三台机器人在火星模拟地形中实现去中心化协同定位建图，揭示通信受限下的挑战与解决方案。
Multi-Robot Decentralized Collaborative SLAM in Planetary Analogue Environments: Dataset, Challenges, and Lessons Learned
- 三机器人通过自组织网络实现去中心化协同定位建图
- 通信中断会显著影响地图一致性与定位精度
- 适用于月球/火星探测的多机器人系统研究
去中心化协同同时定位与建图（C-SLAM）是无需依赖预设定位与通信基础设施，在未知环境中开展多机器人任务的关键技术，对月球、火星等行星探索具有重要意义。本文分享了三台机器人在火星模拟地形上运行的C-SLAM实验经验，考察了有限且间歇性通信对性能的影响，以及行星类环境带来的独特定位挑战。此外，我们发布了实验期间采集的真实时点对点机器人间吞吐量与延迟数据，该数据集旨在支持未来在通信受限条件下开展的去中心化多机器人协同研究。
原文摘要 · Abstract (English)
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.
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