Robust Pedestrian Dead Reckoning for Multiple Poses in Smartphones

Abstract

In this paper, an integration approach (IA) and parametric approach (PA) fusion-based pedestrian dead reckoning (PDR) using low-cost inertial sensors is proposed to improve the indoor position estimation. When moving indoors using a smartphone, various poses such as texting and putting a pocket are possible. In the existing PA-based positioning algorithms, it can be used only when the pedestrian’s walking direction and the device’s direction coincide. In order to solve the problem, this paper proposes an algorithm that constructs state variables based on the IA and uses the position vector from the PA as a measurement. If the walking direction and the device heading do not match based on the poses, the position is updated according to the direction calculated using principal component analysis (PCA) and the step length obtained from the PA. Through experiments considering various operating conditions and paths, it shows that the proposed method stably estimates the position and improves performance even in various indoor environments with the accuracy of 2.13m for Xsens MTw 2.49m for Smartphone.

Keywords

• Indoor navigation
• pedestrian dead reckoning (PDR)
• extended Kalman filter (EKF)
• integration approach (IA)
• parametric approach (PA)
• fusion method