Reliable pose estimation of uncooperative satellites is a key technology for
enabling future on-orbit servicing and debris removal missions. The Kelvins
Satellite Pose Estimation Challenge aims at evaluating and comparing monocular
vision-based approaches and pushing the state-of-the-art on this problem. This
work is based on the Satellite Pose Estimation Dataset, the first publicly
available machine learning set of synthetic and real spacecraft imageries. The
choice of dataset reflects one of the unique challenges associated with
spaceborne computer vision tasks, namely the lack of spaceborne images to train
and validate the developed algorithms. This work briefly reviews the basic
properties and the collection process of the dataset which was made publicly
available. The competition design, including the definition of performance
metrics and the adopted testbed, is also discussed. The main contribution of
this paper is the analysis of the submissions of the 48 competitors, which
compares the performance of different approaches and uncovers what factors make
the satellite pose estimation problem especially challenging.

该研究以卫星姿态估计数据集为基础，对基于单目视觉的方法进行分析，在对 48 名参赛者分析竞赛表现的基础上，探讨了卫星姿态估计问题的挑战和改进方向。

卫星姿态估计挑战赛：数据集、竞赛设计与结果

Satellite Pose Estimation Challenge: Dataset, Competition Design and  Results

We propose an approach to estimate the 6DOF pose of a satellite, relative to
a canonical pose, from a single image. Such a problem is crucial in many space
proximity operations, such as docking, debris removal, and inter-spacecraft
communications. Our approach combines machine learning and geometric
optimisation, by predicting the coordinates of a set of landmarks in the input
image, associating the landmarks to their corresponding 3D points on an a
priori reconstructed 3D model, then solving for the object pose using
non-linear optimisation. Our approach is not only novel for this specific pose
estimation task, which helps to further open up a relatively new domain for
machine learning and computer vision, but it also demonstrates superior
accuracy and won the first place in the recent Kelvins Pose Estimation
Challenge organised by the European Space Agency (ESA).

我们提出了一种从单个图像中估计卫星 6DOF 姿态的方法，该方法结合了机器学习和几何优化，并预测了输入图像中一组地标的坐标，解决了非线性优化以求解物体姿态，不仅在该特定姿态估计任务中具有新颖性，还在欧洲航天局（ESA）组织的 Kelvins 姿态估计挑战赛中展现出优秀的精确度，获得了第一名。