弥合领域差距的飞行级空间borne视觉
Bridging the Domain Gap for Flight-Ready Spaceborne Vision
AI总结：
SPNv3是一种高效、飞行级的神经网络模型，通过数据增强和视觉Transformer架构，在合成图像上训练后实现对真实空间borne图像的高精度姿态估计，适用于近距 rendezvous和接近操作。
摘要：
本文提出了空间姿态网络v3（SPNv3），一种用于估计已知非合作目标航天器单目姿态的神经网络（NN）。SPNv3被设计和训练以在计算效率的同时提供对未在地面离线训练和验证中观察到的空间borne图像的鲁棒性。这些特性对于在空间级边缘设备上部署NN至关重要。这些特性通过精心的NN设计选择和广泛的权衡分析得以实现，揭示了数据增强、迁移学习和视觉Transformer架构等特征，这些特征有助于同时最大化鲁棒性和最小化计算开销。实验表明，最终的SPNv3可以在机器人测试台的硬件在环图像上实现最先进的姿态精度，同时仅在计算机生成的合成图像上进行训练，有效地弥合了合成与真实图像之间的领域差距。同时，SPNv3在具有飞行遗产的代表图形处理单元系统上测试时，其运行速度超过了现代卫星导航滤波器的更新频率。总体而言，SPNv3是一种高效、飞行级的NN模型，可直接应用于近距 rendezvous和接近操作与目标在轨空间物体。
英文摘要：
This work presents Spacecraft Pose Network v3 (SPNv3), a Neural Network (NN) for monocular pose estimation of a known, non-cooperative target spacecraft. SPNv3 is designed and trained to be computationally efficient while providing robustness to spaceborne images that have not been observed during offline training and validation on the ground. These characteristics are essential to deploying NNs on space-grade edge devices. They are achieved through careful NN design choices, and an extensive trade-off analysis reveals features such as data augmentation, transfer learning and vision transformer architecture as a few of those that contribute to simultaneously maximizing robustness and minimizing computational overhead. Experiments demonstrate that the final SPNv3 can achieve state-of-the-art pose accuracy on hardware-in-the-loop images from a robotic testbed while having trained exclusively on computer-generated synthetic images, effectively bridging the domain gap between synthetic and real imagery. At the same time, SPNv3 runs well above the update frequency of modern satellite navigation filters when tested on a representative graphical processing unit system with flight heritage. Overall, SPNv3 is an efficient, flight-ready NN model readily applicable to close-range rendezvous and proximity operations with target resident space objects.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
