一、手动标定

代码工程：GitHub - Livox-SDK/livox_camera_lidar_calibration: Calibrate the extrinsic parameters between Livox LiDAR and camera

        这是Livox提供的手动校准Livox雷达和相机之间外参的方法，并在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。

1.1 环境配置

我的基础环境为 ubuntu20.04   ros neotic pcl 1.10 提前还需要装好 Eigen  Ceres

# install Livox_SDK
git clone https://github.com/Livox-SDK/Livox-SDK.git
cd Livox-SDK
sudo ./third_party/apr/apr_build.sh
cd build && cmake ..
make
sudo make install

# install livox_ros_driver
cd ..
git clone https://github.com/Livox-SDK/livox_ros_driver.git ws_livox/src
cd ws_livox
catkin_make

# install camera/lidar calibration package
cd src
git clone https://github.com/Livox-SDK/livox_camera_lidar_calibration.git
cd ..
catkin_make
source devel/setup.bash

编译过程中遇到的问题：

 /usr/include/pcl-1.10/pcl/point_types.h: In function ‘const pcl::CPPFSignature& pcl::common::operator-=(pcl::CPPFSignature&, const float&)’:
/usr/include/pcl-1.10/pcl/point_types.h:574:1: error: ‘minusscalar’ is not a member of ‘pcl::traits’
  574 | POINT_CLOUD_REGISTER_POINT_STRUCT (pcl::CPPFSignature,
      | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/usr/include/pcl-1.10/pcl/point_types.h:574:1: error: ‘minusscalar’ is not a member of ‘pcl::traits’
  574 | POINT_CLOUD_REGISTER_POINT_STRUCT (pcl::CPPFSignature,
      | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/usr/include/pcl-1.10/pcl/point_types.h:574:1: error: ‘minusscalar’ is not a member of ‘pcl::traits’
  574 | POINT_CLOUD_REGISTER_POINT_STRUCT (pcl::CPPFSignature,
      | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/usr/include/pcl-1.10/pcl/point_types.h:574:1: error: ‘minusscalar’ is not a member of ‘pcl::traits’
  574 | POINT_CLOUD_REGISTER_POINT_STRUCT (pcl::CPPFSignature,

解决办法：修改编译文件，将和c++11的位置进行修改

set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++14 -O3")

cmake_minimum_required(VERSION 2.8.3)
project(camera_lidar_calibration)

## Compile as C++11, supported in ROS Kinetic and newer
# add_compile_options(-std=c++11)
set(CMAKE_BUILD_TYPE "Release")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++14")
#set(CMAKE_CXX_FLAGS "-std=c++11")
set(CMAKE_CXX_FLAGS_RELEASE "-O3 -Wall -g")
## Find catkin macros and libraries
## if COMPONENTS list like find_package(catkin REQUIRED COMPONENTS xyz)
## is used, also find other catkin packages
find_package(catkin REQUIRED COMPONENTS
  roscpp
  rospy
  std_msgs
  pcl_conversions
  pcl_ros
  cv_bridge
)

find_package(PCL 1.10 REQUIRED)
find_package(OpenCV)
find_package(Threads)
find_package(Ceres REQUIRED)

set(CMAKE_AUTOMOC ON)
set(CMAKE_AUTOUIC ON)
set(CMAKE_AUTORCC ON)
set(CMAKE_INCLUDE_CURRENT_DIR ON)

list(REMOVE_ITEM PCL_LIBRARIES "vtkproj4")

catkin_package(
 CATKIN_DEPENDS roscpp rospy std_msgs message_runtime
)

###########
## Build ##
###########

## Specify additional locations of header files
## Your package locations should be listed before other locations
include_directories(
# include
  ${catkin_INCLUDE_DIRS}
  ${PCL_INCLUDE_DIRS}
  ${OpenCV_INCLUDE_DIRS}
)

add_executable(cameraCalib src/cameraCalib.cpp)
target_link_libraries(cameraCalib ${catkin_LIBRARIES} ${OpenCV_LIBRARIES})

add_executable(pcdTransfer src/pcdTransfer.cpp src/common.h)
target_link_libraries(pcdTransfer ${catkin_LIBRARIES} ${PCL_LIBRARIES})

add_executable(cornerPhoto src/corner_photo.cpp src/common.h)
target_link_libraries(cornerPhoto ${catkin_LIBRARIES} ${OpenCV_LIBRARIES})


add_executable(getExt1 src/cam_lid_external1.cpp src/common.h)
target_link_libraries(getExt1 ${catkin_LIBRARIES} ${OpenCV_LIBRARIES} ${CERES_LIBRARIES})

add_executable(getExt2 src/cam_lid_external2.cpp src/common.h)
target_link_libraries(getExt2 ${catkin_LIBRARIES} ${OpenCV_LIBRARIES} ${CERES_LIBRARIES})


add_executable(projectCloud src/projectCloud.cpp src/common.h)
target_link_libraries(projectCloud ${catkin_LIBRARIES} ${OpenCV_LIBRARIES})

add_executable(colorLidar src/color_lidar_display.cpp src/common.h)
target_link_libraries(colorLidar ${catkin_LIBRARIES} ${OpenCV_LIBRARIES} ${CERES_LIBRARIES})

