Floorplan2Guide: LLM-Guided Floorplan Parsing for BLV Indoor Navigation

• URL: http://arxiv.org/abs/2512.12177v1
• Date: Sat, 13 Dec 2025 04:49:26 GMT
• Title: Floorplan2Guide: LLM-Guided Floorplan Parsing for BLV Indoor Navigation
• Authors: Aydin Ayanzadeh, Tim Oates,
• Abstract summary: We propose a novel navigation approach that transforms floor plans into navigable knowledge graphs and generate human-readable navigation instructions.<n> Floorplan2Guide integrates a large language model (LLM) to extract spatial information from architectural layouts.<n>Results indicate that few-shot learning improves navigation accuracy in comparison to zero-shot learning on simulated and real-world evaluations.
• Score: 4.3114959617830015
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Indoor navigation remains a critical challenge for people with visual impairments. The current solutions mainly rely on infrastructure-based systems, which limit their ability to navigate safely in dynamic environments. We propose a novel navigation approach that utilizes a foundation model to transform floor plans into navigable knowledge graphs and generate human-readable navigation instructions. Floorplan2Guide integrates a large language model (LLM) to extract spatial information from architectural layouts, reducing the manual preprocessing required by earlier floorplan parsing methods. Experimental results indicate that few-shot learning improves navigation accuracy in comparison to zero-shot learning on simulated and real-world evaluations. Claude 3.7 Sonnet achieves the highest accuracy among the evaluated models, with 92.31%, 76.92%, and 61.54% on the short, medium, and long routes, respectively, under 5-shot prompting of the MP-1 floor plan. The success rate of graph-based spatial structure is 15.4% higher than that of direct visual reasoning among all models, which confirms that graphical representation and in-context learning enhance navigation performance and make our solution more precise for indoor navigation of Blind and Low Vision (BLV) users.

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