快速迭代五点相对位姿估计
Fast Iterative Five point Relative Pose Estimation
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中文总结 AI 辅助
本文提出一种基于Powell Dog-Leg算法的快速迭代五点相对位姿估计方法，精度与Nister算法相当、速度约快一倍，可扩展至五点以上场景，经三类真值数据集系统评估有效。
中文摘要 AI 辅助
对两个相机间相对位姿的鲁棒估计是结构与运动方法的基础部分。对于已校准的相机，五点法结合RANSAC等鲁棒估计器在多数情况下能给出最优结果。当前求解五点相对位姿问题的最先进方法来自Nister[9]，因其比其他方法更快，且在RANSAC框架中可通过增加迭代次数提升精度。本文提出一种基于Powell Dog-Leg算法的新型迭代方法，该方法精度与Nister算法相同，速度约快一倍。所提方法可轻松扩展至五点以上的情况，同时保留高效误差度量，使其也非常适合作为优化步骤。本文在三类含已知真值的数据集上对所提算法进行了系统评估。
英文摘要
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 Nister [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.
