This paper introduces a novel targetless method for joint intrinsic and extrinsic calibration of LiDAR-camera systems using plane-constrained bundle adjustment (BA). Our method leverages LiDAR point cloud measurements from planes in the scene, alongside visual points derived from those planes. The core novelty of our method lies in the integration of visual BA with the registration between visual points and LiDAR point cloud planes, which is formulated as a unified optimization problem. This formulation achieves concurrent intrinsic and extrinsic calibration, while also imparting depth constraints to the visual points to enhance the accuracy of intrinsic calibration. Experiments are conducted on both public data sequences and self-collected dataset. The results showcase that our approach not only surpasses other state-of-the-art (SOTA) methods but also maintains remarkable calibration accuracy even within challenging environments. For the benefits of the robotics community, we have open sourced our codes.

翻译：本文提出了一种新颖的无需靶标方法，用于LiDAR-相机系统的联合内外参标定，该方法采用平面约束的光束法平差（BA）。该方法利用场景中平面的LiDAR点云测量，以及从这些平面衍生的视觉点。本方法的核心创新在于将视觉BA与视觉点和LiDAR点云平面之间的配准整合为一个统一的优化问题。该公式实现了内外参的同步标定，同时为视觉点赋予深度约束，以提升内参标定的精度。实验在公开数据序列和自采数据集上进行。结果表明，我们的方法不仅超越其他最先进（SOTA）方法，而且在挑战性环境下仍保持卓越的标定精度。为惠及机器人学界，我们已开源相关代码。