Robust Error-State Kalman Filter for Estimating IMU Orientation

Rachel V Vitali; Ryan S McGinnis; Noel C Perkins

doi:10.1109/JSEN.2020.3026895

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Robust Error-State Kalman Filter for Estimating IMU Orientation

Journal article

Peer reviewed

Robust Error-State Kalman Filter for Estimating IMU Orientation

Rachel V Vitali, Ryan S McGinnis and Noel C Perkins

IEEE sensors journal, Vol.21(3), pp.3561-3569

02/01/2021

DOI: 10.1109/JSEN.2020.3026895

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Abstract

Inertial measurement units (IMUs) are increasingly utilized as motion capture devices in human movement studies. Given their high portability, IMUs can be deployed in any environment, importantly those outside of the laboratory. However, a significant challenge limits the adoption of this technology; namely estimating the orientation of the IMUs to a common world frame, which is essential to estimating the rotations across skeletal joints. Common (probabilistic) methods for estimating IMU orientation rely on the ability to update the current orientation estimate using data provided by the IMU. The objective of this work is to present a novel error-state Kalman filter that yields highly accurate estimates of IMU orientation that are robust to poor measurement updates from fluctuations in the local magnetic field and/or highly dynamic movements. The method is validated with ground truth data collected with highly accurate orientation measurements provided by a coordinate measurement machine. As an example, the method yields IMU-estimated orientations that remain within 3.7 degrees (RMS error) over relatively long (25 cumulative minutes) trials even in the presence of large fluctuations in the local magnetic field. For comparison, ignoring the magnetic interference increases the RMS error to 12.8 degrees, more than a threefold increase.

Current measurement

IMU

indirect Kalman filtering

Inertial measurement unit

Kalman filters

Magnetometers

Magnetosphere

Noise measurement

orientation estimation

Pollution measurement

Quaternions

Details

Title: Subtitle: Robust Error-State Kalman Filter for Estimating IMU Orientation
Creators: Rachel V Vitali - University of Iowa
Ryan S McGinnis - University of Vermont
Noel C Perkins - University of Michigan–Ann Arbor
Resource Type: Journal article
Publication Details: IEEE sensors journal, Vol.21(3), pp.3561-3569
Publisher: IEEE
DOI: 10.1109/JSEN.2020.3026895
ISSN: 1530-437X
eISSN: 1558-1748
Grant note: MC10, Inc., and Impellia W911QY-15-C-0053 / U.S. Army Contracting Command-APG, Natick Contracting Division, Natick, MA, USA (10.13039/100010194)
Language: English
Date published: 02/01/2021
Academic Unit: Mechanical Engineering
Record Identifier: 9984196544102771

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