摘要
由于室内环境错综复杂、非成像型定位设备操作困难,室内定位无法保障高稳定性,为提高可见光室内定位精度,提出了一种融合注意力机制与图神经网络(GNN)的可见光室内定位模型,本方法将可见光图像的特征用图来表示,通过聚合可见光图像的图内和图间信息,获得具有几何位置信息的描述符,利用注意力机制模块进一步增强特征描述符,最终经过可微分的最优传输算法输出匹配结果。在搭建的4 m×4 m×3 m的环境中进行仿真实验,结果表明:在定位高度为0、0.75、1.50 m时,平均定位误差分别为5.93、7.21、9.15 cm,达到cm量级的定位效果,为室内可见光定位提供了新的理论支持和实际应用参考价值。
Objective Visible light indoor positioning provides novel theoretical and practical support for high-speed,environmentally-friendly,safe,and economical indoor localization.Traditional non-imaging-based positioning methods face operational challenges,and current algorithms using image sensors primarily capture local appearance information,often overlooking geometric structure,thus affecting positioning accuracy.In this study,we propose a model integrating attention mechanisms and graph neural networks(GNNs),enhancing the accuracy and robustness of indoor visible light positioning.Methods In this study,a novel approach combines attention mechanism with GNNs.Visible light images are represented as graphs,where GNNs aggregate both intra-and inter-graph information,embedding the spatial position of feature points into descriptors,which enriches them with geometric data.The attention module further enhances descriptor quality,improves matching accuracy and realizes precise indoor positioning.Results and Discussions To validate the model,a 4 m×4 m×3 m visible light experimental platform is constructed.Four 10 W LED light sources are positioned at the top of the model,and the visible light area is divided into a grid of equidistant 5 cm×5 cm cells.Visible light images are captured at each grid vertex,creating a fingerprint database with 3510 images.For testing,80%of the database images are used for feature extraction and matching,while the remaining 20%are reserved for model testing.Simulation and practical experiments are conducted with the platform at heights of 0,0.75,and 1.50 m.The results show centimeter-level accuracy,with average errors of 5.93,7.21,and 9.15 cm,at each height.The robustness tests are also conducted,including rotation and tilt transformations of the mobile terminal.Compared to the SuperPoint algorithm,which extracts image features for deep learning,the experimental results show notable improvements in matching rates.When the visible light image is rotated by 5°,the matching rate increases by 9%;at 30°,it increases by 17%with a maximum improvement of 20%observed in the rotation experiments.For tilt angles,a 5°tilt results in a 12%increase in matching rate,while a 30°tilt yields a 13%increase.These results indicate that the algorithm proposed in this study surpasses the SuperPoint algorithm in matching accuracy,demonstrating its superior performance.Conclusions Indoor visible light environments are often complex,with frequent light and background interferences that challenge traditional algorithms.This model,combining attention mechanisms and GNNs,optimizes indoor visible light positioning by enhancing robustness and stability.Using deformable convolutional networks(DCNs)during sampling improves key information in visible light images.GNNs effectively aggregate both intra-and inter-image information,while the attention mechanism dynamically adjusts feature weights to emphasize features with greater discrimination and reliability.This reduces the influence of illumination and occlusion,enhancing matching accuracy.In this study,a 4 m×4 m×3 m visible light indoor positioning model is constructed for simulation testing.The experimental results show an average positioning error of 7.43 cm,highlighting this approach as a viable new algorithm for indoor visible light positioning.
作者
孟祥艳
奚田
赵黎
张峰
Meng Xiangyan;Xi Tian;Zhao Li;Zhang Feng(School of Electronic Information Engineering,Xi’an Technological University,Xi’an 710021,Shaanxi,China)
出处
《光学学报》
北大核心
2025年第2期105-117,共13页
Acta Optica Sinica
基金
陕西省科技计划项目-重点研发计划-一般项目(工业领域)(2024GX-YBXM-105)
西安市科技局高校院所科技人员服务企业项目(24GXFW0026)。
关键词
可见光室内定位
注意力机制
图神经网络
最优传输
visible light indoor positioning
attention mechanism
graph neural network
optimal transmission
