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Bayesian filtering over compressed appearance states

Douillard, B., Upcroft, B., Kaupp, T., Ramos, F., & Durrant-Whyte, H. (2007) Bayesian filtering over compressed appearance states. In Dunbabin, Matthew & Srinivasan, Mandyam (Eds.) Proceedings of the 2007 Australasian Conference on Robotics & Automation, Australian Robotics & Automation Association, Brisbane, Australia.

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Abstract

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.

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ID Code: 40413
Item Type: Conference Paper
Keywords: Bayesian updates, real-time
ISBN: 9780958758390
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Control Systems Robotics and Automation (090602)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Engineering Systems
Copyright Owner: Copyright 2007 [please consult author]
Deposited On: 13 Apr 2011 11:41
Last Modified: 14 Aug 2011 00:21

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