Camera Modelling and Calibration with Machine Vision Applications
Camera modelling and calibration are important parts of machine vision. They can be used
for calculating geometric information from images. A camera model is a mathematical projection
between a 3D object space and a 2D image. The camera calibration is a mathematical
procedure calculating parameters of the camera model, usually based on several images of
reference points. These fundamental parts of machine vision are improved in this thesis.
One large part is the development of a generic camera model, GCM, that is accurate, computationally
efficient and can be used for both conventional, fisheye and even catadioptric
cameras. Different models were used in the past for conventional and omnidirectional cameras
and this is a well-known problem, the solution of which is described in this thesis.
The accuracy of camera models is improved by introducing new ways of compensating
for different distortions, such as radial distortion, varying entrance pupil point and decentring
distortion. Calibration is improved by introducing new means of calculating start estimates of
camera parameters, from analysing shapes, sizes and positions of the reference points in the
images. These start estimates are needed in order to make the calibration converge. Methods
for calculating better reference centre points than the centres of gravity are developed in
order to increase the accuracy further. Non-trivial null spaces that occur during calibration
are identified. Awareness of these improve the calibration.
Calibrations with different camera models are implemented and tested for real cameras
in order to compare their accuracy. Certain models are better for certain situations, but the
overall performance and properties are favourable for the GCM. A stereo vision welding
robot system is developed, using the new model. It determines the geometry of a 3D weld
joint, so that a robot can follow it. The same system is implemented in a virtual environment
using a simulation software. Such simulation is important since it makes it possible to
develop robot vision systems off-line.