From Single Scan to Sequential Consistency: A New Paradigm for LIDAR Relocalization

Minghang Zhu; Zhijing Wang; Yuxin Guo; Wen Li; Sheng Ao; Cheng Wang

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arXiv · 2602.03198

From Single Scan to Sequential Consistency: A New Paradigm for LIDAR Relocalization

Minghang Zhu·Zhijing Wang·Yuxin Guo·Wen Li·Sheng Ao·Cheng Wang

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Abstract

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.

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Minghang Zhu, Zhijing Wang, Yuxin Guo, Wen Li, Sheng Ao, Cheng Wang. 2026-02-03. From Single Scan to Sequential Consistency: A New Paradigm for LIDAR Relocalization. https://arxiv.org/abs/2602.03198

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