Multiple camera systems (MCS) have attracted increasing attention in machine vision. Regarding pose estimation of MCS, a novel integrated reconstruction and orthogonal iteration algorithm is proposed. A general camera model is introduced to formulate MCS to derive the algorithm. To make full use of the redundant measurements of MCS, the observed reference points are classified, and a weighted objective function is constructed. Using the objective function, advantages of the optimal absolute orientation solution (OAOS) and the orthogonal iteration (OI) algorithm are combined. Introduction of 3D reconstructed information not only overcomes the pose ambiguity of OI but also improves convergence and robustness in pose estimation. The effects of reconstructed error on final estimation are reduced using an adaptive weighting method. Simulation results show effectiveness and superiority of the proposed algorithm.%多摄像机系统(multiple camera systems,MCS)在机器视觉领域成为研究热点.针对MCS的位姿估计问题提出了一种新的综合重构与正交迭代算法,并引入广义摄像机模型来描述MCS以方便算法推导.为充分利用MCS提供的冗余测量信息,将观测到的参考点进行分类,并构建了加权目标函数,综合了最优绝对定向解和正交迭代(orthogonal iteration,OI)算法的优点.三维重构信息的引入不仅克服了OI算法的位姿模糊,而且提高了位姿估计的收敛速度和鲁棒性.采用了自适应权值方法降低重构误差对最终估计的影响.仿真结果验证了提出算法的有效性和优越性.
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