Given the rapid convergence characteristic of the stochastic parallel gradient descent (SPGD) algorithm, this study proposes a method that applies the algorithm to a two-step camera calibration method to resolve the frequent iteration and long calibration time deficiencies that exist under the traditional two-step camera calibration method, thereby achieving rapid calibration. The method first uses image coordinates obtained with subpixel positioning technology as initial values of control variables, in addition to positive disturbances produced on a two-dimensional plane, then uses two-step theory to calculate the average value of aberrations. Based on the same rationale, negative disturbances are then produced and the average value of the aberrations is calculated. Finally if, after assessing whether to continue with further iterations based on the difference in these values, continued iterations confirm new control variables based on the SPGD algorithm iteration formula, a new cycle is started until the results satisfy requirements. Theoretical analysis and experimental results show that the proposed rapid calibration method using the SPGD algorithm in the two-step camera calibration method is 3–4 times faster than the traditional two-step calibration method, and that it has significant potential value for use in certain time-constrained projects.
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