Language-EXtended Indoor SLAM (LEXIS): A Versatile System for Real-time Visual Scene Understanding

arXiv:2309.15065v2 Announce Type: replace-cross
Abstract: 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 …

abstract adaptability arxiv autonomous autonomous systems class cs.cv cs.ro eess.iv language localization mapping mobile real-time semantic slam systems type understanding visual work