Satellite pose estimation network based on dual-channel ResNet50

Abstract

In the satellite pose estimation problem, the deep learning method is used to train the network. The satellite pose needs to estimate the rotation (R) and translation (T), which are difficult to be well estimated simultaneously due to the internal coupling interaction. To solve the above problems, a dual-channel satellite pose estimation network based on ResNet50 is proposed to decouple the rotation and translation of satellite, effectively avoid the interaction, and estimate the translation and rotation of satellite respectively through the constructed network, which improves the recognition effect of satellite attitude. Through experimental verification, the network model constructed in this paper has better effect on the estimation of rotation and translation compared with other methods.