Semantic 3D Reconstruction for Robotic Manipulators with an Eye-In-Hand Vision System

Semantic 3D Reconstruction for Robotic Manipulators with an Eye-In-Hand Vision System

Three-dimensional reconstruction and semantic understandings have attracted extensive attention in recent years. However, current reconstruction techniques mainly target large-scale scenes, such as an indoor environment or automatic self-driving cars. There are few studies on small-scale and high-pr...

Full description

Bibliographic Details
Main Authors: Fusheng Zha, Yu Fu, Pengfei Wang, Wei Guo, Mantian Li, Xin Wang, Hegao Cai
Format: Article
Language:English
Published: MDPI AG 2020-02-01
Series:Applied Sciences
Subjects:
semantic 3d reconstruction
eye-in-hand vision system
robotic manipulator
Online Access:https://www.mdpi.com/2076-3417/10/3/1183
id doaj-cdb7ad627fce4399886219d6ef3f6a0c
record_format Article
spelling doaj-cdb7ad627fce4399886219d6ef3f6a0c2020-11-25T01:40:00ZengMDPI AGApplied Sciences2076-34172020-02-01103118310.3390/app10031183app10031183Semantic 3D Reconstruction for Robotic Manipulators with an Eye-In-Hand Vision SystemFusheng Zha0Yu Fu1Pengfei Wang2Wei Guo3Mantian Li4Xin Wang5Hegao Cai6State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150080, ChinaState Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150080, ChinaState Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150080, ChinaState Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150080, ChinaState Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150080, ChinaShenzhen Academy of Aerospace Technology, Shenzhen 518057, ChinaState Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150080, ChinaThree-dimensional reconstruction and semantic understandings have attracted extensive attention in recent years. However, current reconstruction techniques mainly target large-scale scenes, such as an indoor environment or automatic self-driving cars. There are few studies on small-scale and high-precision scene reconstruction for manipulator operation, which plays an essential role in the decision-making and intelligent control system. In this paper, a group of images captured from an eye-in-hand vision system carried on a robotic manipulator are segmented by deep learning and geometric features and create a semantic 3D reconstruction using a map stitching method. The results demonstrate that the quality of segmented images and the precision of semantic 3D reconstruction are effectively improved by our method.https://www.mdpi.com/2076-3417/10/3/1183semantic 3d reconstructioneye-in-hand vision systemrobotic manipulator
collection DOAJ
language English
format Article
sources DOAJ
author Fusheng Zha
Yu Fu
Pengfei Wang
Wei Guo
Mantian Li
Xin Wang
Hegao Cai
spellingShingle Fusheng Zha
Yu Fu
Pengfei Wang
Wei Guo
Mantian Li
Xin Wang
Hegao Cai
Semantic 3D Reconstruction for Robotic Manipulators with an Eye-In-Hand Vision System
Applied Sciences
semantic 3d reconstruction
eye-in-hand vision system
robotic manipulator
author_facet Fusheng Zha
Yu Fu
Pengfei Wang
Wei Guo
Mantian Li
Xin Wang
Hegao Cai
author_sort Fusheng Zha
title Semantic 3D Reconstruction for Robotic Manipulators with an Eye-In-Hand Vision System
title_short Semantic 3D Reconstruction for Robotic Manipulators with an Eye-In-Hand Vision System
title_full Semantic 3D Reconstruction for Robotic Manipulators with an Eye-In-Hand Vision System
title_fullStr Semantic 3D Reconstruction for Robotic Manipulators with an Eye-In-Hand Vision System
title_full_unstemmed Semantic 3D Reconstruction for Robotic Manipulators with an Eye-In-Hand Vision System
title_sort semantic 3d reconstruction for robotic manipulators with an eye-in-hand vision system
publisher MDPI AG
series Applied Sciences
issn 2076-3417
publishDate 2020-02-01
description Three-dimensional reconstruction and semantic understandings have attracted extensive attention in recent years. However, current reconstruction techniques mainly target large-scale scenes, such as an indoor environment or automatic self-driving cars. There are few studies on small-scale and high-precision scene reconstruction for manipulator operation, which plays an essential role in the decision-making and intelligent control system. In this paper, a group of images captured from an eye-in-hand vision system carried on a robotic manipulator are segmented by deep learning and geometric features and create a semantic 3D reconstruction using a map stitching method. The results demonstrate that the quality of segmented images and the precision of semantic 3D reconstruction are effectively improved by our method.
topic semantic 3d reconstruction
eye-in-hand vision system
robotic manipulator
url https://www.mdpi.com/2076-3417/10/3/1183
work_keys_str_mv AT fushengzha semantic3dreconstructionforroboticmanipulatorswithaneyeinhandvisionsystem
AT yufu semantic3dreconstructionforroboticmanipulatorswithaneyeinhandvisionsystem
AT pengfeiwang semantic3dreconstructionforroboticmanipulatorswithaneyeinhandvisionsystem
AT weiguo semantic3dreconstructionforroboticmanipulatorswithaneyeinhandvisionsystem
AT mantianli semantic3dreconstructionforroboticmanipulatorswithaneyeinhandvisionsystem
AT xinwang semantic3dreconstructionforroboticmanipulatorswithaneyeinhandvisionsystem
AT hegaocai semantic3dreconstructionforroboticmanipulatorswithaneyeinhandvisionsystem
_version_ 1725047677033381888

Similar Items