Publication
Towards Inertial Human Motion Tracking with Drift-Free Absolute Orientations using only Sparse Sources of Heading Information
Michael Lorenz; Gabriele Bleser; Didier Stricker; Bertram Taetz
In: Proceedings of the 25th International Conference on Information Fusion in Linköping. International Conference on Information Fusion (FUSION-2022), July 4-7, Linköping, Sweden, IEEE Explore, 2022.
Abstract
Tracking a kinematic chain model with inertial
sensors and magnetometers using a Bayesian Filter approach
typically one magnetometer per segment is used to compensate
for a global heading drift. In this work we present a study
showing that using an appropriate modeling, heading information
can be propagated from one segment to neighboring segments
in a kinematic chain. This implies that the amount of required
magnetometers can be much lower than one per segment. In
particular we elaborate on recent theoretical results and observe
that the absolute orientation of all segments in a kinematic chain
can be estimated drift-free with only sparse sources of heading
information. Our study consists of two parts. The first part is
based on a simulated manipulator consisting of three segments.
The second one includes the lower body (seven segments) of
subjects performing walking trials. Here the inertial sensor data
was generated using position and rotation tracking data from
a marker-based optical reference system. We show that under
certain circumstances the inclusion of a single source of heading
information is enough to capture even under disturbances the
absolute orientation of the remaining segments drift-free.