Real-time particle filters

被引:114
作者
Kwok, C [1 ]
Fox, D
Meila, M
机构
[1] Univ Washington, Dept Comp Sci & Engn, Seattle, WA 98195 USA
[2] Univ Washington, Dept Stat, Seattle, WA 98195 USA
关键词
mixture beliefs; Monte Carlo gradients; particle filter; real time; robot localization; state estimation;
D O I
10.1109/JPROC.2003.823144
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Particle filters estimate the state of dynamic 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 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 on valuable sensor information. Experiments using data collected with a mobile robot show that our approach yields strong improvements over other approaches.
引用
收藏
页码:469 / 484
页数:16
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