提出可识别可靠校准区域的动态支持图，提升农田环境下的激光雷达与摄像头在线标定精度。
Calibration-Informative Region Selection for Online LiDAR--Camera Calibration in Agricultural Environments
- 基于运动与深度点对应关系，构建跨模态残差并生成密集校准支持图。
- 在KITTI数据集上，支持引导优化使平移误差降低，旋转精度改善有限。
- 适用于农田等复杂场景中需实时校准的自动驾驶系统，尤其关注定位可靠性。
可靠的多模态标定需要识别真正约束外参的观测数据，避免噪声或歧义影响。本文提出一种基于支持图的多模态标定方法，将流程解耦为四个模块：初始标定、跨模态残差提取、支持图估计和支持感知精修。针对无目标的在线激光雷达-相机标定，采用基于运动与深度点对应关系的MDPCalib方法，以及预测类似光流的图像平面残差的CMRNext模型。核心贡献是提出一个密集校准支持图，通过聚合对齐观测间的跨模态一致性，突出校准证据稳定可靠的区域。在Bacchus长期（BLT）数据集和KITTI上，发现校准证据在空间和语义上分布不均，部分语义区域提供更强校准线索。在KITTI上，支持图引导的精修提升了平移精度，但旋转精度提升有限。
原文摘要 · Abstract (English)
Reliable multi-modal calibration requires identifying which observations truly constrain the extrinsic parameters and which ones mainly add noise or ambiguity. In this paper, we propose a support-map-driven approach to multi-modal calibration that decouples four functional blocks: initial calibration, cross-modal residual extraction, support-map estimation, and support-aware refinement. We instantiate this formulation for online LiDAR--camera calibration using MDPCalib, a target-less LiDAR--camera calibration method based on motion and deep point correspondences, and CMRNext, a dense LiDAR--camera matching model that predicts optical-flow-like image-plane residuals. The key contribution is a dense calibration support map that aggregates cross-modal agreement over aligned observations and highlights where calibration evidence is consistently reliable. Across the Bacchus Long-Term (BLT) dataset and KITTI, we show that calibration evidence is spatially and semantically non-uniform, indicating that some semantic regions provide stronger cues for calibration than others. On KITTI, support-guided refinement improves the calibration performance with better translation accuracy while rotational gains remain limited.
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