联合标定多相机与激光雷达，提升系统级一致性
Joint Multi-Camera LiDAR Extrinsic Calibration via Learned Pairwise Initialization and Geometric Refinement
- 先用模型预测每对相机-激光雷达的初始位姿，再全局优化
- 在KITTI上达0.89厘米平移误差，Walkley上平移误差从108.6降至3.1厘米
- 适合多相机激光雷达系统，尤其当单个估计不准确时
现有基于学习的相机-激光雷达标定方法通常独立处理每对传感器，忽略了多相机平台中的刚性几何关联。导致各相机估计虽局部准确，但整体不一致。本文提出两阶段联合标定框架：首先用CMRNext独立估计每对相机-激光雷达的初始外参及密集2D-3D对应关系；随后通过多帧捆绑调整，引入重投影、每相机先验和相对位姿先验项，联合优化所有估计。该方法将独立预测转化为全局一致的多相机标定。在KITTI（CMRNext域内）和Walkley（域外）数据集上的实验表明，该方法提升了单相机精度与相机间一致性。在KITTI上实现0.89厘米平移误差和0.038弧度旋转误差；在Walkley上，平移误差由108.6厘米降至3.1厘米，凸显了显式多相机耦合在单相机预测不可靠时的优势。
原文摘要 · Abstract (English)
Most learning-based camera-LiDAR calibration methods treat each camera-LiDAR pair independently, ignoring the rigid geometric coupling in multi-camera platforms. As a result, per-camera estimates may be individually accurate yet inconsistent at the system level. We present a two-stage framework for joint multi-camera LiDAR extrinsic calibration that combines learned pairwise matching with geometric refinement. First, CMRNext is applied independently to each camera to produce initial extrinsic estimates and dense 2D-3D correspondences. These predictions are then jointly refined through a multi-frame bundle adjustment with reprojection, per-camera prior, and relative-pose prior terms. This approach converts pairwise predictions into a globally consistent multi-camera calibration. Experiments on KITTI (in-domain for CMRNext) and Walkley (out-of-domain) datasets show improved per-camera accuracy and inter-camera consistency. On KITTI, the method achieves 0.89 cm translation error and 0.038 rotation error. On Walkley, it reduces translation error from 108.6 cm to 3.1 cm, highlighting the benefit of explicit multi-camera coupling when single-camera predictions are less reliable.
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