Speedup 3-D Texture-Less Object Recognition Against Self-Occlusion for Intelligent Manufacturing
文献类型：期刊论文
| 作者 | Feng Y(冯云)2; Fan BJ(范保杰)1; Yu HB(于海斌)2; Cong Y(丛杨)2; Tian DY(田冬英)2; Yu P(于鹏) ; Liu LQ(刘连庆); Zhao L(赵亮); Yang T(杨铁); Li N(李宁)
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| 刊名 | IEEE Transactions on Cybernetics
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| 出版日期 | 2018 |
| 页码 | 1-11 |
| 关键词 | Hough voting hypothesis generation k-d tree local reference frame (LRF) object recognition pose estimation |
| ISSN号 | 2168-2267 |
| 产权排序 | 1 |
| 通讯作者 | Yu HB(于海斌) |
| 中文摘要 | 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. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.sia.cn/handle/173321/22370]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 作者单位 | 1.College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210042, China 2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China |
| 推荐引用方式 GB/T 7714 | Feng Y,Fan BJ,Yu HB,et al. Speedup 3-D Texture-Less Object Recognition Against Self-Occlusion for Intelligent Manufacturing[J]. IEEE Transactions on Cybernetics,2018:1-11. |
| APA | Feng Y.,Fan BJ.,Yu HB.,Cong Y.,Tian DY.,...&李宁.(2018).Speedup 3-D Texture-Less Object Recognition Against Self-Occlusion for Intelligent Manufacturing.IEEE Transactions on Cybernetics,1-11. |
| MLA | Feng Y,et al."Speedup 3-D Texture-Less Object Recognition Against Self-Occlusion for Intelligent Manufacturing".IEEE Transactions on Cybernetics (2018):1-11. |
入库方式： OAI收割
来源：沈阳自动化研究所
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