Context Identification for Efficient Multiple-Model State Estimation - Robotics Institute Carnegie Mellon University

Context Identification for Efficient Multiple-Model State Estimation

Sarjoun Skaff, Howie Choset, and Alfred Rizzi
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},
}