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A Context-Based State Estimation Technique for Hybrid Systems

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4 Author(s)
Skaff, S. ; Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA; ; Rizzi, A.A. ; Choset, H. ; Pei-Chun Lin

This paper proposes an approach to robust state estimation for mobile robots with intermittent dynamics. The approach consists of identifying the robot’s mode of operation by classifying the output of onboard sensors into mode-specific contexts. The underlying technique seeks to efficiently use available sensor information to enable accurate, high-bandwidth mode identification. Context classification is combined with multiple-model filtering in order to significantly improve the accuracy of state estimates for hybrid systems. This approach is validated in simulation and shown experimentally to produce accurate estimates on a jogging robot using low-cost sensors.

Published in:

Robotics and Automation, 2005. ICRA 2005. Proceedings of the 2005 IEEE International Conference on

Date of Conference:

18-22 April 2005