Although people spend most of their time indoors, outdoor tracking systems, such as the Global Positioning System (GPS), are predominantly used for location-based services. These systems are accurate outdoors, easy to use, and operate autonomously on each mobile device. In contrast, Indoor Tracking Systems~(ITS) lack standardization and are often difficult to operate because they require costly infrastructure. In this paper, we propose an indoor tracking algorithm that uses collected data from inertial sensors embedded in most mobile devices. In this setting, mobile devices autonomously estimate their location, hence removing the burden of deploying and maintaining complex and scattered hardware infrastructure. In addition, these devices collaborate by anonymously exchanging data with other nearby devices, using wireless communication, such as Bluetooth, to correct errors in their location estimates. Our collaborative algorithm relies on low-complexity geometry operations and can be deployed on any recent mobile device with commercial-grade sensors. We evaluate our solution on real-life data collected by different devices. Experimentation with 16 simultaneously moving and collaborating devices shows an average accuracy improvement of 44% compared to the standalone Pedestrian Dead Reckoning algorithm.

翻译：尽管人们大部分时间都在室内活动，但基于位置的服务主要依赖全球定位系统（GPS）等户外追踪系统。这些系统在户外具有高精度、易用性强，且能在每台移动设备上自主运行。相比之下，室内追踪系统（ITS）缺乏标准化，且常因需要昂贵的基础设施而难以部署。本文提出一种室内追踪算法，该算法利用大多数移动设备内置的惯性传感器采集数据。在此框架下，移动设备可自主估计其位置，从而免除了部署和维护复杂分散的硬件基础设施的负担。此外，这些设备通过蓝牙等无线通信方式与附近设备匿名交换数据，以协作修正位置估计误差。我们的协作算法基于低复杂度几何运算，可部署于任何搭载商用级传感器的现代移动设备。我们使用不同设备采集的真实数据对方案进行评估。在16台设备同时移动并协作的实验中，相较于独立的行人航位推算算法，本方案平均精度提升了44%。