Kinematic Model-Based Pedestrian Dead Reckoning for Heading Correction and Lower Body Motion TrackingReport as inadecuate




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Department of Mechanical and Aerospace Engineering, Automation and Systems Research Institute, Seoul National University, Seoul 151-744, Korea

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BK21Plus Transformative Training Program for Creative Mechanical and Aerospace Engineers, Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul 151-744, Korea





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Author to whom correspondence should be addressed.



Academic Editor: Kourosh Khoshelham

Abstract In this paper, we present a method for finding the enhanced heading and position of pedestrians by fusing the Zero velocity UPdaTe ZUPT-based pedestrian dead reckoning PDR and the kinematic constraints of the lower human body. ZUPT is a well known algorithm for PDR, and provides a sufficiently accurate position solution for short term periods, but it cannot guarantee a stable and reliable heading because it suffers from magnetic disturbance in determining heading angles, which degrades the overall position accuracy as time passes. The basic idea of the proposed algorithm is integrating the left and right foot positions obtained by ZUPTs with the heading and position information from an IMU mounted on the waist. To integrate this information, a kinematic model of the lower human body, which is calculated by using orientation sensors mounted on both thighs and calves, is adopted. We note that the position of the left and right feet cannot be apart because of the kinematic constraints of the body, so the kinematic model generates new measurements for the waist position. The Extended Kalman Filter EKF on the waist data that estimates and corrects error states uses these measurements and magnetic heading measurements, which enhances the heading accuracy. The updated position information is fed into the foot mounted sensors, and reupdate processes are performed to correct the position error of each foot. The proposed update-reupdate technique consequently ensures improved observability of error states and position accuracy. Moreover, the proposed method provides all the information about the lower human body, so that it can be applied more effectively to motion tracking. The effectiveness of the proposed algorithm is verified via experimental results, which show that a 1.25% Return Position Error RPE with respect to walking distance is achieved. View Full-Text

Keywords: indoor positioning; pedestrian dead reckoning; wearable sensors; extended Kalman filter; motion tracking indoor positioning; pedestrian dead reckoning; wearable sensors; extended Kalman filter; motion tracking





Author: Min Su Lee 1, Hojin Ju 1, Jin Woo Song 2 and Chan Gook Park 1,*

Source: http://mdpi.com/



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