RGB-D Instance Segmentation-based Suction Point Detection for Grasping-Reference-Cited by-同舟云学术

RGB-D Instance Segmentation-based Suction Point Detection for Grasping

Published:2022-12-05 Issue: Volume: Page:
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Container-title:2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)
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Author:

Fu Yeyu¹,Sun Ting¹,Wang Li²,Yu Shijie¹,Deng Lei¹,Chen Baohua³,Li Lin²,Xie Yusen¹,Deng Shuixin¹,Yin Hanxi³

Affiliation:

1. Beijing Information Science & Technology University,Joint International Research Laboratory of Advanced Photonics and Elextronics,Beijing,China

2. Beijing Institute of Control Engineering,Space Optoelectronic Measurement and Perception Lab.,Department of Perception zhong-guancun Open Lab.,Beijing,China

3. Tsinghua University,Department of Automation,Beijing,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10011626/10011636/10011780.pdf?arnumber=10011780

Reference22 articles.

1. Suction Grasp Region Prediction Using Self-supervised Learning for Object Picking in Dense Clutter

2. GraspFusion: Realizing Complex Motion by Learning and Fusing Grasp Modalities with Instance Segmentation

3. GKNet: Grasp keypoint network for grasp candidates detection

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1. Uncertainty-Aware Suction Grasping for Cluttered Scenes;IEEE Robotics and Automation Letters;2024-06

2. STOPNet: Multiview-based 6-DoF Suction Detection for Transparent Objects on Production Lines;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

3. Instance segmentation of stack composed of unknown objects;Engineering Applications of Artificial Intelligence;2023-11