Long-range relative pose estimation and optimization of a failure satellite

To improve the accuracy of pose estimation of long-range non-cooperative targets with slow rotation, a method for relative pose estimation is proposed based on fusion of image super-resolution and visual Simultaneous Localization And Mapping (SLAM). The method mainly includes three steps. First, a gradient guidance Generative Adversarial Network (GAN)-based super-resolution model is utilized to improve the quality of images, so as to obtain more and higher quality feature points. Second, a feature database is constructed to match the current frame with the feature database, so as to improve tracking stability of the rotating target. Thirdly, pose graph optimization is carried out in multiple frames to optimize the joint pose, so as to eliminate the cumulative error and obtain more accurate estimation results. To stablize the training of GAN, an evolutionary algorithm is introduced. To enhance the generalization and robustness of the model, the dataset is obtained by semi-physical simulation. Experimental results show that when the imaging distance is equivalent to 25 m and the target is rotating at 25 (°)/s, our algorithm can realize continuous stable measurement after the images are enhanced by the super-resolution model. © 2021, Beihang University Aerospace Knowledge Press. All right reserved.