Paper Info
Title
Lattice independent component analysis for mobile robot localization
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).
Year
DOI
Venue
2010
10.1007/978-3-642-13803-4_42
HAIS (2)
Keywords
Field
DocType
linear unmixing,strong lattice independent,lattice independent component analysis,affine independent,independent component analysis,sample path experiment,mobile robot localization,feature vector,data selected endmembers,endmember induction heuristic algorithm,heuristic algorithm
Affine transformation,Endmember,Feature vector,Pattern recognition,Lattice (order),Computer science,Heuristic (computer science),Artificial intelligence,Independent component analysis,Robot,Machine learning,Mobile robot
Conference
Volume
ISSN
ISBN
6077
0302-9743
3-642-13802-0
Citations 
PageRank 
References 
4
0.38
10
Authors
3
Name
Order
Citations
PageRank
Ivan Villaverde11339.82
Borja Fernandez-Gauna2494.89
Ekaitz Zulueta35610.97