Binocular camera calibration based on dual update strategy weighted differential evolution particle swarm optimization

Abstract

Aiming at the problem of camera calibration with multi-parameters in space target pose measurement, a novel calibration method based on dual update strategy weighted differential evolution particle swarm optimization was proposed . An adaptive judgment factor was constructed to control the usage proportion of weighted differential evolution (WDE) algorithm and particle swarm optimization (PSO) algorithm in each iteration process, which allowed the PSO or WDE algorithm to update individuals according to the probability laws. Moreover, through the information exchange mechanism, the individual obtained by the WDE operation was adopted to guide the individual evolution process operated by PSO algorithm. The proposed WDEPSO algorithm could ensure the diversity and effectiveness of the individual evolution of the population, and it was coupled with the camera nonlinear calibration model and parameters. Consequently, the proposed method could simultaneously realize the combined nonlinear and global continuous optimization, which overcomed the local convergence problem caused by the limited feature points arise from the saturated light intensity of the target space ambient. Experiments suggest that the objective function value optimized by the WDEPSO method is smaller, and the proposed method obtains the higher calibration accuracy. The standard gague measurement error obtained by the calibration parameters is less than 0.40 mm, the reconstruction attitude accuracy of the target under large angular motion is better than 0.30°, as well as the repeatability measurement results are stable.