Spacecraft pose estimation: Robust 2-D and 3-D structural losses and unsupervised domain adaptation by intermodel consensus
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...
| Autores: | , , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universidad Autónoma de Madrid |
| Repositorio: | Biblos-e Archivo. Repositorio Institucional de la UAM |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.uam.es:10486/715655 |
| Acceso en línea: | http://hdl.handle.net/10486/715655 https://dx.doi.org/10.1109/TAES.2023.3306731 |
| Access Level: | acceso abierto |
| Palabra clave: | 3-D loss domain adaptation point-n-perspective spacecraft pose estimation uncooperative Telecomunicaciones |
| Sumario: | 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 |
|---|