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.

Fast Graspability Evaluation (FGE) has been proposed as a method for detecting grasping positions on objects and is now being used for industrial robots. FGE uses convolution of hand templates with regions on the target object to estimate the optimum grasping posture. However, the hand opening width and rotation angles must be set with high resolution to achieve highly accurate results and the computational load is high. To address that issue, we propose a method in which hand templates are represented in compact form for faster processing by using singular value decomposition. Applying singular value decomposition enables hand templates to be represented as linear combinations of a small number of eigenvalue templates and eigenfunctions. Eigenfunctions take discrete values, but response values can be calculated with arbitrary parameters by fitting a continuous function. Experimental results show that the proposed method reduces computation time by two thirds while maintaining the same detection accuracy as conventional FGE for both parallel hands and three-finger hands.