Grasping pose estimation for SCARA robot based on deep learning of point cloud

Abstract

With the development of 3D measurement technology, 3D vision sensors and object pose estimation methods have been developed for robotic loading and unloading. In this work, an end-to-end deep learning method on point clouds, PointNetRGPE, is proposed to estimating the grasping pose of SCARA robot. In PointNetRGPE model, the point cloud and class number are fused into a point-class vector, and several PointNet-like networks are used to estimate the robot grasping pose, containing 3D translation and 1D rotation. Considering that rotational symmetry is very common in man-made and industrial environments, a novel architecture is introduced into PointNetRGPE to solve the pose estimation problem with rotational symmetry in the z-axis direction. Additionally, an experimental platform is built containing an industrial robot and a binocular stereo vision system, and a dataset with three subsets is set up. Finally, the PointNetRGPE is tested on the dataset, and the success rates of three subsets are 98.89%, 98.89%, and 94.44% respectively.