Unreal mask: one-shot multi-object class-based pose estimation for robotic manipulation using keypoints with a synthetic dataset

Abstract

Object pose estimation is a prerequisite for many robotic applications. Preparing dataset for network training is a challenging part of the pose estimation approaches, and in most of them, the network can detect just the trained objects. Synthetic data are used to train deep neural networks in robotic manipulation as a promising method for obtaining a huge amount of prelabeled training data, which are generated safely. We are to investigate the reality gap in the pose estimation of intra-category objects from a single RGB-D image using keypoints. The proposed approach in this paper provides a fast and simple procedure for training a deep neural network to identify the object and its keypoints based on synthetic dataset and autolabeling program. To our knowledge, this is the first deep network trained only on synthetic data that can find keypoints of intra-category objects for pose estimation purposes. The speed of training and the simplicity of this method make it very easy to add a new class of objects to the system which is the main advantage of this approach. Using this approach, we demonstrate a near-real-time system estimating object poses with sufficient accuracy for real-world semantic grasping and manipulating of intra-category objects in clutter by a real robot.