Threedimensional Localization of Convolutional Neural Networks Based on Domain Randomization In this paper, we propose a 3D positioning framework based on convolutional neural network to realize the pose estimation of the target object during the mechanical arm grabbing process. First, the image data for training this network is completely synthesized by domain randomization technology to reduce the data acquisition cost and make up the gap between the simulated image and the real image. Then, the 3D positioning framework is designed based on the ResNet architecture, which consists of two parts. One is to obtain the classification of target object, and the other is to obtain the 3D coordinates of the object and the horizontal rotation angle. Finally, the target image classification and pose estimation are tested on the real images with interference and the synthetic images with interference, respectively. The results show that the proposed method has high prediction accuracy, and the effectiveness of the framework is proved by migrating it to the real physical robot arm experimental platform.

A Multitask Learning Convolutional Neural Network for Object Pose Estimation xe2x8bx86 Estimating 6D poses of objects from RGB images is very crucial for robots to interact with the surrounding environment and to cooperate with humans. It is a challenging problem due to the various shapes of objects, the occlusions among objects, as well as the complexity of the scene. In this study, we present a new multitask convolutional neural network for 6D object pose estimation, which also learns the object region in the image to further improve the estimation result. A weighted loss computed from the training samples is adopted to guarantee that the components of the estimated pose are equally accurate. Additionally, we sythesize new virtual data based on the existing training data so as to overcome the possible adverse situation caused by insufficiency of training data. Experiments on the YCBvideo dataset are undertaken to validate the effectiveness of the proposed method.