Abstract
Pedestrian dead reckoning (PDR) is an inertial navigation system that relies on smartphone sensors for estimating a pedestrian’s step movements. However, such systems suffer from poor accuracy due to the drift and inherent noise in sensor readings. In addition, step size variation among pedestrians and device heterogeneity pose further challenges for building a scalable PDR system that can provide uniform performance across various devices and a diverse range of users. Visible light positioning (VLP), which uses LED lights with visible light communication (VLC) capability to provide high-accuracy localization, can achieve precision of a few cm. However, VLP systems suffer from practical limitations due to occasional line-of-sight (LOS) blockage and the sparse density of lighting in large-scale indoor venues. In this work, we propose a light-assisted dead reckoning (LiDR) system, which aims to address the problems of both VLP and PDR. It uses LED lighting as high-accuracy location landmarks to provide regular calibration for the PDR and estimates the individual pedestrians’ step size for increased accuracy. In addition, a light-shape-based heading angle correction algorithm is proposed to reduce the heading angle error and further improve the accuracy. The system is implemented as an Android-based navigation application, with a digital map and cloud-based backend storage for location, device, and user-specific parameters. The real-time performance of the system is evaluated in a 450- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{m}^{2}$ </tex-math></inline-formula> lab and on a 150-m walking track. The experimental results demonstrate that with a maximum light spacing of 15 m, an overall average accuracy of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\lt ~0.7$ </tex-math></inline-formula> m can be achieved for the whole system.
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