基于边缘关联点云的激光雷达与相机外参标定方法

激光雷达的点云和相机的图像经常被融合应用在多个领域。准确的外参标定是融合两者信息的前提。点云特征提取是外参标定的关键步骤。但是点云的低分辨率和低质量会影响标定结果的精度。针对这些问题，提出一种基于边缘关联点云的激光雷达与相机外参标定方法。首先，利用双回波提取标定板边缘关联点云；然后，通过优化方法从边缘关联点云中提取出与实际标定板尺寸大小兼容的标定板角点；最后，将点云中角点和图像中角点匹配。用多点透视方法求解激光雷达与相机之间的外参。实验结果表明，该方法的重投影误差为1.602px，低于同类对比方法，验证了该方法的有效性与准确性。

Extrinsic parameter calibration of LiDAR and camera based on edge correlation point cloud

There are many fields choose to fuse lidar point cloud and camera images for applying. The accurate extrinsic calibration is a prerequisite for fusing their information. The point cloud feature extraction is a key step in extrinsic calibration. However, the low resolution and low quality of point clouds can affect the accuracy of calibration results. To address these problems, this paper proposed a LiDAR and camera extrinsic calibration method based on edge correlation point cloud. Firstly, it extracted the edge correlation point cloud of the calibration plate by using dual echoes. Then, it obtained the corner points from the edge correlation point cloud by the optimization method, where the corner points were compatible with the actual calibrated plate size. Finally, it matched the corner points in the point cloud with corner points in the image, and solved the extrinsic parameter between the LiDAR and the camera by the multi-point perspective method. The experimental results show that the reprojection error of the method is 1.602px, which is lower than that of comparison methods, verifying the effectiveness and accuracy of the method.