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

Yewei Huang, Tixiao Shan, Fanfei Chen, Brendan Englot

Research output: Contribution to journalArticlepeer-review

59 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)1150-1157
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume7
Issue number2
DOIs
StatePublished - 1 Apr 2022

Keywords

  • Multi-robot SLAM
  • distributed robot systems
  • range sensing

Fingerprint

Dive into the research topics of 'DiSCo-SLAM: Distributed Scan Context-Enabled Multi-Robot LiDAR SLAM with Two-Stage Global-Local Graph Optimization'. Together they form a unique fingerprint.

Cite this