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Generalization of Parameter Recovery in Binocular Vision for a Planar Scene

Örnhag, Marcus Valtonen LU and Heyden, Anders LU (2019) In International Journal of Pattern Recognition and Artificial Intelligence
Abstract

In this paper, we consider a mobile platform with two cameras directed towards the floor. In earlier work, this specific problem geometry has been considered under the assumption that the cameras have been mounted at the same height. This paper extends the previous work by removing the height constraint, as it is hard to realize in real-life applications. We develop a method based on an equivalent problem geometry, and show that much of previous work can be reused with small modification to account for the height difference. A fast solver for the resulting nonconvex optimization problem is devised. Furthermore, we propose a second method for estimating the height difference by constraining the mobile platform to pure translations. This... (More)

In this paper, we consider a mobile platform with two cameras directed towards the floor. In earlier work, this specific problem geometry has been considered under the assumption that the cameras have been mounted at the same height. This paper extends the previous work by removing the height constraint, as it is hard to realize in real-life applications. We develop a method based on an equivalent problem geometry, and show that much of previous work can be reused with small modification to account for the height difference. A fast solver for the resulting nonconvex optimization problem is devised. Furthermore, we propose a second method for estimating the height difference by constraining the mobile platform to pure translations. This is intended to simulate a calibration sequence, which is not uncommon to impose. Experiments are conducted using synthetic data, and the results demonstrate a robust method for determining the relative parameters comparable to previous work.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
epub
subject
keywords
binocular vision, homography, planar motion, Relative pose estimation, SLAM, visual odometry
in
International Journal of Pattern Recognition and Artificial Intelligence
publisher
World Scientific
external identifiers
  • scopus:85062627714
ISSN
0218-0014
DOI
10.1142/S0218001419400111
language
English
LU publication?
yes
id
ec0babb2-d012-4f32-856e-ccaada25b8ef
date added to LUP
2019-03-19 11:20:48
date last changed
2019-04-10 04:21:41
@article{ec0babb2-d012-4f32-856e-ccaada25b8ef,
  abstract     = {<p>In this paper, we consider a mobile platform with two cameras directed towards the floor. In earlier work, this specific problem geometry has been considered under the assumption that the cameras have been mounted at the same height. This paper extends the previous work by removing the height constraint, as it is hard to realize in real-life applications. We develop a method based on an equivalent problem geometry, and show that much of previous work can be reused with small modification to account for the height difference. A fast solver for the resulting nonconvex optimization problem is devised. Furthermore, we propose a second method for estimating the height difference by constraining the mobile platform to pure translations. This is intended to simulate a calibration sequence, which is not uncommon to impose. Experiments are conducted using synthetic data, and the results demonstrate a robust method for determining the relative parameters comparable to previous work.</p>},
  author       = {Örnhag, Marcus Valtonen and Heyden, Anders},
  issn         = {0218-0014},
  keyword      = {binocular vision,homography,planar motion,Relative pose estimation,SLAM,visual odometry},
  language     = {eng},
  publisher    = {World Scientific},
  series       = {International Journal of Pattern Recognition and Artificial Intelligence},
  title        = {Generalization of Parameter Recovery in Binocular Vision for a Planar Scene},
  url          = {http://dx.doi.org/10.1142/S0218001419400111},
  year         = {2019},
}