Recovering projective structure and motion from straight lines

This paper aims to develop a complete algorithm to determine the robot motion and the scene structure using a monocular vision system. It is based on straight lines and significant points extracted on them. In this way, an agreement between the problems to extract or to match points and the limitations of infinite lines to compute structure and motion is established. Many geometrical relations of the lines in the scene are exploited to clear up the coupling between the rotation and the translation of the camera. Several real images have been used to validate the proposed method. The algorithm has been considered for navigation of a mobile robot moving in man-made environments.

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An iterative factorization method for projective structure and motion from image sequences

Image and Vision Computing ◽

10.1016/s0262-8856(99)00002-5 ◽

1999 ◽

Vol 17 (13) ◽

pp. 981-991 ◽

Cited By ~ 89

Author(s):

A Heyden ◽

R Berthilsson ◽

G Sparr

Keyword(s):

Factorization Method ◽

Image Sequences ◽

Projective Structure ◽

Structure And Motion

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An iterative method based on 1D subspace for projective reconstruction

Opto-Electronics Review ◽

10.2478/s11772-010-0057-0 ◽

2011 ◽

Vol 19 (1) ◽

Cited By ~ 3

Author(s):

S. Liu ◽

Y. Peng ◽

Z. Zeng ◽

C. Han

Keyword(s):

Iterative Method ◽

Projective Space ◽

Fast Algorithms ◽

Real Data ◽

Factorization Method ◽

Image Sequences ◽

Projective Structure ◽

Projective Reconstruction ◽

3D Motion ◽

Structure And Motion

AbstractHeyden et al. introduced an iterative factorization method for projective reconstruction from image sequences. In their formulation, the projective structure and motion are computed by using an iterative factorization based on 4D subspace. In this paper, the problem is reformulated based on fact that the x, y, and z coordinates of each feature in projective space are known from their projection. The projective reconstruction, i.e., the relative depths w and the 3D motion, is obtained by a simple iterative factorization based on 1D subspace. This allows the use of very fast algorithms even when using a large number of features and large number of frames. The experiments with both simulate and real data show that the method presented in the paper is efficient and has good convergency.

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