3D Object Recognition Based on PointNet and Sparse Point Cloud

Abstract

Due to the importance of factory automation in industries such as manufacturing, which reduces labor costs, repetitive jobs and overcomes dangerous processing environments, this study focuses on developing a 3D visual object recognition and posture estimation method to achieve an automated grasping system. In this study, the object point cloud data is obtained by Ensenso 3D camera, where the 3D object recognition is trained based on the PointNet++ neural network model architecture. For the mechanical arm posture part, an optimal grasping database of the object is established to achieve the tasks. After the point cloud preprocessing (filtering, segmentation, centroid transfer), the iterative closest point algorithm is used to match the target object and then it obtains the rotation and translation matrices to calculate the target object grasping posture.