DGCM-Net: Dense Geometrical Correspondence Matching Network for Incremental Experience-Based Robotic Grasping-Reference-Cited by-同舟云学术

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DGCM-Net: Dense Geometrical Correspondence Matching Network for Incremental Experience-Based Robotic Grasping

Published:2020-09-17 Issue: Volume:7 Page:
ISSN:2296-9144
Container-title:Frontiers in Robotics and AI
language:
Short-container-title:Front. Robot. AI

Author:

Patten Timothy,Park Kiru,Vincze Markus

Funder

Austrian Science Fund

Publisher

Frontiers Media SA

Subject

Artificial Intelligence,Computer Science Applications

Reference70 articles.

1. Global search with Bernoulli alternation kernel for task-oriented grasping informed by simulation;Antonova,2018

2. Pose guided RGBD feature learning for 3D object pose estimation;Balntas,2017

3. Grasping familiar objects using shape context;Bohg,2009

4. Data-driven grasp synthesis–A survey;Bohg;IEEE Trans. Robot,2014

5. Learning to manipulate unknown objects in clutter by reinforcement;Boularias,2015

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2. FastGNet: an efficient 6-DOF grasp detection method with multi-attention mechanisms and point transformer network;Measurement Science and Technology;2024-01-18

3. Robotic Grasp Detection Using Structure Prior Attention and Multiscale Features;IEEE Transactions on Systems, Man, and Cybernetics: Systems;2024

4. Self-supervised Vision Transformers for 3D pose estimation of novel objects;Image and Vision Computing;2023-11

5. PSiamRML: Target Recognition and Matching Integrated Localization Algorithm Based on Pseudo-Siamese Network;International Journal of Aerospace Engineering;2023-10-12

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