Pose estimation for non-cooperative satellites based on a three-dimensional bounding box detection network

Detecting predefined key points on the satellite body in monocular images is a key method for performing satellite pose estimation. However, existing methods always design specialized predefined key points for a single satellite, which results in the low universality of such visual-based methods. Moreover, under remote observation conditions, the information on the surface of the satellite is not clear, and this complex presetting of key points is not easily detected. In this study, a neural network capable of predicting a 3D bounding box with relative depth information detection is proposed as a means of adapting to different satellite targets using a more general and geometrically simple definition of key points. Furthermore, the network is able to estimate satellite rotation accurately based on the aforementioned prediction results. The generalization ability of the proposed CNN-based model was enhanced by conducting network training and testing using a custom-built synthetic image dataset composed of a collection of 15 distinct satellite models captured using a virtual camera. The testing results demonstrate that the proposed pose estimation approach obtains accurate performance for non-cooperative targets with distinct shapes in the absence of predefined key point designs. Moreover, the observed accuracy of pose estimation is consistent with that of existing advanced models trained with only a single satellite target.