用语言指令导航建筑，结合BIM与大模型实现智能引导。
An Embodied AR Navigation Agent: Integrating BIM with Retrieval-Augmented Generation for Language Guidance
- 三阶段语言代理解析自然语言指令，利用BIM数据进行空间推理。
- 用户实测系统可用性得分80.5，显著提升对系统智能性的感知。
- 适合需要自然语言交互的智能建筑导航场景，如导览或无障碍服务。
在增强现实（AR）中提供智能且自适应的导航辅助，不仅需要视觉提示，还需系统能理解灵活的用户意图，并在空间与语义上下文中进行推理。以往的AR导航系统多依赖僵化的输入方式或预设命令，限制了丰富建筑数据的利用并阻碍自然交互。本文提出一种具身化AR导航系统，将建筑信息模型（BIM）与多智能体检索增强生成（RAG）框架结合，支持灵活的语言驱动目标检索与路径规划。系统由三个基于大语言模型（LLM）的语言代理——分诊、搜索和响应——协同工作，实现对开放式查询的鲁棒解析与空间推理。导航通过具备语音交互和移动能力的具身化AR代理完成，提升用户体验。真实世界用户研究显示系统可用性量表（SUS）得分为80.5，表明优秀可用性；对比评估显示，具身界面显著提升了用户对系统智能性的感知。结果凸显语言锚定推理与具身设计在以人为本的AR导航系统中的重要性与潜力。
原文摘要 · Abstract (English)
Delivering intelligent and adaptive navigation assistance in augmented reality (AR) requires more than visual cues, as it demands systems capable of interpreting flexible user intent and reasoning over both spatial and semantic context. Prior AR navigation systems often rely on rigid input schemes or predefined commands, which limit the utility of rich building data and hinder natural interaction. In this work, we propose an embodied AR navigation system that integrates Building Information Modeling (BIM) with a multi-agent retrieval-augmented generation (RAG) framework to support flexible, language-driven goal retrieval and route planning. The system orchestrates three language agents, Triage, Search, and Response, built on large language models (LLMs), which enables robust interpretation of open-ended queries and spatial reasoning using BIM data. Navigation guidance is delivered through an embodied AR agent, equipped with voice interaction and locomotion, to enhance user experience. A real-world user study yields a System Usability Scale (SUS) score of 80.5, indicating excellent usability, and comparative evaluations show that the embodied interface can significantly improves users' perception of system intelligence. These results underscore the importance and potential of language-grounded reasoning and embodiment in the design of user-centered AR navigation systems.
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