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A Survey of the Research Status of Pedestrian Dead Reckoning Systems Based on Inertial Sensors

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Abstract

With the development of micro-electromechanical systems (MEMS), miniaturized, low-power and low-cost inertial measurement units (IMUs) have been widely integrated into mobile terminals and smart wearable devices. This provides the prospect of a broad application for the inertial sensor-based pedestrian dead-reckoning (IPDR) systems. Especially for indoor navigation and indoor positioning, the IPDR systems have many unique advantages that other methods do not have. At present, a large number of technologies and methods for IPDR systems are proposed. In this paper, we have analyzed and outlined the IPDR systems based on about 80 documents in the field of IPDR in recent years. The article is structured in the form of an introduction-elucidation-conclusion framework. First, we proposed a general framework to explore the structure of an IPDR system. Then, according to this framework, the IPDR system was divided into six relatively independent subproblems, which were discussed and summarized separately. Finally, we proposed a graph structure of IPDR systems, and a sub-directed graph, formed by selecting a combined path from the start node to the end node, skillfully constitutes a technical route of one specific IPDR system. At the end of the article, we summarized some key issues that need to be resolved before the IPDR systems are widely used.

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Acknowledgements

This work was supported by National Key Research and Development of China (No. 2017YFB1002800)

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Authors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Yuan Wu, Hai-Bing Zhu, Qing-Xiu Du & Shu-Ming Tang

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Yuan Wu

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Correspondence to Qing-Xiu Du.

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Recommended by Associate Editor Jangmyung Lee

Yuan Wu received the B. Sc. degree in computer science and technology from Southwest University, China in 2016. He is now a master student in computer application technology at Insititute of Automation, Chinese Academy of Sciences, China.

His research interests include motion tracking based on inertial sensors, human-computer interaction and virtual reality.

Hai-Bing Zhu received the B.Sc. degree in automation and the M. Sc. degree in pattern recognition and intelligent control from University of Science and Technology, China in 1997 and 2000, respectively. He received the Ph. D. degree in control theory and control engineering from the Institute of Automation, Chinese Academy of Sciences, China in 2003. He was an assistant researcher at the Institute of Automation, Chinese Academy of Sciences from 2003 to 2007, and has been a senior engineer since 2007.

His research interests include intelligent robots, sensors and automation technologies, and embedded system applications.

Qing-Xiu Du received the B. Sc. and M. Sc. degrees in computer science and engineering from Harbin Institute of Technology, China in 1982 and 1985, respectively. She is currently a professor at Institute of Automation, Chinese Academy of Sciences, China.

Her research interests include intelligent perception, intelligent human interaction, virtual reality, and digital media technology.

Shu-Ming Tang received the Ph. D. degree in control theory and control engineering from Graduate University of Chinese Academy of Sciences, China in 2005. Currently, she is an associate research professor in the Institute of Automation, Chinese Academy of Sciences. She has published extensive papers in her research areas. She is a committee member of Robot Sports in China, an associate editor of the IEEE Transactions on Intelligent Transportation Systems, a co-chair of the Technical Committee on Automatic Train Supervision (ATS) of Intelligent Transportation Systems Society.

Her research interests are include intelligent vehicles and intelligent transportation systems.

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Wu, Y., Zhu, HB., Du, QX. et al. A Survey of the Research Status of Pedestrian Dead Reckoning Systems Based on Inertial Sensors. Int. J. Autom. Comput. 16, 65–83 (2019). https://doi.org/10.1007/s11633-018-1150-y

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  • DOI: https://doi.org/10.1007/s11633-018-1150-y

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