A Grasping Pose Detection Algorithm for Occluded Objects

To solve the problem of difficult grasping detection of occluded objects, this paper proposes a Channel Attention Grasp Convolution Neural Network (CA-GraspNet) based on convolution neural networks and channel attention mechanisms. CA-GraspNet integrates residual module and the SENet module. This module enables CNN to focus more on the no occluded area and less on the occluded area, overcoming the low accuracy of the existing methods in identifying occluded objects. The occluded datasets are obtained through data enhancement of Cornell and Jacquard grasp datasets, and the occluded datasets are used for algorithm verification. The experimental results show that CA-GraspNet can obtain 97% detection accuracy on Cornell occluded datasets, and 88% on Jacquard occluded datasets, which is higher than similar algorithms. Furthermore, CA-GraspNet also shows approximate performance in grasping detection.