Paper Info
Title
Lattice independent component analysis for appearance-based mobile robot localization
Abstract
This paper introduces an approach to appearance-based mobile robot localization using a new approach to dimensional reduction based on the notion of Lattice Independence called Lattice Independent Component Analysis (LICA). Any algorithm that can select a set of Strong Lattice Independent (SLI) vectors from the data can be applied inside LICA, this paper applies a specific Endmember Induction Algorithm (EIA) developed by our research group. The fact that SLI vectors are Affine Independent allows the coupling of non-linear Lattice Associative Memories (LAM) and linear unmixing for data exploration and dimensionality reduction. To perform an appearance-based mobile robot visual localization, images from the on-board camera robot are transformed into low dimension feature vector representations for classification. For validation, we compare LICA against several Independent Component Analysis (ICA) approaches over a collection of recorded image sequences taken from the robot following some predefined paths. Results show that LICA improves most of the ICA approaches, and it is only slightly improved by the Molgedey and Schouster ICA in some data instances.
Year
DOI
Venue
2012
10.1007/s00521-011-0738-8
Neural Computing and Applications
Keywords
DocType
Volume
appearance-based mobile robot localization,Strong Lattice Independent,lattice independent component analysis,non-linear Lattice Associative Memories,Affine Independent,Independent Component Analysis,ICA approach,appearance-based mobile robot,Lattice Independent Component Analysis,on-board camera robot,Lattice Independence
Journal
21
Issue
ISSN
Citations 
5
1433-3058
3
PageRank 
References 
Authors
0.38
28
4
Name
Order
Citations
PageRank
Manuel Graña11367156.11
Ivan Villaverde21339.82
Jose Manuel Lopez-Guede3223.25
Borja Fernandez-Gauna4494.89