Pose estimation of metal workpieces based on RPM-Net for robot grasping from point cloud

Many metal workpieces have the characteristics of less texture, symmetry and reflectivity, which presents a challenge to existing pose estimation methods. The purpose of this paper is to propose a pose estimation method for grasping metal workpieces by industrial robots.

Dual-hypothesis robust point matching registration network (RPM-Net) is proposed to estimate pose from point cloud. The proposed method uses the Point Cloud Library (PCL) to segment workpiece point cloud from scenes and a trained-well robust point matching registration network to estimate pose through dual-hypothesis point cloud registration.

In the experiment section, an experimental platform is built, which contains a six-axis industrial robot, a binocular structured-light sensor. A data set that contains three subsets is set up on the experimental platform. After training with the emulation data set, the dual-hypothesis RPM-Net is tested on the experimental data set, and the success rates of the three real data sets are 94.0%, 92.0% and 96.0%, respectively.

The contributions are as follows: first, dual-hypothesis RPM-Net is proposed which can realize the pose estimation of discrete and less-textured metal workpieces from point cloud, and second, a method of making training data sets is proposed using only CAD models with the visualization algorithm of the PCL.