MLS-SLAM: Multi-Level Submap Guided LiDAR SLAM
Fuqin Shang, Wenbing Yu, Kangxu Wang, Liubin Wang, Wei Liu, Wenming Yang, Wang Lu, Guijin Wang
Abstract
LiDAR Simultaneous Localization and Mapping (SLAM) has been pivotal in various domains, including digital twins, geographic surveying, and autonomous mobile robotics. However, achieving an optimal balance between high precision and computational efficiency remains a significant challenge. In this paper, we propose MLS-SLAM, a multi-level submap guided 3D LiDAR SLAM method that incorporates three levels of submaps (local-frame submap, rotation-aware submap, and loop-optimized submap) and consists of four key modules: LiDAR Odometry, Local Frame Integration, Rotation-Aware Submap Integration, and Loop-Optimized Submap Integration. Firstly, the LiDAR Odometry module provides an initial pose for each LiDAR scan. Subsequently, the Local Frame Integration module performs continuous local refinement with a sliding window and constructs local-frame submaps. Then, the Rotation-Aware Submap Integration module aggregates local-frame submaps into rotation-aware submaps based on the trajectory. Finally, the Loop-Optimized Submap Integration module finds loops between rotation-aware submaps and performs global pose graph optimization. The performance of MLS-SLAM has been rigorously evaluated on public and self-collected datasets. Experiments show that MLS-SLAM achieves state-of-the-art precision and real-time performance at 10 Hz on datasets with diverse scales and environments.
BibTeX
@inproceedings{ral2026_mlsslammultileve,
title = {MLS-SLAM: Multi-Level Submap Guided LiDAR SLAM},
author = {Fuqin Shang and Wenbing Yu and Kangxu Wang and Liubin Wang and Wei Liu and Wenming Yang and Wang Lu and Guijin Wang and Shuang Wang},
booktitle = {RA-L 2026},
year = {2026}
}