Efficient MSPSO Sampling for Object Detection and 6-D Pose Estimation in 3-D Scenes
文献类型：期刊论文
| 作者 | Xing, Xuejun1,2; Guo, Jianwei2,4 ; Nan, Liangliang3; Gu, Qingyi2,4; Zhang, Xiaopeng1; Yan, Dong-Ming2,4
|
| 刊名 | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
 |
| 出版日期 | 2022-10-01 |
| 卷号 | 69期号:10页码:10281-10291 |
| ISSN号 | 0278-0046 |
| 关键词 | 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 |
| 通讯作者 | Guo, Jianwei(jianwei.guo@nlpr.ia.ac.cn) |
| 英文摘要 | 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. |
| 资助项目 | 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] |
| WOS研究方向 | Automation & Control Systems ; Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000790866600059 |
| 资助机构 | 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 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/48452]  |
| 专题 | 模式识别国家重点实验室_三维可视计算 中国科学院自动化研究所 |
| 通讯作者 | Guo, Jianwei |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, NLPR, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Delft Univ Technol, NL-2628 BL Delft, Netherlands 4.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。