基于旋转搜索的相机位姿估计和对应点匹配

Camera pose estimation and corresponding points matching based on rotation search

传统的同步位姿计算和对应点匹配(SPCD)算法采用两阶段交替迭代的方式计算相机位姿,求解精度较低。除此之外,传统SPCD算法依赖于局部搜索策略,无法保证找到全局最优解。为提升求解SPCD问题的精度和全局收敛能力,推导出了一种新的基于点和点约束的SPCD问题求解模型。基于新模型从角度距离出发构建优化目标函数,并利用分支定界搜索的方法寻找最优相机位姿和3D/2D匹配关系。实验证明:相比于传统SoftPOSIT算法,本文算法具有更高的求解精度和收敛率,并且可适用范围更广。

The traditional Simultaneous Pose and Correspondence Determination(SPCD)algorithm uses a two-stage alternate iteration strategy to calculate the camera pose,which leads to poor accuracy.Besides,the traditional SPCD algorithm relies on local search strategies,and have no guarantee of finding the globally optimal solution.To improve the accuracy and the ability of global convergence of solving the SPCD problem,a new solving model based on pointto-point constraints is derived for the SPCD problem in this paper.Based on this new model,an optimal objective function is constructed by using the angle distance,and the branch-and-bound method is used to search the optimal camera pose and the 3D/2D correspondences.Experiments show that the proposed method has higher accuracy and convergence rate compared to the traditional SoftPOSIT algorithm,and would have wide applications.