Robot camera calibration method based on multi-population particle parallel structure algorithm

Abstract

The invention discloses a robot camera calibration method based on a multi-population particle parallel structure algorithm, and the method comprises the steps: firstly introducing a self-adaptive mutation particle swarm optimization algorithm on the basis of a multi-population particle parallel structure, and according to the difference of the impact on the whole process from a local optimal particle and a global optimal particle in an updating iteration process, providing an inertia factor adjustment strategy based on particle distance change, selectively varying population particles according to the self-adaptive variation rate, whereina stronger large local view is achieved, and an optimal solution is found in a global range; secondly, on the basis of a multi-population particle optimization algorithm based on a parallel structure, carrying out optimization, wherein the problem that an original algorithm is too redundant is solved, updating iteration is carried out on three particle swarms at the same time, and the robustness of the algorithm is improved; and finally, solving the optimal value of the camera parameter on the basis of the initial value of the camera parameter byutilizing a multi-population particle parallel structure optimization algorithm to obtain the optimal value of the camera parameter, so that the calibration precision of the robot camera is improved.

Description

technical field [0001] The invention relates to the technical field of visual perception of industrial robots, in particular to a robot camera calibration method based on a multi-group particle parallel structure algorithm. Background technique [0002] Camera calibration technology plays a key role in the visual perception of robots, the visual guidance of robotic arms, and the visual obstacle avoidance of moving cars. The accuracy of camera calibration directly affects the accuracy of stereo matching and target object recognition and positioning. Therefore, effectively improving the accuracy of camera parameter calibration has become a key issue that urgently needs to be solved in industrial applications of robot vision. [0003] At present, camera calibration methods can be divided into: traditional camera calibration methods, active vision camera calibration methods and camera self-calibration methods. The traditional camera calibration method requires high-precision ca...

AI Technical Summary

Problems solved by technology

Qin Ruikang and others introduced their ideas into the process of camera single-target calibration in "Monocular Camera Calibration Based on Full-Parameter Adaptive Mutation Particle Swarm Algorithm", which not only improved the convergence speed of the particle swarm algorithm, but also improved the camera calibration accuracy. However, the algorithm still has problems such as poor diversity and poor robustness.

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