Geometric Camera Calibration Using Circular Control Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonmetric Calibration of Wide-Angle Lenses and Polycameras
IEEE Transactions on Pattern Analysis and Machine Intelligence
Calibration and Orientation of Cameras in Computer Vision
Calibration and Orientation of Cameras in Computer Vision
Lens distortion calibration using point correspondences
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Vision guided manipulation for planetary robotics - position control
Robotics and Autonomous Systems
Camera Models and Fundamental Concepts Used in Geometric Computer Vision
Foundations and Trends® in Computer Graphics and Vision
Analysis and evaluation of a general camera model
Computer Vision and Image Understanding
Traffic observation and situation assessment
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
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A method is described for accurately calibrating cameras including radial lens distortion, by using known points such as those measured from a calibration fixture. Both the intrinsic and extrinsic parameters are calibrated in a single least-squares adjustment, but provision is made for including old values of the intrinsic parameters in the adjustment. The distortion terms are relative to the optical axis, which is included in the model so that it does not have to be orthogonal to the image sensor plane. These distortion terms represent corrections to the basic lens model, which is a generalization that includes the perspective projection and the ideal fish-eye lens as special cases. The position of the entrance pupil point as a function of off-axis angle also is included in the model. (The complete camera model including all of these effects often is called CAHVORE.) A way of adding decentering distortion also is described. A priori standard deviations can be used to apply weight to given initial approximations (which can be zero) for the distortion terms, for the difference between the optical axis and the perpendicular to the sensor plane, and for the terms representing movement of the entrance pupil, so that the solution for these is well determined when there is insufficient information in the calibration data. For the other parameters, initial approximations needed for the nonlinear least-squares adjustment are obtained in a simple manner from the calibration data and other known information. (Weight can be given to these also, if desired.) Outliers among the calibration points that disagree excessively with the other data are removed by means of automatic editing based on analysis of the residuals. The use of the camera model also is described, including partial derivatives for propagating both from object space to image space and vice versa. These methods were used to calibrate the cameras on the Mars Exploration Rovers.