Accurate Internal Camera Calibration Using Rotation, with Analysis of Sources of Error

Abstract

Describes a simple and accurate method for internal camera calibration based on tracking image features through a sequence of images while the camera undergoes pure rotation. A special calibration object is not required and the method can therefore be used both for laboratory calibration and for self calibration in autonomous robots. Experimental results with real images show that focal length and aspect ratio can be found to within 0.15 percent, and lens distortion error can be reduced to a fraction of a pixel. The location of the principal point and the location of the center of radial distortion can each be found to within a few pixels. We perform a simple analysis to show to what extent the various technical details affect the accuracy of the results. We show that having pure rotation is important if the features are derived from objects close to the camera. In the basic method accurate angle measurement is important. The need to accurately measure the angles can be eliminated by rotating the camera through a complete circle while taking an overlapping sequence of images and using the constraint that the sum of the angles must equal 960 degrees.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Cite

Text

Stein. "Accurate Internal Camera Calibration Using Rotation, with Analysis of Sources of Error." IEEE/CVF International Conference on Computer Vision, 1995. doi:10.1109/ICCV.1995.466781

Markdown

[Stein. "Accurate Internal Camera Calibration Using Rotation, with Analysis of Sources of Error." IEEE/CVF International Conference on Computer Vision, 1995.](https://mlanthology.org/iccv/1995/stein1995iccv-accurate/) doi:10.1109/ICCV.1995.466781

BibTeX

@inproceedings{stein1995iccv-accurate,
  title     = {{Accurate Internal Camera Calibration Using Rotation, with Analysis of Sources of Error}},
  author    = {Stein, Gideon P.},
  booktitle = {IEEE/CVF International Conference on Computer Vision},
  year      = {1995},
  pages     = {230-236},
  doi       = {10.1109/ICCV.1995.466781},
  url       = {https://mlanthology.org/iccv/1995/stein1995iccv-accurate/}
}