Pedestrian navigation: how can inertial measurment units assist smartphones?

Abstract

This paper is devoted to construction of reference walking trajectories for developing pedestrian navigation algorithms for smartphones. Such trajectories can be used both for verification of classical algorithms of navigation or for application of machine learning technics. Reconstruction of closed trajectories based on data from foot-mounted inertial measurement units (IMU) is investigated. The advantages of the approach are the use of inexpensive sensors and the simplicity of the presented method. We propose algorithms for reconstruction of smooth 2D pedestrian trajectories based on measurements from a single IMU as well as on combined measurements from two IMU’s. Introduced algorithms are based on application of modified Kalman filter with an assumption of IMU having zero velocity when foot contacts the ground. In case of two measurement units, it is additionally assumed that the positions of the sensors cannot differ significantly from each other. The algorithms were tested on trajectories lasting from 1 to 10 minutes, passing indoors on horizontal surfaces. Obtained results were compared with high precision trajectories acquired with GNSS RTK receivers. Additionally, the process of inter-device time synchronization is investigated and detailed description of the experiments and used equipment is given. The dataset used for verification of proposed algorithms is freely available at: http://gartseev.ru/projects/rtj2021.

Keywords

• pedestrian navigation
• inertial navigation
• inertial navigation system (ins)
• inertial measurement unit (imu)
• zero velocity update (zupt)
• foot mounted device
• rtk
• dataset
• machine learning
• deep learning
• pdr
• smartphone
• synchronization