Automatic LiDAR-Camera Online Calibration Monitoring and Refinement for Ground Platforms

Accurate LiDAR-camera extrinsic calibration is essential for perception in autonomous ground vehicles, but even a precisely known initial transformation can drift over time due to minor collisions or mechanical shifts. In this paper, we present an online calibration method that detects and corrects such drift without relying on special targets. Our approach continuously checks the existing extrinsic parameters by comparing newly estimated ground-plane parameters and edge-based reprojection errors. Whenever the reprojection error exceeds a preset threshold, a joint optimization refines the LiDAR-camera transform by incorporating both plane and edge constraints. Experimental validation in a simulation environment shows that the proposed framework detects small extrinsic misalignments promptly and effectively restores accurate sensor fusion over extended operation.