Title: Pedestrian indoor navigation using foot-mounted IMU with multi-sensor data fusion

Authors: Shengkai Liu; Tingli Su; Binbin Wang; Shiyu Peng; Xuebo Jin; Yuting Bai; Chao Dou

Addresses: School of Computer and Information Engineering, Beijing Technology and Business University, Beijing, 100048, China; Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing, 100048, China ' School of Computer and Information Engineering, Beijing Technology and Business University, Beijing, 100048, China; Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing, 100048, China ' School of Computer and Information Engineering, Beijing Technology and Business University, Beijing, 100048, China; Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing, 100048, China ' School of Computer and Information Engineering, Beijing Technology and Business University, Beijing, 100048, China; Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing, 100048, China ' School of Computer and Information Engineering, Beijing Technology and Business University, Beijing, 100048, China; Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing, 100048, China ' School of Automation, Beijing Institute of Technology, Beijing 100081, China ' Center of Quality Engineering, AVIC China Aero-Polytechnology Establishment, Beijing 100081, China

Abstract: As a widely used indoor navigation technology, the inertial measurement unit (IMU)-based method has caught considerate research interest. However, owing to the significant and inherent drift of the sensors, it is difficult to get the accurate trajectory for pedestrian movement estimation. In this paper, a foot-mounted IMU system was used to improve the accuracy of pedestrian trajectory, by fusing information from the multiple sensors. With the Kalman filter combined with the zero-velocity update (ZUPT) method, a reasonably accurate pedestrian trajectory was then obtained. Furthermore, some adjustable parameters were introduced to better correct the estimation of position and velocity. Effectiveness of the proposed method was well verified through the indoor experiments and the long track performance was also tested in runway verification.

Keywords: inertial measurement unit; IMU; trajectory tracking; multi-sensor data fusion; Kalman filter; zero-velocity update; ZUPT.

DOI: 10.1504/IJMIC.2018.095833

International Journal of Modelling, Identification and Control, 2018 Vol.30 No.4, pp.261 - 272

Available online: 17 Oct 2018 *

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