A statistical framework for natural feature representation

Kumar, S., Ramos, F., Upcroft, B. and Durrant-Whyte. H. (2005). A statistical framework for natural feature representation. In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems 2005 (IROS 2005), Edmonton, Canada, (1582-1587). 2-6 August 2005.


Author Kumar, S.
Ramos, F.
Upcroft, B.
Durrant-Whyte. H.
Title of paper A statistical framework for natural feature representation
Conference name IEEE/RSJ International Conference on Intelligent Robots and Systems 2005 (IROS 2005)
Conference location Edmonton, Canada
Conference dates 2-6 August 2005
Proceedings title 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems
Journal name 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vols 1-4
Place of Publication New York, U.S.A.
Publisher IEEE
Publication Year 2005
Sub-type Fully published paper
DOI 10.1109/IROS.2005.1544950
ISBN 0-7803-8912-3
Start page 1582
End page 1587
Total pages 6
Language eng
Abstract/Summary 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.
Subjects 091405 Mining Engineering
0913 Mechanical Engineering
Keyword Expectation-maximation algorithm
Feature extraction
Probability
Q-Index Code E1

 
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