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Context Identification for Efficient Multiple-Model State Estimation
S. Skaff, H. Choset, and A. Rizzi
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), No. 2, November, 2007, pp. 2435-2440.

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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’s 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.

Associated lab/group: Microdynamic Systems Laboratory
Associated project: RHex Robot

Number of pages: 6

S. Skaff, H. Choset, and A. Rizzi, "Context Identification for Efficient Multiple-Model State Estimation," IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), No. 2, November, 2007, pp. 2435-2440.

@inproceedings{Skaff_2007_6001,
   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)",
   month = "November",
   year = "2007",
   number = "2",
   pages = "2435-2440"
}