A Vision-based Method for 3D Pose Estimation of Non-cooperative Space Target

Establishing correspondence between the target model and the input image is an important step for the pose estimation of non-cooperative space tar get. Current methods always rely on complex image features and generation of candidate, which can be costly and time consuming. To solve the problems above, this paper proposes a pose estimation method that first conducts initial estimation based on deep neural network and then conducts accurate estimation through correspondence between the known tar get model and the input image is proposed. The deep neural network provides the stable initial value which reduces the candidates of correspondence between the tar get model and image. In addition, a more efficient feature extraction and matching method is adopted in this paper instead of complex multi-dimensional features. The simulation results show that the method proposed performs well both in efficiency and accuracy.