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Adapting the Sample Size in Particle Filters Through KLD-Sampling

Department of Computer Science and Engineering University of Washington Seattle, WA 98195, USA, fox{at}cs.washington.edu

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.

Key Words: particle filters • robot localization • non-linear estimation

The International Journal of Robotics Research, Vol. 22, No. 12, 985-1003 (2003)
DOI: 10.1177/0278364903022012001


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