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Bio-inspired visual self-localization in real world scenarios using Slow Feature Analysis

We present a biologically motivated model for visual self-localization which extracts a spatial representation of the environment directly from high dimensional image data by employing a single unsupervised learning rule. The resulting representation encodes the position of the camera as slowly vary...

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Detalles Bibliográficos
Autores principales: Metka, Benjamin, Franzius, Mathias, Bauer-Wersing, Ute
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6150500/
https://www.ncbi.nlm.nih.gov/pubmed/30240451
http://dx.doi.org/10.1371/journal.pone.0203994

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