Robust Error-State Kalman Filter for Estimating IMU Orientation

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.