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

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. Additionall...

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Veröffentlicht in:IEEE robotics and automation letters Jg. 7; H. 2; S. 1150 - 1157
Hauptverfasser: Huang, Yewei, Shan, Tixiao, Chen, Fanfei, Englot, Brendan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2021.3138156