GP-UKF: Unscented kalman filters with Gaussian process prediction and observation models

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 algorith...

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Bibliographic Details
Published in2007 IEEE/RSJ International Conference on Intelligent Robots and Systems pp. 1901 - 1907
Main Authors Ko, Jonathan, Klein, Daniel J., Fox, Dieter, Haehnel, Dirk
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2007
Subjects
Bayesian methods
Gaussian processes
Intelligent robots
Parametric statistics
Particle filters
Predictive models
State estimation
Training data
Uncertainty
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Summary: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.
ISBN:9781424409112
142440911X
ISSN:2153-0858
DOI:10.1109/IROS.2007.4399284