Online computation of exterior orientation with application to hand-eye calibration

  • Authors:

  • C. -P. Lu;E. Mjolsness;G. D. Hager

  • Affiliations:

  • Department of Computer Science, Yale University, New Haven, CT 06520-8285, U.S.A.;Institute for Neural Computation and Department of Computer Science and Engineering University of California, San Diego, La Jolla, CA 92093-0114, U.S.A.;Department of Computer Science, Yale University, New Haven, CT 06520-8285, U.S.A.

  • Venue:
  • Mathematical and Computer Modelling: An International Journal
  • Year:
  • 1996

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Abstract

Computation of the relative position and orientation between a camera and an observed object from a single image is a central problem in computer vision. Although many solution methods have been proposed, several problems remain. Analytical methods do not take into account the issue of noise. Nonlinear least-squares methods depend critically on good initialization. Linear least-squares methods tend to be very sensitive to noise and outliers. These shortcomings limit their use in modern computer vision applications. In this article, we formulate a new least squares objective function that leads to a good initialization scheme based on weak-perspective projection, as well as a robust and efficient descent algorithm using absolute orientation. The new method combines model-based parameter search and data-driven backprojection which, unlike most existing methods, minimizes 3-D object space error rather than 2-D image error. Extensive experiments on simulated data indicate that the new method outperforms commonly used least squares methods under most conditions. Its performance as a kernel in the inner loop of a robust M-estimate algorithm for outlier rejections is also studied. We demonstrate the use of this method in the context of hand-eye calibration.