MACHINE PERCEPTION AND ROBOTICS GROUP

機械知覚&ロボティクスグループ
中部大学

We propose a 6D pose estimation method for an object from a single RGB image for a robotic grasping task. Many approaches estimate pose parameters from images taken from other viewpoints and use deep learning to achieve high accuracy. However, most of these methods are not robust to changes in object texture, and there is a possibility that the correct pose cannot be estimated by only one-time inference. Our aims are to reduce the number of failure cases and improve the accuracy by a novel architecture using the iterative backpropagation of a pose decoder network and pose estimation on intermediate representation. The error between random and target pose parameters are backpropagated to a neural network and the gradient for approaching the target pose is obtained. The pose parameter is updated using the obtained gradient, the error is calculated again, and backpropagation is re-performed. Repeating this process, we estimate a more accurate pose. Experiments using our own dataset show that estimation accuracy is improved and the number of failure cases is reduced. Furthermore, estimation by coarse-to-fine iterative processing is more accurate and faster. We also experiment with grasping using a UR5 robot and show that the robot can grasp objects without depth information when using the pose estimated by the proposed method.