A new technique for three-dimensional (3D) camera calibration for machine vision metrology using off-the-shelf TV cameras and lenses is described. The two-stage technique is aimed at efficient computation of camera external position and orientation relative to object reference coordinate system as well as the effective focal length, radial lens distortion, and image scanning parameters. The two-stage technique has advantage in terms of accuracy, speed, and versatility over existing state of the art. A critical review of the state of the art is given in the beginning. A theoretical framework is established, supported by comprehensive proof in five appendixes, and may pave the way for future research on 3D robotics vision. Test results using real data are described. Both accuracy and speed are reported. The experimental results are analyzed and compared with theoretical prediction. Recent effort indicates that with slight modification, the two-stage calibration can be done in real time.

A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses

A camera model that accounts for major sources of camera distortion, namely, radial, decentering, and thin prism distortions is presented. The proposed calibration procedure consists of two steps: (1) the calibration parameters are estimated using a closed-form solution based on a distribution-free camera model; and (2) the parameters estimated in the first step are improved iteratively through a nonlinear optimization, taking into account camera distortions. According to minimum variance estimation, the objective function to be minimized is the mean-square discrepancy between the observed image points and their inferred image projections computed with the estimated calibration parameters. The authors introduce a type of measure that can be used to directly evaluate the performance of calibration and compare calibrations among different systems. The validity and performance of the calibration procedure are tested with both synthetic data and real images taken by tele- and wide-angle lenses. >

/pdf/camera-calibration-with-distortion-models-and-accuracy-3bqh2r9lcs.pdf

Camera calibration with distortion models and accuracy evaluation

The authors describe a novel technique for computing position and orientation of a camera relative to the last joint of a robot manipulator in an eye-on-hand configuration. It takes only about 100+64N arithmetic operations to compute the hand/eye relationship after the robot finishes the movement, and incurs only additional 64 arithmetic operations for each additional station. The robot makes a series of automatically planned movements with a camera rigidly mounted at the gripper. At the end of each move, it takes a total of 90 ms to grab an image, extract image feature coordinates, and perform camera extrinsic calibration. After the robot finishes all the movements, it takes only a few milliseconds to do the calibration. A series of generic geometric properties or lemmas are presented, leading to the derivation of the final algorithms, which are aimed at simplicity, efficiency, and accuracy while giving ample geometric and algebraic insights. Critical factors influencing the accuracy are analyzed, and procedures for improving accuracy are introduced. Test results of both simulation and real experiments on an IBM Cartesian robot are reported and analyzed. >

A new technique for fully autonomous and efficient 3D robotics hand/eye calibration

A new technique for fully autonomous and efficient 3D robotics hand-eye calibration

A general framework for real-time manipulation-of 3D objects from-medical data sets promises physicians a powerful new tool.

Physician's Workstation with Real-Time Performance

A new method of calibration is proposed for active visual sensing for use in 3D analysis The method, based on an adaptation of the two camera plane m model, permits the calibration of a mobile camera as a function of the position and orientation of the camera

A new development in camera calibration calibrating a pair of mobile cameras

A new method of camera calibration is proposed for active visual sensing for use in three-dimensional (3D) scene analysis. The method permits calibration of a mobile camera as a function of the position and orientation of the camera. The algorithm for identification of the calibration matrices is divided into two steps: first, calibration for a fixed position of the camera (i. e. , the transformation between the real-world coordinates and the coordinates of the corresponding points in the image plane of the camera); second, calibration as a function of the position and orientation of the camera, which uses the prior information of the calibration for a fixed position. The method can be used for any mobile camera as long as the movement of the camera support is known.

A new development in camera calibration: calibrating a pair of mobile cameras

A new method of camera calibration is proposed for active visual sensing for use in 3D analysis. The method, based on a variation of the two plane camera model, permits the calibration of a mobile camera as a function of the position and orientation of the camera. The algorithm for identification of the calibration matrices is divided into two steps; first, calibration for a fixed position of the camera; and second, calibration as a function of the position and orientation of the camera, utilizing the prior information of the fixed position. The calibration procedure for the fixed position is a linear square method; the calibration procedure for the second method has been modified so as to obtain an analytical solution rather than a numerical iteration as in.

Analytical Identification Of The Calibration Matrices Using The Two Plane Model

A new method of camera calibration is proposed for active visual sensing for use in 3D analysis. The method, based on a variation of the two plane camera model, permits the calibration of a mobile camera as a function of the position and orientation of the camera. The algorithm for identification of the calibration matrices is divided into two steps; first, calibration for a fixed position of the camera; and second, calibration as a function of the position and orientation of the camera, utilizing the prior information of the fixed position. The calibration procedure for the fixed position is a linear square method; the calibration procedure for the second method has been modified so as to obtain an analytical solution

Analytical identification of the calibration matrices using the two plane model [3D scene analysis]

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