Decentralized Collaborative Inertial Tracking

TL;DR

This paper introduces a decentralized indoor tracking algorithm that leverages inertial sensors and device collaboration to improve location accuracy without requiring extensive infrastructure.

Contribution

It presents a novel collaborative indoor tracking method using inertial sensors and wireless communication, eliminating the need for costly infrastructure and enabling autonomous device localization.

Findings

44% average accuracy improvement over standalone Pedestrian Dead Reckoning

Effective collaboration among 16 devices in real-life scenarios

Low-complexity geometry operations suitable for mobile devices

Abstract

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.