Vision-based pose estimation for cooperative space objects |
| |
| Institution: | 1. School of Astronautics, Beihang University, Beijing 100191, China;2. Beijing Key Laboratory of Digital Media, Beijing 100191, China;3. Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA;1. National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University, 710072 Xi’an, China;2. School of Astronautics, Northwestern Polytechnical University, 710072 Xi’an, China;3. School of Astronautics, Beihang University, 100191 Beijing, China;1. School of Aerospace Engineering, Tsinghua University, Beijing, China;2. Department of Aerospace Engineering and Engineering Mechanics, University of Cincinnati, Cincinnati, OH 45221-0070, USA;1. Research Center for Intelligent Robotics, School of Astronautics, Northwestern Polytechnical University, Xi’an, Shaanxi, 710072, China;2. National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University, Xi’an, Shaanxi, 710072, China;1. School of Astronautics, Beihang University, Xueyuan Road No. 37, Haidian District, Beijing 100191, China;2. Mechanical Engineering Department, McGill University, Montreal, QC H3A 0C3, Canada |
| |
| Abstract: | 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. |
| |
| Keywords: | Pose estimation Vision-based Space objects Homeomorphic manifold analysis |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
