TY - GEN
T1 - A New Robotic Grasp Detection Method based on RGB-D Deep Fusion∗
AU - Ma, Hao
AU - Yuan, Ding
AU - Wang, Qingke
AU - Zhang, Hong
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Grasping is one of the most widely used tasks of robots. The application of computer vision can improve robot intelligence. Previous methods simply treated the problem of robotic grasping detection similar to object detection, which ignores the characteristics of the grasping problem, leading to a loss of accuracy. Additionally, treating depth images equally with RGBs is unreasonable. This study proposes a new grasp detection model using an RGB-D deep fusion module that combines multi-scale RGB and depth features. An adaptive anchor box-setting method based on a two-step approximation was designed. With the network-sharing structures of target and grasp detection, the target category and appropriate grasp posture can be obtained end-To-end. Experiments show that compared with other models, ours achieves significant improvement in accuracy while maintaining real-Time computing performance.
AB - Grasping is one of the most widely used tasks of robots. The application of computer vision can improve robot intelligence. Previous methods simply treated the problem of robotic grasping detection similar to object detection, which ignores the characteristics of the grasping problem, leading to a loss of accuracy. Additionally, treating depth images equally with RGBs is unreasonable. This study proposes a new grasp detection model using an RGB-D deep fusion module that combines multi-scale RGB and depth features. An adaptive anchor box-setting method based on a two-step approximation was designed. With the network-sharing structures of target and grasp detection, the target category and appropriate grasp posture can be obtained end-To-end. Experiments show that compared with other models, ours achieves significant improvement in accuracy while maintaining real-Time computing performance.
UR - https://www.scopus.com/pages/publications/85138684318
U2 - 10.1109/RCAR54675.2022.9872259
DO - 10.1109/RCAR54675.2022.9872259
M3 - 会议稿件
AN - SCOPUS:85138684318
T3 - 2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022
SP - 425
EP - 430
BT - 2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022
Y2 - 17 July 2022 through 22 July 2022
ER -