# Floorplan2Guide: LLM-Guided Floorplan Parsing for BLV Indoor Navigation

arXiv: https://arxiv.org/abs/2512.12177 · HTML: https://mlsanity.com/paper/2512.12177/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 43 references that are in the map.
- First posted on arXiv 13 Dec 2025.

## Builds on (most cited first, 9 of 9)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language Models](https://mlsanity.com/paper/2305.16986/) (2023)
- [Vision Language Models Can Parse Floor Plan Maps](https://mlsanity.com/paper/2409.12842/) (2024)
- [LLM-Guided Indoor Navigation with Multimodal Map Understanding](https://mlsanity.com/paper/2503.11702/) (2025)
- [Large Language Models for Knowledge Graph Embedding: A Survey](https://mlsanity.com/paper/2501.07766/) (2025)
- [ResVGAE: Going Deeper with Residual Modules for Link Prediction](https://mlsanity.com/paper/2105.00695/) (2021)
- [NaVIP: An Image-Centric Indoor Navigation Solution for Visually Impaired People](https://mlsanity.com/paper/2410.18109/) (2024)
- [Turn-by-Turn Indoor Navigation for the Visually Impaired](https://mlsanity.com/paper/2410.19954/) (2024)

## Built on by (most cited first, 2 of 2)

- [WildfireVLM: AI-powered Analysis for Early Wildfire Detection and Risk Assessment Using Satellite Imagery](https://mlsanity.com/paper/2602.13305/) (2026)
- [LLM-Guided Agentic Floor Plan Parsing for Accessible Indoor Navigation of Blind and Low-Vision People](https://mlsanity.com/paper/2604.23970/) (2026)

## Abstract (arXiv)

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.