Spacecraft Pose Estimation: Robust 2-D and 3-D Structural Losses and Unsupervised Domain Adaptation by Intermodel Consensus

Juan Ignacio Bravo Pérez-Villar, Álvaro García-Martín, Jesús Bescós, Marcos Escudero-Viñolo

Abstract: The accurate estimation of spacecraft pose is crucial for missions involving the navigation of two spacecraft in close proximity. Supervised algorithms are currently the state-of-the-art approach for spacecraft pose estimation. However, the absence of training data acquired in operational scenarios poses a challenge for the supervised algorithms. To address this issue, computer-aided simulators have been introduced to solve the issue of data availability but introduce a large gap between the training domain and test domain. We here describe an algorithm for unsupervised domain adaptation with robust pseudolabeling by model consensus. Moreover, the proposed method incorporates a 3-D structure into the spacecraft pose estimation pipeline to provide robustness against high illumination shifts between domains. Our solution has ranked second in the two categories of the 2021 Pose Estimation Challenge (SPEC2021) organized by the European Space Agency and the Stanford University, achieving the lowest average error over these two categories. Our solution is available at: https://github.com/JotaBravo/spacecraft-uda

External IDs:dblp:journals/taes/PerezVillarGBE24