Vision-based pose estimation for cooperative space objects

Haopeng Zhang, Zhiguo Jiang, Ahmed Elgammal

Research output: Contribution to journalArticle

23 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)115-122
Number of pages8
JournalActa Astronautica
Volume91
DOIs
StatePublished - Jun 28 2013

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering

Keywords

  • Homeomorphic manifold analysis
  • Pose estimation
  • Space objects
  • Vision-based

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