Manipulator grabbing position detection with information fusion of color image and depth image using deep learning

被引：128

作者：

Jiang, Du ^{[1
]}

Li, Gongfa ^{[1
,3
]}

Sun, Ying ^{[1
]}

Hu, Jiabing ^{[1
]}

Yun, Juntong ^{[2
]}

Liu, Ying ^{[2
]}

机构：

[1] Wuhan Univ Sci & Technol, Minist Educ, Key Lab Met Equipment & Control Technol, Wuhan 430081, Peoples R China

[2] Wuhan Univ Sci & Technol, Res Ctr Biomimet Robot & Intelligent Measurement, Wuhan 430081, Peoples R China

[3] Wuhan Univ Sci & Technol, Hubei Key Lab Mech Transmiss & Mfg Engn, Wuhan 430081, Peoples R China

来源：

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING | 2021年 / 12卷 / 12期

基金：

中国国家自然科学基金;

关键词：

Manipulator; Grabbing position detection; Information fusion; Deep learning; GESTURE RECOGNITION; MULTIOBJECT; NETWORK;

D O I：

10.1007/s12652-020-02843-w

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In order to ensure stable gripping performance of manipulator in a dynamic environment, a target object grab setting model based on the candidate region suggestion network is established with the multi-target object and the anchor frame generation measurement strategy overcoming external environmental interference factors such as mutual interference between objects and changes in illumination. In which, the success rate of model detection is improved by adding small-scale anchor values for small area grabbing target position detection. Further, 94.3% crawl detection success rate is achieved on the multi-target detection data sets using the information fusion of color image and depth image. The methods in this paper effectively improve the model's robustness and crawl success rate.

引用

页码：10809 / 10822

页数：14

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