Swarm-SLAM : Sparse Decentralized Collaborative Simultaneous Localization and Mapping Framework for Multi-Robot Systems
arXiv:2301.06230 · doi:10.1109/LRA.2023.3333742
Abstract
Collaborative Simultaneous Localization And Mapping (C-SLAM) is a vital component for successful multi-robot operations in environments without an external positioning system, such as indoors, underground or underwater. In this paper, we introduce Swarm-SLAM, an open-source C-SLAM system that is designed to be scalable, flexible, decentralized, and sparse, which are all key properties in swarm robotics. Our system supports inertial, lidar, stereo, and RGB-D sensing, and it includes a novel inter-robot loop closure prioritization technique that reduces communication and accelerates convergence. We evaluated our ROS-2 implementation on five different datasets, and in a real-world experiment with three robots communicating through an ad-hoc network. Our code is publicly available: https://github.com/MISTLab/Swarm-SLAM
Code: https://github.com/MISTLab/Swarm-SLAM
References in corpus (3)
Cited by in corpus (9)
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- Multi S-Graphs: An Efficient Distributed Semantic-Relational Collaborative SLAM
- SLAM2REF: Advancing Long-Term Mapping with 3D LiDAR and Reference Map Integration for Precise 6-DoF Trajectory Estimation and Map Extension
- Multi-Robot Decentralized Collaborative SLAM in Planetary Analogue Environments: Dataset, Challenges, and Lessons Learned
- A Benchmark Dataset for Collaborative SLAM in Service Environments
- Cooperative and Asynchronous Transformer-based Mission Planning for Heterogeneous Teams of Mobile Robots
- 3D Foundation Model-Based Loop Closing for Decentralized Collaborative SLAM