ACSC: Automatic Calibration for Non-repetitive Scanning Solid-State LiDAR and Camera Systems

TL;DR

This paper introduces an automatic calibration method for non-repetitive scanning solid-state LiDAR and camera systems, addressing challenges from scanning pattern non-uniformity and error distribution, with demonstrated high accuracy and robustness.

Contribution

The paper presents a novel fully automatic calibration approach combining geometric feature refinement and target-based extrinsic calibration for SSL-camera systems.

Findings

Achieved accurate calibration results across different sensor types.

Demonstrated robustness in real-world conditions.

Provided open-source code for the calibration method.

Abstract

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 .