Statistical Sensor Fusion of a 9-DoF MEMS IMU for Indoor Navigation

TL;DR

This paper introduces a robust batch optimization method for indoor navigation using a 9-DoF MEMS IMU, magnetometer, and self-calibration to improve orientation and position accuracy under magnetic distortions.

Contribution

It presents a novel in-situ self-calibration approach within a post-processed framework, enhancing dead-reckoning accuracy without relying on external signals.

Findings

Orientation accuracy improved by up to 89.5%

Positioning error reduced from meter- to decimeter-level

Method tolerates high magnetic distortions

Abstract

Sensor fusion of a MEMS IMU with a magnetometer is a popular system design, because such 9-DoF (degrees of freedom) systems are capable of achieving drift-free 3D orientation tracking. However, these systems are often vulnerable to ambient magnetic distortions and lack useful position information; in the absence of external position aiding (e.g. satellite/ultra-wideband positioning systems) the dead-reckoned position accuracy from a 9-DoF MEMS IMU deteriorates rapidly due to unmodelled errors. Positioning information is valuable in many satellite-denied geomatics applications (e.g. indoor navigation, location-based services, etc.). This paper proposes an improved 9-DoF IMU indoor pose tracking method using batch optimization. By adopting a robust in-situ user self-calibration approach to model the systematic errors of the accelerometer, gyroscope, and magnetometer simultaneously in a tightly-coupled post-processed least-squares framework, the accuracy of the estimated trajectory from a 9-DoF MEMS IMU can be improved. Through a combination of relative magnetic measurement updates and a robust weight function, the method is able to tolerate a high level of magnetic distortions. The proposed auto-calibration method was tested in-use under various heterogeneous magnetic field conditions to mimic a person walking with the sensor in their pocket, a person checking their phone, and a person walking with a smartwatch. In these experiments, the presented algorithm improved the in-situ dead-reckoning orientation accuracy by 79.8 - 89.5% and the dead-reckoned positioning accuracy by 72.9 - 92.8%, thus reducing the relative positioning error from metre-level to decimetre-level after ten seconds of integration, without making assumptions about the user's dynamics.