作者使用的硬件设备为：

1.2 执行标定

1）相机内参标定

将拍摄了标定板的相机的图像，放在data/camera/photos下，然后在data/camera/in.txt中写入所有需要使用的照片名称。可以使用以下的代码实现：

"""
#-*-coding:utf-8-*- 
# @author: wangyu a beginner programmer, striving to be the strongest.
# @date: 2022/7/7 20:25
"""
import os

# 文件夹路径
img_path = r'/home/nvidia/calibration_ws/src/livox_camera_lidar_calibration/data/camera/photos/'
# txt 保存路径
save_txt_path = r'./in.txt'

# 读取文件夹中的所有文件
imgs = os.listdir(img_path)

# 图片名列表
names = []

# 过滤：只保留png结尾的图片
for img in imgs:
    if img.endswith(".png"):
        names.append(img)

txt = open(save_txt_path,'w')

for name in names:
    #name = name[:-4]    # 去掉后缀名.png
    txt.write(name + '\n')  # 逐行写入图片名，'\n'表示换行

txt.close()

作者使用的标定板的样子为：

我使用的标定板， 内点是行向是9点和6点，边长是20mm

修改 对应的launch文件，如下进行修改：

<?xml version="1.0" encoding="UTF-8"?>
<launch>
    <param name="camera_in_path"        value="/home/nvidia/calibration_ws/src/livox_camera_lidar_calibration/data/camera/in.txt" />  <!-- the file to contain all the photos -->
    <param name="camera_folder_path"    value="/home/nvidia/calibration_ws/src/livox_camera_lidar_calibration/data/camera/photos/" />  <!-- the file to contain all the photos -->
    <param name="result_path"           value="/home/nvidia/calibration_ws/src/livox_camera_lidar_calibration/data/camera/result.txt" />  <!-- the file to save the intrinsic data -->
    
    <param name="row_number"            type="int" value="9" />  <!-- the number of block on the row -->
    <param name="col_number"            type="int" value="6" />  <!-- the number of block on the column -->
    <param name="width"                 type="int" value="20" />  <!-- the width of each block on the chessboard(mm) -->
    <param name="height"                type="int" value="20" />  <!-- the height of each block on the chessboard(mm)-->
    
    <node pkg="camera_lidar_calibration" name="cameraCalib" type="cameraCalib" output="screen"></node>

</launch>

执行下面的语句进行标定：

roslaunch camera_lidar_calibration cameraCalib.launch

标定的结果会保存在data/camera/result.txt中，包括重投影误差，内参矩阵和畸变纠正参数。以上文件夹需要创建。

最终得到的结果如图所示： 

 

2）准备图像中的角点坐标

首先需要把步骤２得到的内参和畸变纠正参数以下图的格式保存在data/parameters/intrinsic.txt文件下 [注 4]。distortion下面对应５个畸变纠正参数，按顺序是k1和k2 (RadialDistortion)，p1和p2 (TangentialDistortion)，最后一个是k3，一般默认是０

 

<?xml version="1.0" encoding="UTF-8"?>
<launch>
  
    <param name="intrinsic_path"    value="/home/nvidia/calibration_ws/src/livox_camera_lidar_calibration/data/parameters/intrinsic.txt" />  <!-- intrinsic file -->
    <param name="input_photo_path"  value="/home/nvidia/LVI_ws/src/livox_camera_calib/data/frame4.png" />  <!-- photo to find the corner -->
    <param name="ouput_path"        value="/home/nvidia/calibration_ws/src/livox_camera_lidar_calibration/data/parameters/corner_photo.txt" />  <!-- file to save the photo corner -->
    <node pkg="camera_lidar_calibration" name="cornerPhoto" type="cornerPhoto" output="screen"></node>

</launch>

2）相机和激光的手动标定

查看点云的方式
roslaunch livox_ros_driver livox_lidar_rviz.launch
需要录制rosbag时输入另一个命令
roslaunch livox_ros_driver livox_lidar_msg.launch
需要注意：保存的rosbag数据格式是customMsg，后续程序中处理rosbag是对应的“livox custom msg format”格式。