| 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
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| Department | 机器人学研究室 |
| Source Publication | IEEE Transactions on Cybernetics
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| ISSN | 2168-2267 |
| 2019 | |
| Volume | 49Issue:11Pages:3887-3897 |
| Indexed By | SCI ; EI |
| EI Accession number | 20183005606428 |
| WOS ID | WOS:000476811000005 |
| Contribution Rank | 1 |
| Funding Organization | National Nature Science Foundation ; CAS-Youth Innovation Promotion Association Scholarship |
| Keyword | Hough voting hypothesis generation k-d tree local reference frame (LRF) object recognition pose estimation |
| Abstract | Realtime 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 Subject | Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics |
| WOS Keyword | 3D ; EFFICIENT ; FEATURES ; INVARIANT ; IMAGES ; MODEL |
| WOS Research Area | Automation & Control Systems ; Computer Science |
| Funding Project | CAS-Youth Innovation Promotion Association Scholarship[2012163] ; National Nature Science Foundation[61533015] ; National Nature Science Foundation[U1613214] ; National Nature Science Foundation[61722311] ; CAS-Youth Innovation Promotion Association Scholarship[2012163] ; National Nature Science Foundation[61533015] ; National Nature Science Foundation[U1613214] ; National Nature Science Foundation[61722311] ; National 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 |
| Identifier | http://ir.sia.cn/handle/173321/22312 |
| Collection | 机器人学研究室 |
| Corresponding Author | Yu HB(于海斌) |
| Affiliation | 1.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. |
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| 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-SA | View Download | |
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