Adapting the Sample Size in Particle Filters Through KLD-Sampling  

Oleh:

Fox, Dieter

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: The International Journal of Robotics Research vol. 22 no. 12 (Dec. 2003), page 985-1003.
Topik: particle filters; robot localization; non-linear estimation
Fulltext: 985.pdf (2.66MB)

Isi artikel

Over the past few 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 using 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.

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