Lattice independent component analysis for mobile robot localization

Authors:
Ivan Villaverde;Borja Fernandez-Gauna;Ekaitz Zulueta
Affiliations:
Computational Intelligence Group, Dept CCIA, UPV/EHU, San Sebastian, Spain;Computational Intelligence Group, Dept CCIA, UPV/EHU, San Sebastian, Spain;Computational Intelligence Group, Dept CCIA, UPV/EHU, San Sebastian, Spain
Venue:
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
Year:
2010

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Abstract

This paper introduces an approach to appearance based mobile robot localization using Lattice Independent Component Analysis (LICA) The Endmember Induction Heuristic Algorithm (EIHA) is used to select a set of Strong Lattice Independent (SLI) vectors, which can be assumed to be Affine Independent, and therefore candidates to be the endmembers of the data Selected endmembers are used to compute the linear unmixing of the robot's acquired images The resulting mixing coefficients are used as feature vectors for view recognition through classification We show on a sample path experiment that our approach can recognise the localization of the robot and we compare the results with the Independent Component Analysis (ICA).