Gaussian process based state estimation for a gyroscope-free IMU

A gyroscope-free inertial measurement unit (GF IMU) utilizes only accelerometers to determine the relative movement of a body. We consider the problem of merging the individual accelerometer measurements using an Unscented Kalman filter (UKF) to estimate the body motion. Conventionally, this is realized by a parametric observation model, which gives the expected sensor measurements. In this paper, we replace this model by a Gaussian process (GP). GPs are a state-of-the art non-parametric Bayesian regression framework. Thereby, the measurements are determined based on a sampled set of training data. No physical principles of the system must be described. In addition, we apply sparse GPs using pseudo-inputs to reduce computation time, while the estimation accuracy remains nearly constant. As a result, the filter cycle time decreases by a factor of 1.92. We present accuracy measurements obtained on a 3D rotation table and compare the results to estimates generated with a parametric model.