Efficient MSPSO Sampling for Object Detection and 6-D Pose Estimation in 3-D Scenes

doi:10.1109/TIE.2021.3121721

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 三维可视计算

	Efficient MSPSO Sampling for Object Detection and 6-D Pose Estimation in 3-D Scenes
	Xing, Xuejun 2,3; Guo, Jianwei 2,4; Nan, Liangliang 1; Gu, Qingyi 2,4; Zhang, Xiaopeng 3; Yan, Dong-Ming 2,4
刊名	IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
	2022-10-01
卷号	69 期号:10 页码:10281-10291
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)
ISSN号	0278-0046
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
内容类型	期刊论文
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
通讯作者	Guo, Jianwei
作者单位	1.Delft Univ Technol, NL-2628 BL Delft, Netherlands 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Automat, NLPR, Beijing 100190, Peoples R China 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.