Pedestrian Motion Tracking by Using Inertial Sensors on the Smartphone

TL;DR

This paper introduces a novel IMU-based pedestrian tracking method using an Extended Kalman Filter and learning-based noise updates, achieving accurate indoor position and velocity estimation without restricting phone use.

Contribution

It presents a new approach combining EKF and learning-based noise adaptation for IMU-based pedestrian tracking, validated on real and public datasets.

Findings

Absolute transmit error of 1.28m on RIDI dataset

Accurate planar position, velocity, and heading estimation

Effective indoor motion tracking without restricting phone use

Abstract

Inertial Measurement Unit (IMU) has long been a dream for stable and reliable motion estimation, especially in indoor environments where GPS strength limits. In this paper, we propose a novel method for position and orientation estimation of a moving object only from a sequence of IMU signals collected from the phone. Our main observation is that human motion is monotonous and periodic. We adopt the Extended Kalman Filter and use the learning-based method to dynamically update the measurement noise of the filter. Our pedestrian motion tracking system intends to accurately estimate planar position, velocity, heading direction without restricting the phone's daily use. The method is not only tested on the self-collected signals, but also provides accurate position and velocity estimations on the public RIDI dataset, i.e., the absolute transmit error is 1.28m for a 59-second sequence.