SIA OpenIR  > 机器人学研究室
Speedup 3-D Texture-Less Object Recognition Against Self-Occlusion for Intelligent Manufacturing
Cong Y(丛杨)1; Tian DY(田冬英)1; Feng Y(冯云)1; Fan BJ(范保杰)2; Yu HB(于海斌)1
Department机器人学研究室
Source PublicationIEEE Transactions on Cybernetics
ISSN2168-2267
2019
Volume49Issue:11Pages:3887-3897
Indexed BySCI ; EI
EI Accession number20183005606428
WOS IDWOS:000476811000005
Contribution Rank1
Funding OrganizationNational Nature Science Foundation ; CAS-Youth Innovation Promotion Association Scholarship
KeywordHough voting hypothesis generation k-d tree local reference frame (LRF) object recognition pose estimation
AbstractRealtime 3-D object detection and 6-DOF pose estimation in clutter background is crucial for intelligent manufacturing, for example, robot feeding and assembly, where robustness and efficiency are the two most desirable goals. Especially for various metal parts with a textless surface, it is hard for most state of the arts to extract robust feature from the clutter background with various occlusions. To overcome this, in this paper, we propose an online 3-D object detection and pose estimation method to overcome self-occlusion for textureless objects. For feature representation, we only adopt the raw 3-D point clouds with normal cues to define our local reference frame and we automatically learn the compact 3-D feature from the simple local normal statistics via autoencoder. For a similarity search, a new basis buffer k-d tree method is designed without suffering branch divergence; therefore, ours can maximize the GPU parallel processing capabilities especially in practice. We then generate the hypothesis candidates via the hough voting, filter the false hypotheses, and refine the pose estimation via the iterative closest point strategy. For the experiments, we build a new 3-D dataset including industrial objects with heavy self-occlusions and conduct various comparisons with the state of the arts to justify the effectiveness and efficiency of our method.
Language英语
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS Keyword3D ; EFFICIENT ; FEATURES ; INVARIANT ; IMAGES ; MODEL
WOS Research AreaAutomation & Control Systems ; Computer Science
Funding ProjectNational Nature Science Foundation[61722311] ; National Nature Science Foundation[U1613214] ; National Nature Science Foundation[61533015] ; CAS-Youth Innovation Promotion Association Scholarship[2012163]
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/22312
Collection机器人学研究室
Corresponding AuthorYu HB(于海斌)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210042, China
Recommended Citation
GB/T 7714
Cong Y,Tian DY,Feng Y,et al. Speedup 3-D Texture-Less Object Recognition Against Self-Occlusion for Intelligent Manufacturing[J]. IEEE Transactions on Cybernetics,2019,49(11):3887-3897.
APA Cong Y,Tian DY,Feng Y,Fan BJ,&Yu HB.(2019).Speedup 3-D Texture-Less Object Recognition Against Self-Occlusion for Intelligent Manufacturing.IEEE Transactions on Cybernetics,49(11),3887-3897.
MLA Cong Y,et al."Speedup 3-D Texture-Less Object Recognition Against Self-Occlusion for Intelligent Manufacturing".IEEE Transactions on Cybernetics 49.11(2019):3887-3897.
Files in This Item: Download All
File Name/Size DocType Version Access License
Speedup 3-D Texture-(3419KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Cong Y(丛杨)]'s Articles
[Tian DY(田冬英)]'s Articles
[Feng Y(冯云)]'s Articles
Baidu academic
Similar articles in Baidu academic
[Cong Y(丛杨)]'s Articles
[Tian DY(田冬英)]'s Articles
[Feng Y(冯云)]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Cong Y(丛杨)]'s Articles
[Tian DY(田冬英)]'s Articles
[Feng Y(冯云)]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Speedup 3-D Texture-Less Object Recognition Against Self-Occlusion for Intelligent Manufacturing.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.