Multi S-graphs: A Collaborative Semantic SLAM architecture

Collaborative Simultaneous Localization and Mapping (CSLAM) is a critical
capability for enabling multiple robots to operate in complex environments.
Most CSLAM techniques rely on the transmission of low-level features for visual
and LiDAR-based approaches, which are used for pose graph optimization.
However, these low-level features can lead to incorrect loop closures,
negatively impacting map generation.Recent approaches have proposed the use of
high-level semantic information in the form of Hierarchical Semantic Graphs to
improve the loop closure procedures and overall precision of SLAM algorithms.
In this work, we present Multi S-Graphs, an S-graphs [1] based distributed
CSLAM algorithm that utilizes high-level semantic information for cooperative
map generation while minimizing the amount of information exchanged between
robots. Experimental results demonstrate the promising performance of the
proposed algorithm in map generation tasks.