Research on camera calibration method based on least squares optimization

Camera calibration is a fundamental task in computer vision, aiming to determine the camera's internal and external parameters to accurately acquire three-dimensional object positioning and attitude. This paper presents a camera calibration optimization method based on the least squares method. Using the camera calibration model with reprojection error analysis, an efficient optimization algorithm is designed. Experiments reveal that, although the reprojection error fails to decrease after optimization, and the camera's internal parameter accuracy is degraded, the pre-optimization results are significantly better than the post-optimization results, highlighting method limitations. This study provides new ideas for future work, emphasizing the importance of balancing data stability and optimization effect in camera calibration. Subsequent studies will explore improved optimization strategies to enhance camera calibration accuracy and expand its practical applications.