Abstract
The article presents Deep Coherent Point Drift (DeepCPD), a neural-network approach for estimating the six-degrees-of-freedom pose of noncooperative spacecraft during autonomous rendezvous and docking through point cloud data. The method registers unorganized scan point clouds with their reference model point clouds. DeepCPD replaces the Expectation-Maximization procedure in the Gaussian Mixture Model registration algorithm with a neural network that learns point-to-component correspondence, achieving better estimation performance and acceleration of the registration process. The proposed method is also robust to perturbation, corruption, occlusion, and distance, as validated by simulated experimental results. Our code will be available at https://github.com/Zhang-CV/DeepCPD.
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