Decision-theoretic approach to robust fusion of location data

Abstract 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.

[1]  Max Mintz,et al.  Feature-based localization using fixed ultrasonic transducers , 1995, Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots.

[2]  M. Stone,et al.  Mathematical Statistics: A Decision Theoretic Approach , 1968 .

[3]  Robert Mandelbaum,et al.  Sensor processing for mobile robot localization, exploration and navigation , 1996 .

[4]  G. Kamberova,et al.  Statistical decision theory for mobile robotics: theory and application , 1996, 1996 IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems (Cat. No.96TH8242).

[5]  Gerda Kamberova.  Robust location estimation for MLR and non-MLR distributions , 1992 .

[6]  Max Mintz,et al.  Robust Multi-Sensor Fusion: A Decision-Theoretic Approach , 1990, Other Conferences.

[7]  J. Berger.  Statistical Decision Theory and Bayesian Analysis , 1988 .

[8]  Max Mintz,et al.  Minimax rules under zero–one loss for a restricted location parameter , 1999 .