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Biologically Motivated Novel Localization Paradigm by High-Level Multiple Object Recognition in Panoramic Images

This paper presents the novel paradigm of a global localization method motivated by human visual systems (HVSs). HVSs actively use the information of the object recognition results for self-position localization and for viewing direction. The proposed localization paradigm consisted of three parts:...

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Detalles Bibliográficos
Autores principales: Kim, Sungho, Shim, Min-Sheob
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4589637/
https://www.ncbi.nlm.nih.gov/pubmed/26457323
http://dx.doi.org/10.1155/2015/465290
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author Kim, Sungho
Shim, Min-Sheob
author_facet Kim, Sungho
Shim, Min-Sheob
author_sort Kim, Sungho
collection PubMed
description This paper presents the novel paradigm of a global localization method motivated by human visual systems (HVSs). HVSs actively use the information of the object recognition results for self-position localization and for viewing direction. The proposed localization paradigm consisted of three parts: panoramic image acquisition, multiple object recognition, and grid-based localization. Multiple object recognition information from panoramic images is utilized in the localization part. High-level object information was useful not only for global localization, but also for robot-object interactions. The metric global localization (position, viewing direction) was conducted based on the bearing information of recognized objects from just one panoramic image. The feasibility of the novel localization paradigm was validated experimentally.
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spelling pubmed-45896372015-10-11 Biologically Motivated Novel Localization Paradigm by High-Level Multiple Object Recognition in Panoramic Images Kim, Sungho Shim, Min-Sheob ScientificWorldJournal Research Article This paper presents the novel paradigm of a global localization method motivated by human visual systems (HVSs). HVSs actively use the information of the object recognition results for self-position localization and for viewing direction. The proposed localization paradigm consisted of three parts: panoramic image acquisition, multiple object recognition, and grid-based localization. Multiple object recognition information from panoramic images is utilized in the localization part. High-level object information was useful not only for global localization, but also for robot-object interactions. The metric global localization (position, viewing direction) was conducted based on the bearing information of recognized objects from just one panoramic image. The feasibility of the novel localization paradigm was validated experimentally. Hindawi Publishing Corporation 2015 2015-09-17 /pmc/articles/PMC4589637/ /pubmed/26457323 http://dx.doi.org/10.1155/2015/465290 Text en Copyright © 2015 S. Kim and M.-S. Shim. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Kim, Sungho
Shim, Min-Sheob
Biologically Motivated Novel Localization Paradigm by High-Level Multiple Object Recognition in Panoramic Images
title Biologically Motivated Novel Localization Paradigm by High-Level Multiple Object Recognition in Panoramic Images
title_full Biologically Motivated Novel Localization Paradigm by High-Level Multiple Object Recognition in Panoramic Images
title_fullStr Biologically Motivated Novel Localization Paradigm by High-Level Multiple Object Recognition in Panoramic Images
title_full_unstemmed Biologically Motivated Novel Localization Paradigm by High-Level Multiple Object Recognition in Panoramic Images
title_short Biologically Motivated Novel Localization Paradigm by High-Level Multiple Object Recognition in Panoramic Images
title_sort biologically motivated novel localization paradigm by high-level multiple object recognition in panoramic images
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4589637/
https://www.ncbi.nlm.nih.gov/pubmed/26457323
http://dx.doi.org/10.1155/2015/465290
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