Conference Papers Year : 2025

Uncertainty-Aware Online Extrinsic Calibration: A Conformal Prediction Approach

Abstract

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

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Dates and versions

hal-04881740 , version 1 (12-01-2025)

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Mathieu Cocheteux, Julien Moreau, Franck Davoine. Uncertainty-Aware Online Extrinsic Calibration: A Conformal Prediction Approach. IEEE Winter Conference on Applications of Computer Vision (WACV 2025), IEEE; CVF, Feb 2025, Tucson, United States. pp.6167-6176, ⟨10.1109/WACV61041.2025.00601⟩. ⟨hal-04881740⟩
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