TY - GEN
T1 - Residual Squeeze-and-Excitation Network with Multi-scale Spatial Pyramid Module for Fast Robotic Grasping Detection
AU - Cao, Hu
AU - Chen, Guang
AU - Li, Zhijun
AU - Lin, Jianjie
AU - Knoll, Alois
N1 - Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - This paper proposes an efficient, fully convolutional neural network to generate robotic grasps by using 300×300 depth images as input. Specifically, a residual squeeze- and-excitation network (RSEN) is introduced for deep feature extraction. Following the RSEN block, a multi-scale spatial pyramid module (MSSPM) is developed to obtain multi-scale contextual information. The outputs of each RSEN block and MSSPM are combined as inputs for hierarchical feature fusion. Then, the fused global features are upsampled to perform pixel-wise learning for grasping pose estimation. The experimental results on Cornell and Jacquard grasping datasets indicate that the proposed method has a fast inference speed of 5ms while achieving high grasp detection accuracy of 96.4% and 94.8% on Cornell and Jacquard, respectively, which strikes a balance between accuracy and running speed. Our method also gets a 90% physical grasp success rate with a UR5 robot arm.
AB - This paper proposes an efficient, fully convolutional neural network to generate robotic grasps by using 300×300 depth images as input. Specifically, a residual squeeze- and-excitation network (RSEN) is introduced for deep feature extraction. Following the RSEN block, a multi-scale spatial pyramid module (MSSPM) is developed to obtain multi-scale contextual information. The outputs of each RSEN block and MSSPM are combined as inputs for hierarchical feature fusion. Then, the fused global features are upsampled to perform pixel-wise learning for grasping pose estimation. The experimental results on Cornell and Jacquard grasping datasets indicate that the proposed method has a fast inference speed of 5ms while achieving high grasp detection accuracy of 96.4% and 94.8% on Cornell and Jacquard, respectively, which strikes a balance between accuracy and running speed. Our method also gets a 90% physical grasp success rate with a UR5 robot arm.
UR - http://www.scopus.com/inward/record.url?scp=85125492087&partnerID=8YFLogxK
U2 - 10.1109/ICRA48506.2021.9561836
DO - 10.1109/ICRA48506.2021.9561836
M3 - Conference contribution
AN - SCOPUS:85125492087
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 2783
EP - 2789
BT - 2021 IEEE International Conference on Robotics and Automation, ICRA 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Robotics and Automation, ICRA 2021
Y2 - 30 May 2021 through 5 June 2021
ER -