弹性与分布式多机器人视觉SLAM：数据集、实验与经验教训
Resilient and Distributed Multi-Robot Visual SLAM: Datasets, Experiments, and Lessons Learned
AI总结：
本文改进了分布式多机器人SLAM系统Kimera-Multi以提升其在不可靠通信下的弹性，发布了MIT校园8台机器人8公里的多机器人基准数据集，并定量评估了其相较集中式基线的弹性表现。
摘要：
本文重新审视了Kimera-Multi这一分布式多机器人同步定位与建图（SLAM）系统，旨在实现真实世界中的部署。具体而言，本文有三大主要贡献。首先，我们描述了对Kimera-Multi的改进，使其能够弹性应对大规模真实世界部署，特别强调处理间歇性和不可靠通信。其次，我们收集并发布了在MIT校园进行现场实验中获得的具有挑战性的多机器人基准数据集，并提供了用于评估的准确参考轨迹和地图。这些数据集包含多达8台机器人长距离行驶（长达8公里），并具有许多具有挑战性的元素，如严重的视觉歧义（例如在地下隧道和走廊中）、具有不同光照条件的混合室内外轨迹，以及动态实体（例如行人和汽车）。最后，我们评估了Kimera-Multi在不同通信场景下的弹性，并与集中式基线系统进行了定量比较。基于现场实验和后续分析的结果，我们讨论了Kimera-Multi的优缺点，并为算法和系统设计提出了未来的方向。我们发布了Kimera-Multi的源代码和所有数据集，以促进多机器人SLAM系统在可靠真实世界部署方面的进一步研究。
英文摘要：
This paper revisits Kimera-Multi, a distributed multi-robot Simultaneous Localization and Mapping (SLAM) system, towards the goal of deployment in the real world. In particular, this paper has three main contributions. First, we describe improvements to Kimera-Multi to make it resilient to large-scale real-world deployments, with particular emphasis on handling intermittent and unreliable communication. Second, we collect and release challenging multi-robot benchmarking datasets obtained during live experiments conducted on the MIT campus, with accurate reference trajectories and maps for evaluation. The datasets include up to 8 robots traversing long distances (up to 8 km) and feature many challenging elements such as severe visual ambiguities (e.g., in underground tunnels and hallways), mixed indoor and outdoor trajectories with different lighting conditions, and dynamic entities (e.g., pedestrians and cars). Lastly, we evaluate the resilience of Kimera-Multi under different communication scenarios, and provide a quantitative comparison with a centralized baseline system. Based on the results from both live experiments and subsequent analysis, we discuss the strengths and weaknesses of Kimera-Multi, and suggest future directions for both algorithm and system design. We release the source code of Kimera-Multi and all datasets to facilitate further research towards the reliable real-world deployment of multi-robot SLAM systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
