Swarm-SLAM : Sparse Decentralized Collaborative Simultaneous Localization and Mapping Framework for Multi-Robot Systems
This work addresses the challenge of enabling efficient multi-robot operations in GPS-denied environments like indoors or underwater, representing an incremental improvement with a focus on scalability and decentralization.
The paper tackles the problem of collaborative simultaneous localization and mapping (C-SLAM) for multi-robot systems in environments without external positioning, introducing Swarm-SLAM, a scalable and decentralized framework that includes a novel inter-robot loop closure prioritization technique to reduce communication and accelerate convergence, with evaluation on five datasets and a real-world experiment with three robots.
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