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arXiv · 2305.03441

Multi S-graphs: A Collaborative Semantic SLAM architecture

Miguel Fernandez-Cortizas·Hriday Bavle·Jose Luis Sanchez-Lopez·Pascual Campoy·Holger Voos
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Abstract

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.

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Miguel Fernandez-Cortizas, Hriday Bavle, Jose Luis Sanchez-Lopez, Pascual Campoy, Holger Voos. 2023-05-05. Multi S-graphs: A Collaborative Semantic SLAM architecture. https://arxiv.org/abs/2305.03441

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