SLAM is a fundamental capability of unmanned systems, with LiDAR-based SLAM gaining widespread adoption due to its high precision. Current SLAM systems can achieve centimeter-level accuracy within a short period. However, there are still several challenges when dealing with largescale mapping tasks including significant storage requirements and difficulty of reusing the constructed maps. To address this, we first design an elastic and lightweight map representation called CELLmap, composed of several CELLs, each representing the local map at the corresponding location. Then, we design a general backend including CELL-based bidirectional registration module and loop closure detection module to improve global map consistency. Our experiments have demonstrated that CELLmap can represent the precise geometric structure of large-scale maps of KITTI dataset using only about 60 MB. Additionally, our general backend achieves up to a 26.88% improvement over various LiDAR odometry methods.

翻译：即时定位与地图构建（SLAM）是无人系统的基础能力，基于激光雷达的SLAM因其高精度而获得广泛应用。当前SLAM系统可在短时间内实现厘米级精度。然而，在处理大规模建图任务时仍存在若干挑战，包括显著的存储需求以及已构建地图难以复用的问题。为此，我们首先设计了一种弹性轻量化的地图表示方法CELLmap，该方法由若干CELL单元构成，每个单元表示对应位置的局部地图。随后，我们设计了一个通用后端系统，包含基于CELL的双向配准模块和闭环检测模块，以提升全局地图的一致性。实验表明，CELLmap仅需约60 MB存储空间即可精确表示KITTI数据集大规模地图的几何结构。此外，我们的通用后端系统相较于多种激光雷达里程计方法实现了最高26.88%的性能提升。