Uncertainty in Artificial Intelligence
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Estimation Procedures for Robust Sensor Control
Greg Hager, Max Mintz
Abstract:
Many robotic sensor estimation problems can characterized in terms of nonlinear measurement systems. These systems are contaminated with noise and may be underdetermined from a single observation. In order to get reliable estimation results, the system must choose views which result in an overdetermined system. This is the sensor control problem. Accurate and reliable sensor control requires an estimation procedure which yields both estimates and measures of its own performance. In the case of nonlinear measurement systems, computationally simple closed-form estimation solutions may not exist. However, approximation techniques provide viable alternatives. In this paper, we evaluate three estimation techniques: the extended Kalman filter, a discrete Bayes approximation, and an iterative Bayes approximation. We present mathematical results and simulation statistics illustrating operating conditions where the extended Kalman filter is inappropriate for sensor control, and discuss issues in the use of the discrete Bayes approximation.
Keywords: Sensor Estimation, Kalman Filter, Bayes Approximation
Pages: 404-411
PS Link:
PDF Link: /papers/87/p404-hager.pdf
BibTex:
@INPROCEEDINGS{Hager87,
AUTHOR = "Greg Hager and Max Mintz",
TITLE = "Estimation Procedures for Robust Sensor Control",
BOOKTITLE = "Proceedings of the Third Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-87)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "1987",
PAGES = "404--411"
}


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