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Smartphone Based Indoor Path Estimation and Localization Without Human Intervention
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DOI：10.1109/TMC.2020.3013113.png)
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The growing commercial interest in indoor localization-based services has stimulated the development of many indoor positioning systems. Despite extensive research on localization, system requirements, such as site survey, user intervention, or specific hardware/software, place limitations on the widespread deployment of localization. To overcome these limitations, we propose a path estimation and localization system for indoor environments, termed PYLON, that runs on a smartphone and a server without any human intervention. PYLON uses an actual floor plan and measurements from widely deployed WiFi access points (APs) and Bluetooth Low Energy (BLE) beacons to estimate the user's path. It creates virtual rooms according to received signal strength indicator (RSSI) values and matches them to actual rooms in the real-world floor plan. After room mapping, PYLON uses door passing times to precisely refine a user's estimated path. Unlike conventional path estimation and localization systems, PYLON works independently of device types. We implement PYLON on five Android smartphones and conduct evaluation with three users in an office building. Our experimental results show that PYLON achieves 97 percent floor plan mapping accuracy with a localization error of 1.42 m.
Keyword:
Poles and towers
Wireless fidelity
Estimation
Simultaneous localization and mapping
Localization
path estimation
dead reckoning
simultaneous localization and mapping
smartphones
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An Efficient Algorithm for Nonlinear Model Predictive Control of Large-Scale Systems Part I: Description of the Method (Ein effizienter Algorithmus für die nichtlineare prädiktive Regelung großer Systeme Teil I: Methodenbeschreibung)大型系统非线性模型预测控制的有效算法第一部分: 方法的描述 (Ein effizienter algorithms f ü r die nichtlineare pr ä diktive Regelung gro ß er Systeme Teil I: Methodenbeschreibung).png)
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