  Autonomous vision-based spaceborne navigation is an enabling technology for
future on-orbit servicing and space logistics missions. While computer vision
in general has benefited from Machine Learning (ML), training and validating
spaceborne ML models are extremely challenging due to the impracticality of
acquiring a large-scale labeled dataset of images of the intended target in the
space environment. Existing datasets, such as Spacecraft PosE Estimation
Dataset (SPEED), have so far mostly relied on synthetic images for both
training and validation, which are easy to mass-produce but fail to resemble
the visual features and illumination variability inherent to the target
spaceborne images. In order to bridge the gap between the current practices and
the intended applications in future space missions, this paper introduces
SPEED+: the next generation spacecraft pose estimation dataset with specific
emphasis on domain gap. In addition to 60,000 synthetic images for training,
SPEED+ includes 9,531 hardware-in-the-loop images of a spacecraft mockup model
captured from the Testbed for Rendezvous and Optical Navigation (TRON)
facility. TRON is a first-of-a-kind robotic testbed capable of capturing an
arbitrary number of target images with accurate and maximally diverse pose
labels and high-fidelity spaceborne illumination conditions. SPEED+ is used in
the second international Satellite Pose Estimation Challenge co-hosted by SLAB
and the Advanced Concepts Team of the European Space Agency to evaluate and
compare the robustness of spaceborne ML models trained on synthetic images.
