Recognition of disordered workpieces based on 3D Laser scanner and RS-CNN

Abstract

In industrial production, the disordered grasping operation of the robotic arm is mostly for grasping a single type of workpiece. Effective grasping is not easy when multiple overlapping workpieces are mixed together. The mutual occlusion between workpieces causes the loss of geometric shape information, which makes it difficult to obtain the precise grasping pose of each workpiece. In this paper, a 3D laser scanner is used to acquire the point cloud features of the workpiece. At first, the point clouds are filtered and segmented, and then they are input into the RS-CNN network for recognition and classification. According to the classification results, different models are used to register the point clouds in the scene. At last, the final pose of the workpiece to be grasped is obtained, which realizes the disorderly grasping of various workpieces.