Context Identification for Efficient Multiple-Model State Estimation
Conference Paper, Proceedings of (IROS) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2435 - 2440, October, 2007
Abstract
This paper presents an approach to accurate and scalable multiple-model state estimation for hybrid systems with intermittent, multi-modal dynamics. The approach consists of using discrete-state estimation to identify a system? behavioral context and determine which motion models appropriately represent current dynamics, and which multiple-model filters are appropriate for state estimation. This improves the accuracy and scalability of conventional multiple-model state estimation. This approach is validated experimentally on a mobile robot that exhibits multi-modal dynamics.
BibTeX
@conference{Skaff-2007-9873,author = {Sarjoun Skaff and Howie Choset and Alfred Rizzi},
title = {Context Identification for Efficient Multiple-Model State Estimation},
booktitle = {Proceedings of (IROS) IEEE/RSJ International Conference on Intelligent Robots and Systems},
year = {2007},
month = {October},
pages = {2435 - 2440},
keywords = {Hidden Markov Models, Timed Automata, Multiple-Model Filtering},
}
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.