基于点云中心的激光雷达与相机联合标定方法研究

Joint calibration of camera and lidar based on point cloud center

针对激光雷达与相机联合使用遇到的点云稀疏、相机受环境光照影响失真等问题,提出一种基于点云中心的激光雷达与相机自动配准方法,避免了传统联合标定需要手动选择特征点以及连续采集多帧等问题。该方法在对点云与图像预处理后,利用平面法向量的一致性实现多标定板点云自动分割,提取标定板在激光坐标系和相机坐标下的点云;然后通过点云聚集迭代求解中心点,实现两个传感器标定板对应点云中心的粗配准;最终利用迭代最近点算法进行精配准,获得标定参数,完成联合标定。实测表明,在激光雷达误差±3 cm范围内,点云正确投影比例达到97.93%,可以有效获取高精度联合标定参数,满足空间环境对激光雷达和相机数据融合的要求。

To address the issue of sparse lidar point cloud and camera distortion caused by ambient light,an automatic registration method of lidar and camera based on point cloud center is proposed.The manual selection of feature points and continuous acquisition of multiple frames in traditional joint calibration can be avoided.After preprocessing the data of laser point clouds and camera image,the multi-checker board point clouds are segmented automatically by the consistency of the plane normal vector.The point clouds of each checkerboard are extracted in the laser coordinate system and camera coordinate system,respectively.Then,the center points are solved iteratively by point cloud aggregation.The rough registration of the corresponding relationship between the center points of the two sensor checkerboards is realized.Finally,the iterative closest point algorithm is used for precision registration,and the calibration parameter matrix is obtained to complete the joint calibration.Measurement results show that the correct projection ratio of point cloud can reach 97.93%within the range of lidar error±3 cm.This method can effectively obtain high-precision joint calibration parameters and meet the requirements of data fusion between lidar and camera in the space environment.