Grasp Detection under Occlusions Using SIFT Features

Distinguishing target object under occlusions has become the forefront of research to cope with grasping study in general. In this paper, a novel framework which is able to be utilized for a parallel robotic gripper is proposed. There are two key steps for the proposed method in the process of grasp...

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Autores principales:	Zhaojun Ye, Yi Guo, Chengguang Wang, Haohui Huang, Genke Yang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/439dd8e048c34989a9bd477f043ed4dc
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id	oai:doaj.org-article:439dd8e048c34989a9bd477f043ed4dc
record_format	dspace
spelling	oai:doaj.org-article:439dd8e048c34989a9bd477f043ed4dc2021-11-22T01:10:07ZGrasp Detection under Occlusions Using SIFT Features1099-052610.1155/2021/7619794https://doaj.org/article/439dd8e048c34989a9bd477f043ed4dc2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/7619794https://doaj.org/toc/1099-0526Distinguishing target object under occlusions has become the forefront of research to cope with grasping study in general. In this paper, a novel framework which is able to be utilized for a parallel robotic gripper is proposed. There are two key steps for the proposed method in the process of grasping occluded object: generating template information and grasp detection using the matching algorithm. A neural network, trained by the RGB-D data from the Cornell Grasp Dataset, predicts multiple grasp rectangles on template images. A proposed matching algorithm is utilized to eliminate the influence caused by occluded parts on scene images and generates multiple grasp rectangles for objects under occlusions using the grasp information of matched template images. In order to improve the quality of matching result, the proposed matching algorithm improves the SIFT algorithm and combines it with the improved RANSAC algorithm. In this way, this paper obtains suitable grasp rectangles on scene images and offers a new thought about grasping detection under occlusions. The validation results show the effectiveness and efficiency of this approach.Zhaojun YeYi GuoChengguang WangHaohui HuangGenke YangHindawi-WileyarticleElectronic computers. Computer scienceQA75.5-76.95ENComplexity, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electronic computers. Computer science QA75.5-76.95
spellingShingle	Electronic computers. Computer science QA75.5-76.95 Zhaojun Ye Yi Guo Chengguang Wang Haohui Huang Genke Yang Grasp Detection under Occlusions Using SIFT Features
description	Distinguishing target object under occlusions has become the forefront of research to cope with grasping study in general. In this paper, a novel framework which is able to be utilized for a parallel robotic gripper is proposed. There are two key steps for the proposed method in the process of grasping occluded object: generating template information and grasp detection using the matching algorithm. A neural network, trained by the RGB-D data from the Cornell Grasp Dataset, predicts multiple grasp rectangles on template images. A proposed matching algorithm is utilized to eliminate the influence caused by occluded parts on scene images and generates multiple grasp rectangles for objects under occlusions using the grasp information of matched template images. In order to improve the quality of matching result, the proposed matching algorithm improves the SIFT algorithm and combines it with the improved RANSAC algorithm. In this way, this paper obtains suitable grasp rectangles on scene images and offers a new thought about grasping detection under occlusions. The validation results show the effectiveness and efficiency of this approach.
format	article
author	Zhaojun Ye Yi Guo Chengguang Wang Haohui Huang Genke Yang
author_facet	Zhaojun Ye Yi Guo Chengguang Wang Haohui Huang Genke Yang
author_sort	Zhaojun Ye
title	Grasp Detection under Occlusions Using SIFT Features
title_short	Grasp Detection under Occlusions Using SIFT Features
title_full	Grasp Detection under Occlusions Using SIFT Features
title_fullStr	Grasp Detection under Occlusions Using SIFT Features
title_full_unstemmed	Grasp Detection under Occlusions Using SIFT Features
title_sort	grasp detection under occlusions using sift features
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/439dd8e048c34989a9bd477f043ed4dc
work_keys_str_mv	AT zhaojunye graspdetectionunderocclusionsusingsiftfeatures AT yiguo graspdetectionunderocclusionsusingsiftfeatures AT chengguangwang graspdetectionunderocclusionsusingsiftfeatures AT haohuihuang graspdetectionunderocclusionsusingsiftfeatures AT genkeyang graspdetectionunderocclusionsusingsiftfeatures
_version_	1718418395902246912

Grasp Detection under Occlusions Using SIFT Features

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