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 (6)
- Robot Operating System 2: Design, Architecture, and Uses In The Wild
- RTAB-Map as an Open-Source Lidar and Visual SLAM Library for Large-Scale and Long-Term Online Operation
- Towards Collaborative Simultaneous Localization and Mapping: a Survey of the Current Research Landscape
- maplab 2.0 -- A Modular and Multi-Modal Mapping Framework
- A Robot Web for Distributed Many-Device Localisation
- Self-Supervised Domain Calibration and Uncertainty Estimation for Place Recognition
Cited by in corpus (9)
- DCL-SLAM: A Distributed Collaborative LiDAR SLAM Framework for a Robotic Swarm
- S3E: A Multi-Robot Multimodal Dataset for Collaborative SLAM
- Collaborative Dynamic 3D Scene Graphs for Automated Driving
- 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