J. Ko and D. Fox.
GP-BayesFilters: Bayesian
Filtering Using Gaussian Process
Prediction and Observation Models
Proc. of IROS, 2008
Abstract
Bayesian filtering is a general framework for recursively estimating
the state of a dynamical system. The most common instantiations of
Bayes filters are Kalman filters (extended and unscented) and particle
filters. Key components of each Bayes filter are probabilistic
prediction and observation models. Recently, Gaussian processes have
been introduced as a non-parametric technique for learning such models
from training data. In the context of unscented Kalman filters, these
models have been shown to provide estimates that can be superior to
those achieved with standard, parametric models. In this paper we
show how Gaussian process models can be integrated into other Bayes
filters, namely particle filters and extended Kalman filters. We
provide a complexity analysis of these filters and evaluate the
alternative techniques using data collected with an autonomous
micro-blimp.
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