采用自适应背景聚类的激光雷达与相机外参标定优化方法

针对在复杂外部环境下激光雷达外参标定过程中遇到的标定板三维点云提取不准确的问题，提出一种基于背景聚类的激光雷达和相机外参标定优化方法，避免了在整个三维点云中盲目检测标定板点云，而导致标定结果存在较大误差以及需要人工手动纠正错误特征点的问题。该方法利用无标定板的背景点云与有标定板的目标点云之间部分空间域内的密度差异性，通过自适应空间阈值模型获得标定板点云与背景点云之间的差异系数K,然后聚类两点云中的部分三维点，完成标定板的三维点云提取。实验证明，该方法可以在复杂环境中准确高效地提取标定板三维点云，从而提高激光雷达和相机外参标定的准确性，在此基础上点云正确投影比例可达97.43%,与对比方法相比投影误差降低25.33%左右。

Optimization method for external parameters calibration of lidar and camera using adaptive background clustering

To address the inaccurate extraction of 3D point cloud of the calibration plate encountered in the process of external parametercalibration of lidar in complex external environment, an optimization method of external parameter calibration of lidar and camera basedon background clustering is proposed. The blind detection of the point cloud of the calibration plate is avoided in the whole threedimensional point cloud, which would lead to large error in the calibration results and the need to manually correct the wrong featurepoints. This method uses the density difference between the background point cloud without calibration plate and the target point cloudwith calibration plate in some spatial domains, and obtains the difference coefficient K between the point cloud of calibration plate andthe background point cloud through the adaptive spatial threshold model. Then, some three-dimensional points in the two-point cloud areclustered to complete the three-dimensional point cloud extraction of the calibration plate. Experimental results show that this method canaccurately and efficiently extract 3D point cloud of calibration plate in complex environment to improve the accuracy of laser radar andcamera external parameter calibration. On this basis, the correct projection proportion of point cloud can reach 97. 43% , and theprojection error is reduced by about 25. 33% compared with other methods.