  Versatile and adaptive semantic understanding would enable autonomous systems
to comprehend and interact with their surroundings. Existing fixed-class models
limit the adaptability of indoor mobile and assistive autonomous systems. In
this work, we introduce LEXIS, a real-time indoor Simultaneous Localization and
Mapping (SLAM) system that harnesses the open-vocabulary nature of Large
Language Models (LLMs) to create a unified approach to scene understanding and
place recognition. The approach first builds a topological SLAM graph of the
environment (using visual-inertial odometry) and embeds Contrastive
Language-Image Pretraining (CLIP) features in the graph nodes. We use this
representation for flexible room classification and segmentation, serving as a
basis for room-centric place recognition. This allows loop closure searches to
be directed towards semantically relevant places. Our proposed system is
evaluated using both public, simulated data and real-world data, covering
office and home environments. It successfully categorizes rooms with varying
layouts and dimensions and outperforms the state-of-the-art (SOTA). For place
recognition and trajectory estimation tasks we achieve equivalent performance
to the SOTA, all also utilizing the same pre-trained model. Lastly, we
demonstrate the system's potential for planning.
