A versatile General Camera Model, GCM, has been developed, and is described in detail. The model is general in the sense that it can capture both fisheye and conventional as well as catadioptric cameras in a unified framework. The camera model includes efficient handling of non-central cameras as well as compensations for decentring distortion. A novel way of analysing radial distortion functions of camera models leads to a straightforward improvement of conventional models with respect to generality, accuracy and simplicity. Different camera models are experimentally compared for two cameras with conventional and fisheye lenses, and the results show that the overall performance is favourable for the GCM.

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 istortion, varying entrance pupil point and decentring distortion. Calibration is mproved by introducing newmeans 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.

The paper describes a versatile machine vision system for correcting off-line programmed nominal robot trajectories for advanced welding. Weld trajectory corrections are needed due to slight variations in weld joints. Such variations occur naturally because of varying tolerances in parts and to heat induced deformations during earlier weld sequences. The developed system uses one camera and a weld tool mounted on the robot hand. As a first step, the whole system, including the camera, is calibrated. Then the system takes images of the weld joint from different positions and orientations, and determines the weld joint geometry in 3D using a stereo vision algorithm and a novel camera model. The weld trajectory is then updated in the robot control system, and weld operation is performed. These steps are repeated for all weld sequences of the work piece. The strategy has successfully been demonstrated for a standard industrial welding robot and a standard FireWire CMOS camera. The maximum deviation of the trajectory found by the system compared to a reference (coordinate measuring machine) is 0.7 mm and the mean deviation is 0.23 mm. Thus, the system shows high potential for industrial implementation. ©2010 IEEE.

This paper presents novel strategies for better calibration and pose calculations of a system for determining the pose, i.e. position and orientation, of a camera. The system in question has a camera aimed to be placed on the hand of an industrial robot for welding, but is useful for any application with a need for measuring position and/or orientation. To calculate the pose of the camera circular reference points that can be recognized in the images are distributed in the working area. From their 2D image coordinates the 6D pose of the camera can be calculated. First the system is calibrated, i.e. the positions of the reference points and the camera parameters are determined. This is done by first taking images of the reference points from different locations, and then do a "total calibration" procedure to calculate the unknown parameters. For a specific system, called PosEye, it was concluded that the accuracy needs to be improved for welding applications. Also a method for making the calculations converge more easily, was needed. To meet these demands a new camera model is proposed, and three preprocessing calculation steps are presented. The new camera model increases accuracy by considering more distortion effects. The preprocessing steps give better initial values for more robust convergence of the algorithms and increased accuracy

A position and orientation (pose) measurement system is being developed. The system, called PosEye, is based on a camera and by using the information in the image, the pose of the camera taking the image can be calculated. The system is aimed to be placed on an industrial robot for welding, but it is flexible and can also be used in many other applications. The accuracy has been measured, and it is concluded that the accuracy needs to be improved for welding applications. To make the pose measurement, reference points, that can be recognized in the image, are distributed in the working area. The positions of the reference points and the parameters in a camera model are initially computed automatically from sample images from a number of directions to the reference points. After this calibration, the pose can be calculated at each sample image. For high accuracy there is a need to have a camera model that takes into account a number of distortion effects, which are further developed in this paper. The new model is used to express an optimization cost function that can be used for both the pose calculation, and the extensive calibration, that determines camera parameters in the camera model and the positions of the reference points

The accuracy of a pose measurement system, called PosEye, is investigated. PosEye is a system for real time measurement of the position and orientation, the pose, of a camera (sensor) using the information in its image. This sensor is aimed to be mounted on an industrial robot for welding. The investigation was done by comparing the PosEye system position output to that of a coordinate measuring machine. Sources of errors are identified, and suggestions for improvements are made