论文标题
移动术：用于机载航天器姿势估计的可嵌入神经网络
Mobile-URSONet: an Embeddable Neural Network for Onboard Spacecraft Pose Estimation
论文作者
论文摘要
航天器姿势估计是一种必不可少的计算机视觉应用，可以提高轨内操作的自主权。 ESA/Stanford竞赛带来了似乎与对航天器上的板载计算机的约束几乎不兼容的解决方案。 Ursonet是其概括能力的竞争中最好的之一，但付出了大量参数和较高的计算复杂性。在本文中，我们提出了移动术：一个航天器构成估计卷积神经网络，参数少了178倍，而与Ursonet相比，降解准确性不超过四倍。
Spacecraft pose estimation is an essential computer vision application that can improve the autonomy of in-orbit operations. An ESA/Stanford competition brought out solutions that seem hardly compatible with the constraints imposed on spacecraft onboard computers. URSONet is among the best in the competition for its generalization capabilities but at the cost of a tremendous number of parameters and high computational complexity. In this paper, we propose Mobile-URSONet: a spacecraft pose estimation convolutional neural network with 178 times fewer parameters while degrading accuracy by no more than four times compared to URSONet.
