让激光雷达定位更可靠，能预测误差范围。
UQ-Loc: Uncertainty-Aware LiDAR Scene Coordinate Regression
- 在原有模型基础上增加不确定性预测模块，输出完整协方差矩阵。
- 定位精度提升，且预测的不确定性与真实误差匹配度高。
- 适合需要安全决策的自动驾驶等场景使用。
基于激光雷达的场景坐标回归（SCR）可直接将点云映射到3D场景坐标，实现无需显式地图检索的精确6-DoF定位。然而，现有方法仅输出确定性结果，忽略了可降低鲁棒性风险的偶然不确定性。本文提出UQ-Loc，基于LightLoc架构，引入各向异性高斯协方差头，为每个体素预测完整的3×3正定协方差矩阵。训练采用负对数似然损失，并加入基于kNN的空间平滑正则项；推理时使用改进的SC2-PCR求解器，结合不确定性加权种子评分与马氏距离内点检测。采用期望校准误差（ECE）作为评估不确定性质量的合理指标。实验表明，UQ-Loc在保持6-DoF定位精度提升的同时，生成了具有良好校准性的协方差。
原文摘要 · Abstract (English)
LiDAR-based Scene Coordinate Regression (SCR) maps point clouds directly to 3D scene coordinates, enabling precise 6-DoF localisation without explicit map retrieval. However, existing methods produce deterministic predictions, discarding aleatoric uncertainty that could improve robustness and downstream decision-making. We present UQ-Loc, which extends the LightLoc architecture with an anisotropic Gaussian covariance head that predicts a full 3x3 positive-definite covariance matrix per voxel. Training uses a Negative Log-Likelihood (NLL) loss augmented with a kNN-based spatial smoothness regulariser, while inference employs a modified SC2-PCR solver with uncertainty-weighted seed scoring and a Mahalanobis-distance inlier test. We adopt Expected Calibration Error (ECE) as a principled metric for evaluating the quality of the predicted uncertainty. Experiments demonstrate that UQ-Loc achieves consistent improvement in 6-DoF localization accuracy while producing well-calibrated covariances.
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