IndoorWaze: A Crowdsourcing-Based Context-Aware Indoor Navigation System
IndoorWaze: A Crowdsourcing-Based Context-Aware Indoor Navigation System
复制标题
DOI：
10.1109/twc.2020.2993545
复制
发表时间：
2020-05
影响因子：
10.4
通讯作者：
Tao Li;Dianqi Han;Yimin Chen;Rui Zhang;Yanchao Zhang;Terri Hedgpeth
中科院分区：
文献类型：
--
作者：
Tao Li;Dianqi Han;Yimin Chen;Rui Zhang;Yanchao Zhang;Terri Hedgpeth
室内导航系统在诸如购物中心等大型复杂室内环境中非常有用。当前的系统专注于提高室内定位精度，并且必须与精确标注的楼层平面图相结合，才能提供可用的室内导航服务。然而，此类标注的楼层平面图往往难以获取，或者手动获取的成本过高。在本文中，我们提出了IndoorWaze，这是一种基于众包的新型情境感知室内导航系统，在相关文献中首次实现了自动生成带有室内兴趣点（POI）标注的精确情境感知楼层平面图。IndoorWaze将室内行人的Wi-Fi指纹与POI工作人员提供的Wi-Fi指纹及POI标签相结合，生成高保真的标注楼层平面图。作为一个轻量级的基于众包的系统，IndoorWaze对室内行人和POI工作人员的要求极低。我们在安卓智能手机上搭建了IndoorWaze的原型，并在一个大型购物中心对其进行了评估。结果表明，IndoorWaze能够生成高保真的标注楼层平面图，其中所有店铺都标注正确且布局合理，所有通道和交叉路口都准确显示，店铺尺寸的中位估计误差低于12%。
Indoor navigation systems are very useful in large complex indoor environments such as shopping malls. Current systems focus on improving indoor localization accuracy and must be combined with an accurate labeled floor plan to provide usable indoor navigation services. Such labeled floor plans are often unavailable or involve a prohibitive cost to manually obtain. In this paper, we present IndoorWaze, a novel crowdsourcing-based context-aware indoor navigation system that can automatically generate an accurate context-aware floor plan with labeled indoor POIs for the first time in literature. IndoorWaze combines the Wi-Fi fingerprints of indoor walkers with the Wi-Fi fingerprints and POI labels provided by POI employees to produce a high-fidelity labeled floor plan. As a lightweight crowdsourcing-based system, IndoorWaze involves very little effort from indoor walkers and POI employees. We prototype IndoorWaze on Android smartphones and evaluate it in a large shopping mall. Our results show that IndoorWaze can generate a high-fidelity labeled floor plan, in which all the stores are correctly labeled and arranged, all the pathways and crossings are correctly shown, and the median estimation error for the store dimension is below 12%.