Abstract
For camera calibration based on direct linear transformation (DLT), the camera’s intrinsic and extrinsic parameters are simultaneously calibrated, which may cause coupling errors in the parameters and affect the calibration parameter accuracy. In this paper, we propose an improved direct linear transformation (IDLT) algorithm for calibration parameter decoupling. This algorithm uses a linear relationship of calibration parameter errors and obtains calibration parameters by moving a three-dimensional template. Simulation experiments were conducted to compare the calibration accuracy of DLT and IDLT algorithms with image noise and distortion. The results show that the IDLT algorithm calibration parameters achieve higher accuracy because the algorithm removes the coupling errors.
Highlights
With the technological developments of digital cameras and microprocessors, computer vision has been widely applied to robot navigation, surveillance, three-dimensional (3D) reconstruction and other fields for its high speed, high accuracy and non-contact nature
The results show projection error from the mathematical model and the camera pinhole geometric model were that the coupling error causes small re-projection error variance and a large parameter analyzed
With respect to the coupling error between the focal distance and translation vector, to simplify the analysis model, we assume that the Euler angles of the world coordinate system relative to the camera coordinate system are zero
Summary
With the technological developments of digital cameras and microprocessors, computer vision has been widely applied to robot navigation, surveillance, three-dimensional (3D) reconstruction and other fields for its high speed, high accuracy and non-contact nature. The DLT algorithm is based on the perspective projection between 3D space points and 2D image points It calculates a transformation matrix and obtains the camera’s intrinsic and extrinsic parameters according to the parameter decomposition. When the calibration data contain noise and distortion, coupling exist parameter between themay be extrinsic and intrinsic parameters. This means that an error in anerrors extrinsic camera’s extrinsic and intrinsic parameters. The results show projection error from the mathematical model and the camera pinhole geometric model were that the coupling error causes small re-projection error variance and a large parameter analyzed This variance related to this the template number feature points largevariance.
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