Egomotion Estimation Using Quadruples of Collinear Image Points

  • Manolis I. A. Lourakis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1843)


This paper considers a fundamental problem in visual motion perception, namely the problem of egomotion estimation based on visual input. Many of the existing techniques for solving this problem rely on restrictive assumptions regarding the observer’s motion or even the scene structure. Moreover, they often resort to searching the high dimensional space of possible solutions, a strategy which might be inefficient in terms of computational complexity and exhibit convergence problems if the search is initiated far away from the correct solution. In this work, a novel linear constraint that involves quantities that depend on the egomotion parameters is developed. The constraint is defined in terms of the optical flow vectors pertaining to four collinear image points and is applicable regardless of the egomotion or the scene structure. In addition, it is exact in the sense that no approximations are made for deriving it. Combined with robust linear regression techniques, the constraint enables the recovery of the FOE, thereby decoupling the 3D motion parameters. Extensive simulations as well as experiments with real optical flow fields provide evidence regarding the performance of the proposed method under varying noise levels and camera motions.


Optical Flow Rotational Component Image Motion Translational Component Scene Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Manolis I. A. Lourakis
    • 1
  1. 1.INRIA Sophia-AntipolisSophia-AntipolisFrance

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