Multi-Robot Decentralized Collaborative SLAM in Planetary Analogue Environments: Dataset, Challenges, and Lessons Learned
Pierre-Yves Lajoie, Karthik Soma, Haechan Mark Bong, Alice Lemieux-Bourque, Rongge Zhang, Vivek Shankar Varadharajan, Giovanni Beltrame
TL;DR
The paper addresses the challenge of enabling accurate, decentralized SLAM among multiple robots in planetary analogue environments with limited communications. It proposes Swarm-SLAM, a three-robot system operating over ad-hoc networks, and evaluates performance using LiDAR/IMU data, place recognition, and robust registration, while introducing a novel inter-robot latency/throughput dataset. Key contributions include the system design, the publicly available dataset, and an in-depth analysis of accuracy and resource efficiency that reveals current limitations and open challenges for communication-aware C-SLAM. This work demonstrates the practicality of decentralized collaboration in space-like conditions and provides actionable data, methods, and insights to guide future improvements in autonomous planetary exploration.
Abstract
Decentralized collaborative simultaneous localization and mapping (C-SLAM) is essential to enable multirobot missions in unknown environments without relying on preexisting localization and communication infrastructure. This technology is anticipated to play a key role in the exploration of the Moon, Mars, and other planets. In this article, we share insights and lessons learned from C-SLAM experiments involving three robots operating on a Mars analogue terrain and communicating over an ad hoc network. We examine the impact of limited and intermittent communication on C-SLAM performance, as well as the unique localization challenges posed by planetary-like environments. Additionally, we introduce a novel dataset collected during our experiments, which includes real-time peer-to-peer inter-robot throughput and latency measurements. This dataset aims to support future research on communication-constrained, decentralized multirobot operations.