_DATASET/ rosbag play kitti_2011_09_30_drive_0027_synced.bag

evo评估（KITTI数据）

输出轨迹(tum格式)

vins_estimator/src/utility/visualization.cpp
pubOdometry()函数150+行

        // write result to file
        // ofstream foutC(VINS_RESULT_PATH, ios::app);
        // foutC.setf(ios::fixed, ios::floatfield);
        // foutC.precision(0);
        // foutC << header.stamp.toSec() * 1e9 << ",";
        // foutC.precision(5);
        // foutC << estimator.Ps[WINDOW_SIZE].x() << ","
        //       << estimator.Ps[WINDOW_SIZE].y() << ","
        //       << estimator.Ps[WINDOW_SIZE].z() << ","
        //       << tmp_Q.w() << ","
        //       << tmp_Q.x() << ","
        //       << tmp_Q.y() << ","
        //       << tmp_Q.z() << ","
        //       << estimator.Vs[WINDOW_SIZE].x() << ","
        //       << estimator.Vs[WINDOW_SIZE].y() << ","
        //       << estimator.Vs[WINDOW_SIZE].z() << "," << endl;

         ofstream foutC(VINS_RESULT_PATH, ios::app);
        foutC.setf(ios::fixed, ios::floatfield);
        foutC.precision(9);
        foutC << header.stamp.toSec() << " ";
        foutC.precision(5);
        foutC << estimator.Ps[WINDOW_SIZE].x() << " "
            << estimator.Ps[WINDOW_SIZE].y() << " "
            << estimator.Ps[WINDOW_SIZE].z() << " "
            << tmp_Q.x() << " "
            << tmp_Q.y() << " "
            << tmp_Q.z() << " "
            << tmp_Q.w() << endl;
            foutC.close();

pose_graph/src/pose_graph.cpp
addKeyFrame()函数150+行

    if (SAVE_LOOP_PATH)
    {
        // ofstream loop_path_file(VINS_RESULT_PATH, ios::app);
        // loop_path_file.setf(ios::fixed, ios::floatfield);
        // loop_path_file.precision(0);
        // loop_path_file << cur_kf->time_stamp * 1e9 << ",";
        // loop_path_file.precision(5);
        // loop_path_file  << P.x() << ","
        //       << P.y() << ","
        //       << P.z() << ","
        //       << Q.w() << ","
        //       << Q.x() << ","
        //       << Q.y() << ","
        //       << Q.z() << ","
         //      << endl;
        ofstream loop_path_file(VINS_RESULT_PATH, ios::app);
        loop_path_file.setf(ios::fixed, ios::floatfield);
        loop_path_file.precision(0);
        loop_path_file << cur_kf->time_stamp << " ";
        loop_path_file.precision(5);
        loop_path_file  << P.x() << " "
                        << P.y() << " "
                        << P.z() << " "
                        << Q.x() << " "
                        << Q.y() << " "
                        << Q.z() << " "
                        << Q.w() << endl;    
        loop_path_file.close();
    }

updatePath()函数600+行

        if (SAVE_LOOP_PATH)
        {
            // ofstream loop_path_file(VINS_RESULT_PATH, ios::app);
            // loop_path_file.setf(ios::fixed, ios::floatfield);
            // loop_path_file.precision(0);
            // loop_path_file << (*it)->time_stamp * 1e9 << ",";
            // loop_path_file.precision(5);
            // loop_path_file  << P.x() << ","
            //       << P.y() << ","
            //       << P.z() << ","
            //       << Q.w() << ","
            //       << Q.x() << ","
            //       << Q.y() << ","
            //       << Q.z() << ","
            //       << endl;
            ofstream loop_path_file(VINS_RESULT_PATH, ios::app);
            loop_path_file.setf(ios::fixed, ios::floatfield);
            loop_path_file.precision(0);
            loop_path_file << (*it)->time_stamp << " ";
            loop_path_file.precision(5);
            loop_path_file  << P.x() << " "
                            << P.y() << " "
                            << P.z() << " "
                            << Q.x() << " "
                            << Q.y() << " "
                            << Q.z() << " "
                            << Q.w() << endl;
            loop_path_file.close();
        }

pose_graph_node.cpp中的main()函数

       # VINS_RESULT_PATH = VINS_RESULT_PATH + "/vins_result_loop.csv";
        VINS_RESULT_PATH = VINS_RESULT_PATH + "/vins_result_loop.txt";

对输出的vins_result_loop.txt文件修改时间戳

# 读取txt文件
with open('vins_result_loop.txt', 'r') as file:
    lines = file.readlines()

# 处理数据
first_line = lines[0].strip().split()
first_num = int(first_line[0])
output_lines = []
for line in lines[0:]:
    parts = line.split()
    new_num = float(parts[0]) - first_num
    new_line = str(new_num)  +' '+ ' '.join(parts[1:]) + '\n'
    output_lines.append(new_line)

# 写入txt文件
with open('output.txt', 'w') as file:
    for line in output_lines:
        file.write(''.join(line))

结果

evo_traj tum output.txt 07_gt_tum.txt --ref=07_gt_tum.txt -a -p --plot_mode=xyz

参考链接：
https://blog.csdn.net/m0_49066914/article/details/131814856
https://blog.csdn.net/Hanghang_/article/details/104535370