LiDAR relocalization aims to estimate the global 6-DoF pose of a sensor in the environment. However, existing regression-based approaches are prone to dynamic or ambiguous scenarios, as they either solely rely on single-frame inference or neglect the spatio-temporal consistency across scans. In this paper, we propose TempLoc, a new LiDAR relocalization framework that enhances the robustness of localization by effectively modeling sequential consistency. Specifically, a Global Coordinate Estimation module is first introduced to predict point-wise global coordinates and associated uncertainties for each LiDAR scan. A Prior Coordinate Generation module is then presented to estimate inter-frame point correspondences by the attention mechanism. Lastly, an Uncertainty-Guided Coordinate Fusion module is deployed to integrate both predictions of point correspondence in an end-to-end fashion, yielding a more temporally consistent and accurate global 6-DoF pose. Experimental results on the NCLT and Oxford Robot-Car benchmarks show that our TempLoc outperforms stateof-the-art methods by a large margin, demonstrating the effectiveness of temporal-aware correspondence modeling in LiDAR relocalization. Our code will be released soon.

翻译：激光雷达重定位旨在估计传感器在环境中的全局六自由度位姿。然而，现有的基于回归的方法容易受到动态或模糊场景的影响，因为它们要么仅依赖单帧推理，要么忽略了扫描间的时空一致性。本文提出TempLoc，一种新的激光雷达重定位框架，通过有效建模序列一致性来增强定位的鲁棒性。具体而言，首先引入全局坐标估计模块，为每次激光雷达扫描预测逐点的全局坐标及其关联的不确定性。随后提出先验坐标生成模块，通过注意力机制估计帧间点对应关系。最后，部署不确定性引导的坐标融合模块，以端到端的方式整合两种点对应关系的预测结果，从而产生更具时间一致性和准确性的全局六自由度位姿。在NCLT和Oxford Robot-Car基准测试上的实验结果表明，我们的TempLoc大幅优于现有最先进方法，证明了时序感知对应关系建模在激光雷达重定位中的有效性。我们的代码即将发布。