J. Ko and D. Fox.
Learning GP-BayesFilters via Gaussian Process
Latent Variable Models
Robotics: Science and Systems, 2009
Abstract
GP-BayesFilters are a general framework for integrating
Gaussian process prediction and observation models into
Bayesian filtering techniques, including particle filters and extended
and unscented Kalman filters. GP-BayesFilters learn nonparametric
filter models from training data containing sequences
of control inputs, observations, and ground truth states. The need
for ground truth states limits the applicability of GP-BayesFilters
to systems for which the ground truth can be estimated without
prohibitive overhead. In this paper we introduce GPBF-LEARN,
a framework for training GP-BayesFilters without any ground
truth states. Our approach extends Gaussian Process Latent
Variable Models to the setting of dynamical robotics systems.
We show how weak labels for the ground truth states can be
incorporated into the GPBF-LEARN framework. The approach
is evaluated using a difficult tracking task, namely tracking a
slotcar based on IMU measurements only.
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