A simplified linear optic-flow motion algorithm
Computer Vision, Graphics, and Image Processing
Subspace methods for recovering rigid motion I: algorithm and implementation
International Journal of Computer Vision
3-D interpretation of optical flow by renormalization
International Journal of Computer Vision
Performance of optical flow techniques
International Journal of Computer Vision
Computing egomotion and detecting independent motion from image motion using colinear points
Computer Vision and Image Understanding
Optimal Structure from Motion: Local Ambiguities and Global Estimates
International Journal of Computer Vision
Removal of Translation Bias when Using Subspace Methods
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Robust Instantaneous Rigid Motion Estimation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Optimal instantaneous rigid motion estimation insensitive to local minima
Computer Vision and Image Understanding
On-chip ego-motion estimation based on optical flow
ARC'11 Proceedings of the 7th international conference on Reconfigurable computing: architectures, tools and applications
Determining spatial motion directly from normal flow field: a comprehensive treatment
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
A review and evaluation of methods estimating ego-motion
Computer Vision and Image Understanding
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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.