Multi-Robot Decentralized Collaborative SLAM in Planetary Analogue Environments: Dataset, Challenges, and Lessons Learned

TL;DR

This paper presents insights from decentralized collaborative SLAM experiments with three robots in Mars-like terrain, highlighting communication challenges, introducing a new dataset, and discussing lessons learned for planetary exploration.

Contribution

It provides a novel dataset and analysis of communication constraints in multi-robot C-SLAM in planetary analogue environments, along with practical lessons learned.

Findings

Limited communication significantly affects C-SLAM performance.

The dataset includes real-time inter-robot throughput and latency data.

Experiments reveal unique localization challenges in planetary-like terrains.

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.