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LLM agents parse floor plans for accessible indoor navigation for visually impaired

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

Researchers have developed an agentic framework to assist blind and low-vision individuals with indoor navigation by parsing floor plans into a structured knowledge base. This system uses a multi-agent module for floor plan analysis and a path planner with a safety evaluator to generate navigation instructions. Tested on the UMBC Math and Psychology building, the framework achieved success rates of up to 92.31% for short routes, significantly outperforming baseline models like Claude 3.7 Sonnet. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Provides a scalable, lightweight solution for accessible indoor navigation, potentially improving independence for visually impaired individuals.

RANK_REASON This is a research paper detailing a novel framework for indoor navigation using LLMs.

Read on arXiv cs.CV →

COVERAGE [2]

  1. arXiv cs.CV TIER_1 · Aydin Ayanzadeh, Tim Oates ·

    LLM-Guided Agentic Floor Plan Parsing for Accessible Indoor Navigation of Blind and Low-Vision People

    arXiv:2604.23970v1 Announce Type: cross Abstract: Indoor navigation remains a critical accessibility challenge for the blind and low-vision (BLV) individuals, as existing solutions rely on costly per-building infrastructure. We present an agentic framework that converts a single …

  2. arXiv cs.CV TIER_1 · Tim Oates ·

    LLM-Guided Agentic Floor Plan Parsing for Accessible Indoor Navigation of Blind and Low-Vision People

    Indoor navigation remains a critical accessibility challenge for the blind and low-vision (BLV) individuals, as existing solutions rely on costly per-building infrastructure. We present an agentic framework that converts a single floor plan image into a structured, retrievable kn…

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