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2007
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CRF-Filters: Discriminative Particle Filters for Sequential State Estimation

12 years 6 months ago
CRF-Filters: Discriminative Particle Filters for Sequential State Estimation
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 based on labeled training data. Experiments using a robot equipped with a laser range-finder demonstrate that our t...
Benson Limketkai, Dieter Fox, Lin Liao
Added 03 Jun 2010
Updated 03 Jun 2010
Type Conference
Year 2007
Where ICRA
Authors Benson Limketkai, Dieter Fox, Lin Liao
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