Robot docking using mixtures of Gaussians

Williamson, M., Murray-Smith, R. and Hansen, V. (1999) Robot docking using mixtures of Gaussians. In: Conference on Neural Information Processing Systems, Colorado, USA, 30 November - 5 December 1998, ISBN 0262112450




This paper applies the Mixture of Gaussians probabilistic model, combined with Expectation Maximization optimization to the task of summarizing three dimensionals range data for the mobile robot. This provides a flexible way of dealing with uncertainties in sensor information, and allows the introduction of prior knowledge into low-level perception modules. Problems with the basic approach were solved in several ways: the mixture of Gaussians was reparameterized to reflect the types of objects expected in the scene, and priors on model parameters were included in the optimization process. Both approaches force the optimization to find 'interesting' objects, given the sensor and object characteristics. A higher level classifier was used to interpret the results provided by the model, and to reject spurious solutions.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Murray-Smith, Professor Roderick
Authors: Williamson, M., Murray-Smith, R., and Hansen, V.
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
College/School:College of Science and Engineering > School of Computing Science
Publisher:MIT Press
Copyright Holders:Copyright © 1999 MIT Press
First Published:First published in London
Publisher Policy:Reproduced with the permission of the publisher

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