LLM-Assisted Indoor Scene Assembly with Rule-Based Spatial Validation for Emergency Navigation

Abstract

In emergency scenarios such as fires or earthquakes, rapid and accurate situational awareness of the scene is critical for decision-making processes. Operators may inaccurately visualize descriptions received from victims under stress. In this study, a web-based system is proposed that instantly converts natural language environment descriptions into three-dimensional (3D) scene visualizations. Although Generative AI approaches produce photorealistic images, they carry the risk of hallucination. Therefore, this study adopts a deterministic Scene Assembly approach that prioritizes spatial consistency. The system converts user text into a structured JSON format via an LLM-based parser, validates physical consistency through a rulebased spatial inference layer, and computes evacuation routes using the A* algorithm. Experimental results on 100 scenarios show that while LLM-only achieves 76.3% overall accuracy, the addition of the spatial constraint layer raises this to 85.7%.