CRF-Filters: Discriminative
Particle Filters for Sequential State Estimation
Proc. of the International Conference on Robotics
and Automation (ICRA), 2007
Abstract
Particle filters have been applied with great success to
various state estimation problems in robotics. However, particle
filters often require extensive parameter tweaking in order to work
well in practice. This is based on two observations. First, particle
filters typically rely on independence assumptions such as ``the
beams in a laser scan are independent given the robot's location in
a map''. Second, even when the noise parameters of the dynamical
system are perfectly known, the sample-based approximation can
result in poor filter performance. In this paper we introduce
CRF-Filters, a novel variant of particle filtering for sequential
state estimation. CRF-Filters are based on conditional random fields,
which are discriminative models that can handle arbitrary
dependencies between observations. We show how to learn the
parameters of CRF-Filters s based on labeled training data. Experiments
using a robot equipped with a laser range-finder demonstrate that
our technique is able to learn parameters of the robot's motion and
sensor models that result in good localization performance, without
the need of additional parameter tweaking.