The 6D object pose obtained from single RGB image has broad applications such as robotic manipulation and virtual reality. However, the deep learning-based pose estimation methods usually require a large amount of trainging data to improve the generalization ability of the model, and in general, the common data generation methods have great challenges in high cost of data collection and lack of 3D information. This paper proposes a 6D object pose estimation network with low-quality rendering images. In this network, the feature extraction part takes a single RGB image as the input of the network, and uses the residual network to extract the features of this image. The classification stream of the pose estimation part predicts the category of the target object, and the regression stream returns the rotation angle and translation vector of the target object in 3D space. Moreover, the domain randomization method is used to establish a large-scale low-quality rendering images with the 3D spatial position information in a low collection cost. The experimental results on the established Pose6DDR dataset and the public LineMod dataset verify the superiority of the proposed pose estimation method and the effectiveness of the established large-scale simulation dataset.