单目视觉系统自运动的滚动时域估计算法

摘要

单目视觉系统的自运动估计是计算机视觉领域中的一个关键问题.针对包含有建筑、树木等一般景物特征的应用环境,提出一种单目摄像机位姿估计的滚动时域位姿估计算法.首先分析极线约束方程的不同形式,建立多帧图像闭环之间的时空相关位姿约束,归纳全局最优模型.然后,采用滚动时域方法实现时域窗口内多时刻摄像机位姿的优化估计,实现算法复杂程度和精度的折衷.另外,在室外复杂应用环境下,对常规极约束、冗余极约束和滚动时域冗余极约束这3种位姿估计优化算法进行实验对比,验证该方法的有效性.%Estimating ego-motion in monocular visual systems from the input image sequence is a critical problem in computer vision. A moving horizon estimation ( MHE) based algorithm is proposed to solve the pose estimation problem in most general application environment including buildings and trees. Firstly, different forms of epipolar constraints are analyzed. The time-space related constraints among the closed loop of image sequence are all involved in the global optimization model. In addition, the MHE is adopted to obtain the tradeoff between computation costs and estimation accuracy. Based on the general epipolar equations, the redundant epipolar constraints and the moving horizon constraints, the corresponding three referred pose estimation algorithms are performed comparatively, and the outdoor experimental results validate the effectiveness of the proposed method.