Camera Calibration with Distortion Models and Accuracy Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Euclidean Shape and Motion from Multiple Perspective Views by Affine Iterations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Vision and Applications
The Geometry of Multiple Images: The Laws That Govern The Formation of Images of A Scene and Some of Their Applications
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
A Generic Camera Model and Calibration Method for Conventional, Wide-Angle, and Fish-Eye Lenses
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hand-eye calibration with epipolar constraints: Application to endoscopy
Robotics and Autonomous Systems
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This paper considers the issue of calibrating a camera with narrow angular field of view using standard, perspective methods in computer vision. In doing so, the significance of perspective distortion both for camera calibration and for pose estimation is revealed. Since narrow angular field of view cameras make it difficult to obtain rich images in terms of perspectivity, the accuracy of the calibration results is expectedly low. From this, we propose an alternative method that compensates for this loss by utilizing the pose readings of a robotic manipulator. It facilitates accurate pose estimation by nonlinear optimization, minimizing reprojection errors and errors in the manipulator transformations at the same time. Accurate pose estimation in turn enables accurate parametrization of a perspective camera.