通过分层搜索与仿真激光雷达，实现大场景下快速高精度定位。
Offline-Online Hierarchical 3D Global Relocalization With Synthetic LiDAR Sensing and Descriptor-Space Retrieval
- 离线生成候选位姿与描述符索引，线上分步检索定位
- 平均定位时间仅3秒，精度达8厘米，效率提升一个数量级
- 适合大规模室内外机器人定位，尤其对实时性要求高的场景
3D全局重定位是移动机器人在实际应用中的关键技术。然而，在大尺度环境中，现有方法常因姿态搜索空间庞大和计算开销高导致在线重定位耗时过长。为此，本文提出一种离线-在线分层框架，将搜索空间解耦。离线阶段，在网格地图中模拟激光雷达扫描，生成候选位置及其对应的几何描述符索引；在线阶段，先通过全局检索获得粗略位姿，再经点云配准输出精确的6-DoF位姿。真实世界实验表明，该方法在3D环境中平均重定位时间仅为3秒，平均定位精度达8厘米。相比现有全局重定位方法，计算效率提升一个数量级，同时保持相当的定位精度。
原文摘要 · Abstract (English)
3D global relocalization is one of the key capabilities for mobile robots in practical applications. However, in large scale spaces, existing methods often suffer from prolonged online relocalization time due to factors such as the massive pose search space and high computational overhead. To address these issues, this paper proposes an offline-online hierarchical framework that decouples the search space. In the offline phase, candidate positions and their corresponding geometric descriptor indices are generated in the map by simulating LiDAR scans within the grid map. In the online phase, a coarse pose estimate is first obtained via global retrieval, followed by point cloud registration to output precise 6-DoF pose estimates. Real-world experiments demonstrate that the proposed method achieves an average relocalization time of 3 s and an average localization accuracy of 8 cm in 3D environments. Compared with existing global relocalization methods, the proposed method achieves an order-of-magnitude improvement in computational efficiency while delivering comparable relocalization accuracy.
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