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arXiv · 2605.23580

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

Rajitha de Silva·Grzegorz Cielniak
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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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Rajitha de Silva, Grzegorz Cielniak. 2026-05-22. Calibration-Informative Region Selection for Online LiDAR--Camera Calibration in Agricultural Environments. https://arxiv.org/abs/2605.23580

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