Kinematic Model-Based Pedestrian Dead Reckoning for Heading Correction and Lower Body Motion Tracking

Sensors

Min Su Lee¹  Hojin Ju¹  Jin Woo Song²  Chan Gook Park¹ 

[1] Department of Mechanical and Aerospace Engineering, Automation and Systems Research Institute, Seoul National University, Seoul 151-744, Korea; E-Mails:;BK21Plus Transformative Training Program for Creative Mechanical and Aerospace Engineers, Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul 151-744, Korea; E-Mail:

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

DOI  :  10.3390/s151128129

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.