Floorplan2Guide: LLM-Guided Floorplan Parsing for BLV Indoor Navigation

TL;DR

This paper introduces Floorplan2Guide, a novel indoor navigation system for BLV users that leverages large language models to parse floor plans into knowledge graphs, improving navigation accuracy and reducing manual preprocessing.

Contribution

It presents a new LLM-guided approach to floorplan parsing that enhances indoor navigation for BLV users by transforming layouts into knowledge graphs with minimal manual effort.

Findings

Few-shot learning outperforms zero-shot in navigation accuracy.

Claude 3.7 Sonnet achieves over 92% accuracy on short routes.

Graph-based spatial reasoning improves success rate by 15.4%.

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.