多任务学习提升卫星姿态估计精度，关键任务组合效果更优
Optimizing Multi-Task Learning for Accurate Spacecraft Pose Estimation
- 将姿态估计、关键点预测等任务融合进单一网络，测试不同组合影响
- 直接姿态估计与热图法相互促进，边界框和分割任务反而降低精度
- 适合从事航天自主导航、单目视觉姿态估计的研究者参考
准确的卫星姿态估计对在轨服务任务中的自主制导、导航与控制（GNC）系统至关重要。本文研究了基于单目图像的多任务学习（MTL）框架中不同任务对卫星姿态估计的影响。通过将直接姿态估计、关键点预测、目标定位和分割任务整合至同一卷积神经网络（CNN），利用其模块化特性测试多种多任务配置下的任务间相互作用。采用不同权重策略分析任务间的相互偏差趋势，验证结果的鲁棒性。构建了一个合成数据集用于训练和测试MTL网络。结果显示，直接姿态估计与基于热图的姿态估计通常能相互促进；而边界框和分割任务未带来显著增益，且倾向于降低整体估计精度。
原文摘要 · Abstract (English)
Accurate satellite pose estimation is crucial for autonomous guidance, navigation, and control (GNC) systems in in-orbit servicing (IOS) missions. This paper explores the impact of different tasks within a multi-task learning (MTL) framework for satellite pose estimation using monocular images. By integrating tasks such as direct pose estimation, keypoint prediction, object localization, and segmentation into a single network, the study aims to evaluate the reciprocal influence between tasks by testing different multi-task configurations thanks to the modularity of the convolutional neural network (CNN) used in this work. The trends of mutual bias between the analyzed tasks are found by employing different weighting strategies to further test the robustness of the findings. A synthetic dataset was developed to train and test the MTL network. Results indicate that direct pose estimation and heatmap-based pose estimation positively influence each other in general, while both the bounding box and segmentation tasks do not provide significant contributions and tend to degrade the overall estimation accuracy.
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