提出动态感知的LIO框架，解决动态环境下定位失效问题。
Breaking the Static Assumption: A Dynamic-Aware LIO Framework Via Spatio-Temporal Normal Analysis
- 用时空法向分析改进ICP算法，动态感知融入点云配准
- 在几何稀疏场景下，相对最优方案提升18.3%定位精度
- 适合自动驾驶等动态复杂环境中的高精度定位
本文针对激光惯性里程计（LIO）在动态环境中因静态世界假设而失效的问题。传统LIO算法在动态物体主导且几何结构稀疏的场景中表现不佳。现有动态LIO方法面临根本矛盾：精准定位需可靠静态特征识别，但区分动态物体又依赖精确位姿估计。本文通过将动态感知直接嵌入点云配准过程，打破这一循环依赖。提出一种新型动态感知迭代最近点算法，利用时空法向分析，并结合高效的空间一致性验证方法，提升静态地图构建效果。实验表明，在挑战性动态环境与有限几何结构条件下，该方法相较当前最优LIO系统显著提升性能。代码与数据集已公开于 https://github.com/thisparticle/btsa。
原文摘要 · Abstract (English)
This paper addresses the challenge of Lidar-Inertial Odometry (LIO) in dynamic environments, where conventional methods often fail due to their static-world assumptions. Traditional LIO algorithms perform poorly when dynamic objects dominate the scenes, particularly in geometrically sparse environments. Current approaches to dynamic LIO face a fundamental challenge: accurate localization requires a reliable identification of static features, yet distinguishing dynamic objects necessitates precise pose estimation. Our solution breaks this circular dependency by integrating dynamic awareness directly into the point cloud registration process. We introduce a novel dynamic-aware iterative closest point algorithm that leverages spatio-temporal normal analysis, complemented by an efficient spatial consistency verification method to enhance static map construction. Experimental evaluations demonstrate significant performance improvements over state-of-the-art LIO systems in challenging dynamic environments with limited geometric structure. The code and dataset are available at https://github.com/thisparticle/btsa.
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