用大模型将平面图转为可导航知识图谱，提升视障者室内导航准确率
Floorplan2Guide: LLM-Guided Floorplan Parsing for BLV Indoor Navigation
- 用大语言模型解析建筑平面图，自动提取空间信息
- 5次提示下最高达92.3%导航准确率，长路径仍保持61.5%
- 知识图谱+上下文学习让视障用户导航更精准
室内导航对视障人群仍是重大挑战。现有方案多依赖基础设施，难以适应动态环境。本文提出一种新方法，利用基础模型将平面图转化为可导航知识图谱，并生成自然语言导航指令。Floorplan2Guide通过大语言模型（LLM）从建筑布局中提取空间信息，显著减少早期解析方法所需的手动预处理。实验表明，在模拟与真实场景中，少样本学习相比零样本学习提升了导航准确率。在MP-1平面图上，采用5次提示的Claude 3.7 Sonnet达到92.31%、76.92%和61.54%的短、中、长路径准确率。基于图结构的空间推理成功率比直接视觉推理高出15.4%，验证了图形表示与上下文学习能有效提升导航性能，使本方案更适合盲人及低视力（BLV）用户。
原文摘要 · Abstract (English)
Indoor navigation remains a critical challenge for people with visual impairments. The current solutions mainly rely on infrastructure-based systems, which limit their ability to navigate safely in dynamic environments. We propose a novel navigation approach that utilizes a foundation model to transform floor plans into navigable knowledge graphs and generate human-readable navigation instructions. Floorplan2Guide integrates a large language model (LLM) to extract spatial information from architectural layouts, reducing the manual preprocessing required by earlier floorplan parsing methods. Experimental results indicate that few-shot learning improves navigation accuracy in comparison to zero-shot learning on simulated and real-world evaluations. Claude 3.7 Sonnet achieves the highest accuracy among the evaluated models, with 92.31%, 76.92%, and 61.54% on the short, medium, and long routes, respectively, under 5-shot prompting of the MP-1 floor plan. The success rate of graph-based spatial structure is 15.4% higher than that of direct visual reasoning among all models, which confirms that graphical representation and in-context learning enhance navigation performance and make our solution more precise for indoor navigation of Blind and Low Vision (BLV) users.
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