Uncertainty-Aware Online Extrinsic Calibration: A Conformal Prediction Approach

TL;DR

This paper introduces a novel online extrinsic calibration method that incorporates uncertainty quantification using Monte Carlo Dropout and Conformal Prediction, providing reliable confidence intervals for sensor calibration in autonomous systems.

Contribution

It is the first to integrate uncertainty awareness into online extrinsic calibration, enhancing existing models with guaranteed coverage intervals across various sensor types.

Findings

Effective uncertainty quantification on KITTI and DSEC datasets.

Improved robustness of sensor fusion with confidence measures.

Versatile framework compatible with multiple network architectures.

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.