DBPF: A Framework for Efficient and Robust Dynamic Bin-Picking-Reference-Cited by-同舟云学术

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DBPF: A Framework for Efficient and Robust Dynamic Bin-Picking

Published:2024-06 Issue:6 Volume:9 Page:5166-5173
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Li Yichuan¹^ORCID,Zhao Junkai²^ORCID,Li Yixiao²^ORCID,Wu Zheng³^ORCID,Cao Rui¹^ORCID,Tomizuka Masayoshi³^ORCID,Liu Yun-Hui¹^ORCID

Affiliation:

1. T Stone Robotics Institute, the Department of Mechanical and Automation Engineering, Chinese University of Hong Kong, Hong Kong

2. Hong Kong Centre for Logistics Robotics (HKCLR), Hong Kong

3. Department of Mechanical Engineering, University of California at Berkeley, Berkeley, CA, USA

Funder

Shenzhen Portion of Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone

InnoHK of the Government of the Hong Kong Special Administrative Region via the Hong Kong Centre for Logistics Robotics

RGC

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7083369/10504377/10496146.pdf?arnumber=10496146

Reference32 articles.

1. Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection

2. PPR-Net:Point-wise Pose Regression Network for Instance Segmentation and 6D Pose Estimation in Bin-picking Scenarios

3. ROBI: A Multi-View Dataset for Reflective Objects in Robotic Bin-Picking

4. Two-Stage Grasping: A New Bin Picking Framework for Small Objects

5. Probabilistic Multi-View Fusion of Active Stereo Depth Maps for Robotic Bin-Picking

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