Simultaneous End User Calibration of Multiple Magnetic Inertial Measurement Units With Associated Uncertainty

Hammad Tanveer Butt,Kumar Ankit,Mathias Musahl,Didier Stricker,Maria A Sanchez,Pramod Murthy

doi:10.1109/access.2021.3057579

Abstract

A fast method to simultaneously calibrate multiple MEMS Magnetic Inertial Measurement Units (MIMUs) accurately in the field is needed in many application areas. The MEMS MIMUs require calibration of systematic errors of bias, sensitivity, non-orthogonality and misalignment, which vary with temperature and use. Even after calibration, the sensors undergo stochastic errors in static and dynamic conditions and thus uncertainty of output must also be modeled. We propose a method for easy and fast calibration of multiple MIMUs together, while mounted on a single platform. The precise alignment of sensors is not assumed. Our method calibrates both fixed array of MEMS MIMUs or many independent MIMUs simultaneously using kinematic constraints. The novelty of our approach is that the uncertainty of sensors output is also learned as part of our model. Compared with existing state-of-art methods, our algorithm gives more consistent readings of all MIMUs and our framework also predicts the associated uncertainty of the sensor output. The uncertainty prediction of individual sensors is particularly helpful in the sensor fusion.

Highlights

The Inertial sensors are used both for orientation estimation and navigation [1, 2]
In many applications like wearable body sensor networks for motion capture or pedestrian navigation, often more than one Magnetic Inertial Measurement Units (MIMUs) units are used in a distributed fashion or as an array [3, 4]
Our work proposes a fast and convenient calibration method for simultaneous calibration of multiple MIMU units in the field

Summary

Introduction

Microelectromechanical Systems (MEMS) based inertial sensors such as rate gyro, accelerometer and other sensors like magnetometers have become almost ubiquitous in many devices and different application areas. These are often packaged as one unit and called Magnetic-Inertial Measurement Unit (MIMU). In many applications like wearable body sensor networks for motion capture or pedestrian navigation, often more than one MIMU units are used in a distributed fashion or as an array [3, 4]. The computation of a state or latent features from sensor inputs often implies integration over time, like integration of rate gyro readings for orientation estimation or of accelerometer readings for navigation.

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