A Robot Camera Calibration Method Based on Multi-swarm Particle Parallel Structure Algorithm

Abstract

The invention discloses a robot camera calibration method based on a multi-swarm particle parallel structure algorithm. First, an adaptive variation particle swarm optimization algorithm is introduced on the basis of a multi-swarm particle parallel structure. According to the local optimal particle, The global optimal particles have different influences on the whole process. An inertial factor adjustment strategy based on the change of particle distance is proposed. At the same time, according to the adaptive mutation rate, the selective mutation of the population particles has a stronger overall view, and it is possible to find the optimal particle in the global scope. Secondly, on the basis of the multi-swarm particle optimization algorithm based on parallel structure, optimize it, solve the problem that the original algorithm is too redundant, and update and iterate the three particle groups at the same time, which improves the robustness of the algorithm ;Finally, using multi-swarm particle parallel structure optimization algorithm to solve the optimal value of the camera parameters on the basis of the initial value of the camera parameters, the optimal value of the camera parameters is obtained, which improves the calibration accuracy of the robot camera.

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.

Images