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Found 4 papers, 2 papers with code
Satellite Pose Estimation Challenge: Dataset, Competition Design and Results
Reliable pose estimation of uncooperative satellites is a key technology for enabling future on-orbit servicing and debris removal missions.
Towards Robust Learning-Based Pose Estimation of Noncooperative Spacecraft
It is also shown that when using the texture-randomized spacecraft images during training, regressing 3D bounding box corners leads to better performance on spaceborne images than regressing surface keypoints, as NST inevitably distorts the spacecraft's geometric features to which the surface keypoints have closer relation.
Pose Estimation for Non-Cooperative Rendezvous Using Neural Networks
The SPN method then uses a novel Gauss-Newton algorithm to estimate the position by using the constraints imposed by the detected 2D bounding box and the estimated attitude.
Pose Estimation for Non-Cooperative Spacecraft Rendezvous Using Convolutional Neural Networks
Since reliable training of the CNN requires massive image datasets and computational resources, the parameters of the CNN must be determined prior to the mission with synthetic imagery.
