Mask-RCNN based object segmentation and distance measurement for Robot grasping

被引：0

作者：

Jeong, Dong-Kyo ^{[1
]}

Kang, Ho-Sun ^{[1
]}

Kim, Dong-Eon ^{[1
]}

Lee, Jang-Myung ^{[2
]}

机构：

[1] Pusan Natl Univ, Dept Elect & Comp Engn, Busan 46241, South Korea

[2] Pusan Natl Univ, Dept Elect Engn, Busan 46241, South Korea

来源：

2019 19TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS 2019) | 2019年

关键词：

Mask-RCNN; Object Segmentation; Distance Measurement; Stereo Matching; Object Grasping;

D O I：

10.23919/iccas47443.2019.8971673

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper proposes a new application that the object can be determined by the optimal model. To extract the target from the clutter background accurately, Mask-RCNN (Mask-Region Convolutional Neural Network) model is utilized for the segmentation process. Meanwhile, target object in front of the camera can be localized with the help of the Mask-RCNN segmentation and the geometric stereo matching method. Experiments show that distance values are calculated efficiently. And then the robot manipulator is performed to grasp the target object effectively.

引用

页码：671 / 674

页数：4

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