Abstract: With the advantages of measurement accuracy and low equipment cost, the satellite pose estimation method based on monocular image has a broad prospect in rendezvous and docking, space attack-defense and other applications. Due to the strong power of feature extraction and representation, the convolutional neural network has achieved significantly better performance than traditional methods in monocular pose estimation. However, the existing methods based on convolutional neural network have some problems, such as inductive bias, indirect description of absolute distance, and lack of long-distance modeling ability. Considering the application requirements of satellite monocular pose estimation, this paper applies the transformer model for satellite pose estimation innovatively to overcome the problems above, and proposes a novel end-to-end satellite monocular pose estimation method. A satellite target representation method is proposed based on the set of key points, and the loss function based on the representation method is established. Then, an end-to-end key point regression network model is developed based on characteristics of the key point regression task, and the backbone network structure for feature extraction is improved. Experimental results on public datasets show that the proposed method can achieve reliable and efficient monocular pose estimation of satellite targets, demonstrating better performance than existing similar methods.

Key words: satellite pose estimation, transformer model, no-cooperative target, attention mechanism, key points regression