首次实现在线外参标定的不确定性感知，提升传感器融合可靠性。
Uncertainty-Aware Online Extrinsic Calibration: A Conformal Prediction Approach
- 结合蒙特卡洛丢弃与置信预测，生成有保证覆盖率的预测区间。
- 在KITTI和DSEC数据集上验证，对不同视觉传感器均有效。
- 为标定结果提供可量化置信度，适合追求鲁棒性的自动驾驶研究者。
精确的传感器标定对自主系统至关重要，但其不确定性量化仍缺乏研究。本文提出首个将不确定性感知融入在线外参标定的方法，结合蒙特卡洛丢弃与置信预测，生成具有保证覆盖率的预测区间。该方法可增强现有标定模型的不确定性量化能力，兼容多种网络架构。在KITTI（RGB相机-激光雷达）和DSEC（事件相机-激光雷达）数据集上验证，证明其在不同视觉传感器下的有效性，采用改进指标评估区间效率与可靠性。通过提供带置信度的标定参数，揭示估计可靠性，显著提升动态环境中传感器融合的鲁棒性，对计算机视觉领域具有重要价值。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。