Indoor localization and navigation using smartphone sensory data

Hui-Huang Hsu (), Jung-Kuei Chang, Wei-Jan Peng, Timothy K. Shih, Tun-Wen Pai and Ka Lok Man
Additional contact information
Hui-Huang Hsu: Tamkang University
Jung-Kuei Chang: Tamkang University
Wei-Jan Peng: Tamkang University
Timothy K. Shih: National Central University
Tun-Wen Pai: National Taiwan Ocean University
Ka Lok Man: Xi’an Jiaotong-Liverpool University

Annals of Operations Research, 2018, vol. 265, issue 2, No 2, 187-204

Abstract: Abstract In the cloud age, it is quite easy to collect sensory data from smartphones. With these sensory data, it is desired to provide various kinds of applications to serve the user. In this research, we aim at developing an indoor navigation system on smartphone using solely smartphone sensory data. There are many researches on indoor localization and navigation in the literature. Nevertheless, environmental sensors and/or wearable sensors are usually needed. This can be costly and inconvenient. In this paper, we propose a smartphone indoor localization system using only accelerometer and gyroscope data from the smartphone. The Pedestrian Dead Reckoning (PDR) approach is used to build this system. The PDR approach is simple and efficient though seems traditional. The major weakness of the PDR is that the estimation error would accumulate over time. Thus we propose to add so-called calibration marks which look like short arrows and are placed on both the floor plan and the ground. To use the system, the user first finds a calibration mark on the ground, stands on it and faces the right direction. He/she then moves the android icon (representing the user) on top of the calibration mark on the floor plan on the smartphone. When the user starts to move, the android icon also moves on the floor plan following the real-time estimation of step length and moving direction change for each step from accelerometer and gyroscope data. This is a prototype of an indoor navigation system that can become fully functional after an optimal path planning module is included. Experimental results of estimated walking trace tests show high accuracy. The system is promising and useful as long as a floor plan and calibration marks are built in advance.

Keywords: Pedestrian dead reckoning; Indoor localization; Ambient intelligence; Smartphone applications (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10479-017-2398-2

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