A statistical framework for natural feature representation
Kumar, Suresh , Ramos, Fabio , Upcroft, Ben, & Durrant-Whyte, Hugh (2005) A statistical framework for natural feature representation. In Proceedings 2005 IEEE/RSJ International conference on Intelligent Robots and Systems IROS 2005, IEEE, Shaw Convention Center Edmonton, Alberta, Canada .
This paper presents a robust stochastic framework for the incorporation of visual observations into conventional estimation, data fusion, navigation and control algorithms. The representation combines Isomap, a non-linear dimensionality reduction algorithm, with expectation maximization, a statistical learning scheme. The joint probability distribution of this representation is computed offline based on existing training data. The training phase of the algorithm results in a nonlinear and non-Gaussian likelihood model of natural features conditioned on the underlying visual states. This generative model can be used online to instantiate likelihoods corresponding to observed visual features in real-time. The instantiated likelihoods are expressed as a Gaussian mixture model and are conveniently integrated within existing non-linear filtering algorithms. Example applications based on real visual data from heterogenous, unstructured environments demonstrate the versatility of the generative models.
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|Item Type:||Conference Paper|
|Keywords:||Expectation-maximation algorithm, Feature extraction, Probability, Gaussian mixture model, natural feature representation|
|Subjects:||Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600)|
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 IEEE|
|Copyright Statement:||(c) 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.|
|Deposited On:||13 Apr 2011 12:26|
|Last Modified:||14 Aug 2011 00:21|
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