SugarTrail: Indoor navigation in retail environments without surveys and maps
SugarTrail: Indoor navigation in retail environments without surveys and maps
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DOI：
10.1109/sahcn.2013.6644999
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发表时间：
2013-06
期刊：
影响因子：
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通讯作者：
Aveek Purohit;Zheng Sun;Shijia Pan;Pei Zhang
中科院分区：
文献类型：
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作者：
Aveek Purohit;Zheng Sun;Shijia Pan;Pei Zhang
帮助人们在室内环境中进行细粒度导航的系统可以在零售环境中实现各种普适计算应用程序。现有的室内导航系统依赖于广泛的射频标签调查和准确的平面图。这些先决条件在室内环境中通常是不切实际的。在本文中，我们介绍了 SugarTrail，这是一种用于零售环境中的室内导航辅助系统，可最大程度地减少对主动标记的需求，并且不需要现有地图。通过利用零售店环境中购物者的结构化运动模式，该系统提供比现有无线电指纹方法更高的准确性。通过最少的设置和用户的积极参与，系统可以根据射频和磁特征自动学习室内环境中的用户移动路径。这些路径被聚集起来并用于自动构建环境的可导航虚拟路线图。我们展示了校园测试台和在运营超市收集的实际无线电测量结果，表明 SugarTrail 系统可以以 > 85% 的成功率和 0.7m 的平均精度为用户导航。
A system that helps people navigate in indoor environments on a fine-grained level can enable a variety of pervasive computing applications in retail environments. Existing indoor navigation systems rely on extensive RF tagging surveys and accurate floor plans. These prerequisites are often impractical in indoor environments. In this paper, we present SugarTrail, a system for indoor navigation assistance in retail environments that minimizes the need for active tagging and does not require existing maps. By leveraging the structured movement patterns of shoppers in retail store environments, the system provides higher accuracy than existing radio finger-printing approaches. With minimal setup and active user participation, the system automatically learns user movement pathways in indoor environments from radiofrequency and magnetic signatures. These pathways are clustered and used to automatically build a navigable virtual roadmap of the environment. We present results from a campus testbed and from actual radio measurements collected in an operational supermarket to show that SugarTrail system can navigate users with a success rate of > 85% and an average accuracy of 0.7m.