LLM-assisted indoor scene assembly with rule-based spatial validation for emergency navigation [Acil durum navigasyonu için kural tabanlı mekansal Doğrulama ile LLM destekli iç mekan sahne oluşturma]

Official URL: https://dx.doi.org/10.1109/SIU71813.2026.11636946

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%.