Robotic Grasp Detection With 6-D Pose Estimation Based on Graph Convolution and Refinement-Reference-Cited by-同舟云学术

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Robotic Grasp Detection With 6-D Pose Estimation Based on Graph Convolution and Refinement

Published:2024-06 Issue:6 Volume:54 Page:3783-3795
ISSN:2168-2216
Container-title:IEEE Transactions on Systems, Man, and Cybernetics: Systems
language:
Short-container-title:IEEE Trans. Syst. Man Cybern, Syst.

Author:

Yu Sheng¹^ORCID,Zhai Di-Hua¹^ORCID,Xia Yuanqing¹^ORCID,Wang Wei²,Zhang Chengyu²,Zhao Shiqi²^ORCID

Affiliation:

1. School of Automation, Beijing Institute of Technology, Beijing, China

2. Research Institute of China United Network Communications Corporation Limited, Beijing, China

Funder

National Natural Science Foundation of China

“Xiaomi Young Scholars” from Xiaomi Foundation

China Unicom Innovation Ecological Cooperation Plan

BIT Research and Innovation Promoting Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6221021/10532208/10472971.pdf?arnumber=10472971

Reference41 articles.

1. Development of Sensory-Motor Fusion-Based Manipulation and Grasping Control for a Robotic Hand-Eye System

2. Deep object pose estimation for semantic robotic grasping of household objects;Tremblay

3. A Two-Stream CNN With Simultaneous Detection and Segmentation for Robotic Grasping

4. SE-ResUNet: A Novel Robotic Grasp Detection Method

5. 6-DOF GraspNet: Variational Grasp Generation for Object Manipulation

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