3D Object Recognition and Registration for Robot Grasping System
碩士 === 國立臺北科技大學 === 自動化科技研究所 === 104 === In this paper, we propose a vision guided robot (VGR) grasping system. First, the Kinect sensor was applied to capture the 3D point cloud data. Then, the Viewpoint Feature Histogram (VFH) descriptor for 3D point cloud data encodes geometry and viewpoint, whic...
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ndltd-TW-104TIT051460112019-05-15T22:54:23Z http://ndltd.ncl.edu.tw/handle/x4tgpx 3D Object Recognition and Registration for Robot Grasping System 基於3D物件辨識與對位之機械手臂夾取系統 Chen Po-Chun 陳柏均 碩士 國立臺北科技大學 自動化科技研究所 104 In this paper, we propose a vision guided robot (VGR) grasping system. First, the Kinect sensor was applied to capture the 3D point cloud data. Then, the Viewpoint Feature Histogram (VFH) descriptor for 3D point cloud data encodes geometry and viewpoint, which allows simultaneous recognition of the object and registration with the stable pose on database. However, the wrong pose will be determined when the object is symmetrically placed on the viewpoint. Here, the Modified Viewpoint Feature Histogram (MVFH), is proposed to avoid the ambiguity of symmetric pose. The refine pose is further estimated with iterative closest point (ICP) after the object recognition and rough pose estimation by MVFH on database, which will decrease processing time. Therefore, the information of object pose was sent to the robot grasping system and the robot will automatically grasp the object. The experimental result shows that the proposed grasping system is efficient and affective to improve accuracy, flexibility and intelligence in VGR application. 吳明川 陳金聖 學位論文 ; thesis 0 zh-TW |
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碩士 === 國立臺北科技大學 === 自動化科技研究所 === 104 === In this paper, we propose a vision guided robot (VGR) grasping system. First, the Kinect sensor was applied to capture the 3D point cloud data. Then, the Viewpoint Feature Histogram (VFH) descriptor for 3D point cloud data encodes geometry and viewpoint, which allows simultaneous recognition of the object and registration with the stable pose on database. However, the wrong pose will be determined when the object is symmetrically placed on the viewpoint. Here, the Modified Viewpoint Feature Histogram (MVFH), is proposed to avoid the ambiguity of symmetric pose. The refine pose is further estimated with iterative closest point (ICP) after the object recognition and rough pose estimation by MVFH on database, which will decrease processing time. Therefore, the information of object pose was sent to the robot grasping system and the robot will automatically grasp the object. The experimental result shows that the proposed grasping system is efficient and affective to improve accuracy, flexibility and intelligence in VGR application.
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吳明川 |
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吳明川 Chen Po-Chun 陳柏均 |
author |
Chen Po-Chun 陳柏均 |
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Chen Po-Chun 陳柏均 3D Object Recognition and Registration for Robot Grasping System |
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Chen Po-Chun |
title |
3D Object Recognition and Registration for Robot Grasping System |
title_short |
3D Object Recognition and Registration for Robot Grasping System |
title_full |
3D Object Recognition and Registration for Robot Grasping System |
title_fullStr |
3D Object Recognition and Registration for Robot Grasping System |
title_full_unstemmed |
3D Object Recognition and Registration for Robot Grasping System |
title_sort |
3d object recognition and registration for robot grasping system |
url |
http://ndltd.ncl.edu.tw/handle/x4tgpx |
work_keys_str_mv |
AT chenpochun 3dobjectrecognitionandregistrationforrobotgraspingsystem AT chénbǎijūn 3dobjectrecognitionandregistrationforrobotgraspingsystem AT chenpochun jīyú3dwùjiànbiànshíyǔduìwèizhījīxièshǒubìjiāqǔxìtǒng AT chénbǎijūn jīyú3dwùjiànbiànshíyǔduìwèizhījīxièshǒubìjiāqǔxìtǒng |
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