J. Ko and D. Fox.
GP-BayesFilters: Bayesian
Filtering Using Gaussian Process
Prediction and Observation Models
Autonomous Robots Journal, 2009
Abstract
Bayesian filtering is a general framework for recursively estimating the state
of a dynamical system. Key components of each Bayes filter are probabilistic
prediction and observation models. This paper shows how non-parametric
Gaussian process (GP) regression can be used for learning such models from
training data. We also show how Gaussian process models can be integrated into
different versions of Bayes filters, namely particle filters and extended and
unscented Kalman filters. The resulting GP-BayesFilters can have several advantages
over standard (parametric) filters. Most importantly, GP-BayesFilters do not require
an accurate, parametric model of the system. Given enough training data, they
enable improved tracking accuracy compared to parametric models, and they
degrade gracefully 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 different properties of GP-BayesFilters using
data collected with an autonomous micro-blimp as well as synthetic data.
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