这篇论文用图神经网络把RFID信号变成空间地图，能预测物体移动轨迹，比单纯定位更聪明，室内导航和机器人适用。
该论文提出一种基于图神经网络（GNN）的框架，从RFID观测中推断室内空间几何。系统将RFID阅读器、标签和物理结构建模为图节点，信号强度和平面图语义编码为边特征。GNN被训练来预测线性轨迹、矩形边界区域和物体移动路径等几何模式。该方法相比传统RFID定位，能捕获对象与基础设施之间的高阶空间关系。实验在室内平面图数据上验证了有效性。
Graph Neural Networks for RFID-Based Spatial Geometry Inference in Spatial AI Systems
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.