Uncertainty-Aware Online Extrinsic Calibration: A Conformal Prediction Approach
This work addresses the underexplored issue of uncertainty in sensor calibration, which is crucial for improving the robustness of sensor fusion in dynamic environments for the Computer Vision community, though it appears incremental as it enhances existing models.
The paper tackles the problem of uncertainty quantification in online extrinsic sensor calibration for autonomous systems by integrating Monte Carlo Dropout with Conformal Prediction to generate prediction intervals with guaranteed coverage, validated on KITTI and DSEC datasets across different visual sensor types.
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.