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Keypoint-based robotic grasp detection scheme in multi-object scenes
Li, Tong1; Wang F(王斐)2; Ru CL(茹常磊)1; Jiang Y(姜勇)3; Li JH(李景宏)1
Department工艺装备与智能机器人研究室
Source PublicationSensors
ISSN1424-8220
2021
Volume21Issue:6Pages:1-15
Indexed ByEI
EI Accession number20211210101881
Contribution Rank3
Funding OrganizationFoundation of National Natural Science Foundation of China under Grant 61973065, 52075531 ; Fundamental Research Funds for the Central Universities of China under Grant N182612002
Keywordrobot grasping CNN keypoint multi-object scenes Cornell dataset VMRD
Abstract

Robot grasping is an important direction in intelligent robots. However, how to help robots grasp specific objects in multi-object scenes is still a challenging problem. In recent years, due to the powerful feature extraction capabilities of convolutional neural networks (CNN), various algorithms based on convolutional neural networks have been proposed to solve the problem of grasp detection. Different from anchor-based grasp detection algorithms, in this paper, we propose a keypoint-based scheme to solve this problem. We model an object or a grasp as a single point—the center point of its bounding box. The detector uses keypoint estimation to find the center point and regress to all other object attributes such as size, direction, etc. Experimental results demonstrate that the accuracy of this method is 74.3% in the multi-object grasp dataset VMRD, and the performance on the single-object scene Cornell dataset is competitive with the current state-of-the-art grasp detection algorithm. Robot experiments demonstrate that this method can help robots grasp the target in single-object and multi-object scenes with overall success rates of 94% and 87%, respectively.

Language英语
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/28632
Collection工艺装备与智能机器人研究室
Corresponding AuthorWang F(王斐)
Affiliation1.College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
2.Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110169, China
3.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
Recommended Citation
GB/T 7714
Li, Tong,Wang F,Ru CL,et al. Keypoint-based robotic grasp detection scheme in multi-object scenes[J]. Sensors,2021,21(6):1-15.
APA Li, Tong,Wang F,Ru CL,Jiang Y,&Li JH.(2021).Keypoint-based robotic grasp detection scheme in multi-object scenes.Sensors,21(6),1-15.
MLA Li, Tong,et al."Keypoint-based robotic grasp detection scheme in multi-object scenes".Sensors 21.6(2021):1-15.
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