Robustness Improvement of Visual Templates Matching Based on Frequency-Tuned Model in RatSLAM

This paper describes an improved brain-inspired simultaneous localization and mapping (RatSLAM) that extracts visual features from saliency maps using a frequency-tuned (FT) model. In the traditional RatSLAM algorithm, the visual template feature is organized as a one-dimensional vector whose values...

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Detalles Bibliográficos
Autores principales: Yu, Shumei, Wu, Junyi, Xu, Haidong, Sun, Rongchuan, Sun, Lining
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7546858/
https://www.ncbi.nlm.nih.gov/pubmed/33101002
http://dx.doi.org/10.3389/fnbot.2020.568091

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7546858/
https://www.ncbi.nlm.nih.gov/pubmed/33101002
http://dx.doi.org/10.3389/fnbot.2020.568091

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