【SLAM】VINS-fusion，VINS-Mono 编译，使用T265测试 ，GPS融合的测试

安装 VINS-fusion VINS-Mono

将vins-mono 下载到ros工作空间catkin_ws里面

cd /home/jiangz/catkin_ws/src
git clone https://github.com/HKUST-Aerial-Robotics/VINS-Fusion.git

遇到的bug基本可以通过下面三个解决

进入调试模式 ：https://www.cxybb.com/article/u014527548/86503256

**opencv4 安装 VINS :遇到的bug
千万不要多安装一个opencv3！！！**会因为两个库冲突，导致segment fault！
https://zhuanlan.zhihu.com/p/432167383
https://blog.csdn.net/tjj1998/article/details/110500320

ceres问题 ：
https://blog.csdn.net/weixin_56024057/article/details/128344034

使用T265相机启动

由于VINS-Fusion里面的t265相机的yaml包已经没有了，这次测试 VINS—Mono的

roslaunch vins_estimator realsense_fisheye.launch
roslaunch vins_estimatot vins_rviz.launch 
rosbag play xxx.bag

注意需要修改 realsense_fisheye.launch 里面的相机内参，tpoic和输出路径名字
内参的来源参考了这篇文章：https://blog.csdn.net/weixin_45810108/article/details/116917579

%YAML:1.0

#common parameters
imu_topic: "/camera/imu"
image_topic: "/camera/fisheye1/image_raw"
output_path: "~/catkin_ws/src/VINS-Fusion-master/vins_output"

#camera calibration 
model_type: KANNALA_BRANDT
camera_name: camera
image_width: 848
image_height: 800
projection_parameters:
   k2: -0.00836907513439655
   k3: 0.0468319803476334
   k4: -0.0438220910727978
   k5: 0.00818893685936928
   mu: 285.814514160156
   mv: 285.921997070312
   u0: 424.557891845703
   v0: 394.814086914062

# 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.                        
#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: [0.99992917,  0.00878151,  0.00803387,
           -0.00870674,  0.9999189,  -0.0092943, 
           -0.00811483,  0.00922369,  0.99992453]
#Translation from camera frame to imu frame, imu^T_cam
extrinsicTranslation: !!opencv-matrix
   rows: 3
   cols: 1
   dt: d
   data: [0.00188568, 0.00123801, 0.01044055]

#feature traker paprameters
max_cnt: 120            # 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: 0             # 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: 9.805    # gravity magnitude

#loop closure parameters
loop_closure: 1                    # start loop closure
fast_relocalization: 1             # useful in real-time and large project
load_previous_pose_graph: 0        # load and reuse previous pose graph; load from 'pose_graph_save_path'
pose_graph_save_path: "~/catkin_ws/src/VINS-Fusion-master/vins_output/pose_graph/" # save and load path

#unsynchronization parameters
estimate_td: 1                      # online estimate time offset between camera and imu
td: 0.010                             # 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

效果：

不咋地，会漂移，具体原因还需要进一步的探究，但是终究是跑起来了555

vins-fusion gps融合代码

环境要求：
vision-opencv noetic分支
opencv 4.2
ceres 1.4

rosrun vins vins_node <path>/xxx.yaml
rosrun global_fusion global_fusion_node
rosbag play xxx.bag

输出topic: /globalEstimator/global_odometry