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.