快速迭代五点相对位姿估计

J. Hedborg, M. Felsberg
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引用次数: 11

摘要

两个相机之间的相对姿态的鲁棒估计是结构和运动方法的基本组成部分。对于校准过的相机,五点方法与稳健的估计器(如RANSAC)在大多数情况下给出了最好的结果。目前最先进的从五个点求解相对位姿问题的方法是由于nist[9],因为它比其他方法更快,并且在RANSAC方案中可以通过增加迭代次数来提高精度。本文提出了一种新的基于Powell's Dog Leg算法的迭代方法。新方法具有相同的精度,并且速度大约是Nister算法的两倍。该方法易于扩展到5个点以上,同时保留了有效的误差度量。这使得它也非常适合作为一个细化步骤。该算法在三种已知地面真值的数据集上进行了系统的评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fast iterative five point relative pose estimation
Robust estimation of the relative pose between two cameras is a fundamental part of Structure and Motion methods. For calibrated cameras, the five point method together with a robust estimator such as RANSAC gives the best result in most cases. The current state-of-the-art method for solving the relative pose problem from five points is due to Nistér [9], because it is faster than other methods and in the RANSAC scheme one can improve precision by increasing the number of iterations. In this paper, we propose a new iterative method, which is based on Powell's Dog Leg algorithm. The new method has the same precision and is approximately twice as fast as Nister's algorithm. The proposed method is easily extended to more than five points while retaining a efficient error metrics. This makes it also very suitable as an refinement step. The proposed algorithm is systematically evaluated on three types of datasets with known ground truth.
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