Mobile-URSONet：一种用于航天器在轨位姿估计的可嵌入式神经网络
Mobile-URSONet: an Embeddable Neural Network for Onboard Spacecraft Pose Estimation
AI总结：
针对航天器位姿估计中URSONet参数量大、计算复杂度高的问题，提出可嵌入式网络Mobile-URSONet，在精度下降不超过4倍的前提下将参数量减少了178倍。
摘要：
航天器位姿估计是一项关键的计算机视觉应用，能够提升在轨操作的自主性。ESA/Stanford竞赛提出的方案似乎难以适应航天器机载计算机的约束。URSONet凭借其泛化能力在竞赛中名列前茅，但代价是参数量庞大且计算复杂度高。本文提出Mobile-URSONet：一种位姿估计卷积神经网络，参数量减少178倍，且精度下降不超过4倍。
英文摘要：
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.
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。
