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.

翻译：精确的卫星姿态估计对于在轨服务（IOS）任务中的自主制导、导航与控制（GNC）系统至关重要。本文探讨了在利用单目图像进行卫星姿态估计时，多任务学习（MTL）框架内不同任务的影响。通过将直接姿态估计、关键点预测、目标定位和分割等任务集成到一个单一网络中，本研究旨在利用本工作中使用的卷积神经网络（CNN）的模块化特性，通过测试不同的多任务配置来评估任务间的相互影响。通过采用不同的加权策略来进一步验证研究结果的鲁棒性，从而发现了所分析任务间相互偏差的趋势。研究开发了一个合成数据集用于训练和测试该MTTL网络。结果表明，直接姿态估计和基于热图的姿态估计总体上相互促进，而边界框和分割任务均未提供显著贡献，且往往会降低整体估计精度。