Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月10日

查看全文
  • Xin Liu,
  • Hongping Wang,
  • Shouxin Ruan,
  • Linsen Song,
  • Yiwen Zhang,
  • Xiaoxu Zhang,
  • XinYang Liu,
  • Hainan Li

摘要

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.