\begin{abstract}

% Indoor navigation for blind and low-vision (BLV) individuals remains an open accessibility problem. Most existing solutions either rely on infrastructure and computer vision pipelines that need to be retrained across different environments due to the absence of a clear ground plane, or both. Unlike in such challenging scenarios, indoor navigation for the BLV individual has a unique advantage: publicly accessible architectural floor plans for public buildings are readily available. In this paper, we present a multi-agent framework that converts a single floor plan image into a structured, retrievable knowledge base and generates safe, accessible navigation instructions. Firstly, we design a multi-agent pipeline to parse floor plans, perform pathfinding, use a Self-Critic, and employ a Safety Evaluator, which can robustly retry to obtain a spatial graph from a floor plan image. Moreover, we develop a Retrieval-Augmented Generation (RAG) to store and retrieve the extracted knowledge graph.  We conduct extensive experiments on real-world buildings and on the CVC-FP benchmark. Our approach achieved the best success rates of 92.30\%, 76.92\%, and 61.53\%, respectively. Compared with the best single-call baseline using Claude~3.7 Sonnet, which achieved 84.62\%, 69.23\%, and 53.85\% on the Math\&Psychology Building Floor~1 (MP1). Results on both the real-world floorplan and the CVC-FP benchmark further demonstrate that our approach is a scalable solution for accessible indoor navigation.

Indoor navigation remains a critical accessibility challenge for blind and low-vision (BLV) individuals, as existing solutions rely on costly per-building infrastructure. We present a multi-agentic framework that converts a single floor plan image into a structured, retrievable knowledge base to generate safe, accessible navigation instructions using lightweight infrastructure for BLV users. Our system has two main phases: a multi-agent system that constructs a knowledge graph by parsing the floorplan through a self-correcting pipeline, which employs iterative retry loops and corrective feedback to extract valid spatial knowledge graphs; and a Path Planner for generating accessible navigation instructions, evaluated by a Safety Evaluator to measure potential hazards during navigation. We evaluate our system in the real-world UMBC Math and Psychology building, floors 1 and 3. Our model surpasses baselines across all route types on MP-1 and MP-3. We achieve success rates of 92.31\%, 76.92\%, and 61.54\% for short, medium, and long routes, respectively, outperforming the best single-call baseline, Claude~3.7 Sonnet, with 84.62\%, 69.23\%, and 53.85\%, respectively. On MP-3, our workflow achieves success rates of 76.92\%, 61.54\%, and 38.46\%, improving over the best baseline of 61.54\%, 46.15\%, and 23.08\%. These results demonstrate the superiority of our method over single-call LLM baselines. Results on both the real-world floorplan and the CVC-FP benchmark further demonstrate that our workflow is a scalable solution for generating more accessible navigation for BLV individuals.

\end{abstract}