CELLmap: Enhancing LiDAR SLAM through Elastic and Lightweight Spherical Map Representation

TL;DR

CELLmap introduces an elastic, lightweight spherical map representation for LiDAR SLAM that reduces storage needs and enhances global map consistency, enabling efficient large-scale mapping and improved odometry accuracy.

Contribution

The paper proposes CELLmap, a novel elastic and lightweight map representation, along with a backend for bidirectional registration and loop closure detection, improving large-scale LiDAR SLAM performance.

Findings

CELLmap can represent large-scale maps with only 60 MB.

The backend improves LiDAR odometry accuracy by up to 26.88%.

CELLmap maintains high geometric fidelity in large-scale environments.

Abstract

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.