Particle filters for sequential state estimation

	Dieter Fox
        University of Washington

This talk will provide an introduction to the problem of
estimating the state of a dynamic system from noisy sensor 
data.  Bayes filters phrase this problem as probabilistic 
posterior estimation.  The focus of this talk will be on 
particle filters, a sample-based implementation of Bayes 
filters.  Over the last years, particle filters have been 
applied with great success to various problems in vision and 
robotics. After introducing the basic algorithm underlying 
particle filters, I will discuss extensions such as 
Rao-Blackwellised particle filters. All methods will be 
illustrated using problems from robotics and people tracking.