An Automatic LiDAR-Camera Extrinsic Calibration Method for Sparse Point Clouds Using Boundary Features

Abstract

Extrinsic calibration for LiDAR and camera using sparse point clouds can significantly reduce cost and improve efficiency. However, most target-based methods are designed for dense point clouds and are less effective in sparse scenarios, while targetless methods primarily rely on environmental features. To address this limitation, a LiDAR–camera extrinsic calibration method for sparse point clouds is proposed in this paper. First, the proposed method extracts the complete checkerboard image via line-segment direction clustering and midpoint-to-normal projection. Second, a constructed theoretical checkerboard boundary point cloud is aligned to the scanned boundary point cloud using a proposed dimension-reduced, global-search and local-refinement (DGL) method. Third, coarse calibration is derived from the centroids of the checkerboard in images and aligned point clouds, followed by refinement through joint optimization of reprojection error and normal consistency error. Finally, experiments on the simulated dataset yield translation and rotation errors below 0.015 m and 0.3°, respectively. On a self-collected dataset, the method achieves an mIoU of 90.9% between the checkerboard region reprojected from point clouds and its image counterpart, outperforming state-of-the-art methods under sparse point cloud conditions.