Indoor pedestrian navigation using an INS/EKF framework for yaw drift reduction and a foot-mounted IMU

TLDR

Position estimation inside buildings is essential for Intelligent Spaces, yet current LPS require complex infrastructure, whereas IMU‑based PDR offers a simpler alternative. The paper proposes a Kalman‑based INS‑EKF‑ZUPT framework (IEZ) and extends it with heading drift reduction algorithms (IEZ+) to enable accurate indoor pedestrian navigation even with local magnetic disturbances. IEZ employs an EKF, INS mechanization, ZUPT, stance detection, and heading drift reduction methods (ZARU, HDR, Compass) to estimate position and attitude. IEZ+ achieved positioning errors of about 1 % of the total travelled distance, outperforming other works that use higher‑performance IMUs.

Abstract

The estimation of the position of a person in a building is a must for creating Intelligent Spaces. State-of-the-art Local Positioning Systems (LPS) require a complex sensor-network infrastructure to locate with enough accuracy and coverage. Alternatively, Inertial Measuring Units (IMU) can be used to estimate the movement of a person; a methodology that is called Pedestrian Dead-Reckoning (PDR). In this paper, we describe and implement a Kalman-based framework, called INS-EKF-ZUPT (IEZ), to estimate the position and attitude of a person while walking. IEZ makes use of an Extended Kalman filter (EKF), an INS mechanization algorithm, a Zero Velocity Update (ZUPT) methodology, as well as, a stance detection algorithm. As the IEZ methodology is not able to estimate the heading and its drift (non-observable variables), then several methods are used for heading drift reduction: ZARU, HDR and Compass. The main contribution of the paper is the integration of the heading drift reduction algorithms into a Kalman-based IEZ platform, which represents an extended PDR methodology (IEZ+) valid for operation in indoor spaces with local magnetic disturbances. The IEZ+ PDR methodology was tested in several simulated and real indoor scenarios with a low-performance IMU mounted on the foot. The positioning errors were about 1% of the total travelled distance, which are good figures if compared with other works using IMUs of higher performance.