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ISPRS Int. J. Geo-Inf. 2015, 4(4), 2638-2659; doi:10.3390/ijgi4042638

An Improved PDR/Magnetometer/Floor Map Integration Algorithm for Ubiquitous Positioning Using the Adaptive Unscented Kalman Filter

1,* , 1,2
,
3
and
4
1
School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
2
School of Mathematical and Geospatial Sciences, Royal Melbourne Institute of Technology University, Melbourne, Vic 3001, Australia
3
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
4
Chemical Engineering Institute, China University of Mining and Technology, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
Received: 25 August 2015 / Accepted: 17 September 2015 / Published: 25 November 2015
View Full-Text   |   Download PDF [1325 KB, uploaded 25 November 2015]   |  

Abstract

In this paper, a scheme is presented for fusing a foot-mounted Inertial Measurement Unit (IMU) and a floor map to provide ubiquitous positioning in a number of settings, such as in a supermarket as a shopping guide, in a fire emergency service for navigation, or with a hospital patient to be tracked. First, several Zero-Velocity Detection (ZDET) algorithms are compared and discussed when used in the static detection of a pedestrian. By introducing information on the Zero Velocity of the pedestrian, fused with a magnetometer measurement, an improved Pedestrian Dead Reckoning (PDR) model is developed to constrain the accumulating errors associated with the PDR positioning. Second, a Correlation Matching Algorithm based on map projection (CMAP) is presented, and a zone division of a floor map is demonstrated for fusion of the PDR algorithm. Finally, in order to use the dynamic characteristics of a pedestrian’s trajectory, the Adaptive Unscented Kalman Filter (A-UKF) is applied to tightly integrate the IMU, magnetometers and floor map for ubiquitous positioning. The results of a field experiment performed on the fourth floor of the School of Environmental Science and Spatial Informatics (SESSI) building on the China University of Mining and Technology (CUMT) campus confirm that the proposed scheme can reliably achieve meter-level positioning. View Full-Text
Keywords: zero-velocity detection; adaptive unscented Kalman filter; heading angle; floor map matching; Inertial Measurement Unit; Pedestrian Dead Reckoning zero-velocity detection; adaptive unscented Kalman filter; heading angle; floor map matching; Inertial Measurement Unit; Pedestrian Dead Reckoning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Wang, J.; Hu, A.; Li, X.; Wang, Y. An Improved PDR/Magnetometer/Floor Map Integration Algorithm for Ubiquitous Positioning Using the Adaptive Unscented Kalman Filter. ISPRS Int. J. Geo-Inf. 2015, 4, 2638-2659.

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