Uncertainty-Aware Online Extrinsic Calibration: A Conformal Prediction Approach

Accurate sensor calibration is crucial for autonomous systems, yet its uncertainty quantification remains under-explored. We present the first approach to integrate un-certainty awareness into online extrinsic calibration, com-bining Monte Carlo Dropout with Conformal Prediction to generate prediction intervals with a guaranteed level of coverage. Our method proposes a framework to en-hance existing calibration models with uncertainty quan-tification, compatible with various network architectures. Validated on KITTI (RGB Camera-LiDAR) and DSEC (Event Camera-LiDAR) datasets, we demonstrate effective-ness across different visual sensor types, measuring performance with adapted metrics to evaluate the efficiency and reliability of the intervals. By providing calibration pa-rameters with quantifiable confidence measures, we offer insights into the reliability of calibration estimates, which can greatly improve the robustness of sensor fusion in dy-namic environments and usefully serve the Computer Vision community.