Robot Docking Using Mixtures of Gaussians

Part of Advances in Neural Information Processing Systems 11 (NIPS 1998)

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Matthew Williamson, Roderick Murray-Smith, Volker Hansen


This paper applies the Mixture of Gaussians probabilistic model, com(cid:173) bined with Expectation Maximization optimization to the task of sum(cid:173) marizing three dimensional range data for a mobile robot. This provides a flexible way of dealing with uncertainties in sensor information, and al(cid:173) lows the introduction of prior knowledge into low-level perception mod(cid:173) ules. 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.