Context Identification for Efficient Multiple-Model State Estimation

Sarjoun Skaff, Howie Choset and Alfred Rizzi
Conference Paper, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), No. 2, pp. 2435-2440, November, 2007

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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.

author = {Sarjoun Skaff and Howie Choset and Alfred Rizzi},
title = {Context Identification for Efficient Multiple-Model State Estimation},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2007},
month = {November},
pages = {2435-2440},
keywords = {Hidden Markov Models, Timed Automata, Multiple-Model Filtering},
} 2017-09-13T10:41:55-04:00