An Efficient Linear Method for the Estimation of Ego-Motion from Optical Flow

  • Florian Raudies
  • Heiko Neumann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5748)


Approaches to visual navigation, e.g. used in robotics, require computationally efficient, numerically stable, and robust methods for the estimation of ego-motion. One of the main problems for ego-motion estimation is the segregation of the translational and rotational component of ego-motion in order to utilize the translation component, e.g. for computing spatial navigation direction. Most of the existing methods solve this segregation task by means of formulating a nonlinear optimization problem. One exception is the subspace method, a well-known linear method, which applies a computationally high-cost singular value decomposition (SVD). In order to be computationally efficient a novel linear method for the segregation of translation and rotation is introduced. For robust estimation of ego-motion the new method is integrated into the Random Sample Consensus (RANSAC) algorithm. Different scenarios show perspectives of the new method compared to existing approaches.


Singular Value Decomposition Auxiliary Variable Subspace Method Angular Error Scatter Matrix 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Florian Raudies
    • 1
  • Heiko Neumann
    • 1
  1. 1.Institute of Neural Information ProcessingUniversity of UlmUlmGermany

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