  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.
