Affiliation:
1. Department of Aerospace Engineering and Technology, Space Engineering University, Beijing 101416, China
2. Department of Basic Course, Space Engineering University, Beijing 101416, China
3. Beijing Institute of Special Electromechanical Technology, Beijing 100191, China
Abstract
Satellite pose estimation plays a crucial role within the aerospace field, impacting satellite positioning, navigation, control, orbit design, on-orbit maintenance (OOM), and collision avoidance. However, the accuracy of vision-based pose estimation is severely constrained by the complex spatial environment, including variable solar illumination and the diffuse reflection of the Earth’s background. To overcome these problems, we introduce a novel satellite pose estimation network, FilterformerPose, which uses a convolutional neural network (CNN) backbone for feature learning and extracts feature maps at various CNN layers. Subsequently, these maps are fed into distinct translation and orientation regression networks, effectively decoupling object translation and orientation information. Within the pose regression network, we have devised a filter-based transformer encoder model, named filterformer, and constructed a hypernetwork-like design based on the filter self-attention mechanism to effectively remove noise and generate adaptive weight information. The related experiments were conducted using the Unreal Rendered Spacecraft On-Orbit (URSO) dataset, yielding superior results compared to alternative methods. We also achieved better results in the camera pose localization task, indicating that FilterformerPose can be adapted to other computer vision downstream tasks.
Funder
National Natural Science Foundation of China
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Cited by
1 articles.
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