改进基本矩阵计算和优化的多摄像机并行标定算法

多摄像机系统具有摄像机数目多、空间位置分布复杂特点，导致多摄像机标定效率低。基本矩阵计算和非线性优化是摄像机标定算法的关键步骤。针对标定物空间位置相互独立性，改进随机抽样一致性(RANSAC)的基本矩阵计算和简化非线性优化的增量方程，提出多摄像机系统的并行标定算法。该算法挖掘多摄像机标定过程的内在并行化，从而提高了标定的时间效率。相比于传统的多摄像机标定算法，并行算法的时间复杂度从O(n3)降为O(n)。实验结果表明：使用多摄像机系统并行标定算法在不损失精度的同时能够减少标定时间，实现多摄像机系统的快速标定。

Improved foundamental matrix computation and optimization method in parallel calibration of multi-camera system

Multi-camera system has a number of cameras and complex space distribution, hence the calibration in multi-camera system is inefficient. The calculation of fundamental matrix and nonlinear optimization are the key steps in camera calibration algorithm. In the light of independent marked points in multi-camera system calibration, fundamental matrix based on RANdom SAmple Consensus (RANSAC) and increment equation in nonlinear optimization were improved, and a parallel calibration algorithm for multi-camera system was proposed. Based on the analysis of the parallel process of calibration, this algorithm improved the efficiency of the calibration time. Compared with the traditional camera calibration algorithm, it makes the time complexity of calibration reduce from O(n3) to O(n). The experiments show that the parallel algorithm reduces the time obviously without any loss in precision, thus realizing fast multi-camera system calibration.