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

TL;DR

This paper introduces an agentic framework that converts floor plan images into a knowledge base to generate safe, accessible indoor navigation instructions for blind and low-vision individuals, outperforming existing methods.

Contribution

The work presents a novel multi-agent system with a self-correcting pipeline for parsing floor plans and generating navigation instructions, requiring only a single floor plan image and lightweight infrastructure.

Findings

Achieved success rates of over 92% on short routes in real-world building

Outperformed baseline models in success rates across multiple route lengths

Demonstrated scalability and effectiveness in real-world indoor navigation scenarios

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 floor plan image into a structured, retrievable knowledge base to generate safe, accessible navigation instructions with lightweight infrastructure. The system has two phases: a multi-agent module that parses the floor plan into a spatial knowledge graph through a self-correcting pipeline with iterative retry loops and corrective feedback; and a Path Planner that generates accessible navigation instructions, with a Safety Evaluator agent assessing potential hazards along each route. We evaluate the system on the real-world UMBC Math and Psychology building (floors MP-1 and MP-3) and on the CVC-FP benchmark. On MP-1, we achieve success rates of 92.31%, 76.92%, and 61.54% for short, medium, and long routes, outperforming the strongest single-call baseline (Claude 3.7 Sonnet) at 84.62%, 69.23%, and 53.85%. On MP-3, we reach 76.92%, 61.54%, and 38.46%, compared to the best baseline at 61.54%, 46.15%, and 23.08%. These results show consistent gains over single-call LLM baselines and demonstrate that our workflow is a scalable solution for accessible indoor navigation for BLV individuals.