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).

翻译：激光雷达点云地图因其高度一致性而在道路机器人导航中得到广泛应用。然而，稠密点云面临内存消耗高、长期运行可维护性降低的挑战。本研究提出了SLIM，一种用于城市环境长期激光雷达建图的可扩展轻量化建图系统。该系统首先将结构点云参数化为线和平面。这些轻量化的结构表示满足了地图融合、位姿图优化与光束法平差的要求，确保了增量式管理与局部一致性。针对长期运行，设计了一种以地图为中心的非线性因子恢复方法，在保持建图精度的同时实现位姿稀疏化。我们使用来自经典激光雷达建图数据集（包括KITTI、NCLT、HeLiPR和M2DGR）的多时段真实世界激光雷达数据验证了SLIM系统。实验证明了其在建图精度、轻量化与可扩展性方面的能力。通过基于地图的机器人定位也验证了地图复用性。最终，利用多时段激光雷达数据，SLIM系统能够以低内存消耗（在KITTI数据集上约130 KB/km）提供全局一致的地图。