VINS-Fusion - 编译及使用（单/双目+IMU）

Posted 2021-12-09 Techblog of HaoWANG

1. VINS-Fusion 基本介绍

  GitHub - HKUST-Aerial-Robotics/VINS-Fusion: An optimization-based multi-sensor state estimatorhttps://github.com/HKUST-Aerial-Robotics/VINS-Fusion 

         VINS-Fusion 是继 VINS-Mono 和 VINS-Mobile（单目视觉惯导 SLAM 方案）后，香港科技大学沈劭劼老师开源的双目视觉惯导 SLAM 方案，VINS-Fusion 是一种基于优化的多传感器状态估计器，可实现自主应用（无人机，汽车和 AR / VR）的精确自定位。 VINS-Fusion 是 VINS-Mono 的扩展，支持多种视觉惯性传感器类型（单目摄像机+ IMU，双目摄像机+ IMU，甚至仅限双目摄像机）。开源项目组还展示了将 VINS 与 GPS 融合的模组示例。

特征如下：

1. 多传感器支持（立体摄像机/单声道摄像机+ IMU /立体摄像机+ IMU）
2. 在线空间校准（相机和 IMU 之间的转换）
3. 在线时间校准（相机和 IMU 之间的时间偏移）
4. 视觉循环闭合

3. 公开数据集测试

 EuRoC Example

Download EuRoC MAV Dataset to YOUR_DATASET_FOLDER. Take MH_01 for example, you can run VINS-Fusion with three sensor types (monocular camera + IMU, stereo cameras + IMU and stereo cameras). Open four terminals, run vins odometry, visual loop closure(optional), rviz and play the bag file respectively. Green path is VIO odometry; red path is odometry under visual loop closure.

3.1 Monocualr camera + IMU

    roslaunch vins vins_rviz.launch
    rosrun vins vins_node ~/catkin_ws/src/VINS-Fusion/config/euroc/euroc_mono_imu_config.yaml 
    (optional) rosrun loop_fusion loop_fusion_node ~/catkin_ws/src/VINS-Fusion/config/euroc/euroc_mono_imu_config.yaml 
    rosbag play YOUR_DATASET_FOLDER/MH_01_easy.bag

3.2 Stereo cameras + IMU

    roslaunch vins vins_rviz.launch
    rosrun vins vins_node ~/catkin_ws/src/VINS-Fusion/config/euroc/euroc_stereo_imu_config.yaml 
    (optional) rosrun loop_fusion loop_fusion_node ~/catkin_ws/src/VINS-Fusion/config/euroc/euroc_stereo_imu_config.yaml 
    rosbag play YOUR_DATASET_FOLDER/MH_01_easy.bag

3.3 Stereo cameras

    roslaunch vins vins_rviz.launch
    rosrun vins vins_node ~/catkin_ws/src/VINS-Fusion/config/euroc/euroc_stereo_config.yaml 
    (optional) rosrun loop_fusion loop_fusion_node ~/catkin_ws/src/VINS-Fusion/config/euroc/euroc_stereo_config.yaml 
    rosbag play YOUR_DATASET_FOLDER/MH_01_easy.bag

4. KITTI Example

4.1 KITTI Odometry (Stereo)

Download KITTI Odometry dataset to YOUR_DATASET_FOLDER. Take sequences 00 for example, Open two terminals, run vins and rviz respectively. (We evaluated odometry on KITTI benchmark without loop closure funtion)

    roslaunch vins vins_rviz.launch
    (optional) rosrun loop_fusion loop_fusion_node ~/catkin_ws/src/VINS-Fusion/config/kitti_odom/kitti_config00-02.yaml
    rosrun vins kitti_odom_test ~/catkin_ws/src/VINS-Fusion/config/kitti_odom/kitti_config00-02.yaml YOUR_DATASET_FOLDER/sequences/00/ 

4.2 KITTI GPS Fusion (Stereo + GPS)

Download KITTI raw dataset to YOUR_DATASET_FOLDER. Take 2011_10_03_drive_0027_synced for example. Open three terminals, run vins, global fusion and rviz respectively. Green path is VIO odometry; blue path is odometry under GPS global fusion.

    roslaunch vins vins_rviz.launch
    rosrun vins kitti_gps_test ~/catkin_ws/src/VINS-Fusion/config/kitti_raw/kitti_10_03_config.yaml YOUR_DATASET_FOLDER/2011_10_03_drive_0027_sync/ 
    rosrun global_fusion global_fusion_node

5. VINS-Fusion on car demonstration

Download car bag to YOUR_DATASET_FOLDER. Open four terminals, run vins odometry, visual loop closure(optional), rviz and play the bag file respectively. Green path is VIO odometry; red path is odometry under visual loop closure.

    roslaunch vins vins_rviz.launch
    rosrun vins vins_node ~/catkin_ws/src/VINS-Fusion/config/vi_car/vi_car.yaml 
    (optional) rosrun loop_fusion loop_fusion_node ~/catkin_ws/src/VINS-Fusion/config/vi_car/vi_car.yaml 
    rosbag play YOUR_DATASET_FOLDER/car.bag