Abstract
The calibration of cameras from external orientation information and image processing is addressed in this paper. We will show that in the case of known rotation the calibration of rotating cameras is linear even in the case of fully varying parameters. For freely moving cameras the calibration problem is also linear but underdetermined for fully varying internal parameters. We show one possible set of contraints to reach a fully determined calibration problem. Furthermore we show that these linear calibration techniques tend to fit to noise for some of the intrinsics. To avoid this fit to noise we introduce a statistical calibration technique which uses the robust components of linear calibration and prior knowledge about cameras. This statistical calibration is fully determined even for freely moving cameras.
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Frahm, JM., Koch, R. (2003). Robust Camera Calibration from Images and Rotation Data. In: Michaelis, B., Krell, G. (eds) Pattern Recognition. DAGM 2003. Lecture Notes in Computer Science, vol 2781. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45243-0_33
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DOI: https://doi.org/10.1007/978-3-540-45243-0_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40861-1
Online ISBN: 978-3-540-45243-0
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