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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Main Authors: Sungho Kim, Min-Sheob Shim
Format: Article
Language:English
Published: Wiley 2015-01-01
Series:The Scientific World Journal
Online Access:http://dx.doi.org/10.1155/2015/465290
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author Sungho Kim
Min-Sheob Shim
author_facet Sungho Kim
Min-Sheob Shim
author_sort Sungho Kim
collection DOAJ
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.
format Article
id doaj-art-fab1cbb17b134461873790d2d45797a7
institution Kabale University
issn 2356-6140
1537-744X
language English
publishDate 2015-01-01
publisher Wiley
record_format Article
series The Scientific World Journal
spelling doaj-art-fab1cbb17b134461873790d2d45797a72025-02-03T06:08:16ZengWileyThe Scientific World Journal2356-61401537-744X2015-01-01201510.1155/2015/465290465290Biologically Motivated Novel Localization Paradigm by High-Level Multiple Object Recognition in Panoramic ImagesSungho Kim0Min-Sheob Shim1Advanced Visual Intelligence Laboratory, Yeungnam University, 280 Daehak-ro, Gyeongsan, Gyeongbuk 712-749, Republic of KoreaAdvanced Visual Intelligence Laboratory, Yeungnam University, 280 Daehak-ro, Gyeongsan, Gyeongbuk 712-749, Republic of KoreaThis 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.http://dx.doi.org/10.1155/2015/465290
spellingShingle Sungho Kim
Min-Sheob Shim
Biologically Motivated Novel Localization Paradigm by High-Level Multiple Object Recognition in Panoramic Images
The Scientific World Journal
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
url http://dx.doi.org/10.1155/2015/465290
work_keys_str_mv AT sunghokim biologicallymotivatednovellocalizationparadigmbyhighlevelmultipleobjectrecognitioninpanoramicimages
AT minsheobshim biologicallymotivatednovellocalizationparadigmbyhighlevelmultipleobjectrecognitioninpanoramicimages