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.

翻译：本文重新审视了Kimera-Multi（一种面向真实世界部署的分布式多机器人同时定位与地图构建（SLAM）系统），并主要做出三项贡献。首先，我们描述了为提升Kimera-Multi在大规模真实场景部署中的韧性而进行的改进，重点处理间歇性不可靠通信问题。其次，我们在麻省理工学院校园进行的实际实验中收集并发布了具有挑战性的多机器人基准数据集，包含用于评估的精确参考轨迹和地图。这些数据集涉及最多8台机器人长距离穿越（最远8公里），包含诸多挑战性要素，如严重视觉歧义（例如地下隧道和走廊）、不同光照条件下的室内外混合轨迹，以及动态实体（如行人和车辆）。最后，我们评估了Kimera-Multi在不同通信场景下的韧性，并与集中式基线系统进行了定量比较。基于实际实验和后续分析结果，我们讨论了Kimera-Multi的优势与不足，并为算法和系统设计提出了未来方向。我们公开了Kimera-Multi的源代码及所有数据集，以促进多机器人SLAM系统可靠真实部署的进一步研究。