发表机构
Warsaw University of Technology（华沙理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
UQ-Loc扩展LightLoc架构，添加各向同性高斯协方差头，采用NLL损失结合kNN正则化训练，改进SC2-PCR求解器推理，提升6自由度定位精度并校准不确定性。
AI 中文摘要
基于激光雷达的场景坐标回归（SCR）直接将点云映射到3D场景坐标，无需显式地图检索即可实现精确的6自由度定位。但现有方法输出确定性预测，丢弃了可提升鲁棒性及下游决策的偶然不确定性。本文提出UQ-Loc，它扩展了LightLoc架构，添加各向同性高斯协方差头，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（NLL）损失，辅以基于k近邻（kNN）的空间平滑正则化项；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测试。本文采用预期校准误差（ECE）作为评估预测不确定性质量的合理指标。实验表明，UQ-Loc在6自由度定位精度上实现持续提升，同时生成校准良好的协方差矩阵。
英文摘要
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.
