发表机构
Politecnico di Torino（都灵理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出循环卷积神经网络，利用LiDAR深度图像序列对旋转航天器进行姿态初始化，在仿真中相比传统CNN降低了误差并提高了收敛率。
AI 中文摘要
精确的姿态估计对于自主在轨服务和近距离操作至关重要。本工作提出了一种循环卷积神经网络（RCNN），用于利用LiDAR导出的深度图像对已知的、可能翻滚的航天器进行粗略姿态初始化。通过处理二维点云投影的时间序列，RCNN有效处理了对称性、遮挡和退化传感问题。在多种航天器几何形状、角速度和距离下的仿真表明，在所采用的实验框架内，RCNN比传统CNN基线产生更低的初始化误差和更高的收敛率，其性能在不同角速度条件下有所变化。
英文摘要
Accurate attitude estimation is essential for autonomous in-orbit servicing and proximity operations. This work proposes a Recurrent Convolutional Neural Network (RCNN) used in coarse attitude initialization of known, possibly tumbling spacecraft using LiDAR-derived depth images. By processing temporal sequences of 2D point-cloud projections, the RCNN effectively handles symmetries, occlusions, and degraded sensing. Simulations across various spacecraft geometries, angular velocities, and ranges show that the RCNN yields lower initialization errors and higher convergence rates than conventional CNN baseline within the adopted experimental framework, with performance varying across angular velocity conditions.
Journal refControl Engineering Practice, Volume 175, 2026, 107134, ISSN 0967-0661
DOI：10.1016/j.conengprac.2026.107134
