基于多适应值全参数自主变异粒子群的立体相机标定

The acquisition of target parameters based on visual measurement provides reliable data support for performance analysis and evaluation of simulation system. The precision of measurement results is determined by the accuracy of camera calibration. A calibration method based on full parameter autonomous mutation particle swarm optimization is proposed. Traditional calibration method is utilized to obtain the initial internal parameters. The fast and global calibration algorithm based on particle swarm optimization is achieved by inertial coefficient contraction adjustment, global factor learning adjustment strategy based on particle distance, multi-adaptation function and the independent variation law. The experimental results show that the proposed method can improve the calibration accuracy to a certain extent and can be used in practical engineering.