空间在轨服务中基于单目相机的航天器非合作相对导航

Aodi Wu, Shengyang Zhang, Leizheng Shu, Chaoming Si, Xue Wan
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引用次数: 0

摘要

随着空间技术的发展,对航天器在轨服务(如在轨装配)的需求越来越大。航天器相对导航是在轨服务中的一项关键技术,它保证了两个航天器安全、准确地接近。非合作航天器是指没有与之通信的航天器,因此无法从差分GNSS中获得精确的相对位置和姿态。地面制导采用地基定轨技术,可以引导两颗航天器接近200米。然而,当相对距离小于200m时,需要更高精度的相对导航。双目相机可以提供近距离内的相对位置和姿态,但由于双目相机的焦距通常较小,不适合中距离导航。因此,在使用单目摄像机时,由于距离比例尺不确定,200m ~ 10m等中距离的相对导航成为一个挑战。为了解决这一问题,本文提出了一种基于深度学习技术的单目相机相对位置视觉导航算法。导航算法采用YOLOv5目标检测技术获取航天器在图像中的位置,然后根据针孔相机模型计算出航天器在空间中的真实相对位置。该算法在NVIDIA TX2计算设备上的速度可达10fps,在200m-10m范围内的平均相对位置误差为5.16%。该算法已成功应用于在轨视觉导航任务中,实现了快速、鲁棒的导航效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Non-cooperative Spacecraft Relative Navigation Based on Monocular Camera in Space On-orbit Servicing
With the development of space technology, there is an increasing demand for spacecraft on-orbit servicing, such as on-orbit assembly. Relative navigation of spacecraft is a key technology in on-orbit servicing because it ensures the safe and accurate approach of the two spacecraft. Non-cooperative spacecraft means that there is no communication with it and therefore cannot get precise relative position and attitude from differential GNSS. The ground guidance using ground-based orbit determination technology can guide the two spacecraft to be close as 200m. However, when the relative distance is less than 200m, higher-precision relative navigation is required. Binocular cameras can provide relative position and attitude within close range, however, as the focal length of binocular cameras is usually small, they are not suitable for medium range navigation. Thus, the relative navigation in medium range, such as 200m to 10m, becomes a challenge as the distance scale is uncertain using monocular camera. To solve this problem, this paper proposes a relative position visual navigation algorithm based on deep learning technology using monocular cameras. The navigation algorithm uses YOLOv5 target detection technology to obtain the position of the spacecraft in the image, and then calculates the real relative position in space based on the pinhole camera model. The speed of the proposed algorithm can reach 10 FPS on the NVIDIA TX2 computing device, and the average relative position error is 5.16% at 200m-10m. The proposed algorithm has been successfully applied to an on-orbit visual navigation task and achieve fast and robust navigation result.
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