Currently, indoor localization technology and indoor location-based services are becoming increasingly important in the area of mobile and ubiquitous computing. However, the design of an indoor location-based system confronts two challenges: 1) achieving high-precision location recognition and 2) identifying what indoor objects actually are (which is called semantic labeling). In this article, we propose AtLAS, an activity-based indoor localization and semantic labeling mechanism. The key idea is that some objects in an indoor environment, such as doors and toilets, determine predictable human behaviors in small areas, which can be reflected in unique sensor readings. AtLAS leverages this idea to determine a user’s accurate location by identifying users’ activities. Furthermore, we leverage the topological structure of indoor objects to mine the semantic knowledge and label the objects through gained knowledge automatically. To the best of our knowledge, AtLAS is the first attempt to build a system that leverages users’ activities to conduct a high-precision indoor localization and semantic labeling system for the case of residences. The experimental results show that AtLAS can achieve a median localization accuracy of 0.57 m, and the system can localize the landmarks with a median accuracy of 0.43 m on average without 5% worst errors. AtLAS can label the objects semantically with a 5.7% false-positive rate and a 5.8% false-negative rate on average.

中文翻译：

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AtLAS：基于活动的住宅室内定位和语义标记机制

当前，室内定位技术和基于室内位置的服务在移动和普适计算领域变得越来越重要。但是，基于室内位置的系统的设计面临两个挑战：1）实现高精度的位置识别； 2）识别室内对象的实际含义（称为语义标记）。在本文中，我们提出了AtLAS，一种基于活动的室内定位和语义标记机制。关键思想是，室内环境中的某些物体（例如门和厕所）会确定小区域内可预测的人类行为，这可以反映在独特的传感器读数中。AtLAS利用这一想法通过识别用户的活动来确定用户的准确位置。此外，我们利用室内物体的拓扑结构来挖掘语义知识，并通过获得的知识自动标记物体。据我们所知，AtLAS是首次尝试构建一个利用用户活动来针对住宅案例进行高精度室内定位和语义标记系统的系统。实验结果表明，AtLAS可以实现0.57 m的中位定位精度，并且该系统可以平均以0.43 m的中位精度定位界标，而不会出现5％的最差错误。AtLAS可以平均以5.7％的假阳性率和5.8％的假阴性率在语义上标记对象。AtLAS是首次尝试构建一种系统，该系统可以利用用户的活动针对住宅案例进行高精度的室内定位和语义标记系统。实验结果表明，AtLAS可以实现0.57 m的中位定位精度，并且该系统可以平均以0.43 m的中位精度定位界标，而不会出现5％的最差错误。AtLAS可以平均以5.7％的假阳性率和5.8％的假阴性率在语义上标记对象。AtLAS是首次尝试构建一种系统，该系统可以利用用户的活动针对住宅案例进行高精度的室内定位和语义标记系统。实验结果表明，AtLAS可以实现0.57 m的中位定位精度，并且该系统可以平均以0.43 m的中位精度定位界标，而不会出现5％的最差错误。AtLAS可以平均以5.7％的假阳性率和5.8％的假阴性率在语义上标记对象。