\section{Introduction}
\label{sec:intro}
\begin{figure}[!t]
    \centering
    \includegraphics[width=0.7\columnwidth]{1st_page.png}
  \caption{Overview of our agentic pipeline showing the five-agent workflow and three-tier RAG knowledge store.}
  \label{fig:schematic}
\end{figure}


Approximately 2.2 billion people live with some form of visual impairment~\cite{who2019}. While outdoor navigation is largely addressed by GPS-based smartphone applications, indoor wayfinding for blind and low-vision (BLV) users remains an open and consequential challenge. GPS signals attenuate inside buildings, and complex indoor topologies (e.g., hallways, doors, elevators) are difficult to communicate through audio alone~\cite{kuriakose2022indoor}. Existing indoor assistive systems generally follow one of two paradigms. Most approaches require deploying Bluetooth beacons or Wi-Fi fingerprinting, entail physical installation and maintenance in dynamic environments, and rely on vision-based methods that estimate depth from real-time camera feeds. These systems usually need to be retrained for each building. Although they can achieve accurate localization and support safe navigation, their dependence on environment-specific training limits their usability for users navigating among different indoor spaces. In our previous study \cite{ayanzadeh2025floorplan2guide}, we addressed this problem by transforming floor plans into a more navigable representation. Specifically, we create a structured, graphical representation of floor plan elements and convert the floor plan into a navigable system. This approach decreases preprocessing requirements while enabling the extraction of a traversable spatial graph directly from a single floor plan image and translating into navigation instructions. Our developed system takes a single floor plan image and a navigation query (start room and destination) as input and generates first-person, heading-tracked navigation steps enriched with landmark references, door identifiers, sensory confirmation cues, and safety warnings. Algorithm~\ref{alg:orchestrator} details the complete pipeline execution. The exact LLM prompts used by each agent are provided in the Appendix.

In this study, we extend our approach to improve the robustness of floor plan parsing by introducing an agentic pipeline that implements a hybrid Retrieval-Augmented Generation (RAG) framework. This system stores multiple perspectives of the floor plan, making the system more robust for navigation. Moreover, we employ fiducial markers~\cite{zafari2019survey,nakajima2023aruco} as navigation checkpoints. Since the primary focus of this research is floor plan parsing and the generation of safe navigation instructions, these markers perform as reliable reference points to support navigation without shifting the study's core emphasis.


\smallskip
\noindent Our contributions are summarized as follows:
\begin{enumerate}
    \item A Multi-agentic pipeline with self-correcting feedback loops for strong spatial graph extraction from architectural floor plan images.
    \item Integrate Retrieval-Augmented Generation (RAG) that uses multimodal information for grounded path planning which is safety-aware for BLV users.
    \item Evaluation on real-world public buildings and the CVC-FP benchmark, including physical walk-through validation, showing the effectiveness of the proposed method over single-call LLM baselines.


\end{enumerate}


The rest of this paper is structured as follows. Section~\ref{sec:related} reviews related work. Section~\ref{sec:method} details the proposed agentic pipeline and the hybrid RAG architecture. Section~\ref{sec:experiments} presents experiments and results on the CVC-FP benchmark and two real-world UMBC buildings~(MP-1 and MP-3), and Section~\ref{sec:conclusion} concludes the paper.

%=====================================================================


\begin{figure*}[!t]
    \centering
    \includegraphics[width=\textwidth]{floorplan_.png}
    \caption{Sample floor plan images used in this study. (a) MP-3 and (b) MP-1 are architectural floor plans with detailed room and structural annotations. (c) and (d) are sample floor plans from the CVC-FP dataset.}
    \label{fig:floorplan_1}
\end{figure*}
\section{Related Work}\label{sec:related}

\subsection{Indoor Navigation for BLV Users}
Assistive indoor wayfinding has been investigated through both infrastructure-dependent and infrastructure-free paradigms.
Zafari~\emph{et al.}~\cite{zafari2019survey} survey Bluetooth Low Energy (BLE) beacon deployments, which achieve sub-meter accuracy but incur per-building installation costs.
Nakajima~\emph{et al.}~\cite{nakajima2023aruco} combine ArUco marker tracking with graph-based pathfinding for real-time guidance.
Vision-based approaches leverage depth estimation but degrade in textureless institutional corridors.
Kuriakose~\emph{et al.}~\cite{kuriakose2022indoor} comprehensively review smartphone-based solutions, concluding that no single modality provides the coverage and robustness needed for widespread BLV adoption. More recently, large language models (LLMs) have been harnessed for explicit spatial reasoning and navigation planning (e.g., NavGPT~\cite{zhou2024navgpt}, MapGPT~\cite{chen2024mapgpt}), occasionally leveraging multi-agent consultation to improve zero-shot routing~\cite{liang2024discussnav}.



