Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)
Cody Kwok, Dieter Fox, Marina Meila
Particle filters estimate the state of dynamical systems from sensor infor- mation. 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 fil- ters, 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 represent- ing 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. Exper- iments using data collected with a mobile robot show that our approach yields strong improvements over other approaches.