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C. Kwok, D. Fox and M. Meila
Real-time particle filters
Technical Report UW-CSE-02-07-01
AbstractParticle filters estimate the state of dynamical systems from sensor information. In many real time applications of particle filters, however, sensor information arrives at a significantly higher rate than the update rate of the filter. The prevalent approach to dealing with such situations is to update the particle filter as often as possible and to discard sensor information that cannot be processed in time. In this paper we present real-time particle filters, which make use of all sensor information even when the filter update rate is below the update rate of the sensors. This is achieved by distributing samples among the different observations arriving during a filter update. Hence the approach represents posteriors by mixtures of sample sets. The weights of the mixture components are set so as to minimize the approximation error introduced by the mixture representation. Minimization is achieved by gradient descent using efficient Monte Carlo approximation of the gradients. Thereby, our approach focuses computational resources (samples) on valuable sensor information. Experiments using data collected with a mobile robot show that our approach yields strong improvements over other approaches.
Full paper [.ps.gz] (340 kb, 12 pages)
Animations:The following animations illustrate how the real time particle filters work.
Department of Computer Science & Engineering|
University of Washington
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