A Convenient and High-Accuracy Multicamera Calibration Method Based on Imperfect Spherical Objects

Multicamera systems have important applications in industrial online measurement, attracting wide interest due to their encouraging performance. However, how to develop a convenient and high-accuracy multicamera calibration model is a big challenge. Traditional calibration methods based on 2-D objects require the preparation of high-precision and large-scale calibration objects and have poor adaptability to the spatial distribution of cameras. To address these issues, a convenient and accurate multicamera calibration method is proposed based on an imperfect spherical object. Specifically, a special calibration object, i.e., an imperfect sphere with many coded targets is designed for calibrating the multicamera system, where the optical axes of the cameras are different in orientations and converge into the measurement field. Then, a Euclidean reconstruction method is employed to calculate the camera poses and the spatial coordinates of feature points in the local coordinate system after the adaptive grouping of cameras is completed. Moreover, a graph theory-based optimal path transformation algorithm is developed to obtain the camera poses and spatial point coordinates in the global coordinate system (GCS), and a spherical projection optimization algorithm is designed to spheroidize the spatial coordinates of feature points. In the end, the camera poses and the spatial coordinates of feature points in the GCS are optimized by a bundle adjustment algorithm. We build a multicamera system and conduct extensive experiments to demonstrate the superiority of the proposed method.