Cameras are rapidly becoming the choice for on-board sensors towards space rendezvous due to their small form factor and inexpensive power, mass, and volume costs. When it comes to docking, however, they typically serve a secondary role, whereas the main work is done by active sensors such as lidar. This paper documents the development of a proposed AI-based (artificial intelligence) navigation algorithm intending to mature the use of on-board visible wavelength cameras as a main sensor for docking and on-orbit servicing (OOS), reducing the dependency on lidar and greatly reducing costs. Specifically, the use of AI enables the expansion of the relative navigation solution towards multiple classes of scenarios, e.g., in terms of targets or illumination conditions, which would otherwise have to be crafted on a case-by-case manner using classical image processing methods. Multiple convolutional neural network (CNN) backbone architectures are benchmarked on synthetically generated data of docking manoeuvres with the International Space Station (ISS), achieving position and attitude estimates close to 1% range-normalised and 1deg, respectively, an established rule of thumb for the navigation measurement accuracy during final approach. The integration of the solution with a physical prototype of the refuelling mechanism is validated in laboratory using a robotic arm to simulate a berthing procedure.

中文翻译：

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基于人工智能的单目姿态估计，用于自主太空加油

由于其外形尺寸小且功耗、质量和体积成本低廉，相机正迅速成为太空交会机载传感器的选择。然而，在对接方面，它们通常扮演次要角色，而主要工作是由激光雷达等主动传感器完成的。本文记录了一种拟议的基于人工智能（人工智能）的导航算法的开发，旨在成熟地使用星载可见波长相机作为对接和在轨服务（OOS）的主要传感器，减少对激光雷达和在轨服务（OOS）的依赖。大大降低成本。具体来说，人工智能的使用可以将相对导航解决方案扩展到多类场景，例如在目标或照明条件方面，否则必须使用经典图像处理方法根据具体情况进行设计。多个卷积神经网络 (CNN) 主干架构以与国际空间站 (ISS) 对接操作的综合生成数据为基准，实现位置和姿态估计分别接近 1% 范围归一化和 1deg，这是已建立的经验法则最终进场时的导航测量精度。该解决方案与加油机构物理原型的集成在实验室中使用机械臂模拟停泊过程进行了验证。