Calibration-Informative Region Selection for Online LiDAR--Camera Calibration in Agricultural Environments

• URL: http://arxiv.org/abs/2605.23580v1
• Date: Fri, 22 May 2026 12:48:56 GMT
• Title: Calibration-Informative Region Selection for Online LiDAR--Camera Calibration in Agricultural Environments
• Abstract summary: We propose a support-map-driven approach to multi-modal calibration.<n>We instantiate this formulation for online LiDAR--camera calibration using MDPCalib.<n>Key contribution is a dense calibration support map that aggregates cross-modal agreement over aligned observations.
• Score: 6.51006117672225
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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