相机与激光雷达标定:gazebo仿真livox_camera_lidar_calibration---R3live算法验证

ROS功能包:livox_camera_lidar_calibration提供了一个手动校准Livox雷达和相机之间外参的方法，已经在Mid-40，Horizon和Tele-15上进行了验证。其中包含了计算相机内参，获得标定数据，优化计算外参和雷达相机融合应用相关的代码。本方案中使用了标定板角点作为标定目标物，由于Livox雷达非重复性扫描的特点，点云的密度较大，比较易于找到雷达点云中角点的准确位置。相机雷达的标定和融合也可以得到不错的结果。

在前几篇中介绍了livox_camera_lidar_calibration功能包.以及在gazebo中搭建了标定场景.并进行外参标定,进行了简单的验证.

本篇使用标定的外参,进行R3live算法的验证,看下效果怎么样

将得到的内外参数进行R3live的配置
打开r3live_config.yaml文件
修改:

• camera_intrinsic
• camera_dist_coeffs
• camera_ext_t
• camera_ext_R

注意之前标定的是相机到雷达的,R3live下面配置的雷达到相机的,所以要把得到外参旋转矩阵求逆,再填入,平移矩阵则取负

//之前的
0.00199437  -0.999998  -0.000472109 
-0.00306955  0.000465986  -0.999995  
0.999993  0.00199581  -0.00306862 
//求逆后
 0.00199437   	-0.00306956   	0.99999360   
-0.99999780   	0.00046599   	0.00199581   
-0.00047211   	-0.99999536   	-0.00306862   

  

 

  
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Lidar_front_end:
   lidar_type: 1   # 1 for Livox-avia, 3 for Ouster-OS1-64
   N_SCANS: 6
   using_raw_point: 1
   point_step: 1
   
r3live_common:
   if_dump_log: 0                   # If recording ESIKF update log. [default = 0]
   record_offline_map: 1            # If recording offline map. [default = 1]
   pub_pt_minimum_views: 3          # Publish points which have been render up to "pub_pt_minimum_views" time. [default = 3]
   minimum_pts_size: 0.01           # The minimum distance for every two points in Global map (unit in meter). [default = 0.01] 
   image_downsample_ratio: 1        # The downsample ratio of the input image. [default = 1]
   estimate_i2c_extrinsic: 1        # If enable estimate the extrinsic between camera and IMU. [default = 1] 
   estimate_intrinsic: 1            # If enable estimate the online intrinsic calibration of the camera lens. [default = 1] 
   maximum_vio_tracked_pts: 600     # The maximum points for tracking. [default = 600]
   append_global_map_point_step: 4  # The point step of append point to global map. [default = 4]

r3live_vio:
   image_width: 1024
   image_height: 960
   camera_intrinsic:
      [715.521, 0.0, 511.578,
      0.0,  717.146, 481.681,
      0.0, 0.0, 1.0 ] 
   camera_dist_coeffs: [0.000047, 0.000106, 0.000019, 0.000009, 0.000000]  #k1, k2, p1, p2, k3
   # Fine extrinsic value. form camera-LiDAR calibration.
   camera_ext_R:
        [ 0.00199437, -0.00306956, 0.99999360,   
         -0.99999780, 0.00046599, 0.00199581,   
         -0.00047211, -0.99999536, -0.00306862 ]

   camera_ext_t: [-0.191228, 0.00300958, 0.0678278] 
   #camera_ext_t: [0,0,0] 
   # Rough extrinsic value, form CAD model, is not correct enough, but can be online calibrated in our datasets.
   #camera_ext_R:
   #    [0, 0, 1,
   #     -1, 0, 0,
   #     0, -1, 0]
   # camera_ext_t: [0,0,0] 
   
r3live_lio:        
   lio_update_point_step: 4   # Point step used for LIO update.  
   max_iteration: 2           # Maximum times of LIO esikf.
   lidar_time_delay: 0        # The time-offset between LiDAR and IMU, provided by user. 
   filter_size_corner: 0.30   
   filter_size_surf: 0.30
   filter_size_surf_z: 0.30
   filter_size_map: 0.30

  

 

  
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验证的场景如下:
gazebo中的场景是这样的

控制无人机做了些旋转和平移

建立的三维模型如下:

拼接效果还可以

其中厂房的管道细节:

文章来源: blog.csdn.net，作者：月照银海似蛟龙，版权归原作者所有，如需转载，请联系作者。

原文链接：blog.csdn.net/qq_32761549/article/details/125741152