Uncertainty-Aware Online Extrinsic Calibration: A Conformal Prediction Approach (2501.06878v1)

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.

• The paper introduces a novel framework for online extrinsic calibration using Monte Carlo Dropout and Conformal Prediction to provide statistically-guaranteed uncertainty quantification.
• Key results on KITTI and DSEC datasets show the method achieves reliable prediction interval coverage (PICP) close to target levels with narrow interval widths (MPIW) and good interval scores (IS).
• Integrating uncertainty awareness into online calibration enhances the robustness of sensor fusion and improves safety in autonomous systems by identifying when recalibration is needed.

Uncertainty-Aware Online Extrinsic Calibration: A Conformal Prediction Approach

This paper introduces a methodological advancement in the field of autonomous systems and computer vision, focusing on the challenge of online extrinsic calibration with integrated uncertainty quantification. The authors propose a novel approach that leverages Monte Carlo Dropout (MCD) combined with Conformal Prediction (CP) to generate statistically-guaranteed prediction intervals for sensor calibration parameters, addressing a significant gap in the current literature regarding uncertainty awareness in calibration processes.

Context and Motivation

Extrinsic calibration is critical for determining the spatial relationships between various sensors in autonomous systems, such as LiDAR and cameras. Accurate calibration is paramount for tasks like object detection and segmentation, especially in dynamic environments. Traditional calibration methods, often requiring controlled conditions and manual intervention, fall short in real-time applications where dynamic recalibration is essential. While recent advances have introduced deep learning-based calibration techniques that enhance calibration efficiency, they lack robust uncertainty quantification. This paper aims to bridge this gap by introducing a framework that ensures reliable, real-time calibration.

Methodological Approach

The proposed approach integrates two key components: MCD and CP.

1. Monte Carlo Dropout (MCD): This technique is employed to introduce uncertainty estimation into the deep learning model by interpreting dropout as Bayesian approximation. During inference, dropout is applied to generate multiple predictions, from which the mean and standard deviation (uncertainty) of predictions are derived. This reduces model uncertainty (epistemic uncertainty), providing insights into the confidence of the calibration estimates.
2. Conformal Prediction (CP): To offer statistically-guaranteed prediction intervals, CP is incorporated. This method is distribution-free and provides valid prediction intervals under minimal assumptions of exchangeability. By calibrating CP with a separate dataset, the approach generates intervals that theoretically cover the true calibration parameters with a specified confidence level.

Key Results

Experiments were conducted on real-world datasets, specifically KITTI (RGB Camera-LiDAR) and DSEC (Event Camera-LiDAR), demonstrating the approach's applicability across different sensor modalities. Performance was measured using Prediction Interval Coverage Probability (PICP), Mean Prediction Interval Width (MPIW), and Interval Score (IS), ensuring a comprehensive evaluation of the prediction intervals' reliability and efficiency.

• PICP: Results showed close alignment with target coverage levels (e.g., 90%, 95%, 99%), indicating reliable prediction intervals.
• MPIW: The method achieved narrow intervals, reflecting the precision of calibration estimates, particularly on the KITTI dataset with translational and rotational parameters.
• IS: Lower interval scores were observed, demonstrating balanced reliability and tightness in prediction intervals.

Implications and Future Directions

The introduction of uncertainty awareness in online extrinsic calibration has profound implications for autonomous systems, enhancing the robustness of sensor fusion and ensuring consistent calibration quality. This approach not only improves confidence in autonomous applications but also aids in recognizing situations where recalibration might be necessary, thus enhancing safety in real-world deployments.

Future research directions could explore the integration of this framework into more complex sensor networks and investigate its performance under different environmental conditions. Additionally, optimizing the computational efficiency of MCD and CP for real-time applications remains a significant area for development. The application of such uncertainty-aware calibration methods could extend beyond autonomous vehicles to other domains requiring precise sensor coordination and robust data fusion.