不合作空间驻留物体的位姿估计是近距离操作中实现自主性的关键资产。在这种情况下，单目相机是一种有价值的解决方案，因为它们的系统要求低。然而，相关联的图像处理算法对于实时的板上实现来说在计算上太昂贵，或者不够准确。在本文中，我们提出了一个姿态估计软件，利用神经网络架构，可以扩展到不同的精度-延迟权衡。我们设计了与边缘张量处理单元兼容的管道，以展示低功耗机器学习加速器如何在太空中实现人工智能开发。测试的神经网络的基准航天器姿态估计数据集，并特意开发的Cosmo真实感数据集，它描绘了一个COSMO-SkyMed卫星在各种随机姿势和可操纵的太阳能电池板的方向。我们架构的最轻版本在两个数据集上都实现了最先进的准确性，但网络复杂性只有一小部分，在Coral Dev Board Mini上以每秒7.7帧的速度运行，仅消耗2.2W。
英文摘要：
Pose estimation of an uncooperative space resident object is a key asset towards autonomy in close proximity operations. In this context monocular cameras are a valuable solution because of their low system requirements. However, the associated image processing algorithms are either too computationally expensive for real time on-board implementation, or not enough accurate. In this paper we propose a pose estimation software exploiting neural network architectures which can be scaled to different accuracy-latency trade-offs. We designed our pipeline to be compatible with Edge Tensor Processing Units to show how low power machine learning accelerators could enable Artificial Intelligence exploitation in space. The neural networks were tested both on the benchmark Spacecraft Pose Estimation Dataset, and on the purposely developed Cosmo Photorealistic Dataset, which depicts a COSMO-SkyMed satellite in a variety of random poses and steerable solar panels orientations. The lightest version of our architecture achieves state-of-the-art accuracy on both datasets but at a fraction of networks complexity, running at 7.7 frames per second on a Coral Dev Board Mini consuming just 2.2W.
SatelliteCalculator: A Multi-Task Vision Foundation Model for Quantitative Remote Sensing Inversion
Faculty of Computer Science and Information Technology, Universiti Malaya（马来亚大学计算机科学与信息技术学院）；Faculty of Information Engineering and Automation, Kunming University of Science and Technology（昆明理工大学信息工程与自动化学院）
Test-Time Certifiable Self-Supervision to Bridge the Sim2Real Gap in Event-Based Satellite Pose Estimation
Sentient Satellites Laboratory (SSL), Australian Institute for Machine Learning (AIML), The University of Adelaide（感知卫星实验室（SSL）、澳大利亚机器学习研究所（AIML）、阿德莱德大学）；Laboratory for Information and Decision Systems (LIDS), Massachusetts Institute of Technology（信息与决策系统实验室（LIDS）、麻省理工学院）
Unifying UAV Cross-View Geo-Localization via 3D Geometric Perception
School of Electronic Information, Wuhan University（武汉大学电子信息学院）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）；Technology and Engineering Center for Space Utilization and the Key Laboratory of Space Utilization, Chinese Academy of Sciences（中国科学院空间应用工程与技术中心暨空间利用重点实验室）；School of Aeronautics and Astronautics, University of Chinese Academy of Sciences（中国科学院大学航空宇航学院）
Reconstructing Satellites in 3D from Amateur Telescope Images
College of Future Technology（未来技术学院）；National Biomedical Imaging Center（国家生物医学成像中心）；College of Engineering（工程学院）；State Key Laboratory of Multimedia Information Processing（多媒体信息处理国家重点实验室）；National Engineering Research Center of Visual Technology（视觉技术国家工程研究中心）；School of Computer Science（计算机科学学院）；Beijing Jingdun Technology Co., Ltd.（北京竞 Dunn 技术有限公司）；National Laboratory on Adaptive Optics（自适应光学国家实验室）；Institute of Optics and Electronics（光学与电子学研究所）；Chinese Academy of Sciences（中国科学院）
