ACSC: Automatic Calibration for Non-repetitive Scanning Solid-State LiDAR and Camera Systems
This work provides an automatic and robust calibration solution for non-repetitive scanning SSL and camera systems, which is crucial for applications relying on accurate 3D perception in robotics and autonomous driving.
This paper addresses the challenge of calibrating non-repetitive scanning Solid-State LiDAR (SSL) and camera systems, which suffer from non-uniform scanning patterns and inconsistent ranging error distributions. The authors propose a fully automatic calibration method that refines geometric features from SSL point clouds and estimates 3D corners using reflectance distribution, leading to accurate and robust calibration results across various LiDAR-camera setups.
Recently, the rapid development of Solid-State LiDAR (SSL) enables low-cost and efficient obtainment of 3D point clouds from the environment, which has inspired a large quantity of studies and applications. However, the non-uniformity of its scanning pattern, and the inconsistency of the ranging error distribution bring challenges to its calibration task. In this paper, we proposed a fully automatic calibration method for the non-repetitive scanning SSL and camera systems. First, a temporal-spatial-based geometric feature refinement method is presented, to extract effective features from SSL point clouds; then, the 3D corners of the calibration target (a printed checkerboard) are estimated with the reflectance distribution of points. Based on the above, a target-based extrinsic calibration method is finally proposed. We evaluate the proposed method on different types of LiDAR and camera sensor combinations in real conditions, and achieve accuracy and robustness calibration results. The code is available at https://github.com/HViktorTsoi/ACSC.git .