A Novel Vision-Based Grasping Method Under Occlusion for Manipulating Robotic System

Abstract

The capability to grasp the target object is significant for manipulating robotic systems to offer better services, and it is still challenging under occlusion. This paper proposes a novel vision-based grasping method with a SSD-based detector, an image inpainting and recognition network (IRNet), and a deep grasping guidance network (DgGNet). Based on the clustering of point cloud, IRNet with the combination of a three-stage image inpainting network and a recognition network MobileNet v2 is introduced to detect the occluded object that cannot be found by the detector. Then, the best grasp for the object to be grasped is obtained by DgGNet, which provides the guidance of the manipulator movement. The image inpainting is firstly introduced into the object detection of manipulating robotic system where the recognition based on inpainting result improves the robustness to occlusion. Experimental results validate the effectiveness of the proposed method.