Self-calibration for linear structured light 3D measurement system based on quantum genetic algorithm and feature matching

Abstract

In this paper, a self-calibration method for a linear structured light 3D measurement system and its advantages are presented. According to the mathematical model of the linear-structured light 3D measurement system, the calibration problem is a highdimensional optimization problem. The principle of this self-calibration method can be drawn from a traditional calibration method. In this method, quantum genetic algorithm and feature matching are applied to self-calibration. Feature matching is used to derive two points with fixed spatial relationships, and an optimal solution of system parameters can be obtained by quantum genetic algorithm. Finally, experimental results are given. The measurement error is 0.05 mm and the ratio of the error to scanning distance is 1.54e-4.In this paper, a self-calibration method for a linear structured light 3D measurement system and its advantages are presented. According to the mathematical model of the linear-structured light 3D measurement system, the calibration problem is a high-dimensional optimization problem. The principle of this self-calibration method can be drawn from a traditional calibration method. In this method, quantum genetic algorithm and feature matching are applied to selfcalibration. Feature matching is used to derive two points with fixed spatial relationships, and an optimal solution of system parameters can be obtained by quantum genetic algorithm. Finally, experimental results are given. The measurement error is 0.05 mm and the ratio of the error to scanning distance is 1.54e-4.