Decision-theoretic approach to robust fusion of location data

The purpose of this paper is to introduce the reader to a novel approach to data fusion. We focus on the latest results which have immediate practical implications. Many tasks in active perception require the ability to combine information from a variety of sensors. Prior to combination, the data must be tested for consistency. Both of these tasks can be viewed as data fusion problems. We examine such problems for location data models. Our approach is based on statistical decision theory. We present the application of the theory to mobile robot localization.