D. Fox
Adapting the sample size in
particle filters through KLD-sampling
International Journal of Robotics Research
(IJRR), 2003
Abstract
Over the last years, particle filters have been applied
with great success to a variety of state estimation problems. In
this paper we present a statistical approach to increasing the
efficiency of particle filters by adapting the size of sample sets
during the estimation process. The key idea of the KLD-sampling
method is to bound the approximation error introduced by the
sample-based representation of the particle filter. The name
KLD-sampling is due to the fact that we measure the approximation
error by the Kullback-Leibler distance. Our adaptation approach
chooses a small number of samples if the density is focused on a
small part of the state space, and it chooses a large number of
samples if the state uncertainty is high. Both the implementation
and computation overhead of this approach are small. Extensive
experiments using mobile robot localization as a test application
show that our approach yields drastic improvements over particle
filters with fixed sample set sizes and over a previously introduced
adaptation technique.
Download
Full paper [.pdf]
(3,408 kb, 27 pages)
[To the RSE-lab]