Laser Range Scanners for Enabling Zero-overhead WiFi-based Indoor Localization System
Laser Range Scanners for Enabling Zero-overhead WiFi-based Indoor Localization System
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DOI：
10.1145/3539659
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发表时间：
2022-06
影响因子：
1.9
通讯作者：
Hamada Rizk;Hirozumi Yamaguchi;Maged A. Youssef;T. Higashino
中科院分区：
文献类型：
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作者：
Hamada Rizk;Hirozumi Yamaguchi;Maged A. Youssef;T. Higashino
在过去的十年中，鲁棒和准确的室内定位一直是几项研究工作的目标。为了实现这一目标，已经提出了基于WiFi指纹的室内定位系统。然而，指纹识别涉及到大量的工作，特别是在高密度下完成时，并且需要随着部署区域的任何变化而重复。虽然最近已经引入了一些系统来减少校准工作，但这些系统仍然以开销与准确性为代价。本文介绍了LiPhi++，这是一种精确的系统，用于实现基于指纹的室内定位系统，而无需相关的数据收集开销。这是通过利用便携式激光测距扫描仪的传感能力来自动标记WiFi扫描来实现的，随后可以用于构建（和维护）指纹数据库。作为其设计的一部分，LiPhi++利用该数据库，利用检测到的接入点的信号强度历史来训练深度长期短期记忆网络。LiPhi++还提供了处理实际部署问题的规定，包括嘈杂的无线环境，异构设备等。在两个真实测试台中使用Android手机对LiPhi++进行的评估表明，在相同的部署条件下，它可以与手动指纹识别技术的性能相匹配，而无需与传统指纹识别过程相关的开销。此外，LiPhi++在使用几个月后收集的数据进行测试时，将基于众包和基于指纹识别的系统的中值定位准确度分别提高了284%和418%。
Robust and accurate indoor localization has been the goal of several research efforts over the past decade. Toward achieving this goal, WiFi fingerprinting-based indoor localization systems have been proposed. However, fingerprinting involves significant effort—especially when done at high density—and needs to be repeated with any change in the deployment area. While a number of recent systems have been introduced to reduce the calibration effort, these still trade overhead with accuracy. This article presents LiPhi++, an accurate system for enabling fingerprinting-based indoor localization systems without the associated data collection overhead. This is achieved by leveraging the sensing capability of transportable laser range scanners to automatically label WiFi scans, which can subsequently be used to build (and maintain) a fingerprint database. As part of its design, LiPhi++ leverages this database to train a deep long short-term memory network utilizing the signal strength history from the detected access points. LiPhi++ also has provisions for handling practical deployment issues, including the noisy wireless environment, heterogeneous devices, among others. Evaluation of LiPhi++ using Android phones in two realistic testbeds shows that it can match the performance of manual fingerprinting techniques under the same deployment conditions without the overhead associated with the traditional fingerprinting process. In addition, LiPhi++ improves upon the median localization accuracy obtained from crowdsourcing-based and fingerprinting-based systems by 284% and 418%, respectively, when tested with data collected a few months later.