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Pose Estimation with Radial Distortion and Unknown Focal Length

Josephson, Klas LU and Byröd, Martin LU (2009) IEEE-Computer-Society Conference on Computer Vision and Pattern Recognition Workshops, 2009 p.2411-2418
Abstract
This paper presents a solution to the problem of pose estimation in the presence of heavy radial distortion and a potentially large number of outliers. The main contribution is an algorithm that solves for radial distortion, focal length and camera pose using a minimal set of four point correspondences between 3D world points and image points. We use a RANSAC loop to find a set of inliers and an initial estimate for bundle adjustment. Unlike previous approaches where one starts out by assuming a linear projection model, our minimal solver allows us to handle large radial distortions already at the RANSAC stage. We demonstrate that with the inclusion of radial distortion in an early stage of the process, a broader variety of cameras can be... (More)
This paper presents a solution to the problem of pose estimation in the presence of heavy radial distortion and a potentially large number of outliers. The main contribution is an algorithm that solves for radial distortion, focal length and camera pose using a minimal set of four point correspondences between 3D world points and image points. We use a RANSAC loop to find a set of inliers and an initial estimate for bundle adjustment. Unlike previous approaches where one starts out by assuming a linear projection model, our minimal solver allows us to handle large radial distortions already at the RANSAC stage. We demonstrate that with the inclusion of radial distortion in an early stage of the process, a broader variety of cameras can be handled than was previously possible. In the experiments, no calibration whatsoever is applied to the camera. Instead we assume square pixels, zero skew and centered principal point. Although these assumptions are not strictly true, we show that good results are still obtained and by that conclude that the proposed method is applicable to uncalibrated photographs. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to conference
publication status
published
subject
keywords
Gröbner Bases, Localization, Global Pose
pages
8 pages
conference name
IEEE-Computer-Society Conference on Computer Vision and Pattern Recognition Workshops, 2009
external identifiers
  • WOS:000279038001118
  • Scopus:70450205307
language
English
LU publication?
yes
id
b3ae731e-5c89-45ac-8081-d9ffee9a4a8d (old id 1391281)
date added to LUP
2009-05-05 13:03:55
date last changed
2016-10-23 04:39:28
@misc{b3ae731e-5c89-45ac-8081-d9ffee9a4a8d,
  abstract     = {This paper presents a solution to the problem of pose estimation in the presence of heavy radial distortion and a potentially large number of outliers. The main contribution is an algorithm that solves for radial distortion, focal length and camera pose using a minimal set of four point correspondences between 3D world points and image points. We use a RANSAC loop to find a set of inliers and an initial estimate for bundle adjustment. Unlike previous approaches where one starts out by assuming a linear projection model, our minimal solver allows us to handle large radial distortions already at the RANSAC stage. We demonstrate that with the inclusion of radial distortion in an early stage of the process, a broader variety of cameras can be handled than was previously possible. In the experiments, no calibration whatsoever is applied to the camera. Instead we assume square pixels, zero skew and centered principal point. Although these assumptions are not strictly true, we show that good results are still obtained and by that conclude that the proposed method is applicable to uncalibrated photographs.},
  author       = {Josephson, Klas and Byröd, Martin},
  keyword      = {Gröbner Bases,Localization,Global Pose},
  language     = {eng},
  pages        = {2411--2418},
  title        = {Pose Estimation with Radial Distortion and Unknown Focal Length},
  year         = {2009},
}