  Collaborative Simultaneous Localization and Mapping (CSLAM) is critical to
enable multiple robots to operate in complex environments. Most CSLAM
techniques rely on raw sensor measurement or low-level features such as
keyframe descriptors, which can lead to wrong loop closures due to the lack of
deep understanding of the environment. Moreover, the exchange of these
measurements and low-level features among the robots requires the transmission
of a significant amount of data, which limits the scalability of the system. To
overcome these limitations, we present Multi S-Graphs, a decentralized CSLAM
system that utilizes high-level semantic-relational information embedded in the
four-layered hierarchical and optimizable situational graphs for cooperative
map generation and localization in structured environments while minimizing the
information exchanged between the robots. To support this, we present a novel
room-based descriptor which, along with its connected walls, is used to perform
inter-robot loop closures, addressing the challenges of multi-robot kidnapped
problem initialization. Multiple experiments in simulated and real environments
validate the improvement in accuracy and robustness of the proposed approach
while reducing the amount of data exchanged between robots compared to other
state-of-the-art approaches.
  Software available within a docker image:
https://github.com/snt-arg/multi_s_graphs_docker
