An extrinsic calibration method for multiple RGB-D cameras in a limited field of view

Abstract

A 3D scanning system involving multiple RGB-D cameras has the potential to accelerate the reconstruction of an object and improve the measurement accuracy because it can capture an object in a comprehensive way. However, the extrinsic calibration of a multi-RGB-D-camera system is a fundamental and challenging problem, especially in a limited field of view. In this work, a system with four SR300 cameras on a platform with a size of approximately 0.64 m2 and an extrinsic calibration method assisted by a tower calibration pattern with circular markers in a limited field of view are proposed. The Hough transform algorithm is used to identify the centres of the circular markers in a colour image, and then the 3D coordinates are extracted by employing the alignment relationship between the colour image and the depth image. An improved adaptive cuckoo search (IACS) algorithm and unit quaternion are proposed to optimize the extrinsic parameters. The effectiveness of the IACS is verified by a comparison with the singular value decomposition (SVD), standard cuckoo search (CS) and two CS variant algorithms. In addition, the accuracy of the proposed extrinsic calibration method is verified by reconstructing and measuring a human foot and a cube box. The mean errors from the standard values of the length and width of the reconstructed foot mode are 0.18 cm and 0.13 cm, and the diameter of the yellow sphere are 0.17 cm, respectively. All of the experimental results show that the proposed extrinsic calibration method has a high calibration accuracy and good potential application prospects.