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

arXiv: https://arxiv.org/abs/2605.23580 · HTML: https://mlsanity.com/paper/2605.23580/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 6 references that are in the map.
- First posted on arXiv 22 May 2026.

## Builds on (most cited first, 3 of 3)

- [CMRNext: Camera to LiDAR Matching in the Wild for Localization and Extrinsic Calibration](https://mlsanity.com/paper/2402.00129/) (2024)
- [Automatic Target-Less Camera-LiDAR Calibration From Motion and Deep Point Correspondences](https://mlsanity.com/paper/2404.17298/) (2024)
- [Keypoint Semantic Integration for Improved Feature Matching in Outdoor Agricultural Environments](https://mlsanity.com/paper/2503.08843/) (2025)

## Abstract (arXiv)

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.