DiSCo-SLAM: Distributed Scan Context-Enabled Multi-Robot LiDAR SLAM With Two-Stage Global-Local Graph Optimization

• Published in: IEEE robotics and automation letters Vol. 7; no. 2; pp. 1150 - 1157
• Main Authors: Huang, Yewei, Shan, Tixiao, Chen, Fanfei, Englot, Brendan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.04.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Context; distributed robot systems; Feature extraction; Initial conditions; Laser radar; Lidar; Local optimization; Multi-robot SLAM; Multiple robots; Optimization; range sensing; Robot kinematics; Robot sensing systems; Robots; Simultaneous localization and mapping
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2021.3138156

We propose a novel framework for distributed,multi-robot SLAM intended for use with 3D LiDAR observations. The framework, DiSCo-SLAM, is the first to use the lightweight Scan Context descriptor for multi-robot SLAM, permitting a data-efficient exchange of LiDAR observations among robots. Additionally, our framework includes a two-stage global and local optimization framework for distributed multi-robot SLAM which provides stable localization results that are resilient to the unknown initial conditions that typify the search for inter-robot loop closures. We compare our proposed framework with the widely used distributed Gauss-Seidel (DGS) approach, over a variety of multi-robot datasets, quantitatively demonstrating its accuracy, stability, and data-efficiency.