  Data acquired in space operational conditions is scarce due to the costs and
complexity of space operations. This poses a challenge to learning-based
visual-based navigation algorithms employed in autonomous spacecraft
navigation. Existing datasets, which largely depend on computer-simulated data,
have partially filled this gap. However, the image generation tools they use
are proprietary, which limits the evaluation of methods to unseen scenarios.
Furthermore, these datasets provide limited ground-truth data, primarily
focusing on the spacecraft's translation and rotation relative to the camera.
To address these limitations, we present SPIN (SPacecraft Imagery for
Navigation), an open-source realistic spacecraft image generation tool for
relative navigation between two spacecrafts. SPIN provides a wide variety of
ground-truth data and allows researchers to employ custom 3D models of
satellites, define specific camera-relative poses, and adjust various settings
such as camera parameters and environmental illumination conditions. For the
task of spacecraft pose estimation, we compare the results of training with a
SPIN-generated dataset against existing synthetic datasets. We show a %50
average error reduction in common testbed data (that simulates realistic space
conditions). Both the SPIN tool (and source code) and our enhanced version of
the synthetic datasets will be publicly released upon paper acceptance on
GitHub https://github.com/vpulab/SPIN.
