SLIM用线条平面压缩点云，实现城市环境下的轻量高保真长期建图。
SLIM: Scalable and Lightweight LiDAR Mapping in Urban Environments
- 将点云参数化为线和面，降低存储与维护成本。
- 在KITTI数据上实现约130 KB/km的内存占用，保持全局一致性。
- 适合需长期运行的机器人定位与地图复用场景。
LiDAR点云地图在道路机器人导航中广泛应用，因其高一致性。然而密集点云在长期运行中面临内存消耗大、可维护性差的问题。本文提出SLIM系统，用于城市环境中长期、可扩展且轻量的LiDAR建图。该系统首先将结构化点云参数化为线与面，生成轻量且具结构性的表示形式，满足地图融合、位姿图优化与捆绑调整需求，保障增量管理与局部一致性。针对长期运行，设计了以地图为中心的非线性因子恢复方法，实现位姿稀疏化同时保持建图精度。通过经典数据集KITTI、NCLT、HeLiPR和M2DGR的多时段真实数据验证，SLIM在建图精度、轻量化与可扩展性方面表现优异。地图复用通过基于地图的机器人定位得以验证。最终，使用多时段数据，系统生成全局一致地图，内存消耗低至约130 KB/km（KITTI）。
原文摘要 · Abstract (English)
LiDAR point cloud maps are extensively utilized on roads for robot navigation due to their high consistency. However, dense point clouds face challenges of high memory consumption and reduced maintainability for long-term operations. In this study, we introduce SLIM, a scalable and lightweight mapping system for long-term LiDAR mapping in urban environments. The system begins by parameterizing structural point clouds into lines and planes. These lightweight and structural representations meet the requirements of map merging, pose graph optimization, and bundle adjustment, ensuring incremental management and local consistency. For long-term operations, a map-centric nonlinear factor recovery method is designed to sparsify poses while preserving mapping accuracy. We validate the SLIM system with multi-session real-world LiDAR data from classical LiDAR mapping datasets, including KITTI, NCLT, HeLiPR and M2DGR. The experiments demonstrate its capabilities in mapping accuracy, lightweightness, and scalability. Map re-use is also verified through map-based robot localization. Finally, with multi-session LiDAR data, the SLIM system provides a globally consistent map with low memory consumption (~130 KB/km on KITTI).
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