Lang2Graph: Towards Leveraging Human Language for Indoor Topology Inference Using LLMs

Abstract

Edge-deployed systems such as autonomous robots, AR/XR devices, and emergency-response handhelds require accurate indoor topological representations, yet existing sensor-based and expert-curated mapping methods are impractical for crowd-sourced, resource-constrained deployment. Additionally, current large language model (LLM) approaches to indoor topology inference lack a systematic framework for evaluating the factors that govern performance. We present Lang2Graph, an experimental framework for indoor topological graph inference from natural-language navigational instructions that isolates four governing factors: instruction structure, metadata clarity, prompting strategy, and model size and reasoning capability. We propose the Independent Prompt Executor (IPE), a prompting strategy that decomposes graph construction into independent per-instruction reasoning steps, preventing error propagation. To support factor-level evaluation, we develop a fully synthetic dataset and an augmented benchmark (R2R-AUG) covering idealized and real-world conditions. Our evaluation across multiple model families show that structured instructions, clear metadata, and IPE improve precision, recall, and F1 by 44%, 45%, and 47%, respectively. Reasoning-aligned open-source models of moderate scale (14B parameters) outperform larger proprietary models on the most challenging instruction categories, indicating that reliable indoor topology inference is achievable without cloud-scale dependencies and establishes a viable path toward on-device edge deployment.