GP-UKF: Unscented Kalman Filters with
Gaussian Process Prediction and Observation Models
Best Student Paper Award
Proc. of IROS, 2007
Abstract
This paper considers the use of non-parametric system models for
sequential state estimation. In particular, motion and observation
models are learned from training examples using Gaussian Process (GP)
regression. The state estimator is an Unscented Kalman Filter (UKF).
The resulting GP-UKF algorithm has a number of advantages over
standard (parametric) UKFs. These include the ability to estimate the
state of arbitrary nonlinear systems, improved tracking quality
compared to a parametric UKF, and graceful degradation with increased
model uncertainty. These advantages stem from the fact that GPs
consider both the noise in the system and the uncertainty in the
model. If an approximate parametric model is available, it can be
incorporated into the GP; resulting in further performance
improvements. In experiments, we show how the GP-UKF algorithm can
be applied to the problem of tracking an autonomous micro-blimp.