Pose estimation of cyclic movement using inertial sensor data

We propose a method for estimating the rotation and displacement of a rigid body from inertial sensor data based on the assumption that the movement is cyclic in nature, meaning that the body returns to the same position and orientation at regular time intervals. The method builds on a parameterization of the movement by sums of sinusoids, and the amplitude and phase of the sinusoids are estimated from the data using measurement models with Gaussian noise. The maximum likelihood estimate is then equivalent to a weighted nonlinear least squares estimate. The performance of the method is demonstrated on simulated data and on experimental data.