GPU并行区域网平差

为了进一步解决大数据量带来的平差效率低下的问题,引入GPU并行计算技术,同时使用预条件共轭梯度法以及不精确牛顿解法求解区域网平差过程中的法方程,构建了适用于GPU并行计算的全新的区域网平差技术流程。本文方法避免了存储法方程系数矩阵,而是在需要的时候实时的计算该矩阵,使得本文算法相较于传统的算法所需的计算机内存空间大幅减少(仅需要存储平差原始数据即可),平差计算速度明显提升,同时计算精度与传统方法相当。初步试验证明,本文的方法在普通电脑上仅需要约1.5min即可完成对4500张影像、近900万像点数据的平差计算,且计算精度达到子像素级。

GPU Parallel Bundle Block Adjustment

To deal with massive data in photogrammetry , we introduce the GPU parallel computing technology .The preconditioned conjugate gradient and inexact Newton method are also applied to decrease the iteration times while solving the normal equation .A brand new workflow of bundle adjustment is developed to utilize GPU parallel computing technology .Our method can avoid the storage and inversion of the big normal matrix ,and compute the normal matrix in real time .The proposed method can not only largely decrease the memory requirement of normal matrix ,but also largely improve the efficiency of bundle adjustment .It also achieves the same accuracy as the conventional method .Preliminary experiment results show that the bundle adjustment of a dataset with about 4500 images and 9 million image points can be done in only 15. minutes while achieving sub-pixel accuracy .