\subsection{Floor Plan Parsing}

In our prior work, we explored building on graph embeddings and structural relationship capture for link prediction~\cite{ayanzadeh2020resvgae, nallbani2021resvgae} and improved floor plan parsing for BLV individuals by converting floor plans into a structured knowledge graph and leveraging foundation models for step-by-step navigation instructions~\cite{ayanzadeh2025floorplan2guide}. To our knowledge, this was among the first approaches to parse floor plans using an explicit graphical representation rather than relying solely on direct visual reasoning, as in prior Vision-Language and vision-based methods~\cite{li2024vlmparse}. Our results demonstrated the efficiency of structured floor plan parsing for Vision-Language Navigation (VLN) and greater robustness than direct visual prompting alone~\cite{ayanzadeh2025floorplan2guide}.

Coffrini~et~al.~\cite{coffrini2025llm} prompt Multimodal Language Models (MLLMs) to generate step-by-step indoor navigation instructions from floor plan images, showing that converting raw floor plans into graph-style schematics substantially improves spatial grounding and instruction accuracy. However, this structured representation is constructed manually, leaving automated extraction an open problem. ViLLA~\cite{singh2024villa} integrates LLMs with vision transformers for automatic floor plan analysis and feature extraction, while WAFFLE~\cite{yang2024waffle} uses LLMs and VLMs to structure textual metadata. Advances in spatial reasoning~\cite{chen2024spatialvlm} and large-scale architectural datasets~\cite{fan2021floorplancad} have similarly driven progress in floor plan vectorization and semantic generation~\cite{shabani2023housediffusion}. However, these approaches focus primarily on feature extraction or synthesis rather than evaluating layout accessibility for navigation. We build on these principles, introducing domain-specific agents, self-critics, and an evaluator to improve parsing accuracy and reduce hallucinations in path planning. Our approach is tailored to the spatial structure of floor plans and emphasizes safety, a critical factor for BLV users during indoor navigation.



\begin{figure*}[!t]
    \centering
    \includegraphics[width=\textwidth]{main_diagram.png}
    \caption{End-to-end architecture of the workflow. Left: The Parser extracts rooms and adjacencies, the Graph Builder constructs a validated floor-plan graph, and the Self-Critic enforces structural checks, triggering feedback as needed. A Safety Evaluator flags hazards such as narrow passages or dead ends. Center: Results are stored in a three-tier RAG structure (graph, vector, visual). Right: For a query, the RAG Navigator retrieves context, and the Planner generates BFS-based navigation with landmarks and safety notes, with re-routing on hazard alerts.}
    \label{fig:workflow}
\end{figure*}

\subsection{Retrieval-Augmented Generation}
Retrieval-Augmented Generation (RAG)~\cite{lewis2020rag} mitigates LLM hallucination by grounding generation in retrieved evidence. GraphRAG~\cite{edge2024graphrag} extends this paradigm to knowledge-graph-structured retrieval, demonstrating superior multi-hop reasoning.
UrbanKGent~\cite{ning2024urbankgent} applies LLM agents to urban knowledge graph construction.
While these works establish the power of structured retrieval, none have applied RAG to indoor spatial navigation from architectural documents.
Our three-tier architecture uniquely combines deterministic pathfinding with semantic and visual retrieval.

\subsection{Agentic Systems for Vision-Language Models}
Recent agentic systems adopt LLMs as embodied controllers for explicit reasoning and tool use~\cite{yao2023react, wang2023voyager}. Extending these paradigms to vision, frameworks such as MM-ReAct~\cite{yang2023mmreact} enable iterative multimodal reasoning over images and text. In this study, we build upon and extend the floor plan parsing paradigm by introducing an agentic RAG controller over the floor plan parsing pipeline. Specifically, we integrate a multi-step agent that retrieves relevant subgraphs and contextual information and performs hierarchical chain-of-thought reasoning, moving from global route planning to local turn-by-turn instructions. In contrast to prior VLM-based floor plan parsing systems that rely on few-shot prompting or fixed pipelines~\cite{li2024vlmparse,ganon2025waffle}, our agent can progressively refine, verify, and repair navigation plans. This enables more context-aware guidance that is consistent with the safety requirements for a solid workflow designed for BLV users~\cite{ayanzadeh2025floorplan2guide}.

\section{Method}\label{sec:method}

