A long-range LiDAR–camera extrinsic calibration method for rail transit

In autonomous driving systems, sensor-based environmental perception is paramount. However, in long-distance perception for rail transit, the extrinsic calibration of Light Detection and Ranging (LiDAR) and telephoto cameras is hindered by sparse point clouds and intrinsic parameter inaccuracies. To address these challenges, we propose a novel calibration board design and a corresponding extrinsic calibration method. Inspired by engineering positioning principles, this calibration board builds upon the traditional checkerboard by integrating circular positioning holes. By coupling spatial re-projection constraints with geometric feature alignment, the proposed approach markedly improves feature point extraction and 2D–3D correspondences. Experimental results demonstrate that the method substantially enhances calibration accuracy, offering solid technical support for environmental perception in rail transit.