光子学报, 2014, 43 (1): 0111004, 网络出版: 2021-08-31
基于粒子群优化的水下成像系统标定
Calibration Algorithm of Underwater Imaging System Based on PSO
机器视觉 摄像机标定 双目立体视觉 粒子群优化算法 水下成像系统 Machine vision Camera calibration Binocular stereo vision Particle swarm optimization algorithm Underwater imaging system
摘要
摄像机在水下拍摄时,经过不同介质的成像光线会发生折射,小孔成像模型不再成立,现有标定方法无法准确标定系统参量.针对此问题,本文提出一种基于粒子群优化的水下成像系统标定方法.在空气中应用张正友平面标定法得到摄像机内、外参量,通过求取特征点间相对定位距离,建立系统对水中物体的标定评价函数,并用粒子群算法对其优化,从而标定得到光心到防水罩距离、防水罩平面法向量和防水罩厚度.结果表明:基于粒子群标定算法得到的相对定位误差平均值分别为1.99%和0.62%,而应用高阶畸变折射补偿法得到的相对定位误差平均值分别为3.29%和2.68%;当被测物位于不同拍摄距离以及改变不同姿态时,由本文算法得到的相对定位误差均低于高阶畸变折射补偿法,且本文提出的标定算法可以得到高准确度的系统参量,为水下视觉研究提供了可靠的参量依据.
Abstract
Light refraction occurs when imaging in the water, and the pin-hole imaging model is no longer valid. Calibration methods existing can′t calibrate the system parameters correctly. This paper presents an underwater calibration algorithm based on particle swarm optimization. Firstly, Zhang′s calibration method is employed to calculate intrinsic parameters and external parameters of the two cameras in the air. The evaluation function is accomplished via length measurement, and then the distance from optical center to refractive interface d, the normal vector nπ and the thickness of waterproof cover h are calibrated by using particle swarm optimization. Trough experimental comparison, the relative positioning errors are 3.29% and 2.68% in the method of high order distortion compensate refraction, while those are 1.99% and 0.62% in the proposed algorithm. Compared with previous method, the proposed algorithm can calibrate parameters accurately and decrease the relative positioning errors obviously, which is important to positioning research in submerged circumstance.
张强, 王鑫, 李海滨. 基于粒子群优化的水下成像系统标定[J]. 光子学报, 2014, 43(1): 0111004. ZHANG Qiang, WANG Xin, LI Hai-bin. Calibration Algorithm of Underwater Imaging System Based on PSO[J]. ACTA PHOTONICA SINICA, 2014, 43(1): 0111004.