Adapting the Sample Size in Particle Filters Through KLD-Sampling
Submitted by mkrainin on Tue, 2012-02-28 12:21
| Title | Adapting the Sample Size in Particle Filters Through KLD-Sampling |
| Publication Type | Journal Article |
| Year of Publication | 2003 |
| Authors | Fox D |
| Journal | International Journal of Robotics Research |
| Volume | 22 |
| Abstract | <p>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.</p> |
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| Citation Key | Fox03Ada |
Last changed Wed, 2012-03-07 15:08

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