VERGNet: Visual Enhancement Guided Robotic Grasp Detection Under Low-Light Condition-Reference-Cited by-同舟云学术

VERGNet: Visual Enhancement Guided Robotic Grasp Detection Under Low-Light Condition

Published:2023-12 Issue:12 Volume:8 Page:8541-8548
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Niu Mingdi¹^ORCID,Lu Zhenyu²^ORCID,Chen Lu¹^ORCID,Yang Jing³^ORCID,Yang Chenguang²^ORCID

Affiliation:

1. Institute of Big Data Science and Industry, School of Computer and Information Technology, Shanxi University, Taiyuan, China

2. Faculty of Environment and Technology, Bristol Robotics Lab, University of the West of England, Bristol, U.K.

3. School of Automation and Software Engineering, Shanxi University, Taiyuan, China

Funder

National Natural Science Foundation of China

H2020 Marie Skodowska-Curie Actions Individual Fellowship

Fundamental Research Program of Shanxi Province

Science and Technology Major Project of Shanxi Province

Central Guidance for Local Scientific and Technological Development Funds

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/ielx7/7083369/10287676/10310093.pdf?arnumber=10310093

Reference28 articles.

1. A Context-Free Method for Robust Grasp Detection: Learning to Overcome Contextual Bias

2. Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network

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

4. Visual-tactile robot grasping based on human skill learning from demonstrations using a wearable parallel hand exoskeleton;lu;IEEE Robot Automat Lett,2023

5. Convolutional multi-grasp detection using grasp path for RGBD images

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