KLD-Sampling: Adaptive Particle Filters
Submitted by mkrainin on Tue, 2012-02-28 12:21
| Title | KLD-Sampling: Adaptive Particle Filters |
| Publication Type | Conference Paper |
| Year of Publication | 2001 |
| Authors | Fox D |
| Editor | Dietterich TG, Becker S, Ghahramani Z |
| Conference Name | NIPS |
| Publisher | MIT Press |
| Conference Location | Cambridge, MA |
| Abstract | <p>Over the last years, particle filters have been applied with great success to a variety of state estimation problems. We present a statistical approach to increasing the efficiency of particle filters by adapting the size of sample sets on-the-fly. 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.</p> |
| Downloads | PS |
| Citation Key | Fox01KLD |
Last changed Wed, 2012-03-07 15:08

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