  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.
