时间感知融合用于鲁棒室外LiDAR定位
Temporal-Aware Fusion for Robust Outdoor LiDAR Localization
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中文总结 AI 辅助
提出时间感知融合框架TempLoc，通过建模序列一致性增强室外LiDAR定位鲁棒性，在NCLT和Oxford RobotCar基准上大幅超越现有方法。
中文摘要 AI 辅助
LiDAR重定位旨在估计传感器在环境中的全局6自由度位姿。然而，现有的基于回归的方法在动态或模糊场景中常常遇到限制，因为它们通常优先考虑单帧推理，未充分利用跨扫描的空间-时间一致性潜力。在本文中，我们提出了一种时间感知定位框架（TempLoc），旨在通过有效建模序列一致性来增强室外定位的鲁棒性。具体来说，首先引入全局坐标估计模块，为每次LiDAR扫描预测逐点全局坐标及相关的不确定性。然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点对应的两种预测，产生更时间一致且准确的全局6自由度位姿。在NCLT和Oxford RobotCar基准上的实验结果表明，我们的TempLoc大幅优于最先进的方法，证明了时间感知对应建模在LiDAR重定位中的有效性。
英文摘要
LiDAR relocalization aims to estimate the global 6-DoF pose of a sensor in the environment. However, existing regression-based approaches often encounter limitations in dynamic or ambiguous scenarios, as they typically prioritize single-frame inference, leaving the potential of spatio-temporal consistency across scans not fully explored. In this paper, we propose a Temporal-aware Localization framework (TempLoc) designed to enhance the robustness of outdoor 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 RobotCar benchmarks show that our TempLoc outperforms state-of-the-art methods by a large margin, demonstrating the effectiveness of temporal-aware correspondence modeling in LiDAR relocalization.
发表机构
- Xiamen University（厦门大学）
- University of Bristol（布里斯托大学）
机构由 AI 辅助整理，请以论文原文为准。
