A Sim-to-Real Object Recognition and Localization Framework for Industrial Robotic Bin Picking-Reference-Cited by-同舟云学术

A Sim-to-Real Object Recognition and Localization Framework for Industrial Robotic Bin Picking

Published:2022-04 Issue:2 Volume:7 Page:3961-3968
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Li Xianzhi¹^ORCID,Cao Rui¹^ORCID,Feng Yidan¹,Chen Kai¹,Yang Biqi¹,Fu Chi-Wing¹^ORCID,Li Yichuan²^ORCID,Dou Qi¹^ORCID,Liu Yun-Hui²^ORCID,Heng Pheng-Ann¹^ORCID

Affiliation:

1. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong kong

2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong kong

Funder

Hong Kong Centre for Logistics Robotics

Hong Kong Research Grants Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Computer Vision and Pattern Recognition,Mechanical Engineering,Human-Computer Interaction,Biomedical Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/7083369/9647862/09706346.pdf?arnumber=9706346

Reference49 articles.

2. Optimizing RGB-D Fusion for Accurate 6DoF Pose Estimation

3. Flexible 3D localization of planar objects for industrial bin-picking with monocamera vision system

4. PVNet: Pixel-Wise Voting Network for 6DoF Pose Estimation

5. Development of structured light based bin picking system using primitive models

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