空间人工智能系统中基于 RFID 的空间几何推理的图神经网络
Graph Neural Networks for RFID-Based Spatial Geometry Inference in Spatial AI Systems
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中文总结 AI 辅助
针对室内空间理解难题，本文提出基于图神经网络的学习框架，整合信号强度等数据构建图表示，通过 GNN 训练预测空间几何模式，如线性轨迹等，为室内定位等提供新方法。
中文摘要 AI 辅助
室内空间理解对运行于物理环境的智能系统而言仍是一项基本挑战。传统 RFID 定位技术通常利用信号强度测量来估计标签位置，但无法捕捉物体与基础设施间的高阶空间关系。近期关于 RFID 和无线室内定位的工作越来越强调在噪声传播下的稳健学习，而基于图的定位方法展示了关系建模相对于孤立样本的价值。本文引入一个基于图的学习框架，利用图神经网络（GNN）从 RFID 观测中推断空间几何。该系统不对孤立坐标进行预测，而是对室内平面图中 RFID 读数、天线和物理结构之间的关系进行建模。此框架与近期基于图的室内定位及图构建文献一致，其中拓扑是下游推理的首要信息源。该方法将信号强度数据、平面图语义和空间约束整合到一个图表示中，节点对应 RFID 观测，边编码邻近性和上下文关系。然后训练一个 GNN 来预测几何模式，如线性轨迹、矩形边界区域和物体在空间中的移动路径。
英文摘要
Indoor spatial understanding remains a fundamental challenge for intelligent systems operating in physical environments. Traditional RFID localization techniques typically estimate positions of tags using signal strength measurements but fail to capture higher-order spatial relationships between objects and infrastructure. Recent work on RFID and wireless indoor localization has increasingly emphasized robust learning under noisy propagation, while recent graph-based localization methods demonstrate the value of relational modeling over isolated samples. This paper introduces a graph-based learning framework that leverages Graph Neural Networks (GNNs) to infer spatial geometry from RFID observations. Rather than predicting isolated coordinates, the proposed system models relationships between RFID readings, antennas, and physical structures within an indoor floorplan. This framing is aligned with recent graph-based indoor positioning and graph construction literature, where topology is a first-class source of information for downstream inference. The approach integrates signal strength data, floorplan semantics, and spatial constraints into a graph representation where nodes correspond to RFID observations and edges encode proximity and contextual relationships. A GNN is then trained to predict geometric patterns such as linear trajectories, rectangular bounding regions, and movement paths of objects in space.
