Bayesian filtering over compressed appearance states

Douillard, B., Upcroft, B., Kaupp, T., Ramos, F. and Durrant-Whyte, H. (2007). Bayesian filtering over compressed appearance states. In: Matthew Dunbabin and Mandyam Srinivasan, Proceedings of the Australasian Conference on Robotics and Automation 2007. Australasian Conference on Robotics and Automation 2007 - ACRA 2007, Brisbane, Australia, (). 10-12 December, 2007.


Author Douillard, B.
Upcroft, B.
Kaupp, T.
Ramos, F.
Durrant-Whyte, H.
Title of paper Bayesian filtering over compressed appearance states
Conference name Australasian Conference on Robotics and Automation 2007 - ACRA 2007
Conference location Brisbane, Australia
Conference dates 10-12 December, 2007
Proceedings title Proceedings of the Australasian Conference on Robotics and Automation 2007
Publisher Australian Robotics and Automation Association
Publication Year 2007
Sub-type Fully published paper
ISBN 978-0-9587583-9-0
Editor Matthew Dunbabin
Mandyam Srinivasan
Total pages 10
Language eng
Formatted Abstract/Summary This paper presents a framework for performing real-time recursive estimation of landmarks’ visual appearance. Imaging data in its original high dimensional space is probabilistically mapped to a compressed low dimensional space through the definition of likelihood functions.

The likelihoods are subsequently fused with prior information using a Bayesian update.
This process produces a probabilistic estimate of the low dimensional representation of the landmark visual appearance. The overall filtering provides information complementary to the conventional position estimates which is used to enhance data association.

In addition to robotics observations, the filter integrates human observations in the appearance estimates. The appearance tracks as computed by the filter allow landmark classification. The set of labels involved in the classification task is thought of as an observation space where human observations are made by selecting a label.

The low dimensional appearance estimates returned by the filter allow for low cost communication in low bandwidth sensor networks.
Deployment of the filter in such a network is demonstrated in an outdoor mapping application involving a human operator, a ground and an air vehicle.
Subjects 091007 Manufacturing Robotics and Mechatronics (excl. Automotive Mechatronics)
0910 Manufacturing Engineering
0913 Mechanical Engineering
090602 Control Systems, Robotics and Automation
Keyword Bayesian updates
real-time
Q-Index Code EX

 
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Created: Wed, 14 Apr 2010, 14:34:03 EST by Maria Campbell on behalf of Faculty Of Engineering, Architecture & Info Tech