Camera calibration based on improved differential evolution particle swarm - Wei Fu; Lushen Wu (Measurement + Control, 2023)
Aiming at the problem of camera calibration with multiple parameters, this paper proposes the fusion optimization algorithm of improved differential evolution and particle swarm in the calibration of the camera. Adaptive judgment factors is Introduced to control the improvement of differential evolution during each iteration (IDE) algorithm and particle swarm optimization (PSO) algorithm solve the multiple parameters of traditional camera calibration algorithm. The proposed algorithm can ensure the diversity and effectiveness of individual evolution of the population. Experimental results show that the algorithm has excellent global search capabilities and local optimizations ability. It can accurately calibrate the camera. The convergence precision, speed and robustness performance significantly is superior to PSO (Particle swarm optimization algorithm), DE (differential evolution algorithm), GA (Genetic algorithm) and Zhang’s method. It improves the precision and speed of the proposed calibration method. The root mean square error of the calibration algorithm proposed in this paper is only 0.182, the calibration error result is smaller than other several algorithms. The reprojection error of our method is 0.05938 (Ex/pixel) and 0.02988 (Ey/pixel). It is smaller than GA, PSO, DE, and Zhang’s method. So, the algorithm performance is excellent.