Fast re-parameterisation of Gaussian mixture models for robotics applications

Upcroft, Ben, Kumar, Suresh, Ridley, Matthew, Ling Ong, Lee, & Durrant-Whyte, Hugh (2004) Fast re-parameterisation of Gaussian mixture models for robotics applications. In Barnes, Nick & Austin, David (Eds.) Proceedings of the 2004 Australasian Conference on Robotics & Automation, Australian Robotics & Automation Association, Canberra, Australia, pp. 1-7.

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Autonomous navigation and picture compilation tasks require robust feature descriptions or models. Given the non Gaussian nature of sensor observations, it will be shown that Gaussian mixture models provide a general probabilistic representation allowing analytical solutions to the update and prediction operations in the general Bayesian filtering problem. Each operation in the Bayesian filter for Gaussian mixture models multiplicatively increases the number of parameters in the representation leading to the need for a re-parameterisation step. A computationally efficient re-parameterisation step will be demonstrated resulting in a compact and accurate estimate of the true distribution.

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ID Code: 70474
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Autonomous navigation, Robotics, Gaussian mixture models
ISBN: 0958758360
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 24 Apr 2014 04:37
Last Modified: 24 Apr 2014 04:37

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