CubeSat-CDT: A Cross-Domain Dataset for 6-DoF Trajectory Estimation of a Symmetric Spacecraft

This paper introduces a new cross-domain dataset, CubeSat-
CDT, that includes 21 trajectories of a real CubeSat acquired in a labora-
tory setup, combined with 65 trajectories generated using two rendering
engines – i.e. Unity and Blender. The three data sources incorporate the
same 1U CubeSat and share the same camera intrinsic parameters. In ad-
dition, we conduct experiments to show the characteristics of the dataset
using a novel and efficient spacecraft trajectory estimation method, that
leverages the information provided from the three data domains. Given a
video input of a target spacecraft, the proposed end-to-end approach re-
lies on a Temporal Convolutional Network that enforces the inter-frame
coherence of the estimated 6-Degree-of-Freedom spacecraft poses. The
pipeline is decomposed into two stages; first, spatial features are ex-
tracted from each frame in parallel; second, these features are lifted to
the space of camera poses while preserving temporal information. Our re-
sults highlight the importance of addressing the domain gap problem to
propose reliable solutions for close-range autonomous relative navigation
between spacecrafts. Since the nature of the data used during training
impacts directly the performance of the final solution, the CubeSat-CDT
dataset is provided to advance research into this direction.