Object Pose from 2-D to 3-D Point and Line Correspondences

Key points

• First we devise an error function and second we show how to minimize this error function.1
• The quadratic nature of this function is made possible by representing rotation and translation with a dual number quaternion.1
• This method compares favourably with Newton's method which has extensively been used to solve the problem at hand, with Faugeras-Toscani linear method [6] for calibrating a camera, and with the Levenberg-Marquardt non-linear optimization method.1
• Finally we present some experimental results which demonstrate the robustness of our method with respect to image noise and matching errors.1

Sources (1)

• [1]Object Pose from 2-D to 3-D Point and Line Correspondences
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 07:55 AM

  In this paper we present a method for optimally estimating the rotation and translation between a camera and a 3-D object from point and/or line correspondences.

  First we devise an error function and second we show how to minimize this error function.

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