Lattice independent component analysis for appearance-based mobile robot localization

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.