C. Kwok, D. Fox, and M. Meila
Real-Time Particle Filters
Proceedings of NIPS-2002
Abstract
Particle 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 \emph{all}
sensor information even when the filter update rate is below the
update rate of the sensors. This is achieved by representing
posteriors as mixtures of sample sets, where each mixture component
integrates one observation arriving during a filter update. The
weights of the mixture components are set so as to minimize the
approximation error introduced by the mixture representation.
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.
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