Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
| Efficient MSPSO Sampling for Object Detection and 6-D Pose Estimation in 3-D Scenes | |
Xing, Xuejun1,2; Guo, Jianwei1,3 ; Nan, Liangliang4; Gu, Qingyi1,3; Zhang, Xiaopeng2; Yan, Dong-Ming1,3
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| Source Publication | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
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| ISSN | 0278-0046 |
| 2022-10-01 | |
| Volume | 69Issue:10Pages:10281-10291 |
| Corresponding Author | Guo, Jianwei(jianwei.guo@nlpr.ia.ac.cn) |
| Abstract | The point pair feature (PPF) is widely used in manufacturing for estimating 6-D poses. The key to the success of PPF matching is to establish correct 3-D correspondences between the object and the scene, i.e., finding as many valid similar point pairs as possible. However, efficient sampling of point pairs has been overlooked in existing frameworks. In this article, we propose a revised PPF matching pipeline to improve the efficiency of 6-D pose estimation. Our basic idea is that the valid scene reference points are lying on the object's surface and the previously sampled reference points can provide prior information for locating new reference points. The novelty of our approach is a new sampling algorithm for selecting scene reference points based on the multisubpopulation particle swarm optimization guided by a probability map. We also introduce an effective pose clustering and hypotheses verification method to obtain the optimal pose. Moreover, we optimize the progressive sampling for multiframe point clouds to improve processing efficiency. The experimental results show that our method outperforms previous methods by 6.6%, 3.9% in terms of accuracy on the public DTU and LineMOD datasets, respectively. We further validate our approach by applying it in a real robot grasping task. |
| Keyword | Pose estimation Three-dimensional displays Robustness Robot kinematics Image segmentation Deep learning Clustering algorithms 3-D point cloud 6-D pose estimation multisubpopulation particle swarm optimization (MSPSO) point pair features (PPF) |
| DOI | 10.1109/TIE.2021.3121721 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Key RD Program[2018YFB2100602] ; National Natural Science Foundation of China[62172416] ; National Natural Science Foundation of China[62172415] ; National Natural Science Foundation of China[61802406] ; National Natural Science Foundation of China[61972459] ; Open Research Fund Program of State Key Laboratory of Hydroscience and Engineering, Tsinghua University[sklhse-2020-D-07] ; Scientific Instrument Developing Project of the Chinese Academy of Sciences[YJKYYQ20200045] ; Open Research Projects of Zhejiang Lab[2021KE0AB07] ; Open Research Projects of Zhejiang Lab[TC210H00L/42] |
| Funding Organization | National Key RD Program ; National Natural Science Foundation of China ; Open Research Fund Program of State Key Laboratory of Hydroscience and Engineering, Tsinghua University ; Scientific Instrument Developing Project of the Chinese Academy of Sciences ; Open Research Projects of Zhejiang Lab |
| WOS Research Area | Automation & Control Systems ; Engineering ; Instruments & Instrumentation |
| WOS Subject | Automation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation |
| WOS ID | WOS:000790866600059 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/48453 |
| Collection | 模式识别国家重点实验室_三维可视计算 |
| Corresponding Author | Guo, Jianwei |
| Affiliation | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, NLPR, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 4.Delft Univ Technol, NL-2628 BL Delft, Netherlands |
| First Author Affilication | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| Corresponding Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Recommended Citation GB/T 7714 | Xing, Xuejun,Guo, Jianwei,Nan, Liangliang,et al. Efficient MSPSO Sampling for Object Detection and 6-D Pose Estimation in 3-D Scenes[J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,2022,69(10):10281-10291. |
| APA | Xing, Xuejun,Guo, Jianwei,Nan, Liangliang,Gu, Qingyi,Zhang, Xiaopeng,&Yan, Dong-Ming.(2022).Efficient MSPSO Sampling for Object Detection and 6-D Pose Estimation in 3-D Scenes.IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,69(10),10281-10291. |
| MLA | Xing, Xuejun,et al."Efficient MSPSO Sampling for Object Detection and 6-D Pose Estimation in 3-D Scenes".IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 69.10(2022):10281-10291. |
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