用轻量弹性地图表示法提升大场景激光SLAM精度与存储效率
CELLmap: Enhancing LiDAR SLAM through Elastic and Lightweight Spherical Map Representation
- 将地图划分为局部单元CELL，实现弹性压缩与高效存储
- 在KITTI数据集上仅用60MB即保留精确几何结构
- 后端模块使全局一致性提升26.88%，适合大规模自动驾驶建图
SLAM是无人系统的核心能力，基于激光雷达的SLAM因高精度而广泛应用。现有系统虽能在短时间内达到厘米级精度，但在大规模建图中仍面临存储开销大、地图复用难等问题。为此，本文提出一种名为CELLmap的弹性轻量级地图表示方法，由多个代表局部地图的CELL组成。进一步设计通用后端，包含基于CELL的双向配准模块和回环检测模块，以提升全局地图一致性。实验表明，CELLmap可在仅60MB存储下完整表达KITTI数据集的大规模地图几何结构；其通用后端相较多种激光里程计方法，最多提升26.88%的性能。
原文摘要 · Abstract (English)
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.
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