Abstract

Magnetic-Inertial Measurement Units (MIMUs) based on microelectromechanical (MEMS) technologies are widespread in contexts such as human motion tracking. Although they present several advantages (lightweight, size, cost), their orientation estimation accuracy might be poor. Indoor magnetic disturbances represent one of the limiting factors for their accuracy, and, therefore, a variety of work was done to characterize and compensate them. In this paper, the main compensation strategies included within Kalman-based orientation estimators are surveyed and classified according to which degrees of freedom are affected by the magnetic data and to the magnetic disturbance rejection methods implemented. By selecting a representative method from each category, four algorithms were obtained and compared in two different magnetic environments: (1) small workspace with an active magnetic source; (2) large workspace without active magnetic sources. A wrist-worn MIMU was used to acquire data from a healthy subject, whereas a stereophotogrammetric system was adopted to obtain ground-truth data. The results suggested that the model-based approaches represent the best compromise between the two testbeds. This is particularly true when the magnetic data are prevented to affect the estimation of the angles with respect to the vertical direction.

Highlights

  • Sensing Earth’s magnetic field can aid large-scale navigation

  • Microelectromechanical systems (MEMS) technologies allow the inclusion of low-grade magnetic sensors in Magnetic-Inertial Measurement Units (MIMUs), as well as in many other mobile devices, including smartphones

  • Magnetic MEMS are being successfully employed in several new applications from robotic or unmanned vehicles navigation [7,8] and human motion tracking [9,10,11]

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Summary

Introduction

Sensing Earth’s magnetic field can aid large-scale navigation. Nowadays, sensing Earth’s magnetic field is widely exploited for estimating the three-dimensional (3D) orientation. Microelectromechanical systems (MEMS) technologies allow the inclusion of low-grade magnetic sensors in Magnetic-Inertial Measurement Units (MIMUs), as well as in many other mobile devices, including smartphones. The orientation of a body (mobile) reference frame {b} with respect to a navigation (fixed) reference frame {n} can be expressed in several ways. Euler angles are by in large the most widely used orientation parameterizations. Rbn and qbn are, respectively, the orientation matrix and the quaternion that transform a generic vector pn (observed in the navigation frame) in pb (observed in the body reference frame). The “ZYX” convention is adopted (first rotation around the z-axis, rotations being considered about local axes), yielding the yaw (ψ), pitch (θ) and roll angles (φ) [17]:

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