论文标题
ACSC：自动校准，用于非重复扫描固态激光雷达和相机系统
ACSC: Automatic Calibration for Non-repetitive Scanning Solid-State LiDAR and Camera Systems
论文作者
论文摘要
最近，固态激光雷达（SSL）的快速发展可以从环境中获得低成本，有效的3D点云，这启发了大量的研究和应用。但是，其扫描模式的不均匀性以及范围误差分布的不一致性给其校准任务带来了挑战。在本文中，我们为非重复扫描SSL和相机系统提出了一种全自动校准方法。首先，提出了一种基于时间空间的几何特征改进方法，以从SSL点云中提取有效特征。然后，估计校准目标的3D角（打印的棋盘板），并使用点的反射率分布进行估算。基于上述，最终提出了一种基于目标的外部校准方法。我们在实际条件下对不同类型的LIDAR和相机传感器组合评估了提出的方法，并实现准确性和稳健性校准结果。该代码可在https://github.com/hviktortsoi/acsc.git上找到。
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 .
