Vision-based pose estimation for cooperative space objects

• A vision-based approach for estimating the pose of cooperative space objects is proposed.
• No other data but only images are needed for our approach.
• The first time to apply homeomorphic manifold analysis technology to aerospace area.
• 1D and 2D experiments give effective pose estimation results.
• The robustness of our method against noise and lighting is analyzed.

Imaging sensors are widely used in aerospace recently. In this paper, a vision-based approach for estimating the pose of cooperative space objects is proposed. We learn generative model for each space object based on homeomorphic manifold analysis. Conceptual manifold is used to represent pose variation of captured images of the object in visual space, and nonlinear functions mapping between conceptual manifold representation and visual inputs are learned. Given such learned model, we estimate the pose of a new image by minimizing a reconstruction error via a traversal procedure along the conceptual manifold. Experimental results on the simulated image dataset show that our approach is effective for 1D and 2D pose estimation.