An Optimized Particle Swarm Algorithm for High-Precision Camera Calibration with Enhanced Wide-Angle Distortion Correction

Camera calibration is crucial to accurate vision tasks for its establishment of the mapping between 3D space and 2D image space. Traditional calibration methods often suffer from limited accuracy and time-consuming processes. In this study, we propose an automatic guidance system leveraging an improved particle swarm optimization algorithm to achieve fast and high-precision camera calibration. Our system dynamically recommends optimal camera poses for the next calibration image, effectively reducing calibration uncertainty and enhancing accuracy. Furthermore, for wide-angle cameras, we introduce a pre-estimation of distortion coefficients to guide the calibration process, significantly improving the calibration of distortion parameters. Experimental results demonstrate that our method outperforms existing guidance systems, achieving higher calibration accuracy with fewer images and shorter calculation time. The results of camera parameters calibrated by the system are applied to the reconstruction based on point clouds, and can achieve desirable reconstruction effect. The proposed system holds promise for applications in film and television shooting, promoting the development of the industry by reducing calibration errors and equipment debugging time.