CASIA OpenIR
A pose estimation system based on deep neural network and ICP registration for robotic spray painting application
Wang, Zhe1,2; Fan, Junfeng1,2; Jing, Fengshui1,2; Liu, Zhaoyang1,2; Tan, Min1,2
Source PublicationINTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
ISSN0268-3768
2019-09-01
Volume104Issue:1-4Pages:285-299
Corresponding AuthorJing, Fengshui(fengshui.jing@ia.ac.cn)
AbstractNowadays, off-line robot trajectory generation methods based on pre-scanned target model are highly desirable for robotic spray painting application. For actual implementation of the generated trajectory, the relative pose between the actual target and the model needs to be calibrated in the first place. However, obtaining this relative pose remains a challenge, especially from a safe distance in industrial setting. In this paper, a pose estimation system that is able to meet the robotic spray painting requirements is proposed to estimate the pose accurately. The system captures the image of the target using RGB-D vision sensor. The image is then segmented using a modified U-SegNet segmentation network and the resulting segmentation is registered with the pre-scanned model candidates using iterative closest point (ICP) registration to obtain the estimated pose. To strengthen the robustness, a deep convolutional neural network is proposed to determine the rough orientation of the target and guide the selection of model candidates accordingly thus preventing misalignment during registration. The experimental results are compared with relevant researches and validate the accuracy and effectiveness of the proposed system.
KeywordPose estimation Spray painting RGB-D sensor Deep neural network ICP registration
DOI10.1007/s00170-019-03901-0
WOS KeywordRECOGNITION ; TOOL
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[U1813208] ; National Natural Science Foundation of China[61573358]
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaAutomation & Control Systems ; Engineering
WOS SubjectAutomation & Control Systems ; Engineering, Manufacturing
WOS IDWOS:000483808200016
PublisherSPRINGER LONDON LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/27218
Collection中国科学院自动化研究所
Corresponding AuthorJing, Fengshui
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wang, Zhe,Fan, Junfeng,Jing, Fengshui,et al. A pose estimation system based on deep neural network and ICP registration for robotic spray painting application[J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY,2019,104(1-4):285-299.
APA Wang, Zhe,Fan, Junfeng,Jing, Fengshui,Liu, Zhaoyang,&Tan, Min.(2019).A pose estimation system based on deep neural network and ICP registration for robotic spray painting application.INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY,104(1-4),285-299.
MLA Wang, Zhe,et al."A pose estimation system based on deep neural network and ICP registration for robotic spray painting application".INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 104.1-4(2019):285-299.
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