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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...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Public Library of Science
2018
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| 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 |