A Picture is Worth 1,000 Millimeters: Combining Vision and Wi-Fi to Improve Localization
A Picture is Worth 1,000 Millimeters: Combining Vision and Wi-Fi to Improve Localization
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DOI：
10.1109/wowmom57956.2023.00020
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Shazal Irshad;Eric Rozner;Apurv Bhartia;Bo Chen
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文献类型：
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作者：
Shazal Irshad;Eric Rozner;Apurv Bhartia;Bo Chen
过去，研究人员设计、部署和评估了基于Wi-Fi的定位技术，以便在不增加额外或昂贵的基础设施的情况下定位用户和设备。然而，随着基础设施部署的变化，人们必须重新审视Wi-Fi本地化的作用。如今，摄像头的部署越来越多，因此这项工作研究了如何将从摄像头获得的上下文和视觉数据与Wi-Fi定位技术集成在一起。我们提出了一种名为CALM的方法，适用于商品AP和摄像头。我们的方法包含几个贡献：相机线拟合技术，以限制候选位置的搜索空间，单个AP和相机定位通过一个解投影方案的启发，从3D相机，简单而强大的AP加权，通过相机分析用户的上下文，和一个新的虚拟相机的方法来规模分析。我们激励我们的计划通过分析真实的摄像头和AP拓扑结构的主要供应商。我们对9个房间和102，300个无线读数的评估表明，CALM可以获得分米级的精度，比以前的Wi-Fi技术（如FTM和SpotFi）提高2.7\times $和2.3\times $的性能。
In the past, researchers designed, deployed, and evaluated Wi-Fi based localization techniques in order to locate users and devices without adding extra or costly infrastructure. However, as infrastructure deployments change, one must reexamine the role of Wi-Fi localization. Today, cameras are becoming increasingly deployed, and therefore this work examines how contextual and vision data obtained from cameras can be integrated with Wi-Fi localization techniques. We present an approach called CALM that works on commodity APs and cameras. Our approach contains several contributions: a camera line fitting technique to restrict the search space of candidate locations, single AP and camera localization via a deprojection scheme inspired from 3D cameras, simple and robust AP weighting that analyzes the context of users via the camera, and a new virtual camera methodology to scale analysis. We motivate our scheme by analyzing real camera and AP topologies from a major vendor. Our evaluation over 9 rooms and 102,300 wireless readings shows CALM can obtain decimeter-level accuracy, improving performance over previous Wi-Fi techniques like FTM by $2.7\times$ and SpotFi by $2.3\times$.