为了快速、准确地对含有高比例外点的数据进行模型参数估计，提出一种重抽样优化的快速RANSAC算法。首先在模型检验之前增设预检验，并采用一种基于样条曲线的损失函数来评价模型的质量；然后通过反复重抽样和模型检验来优化内点集；再依据双阈值对内点集进行渐近提纯；最后利用最优内点集来计算模型的参数。特征匹配和基础矩阵估计的实验结果表明，该算法具有较高的精度和效率；当外点比例高于50%时，运行速度比传统算法提高大于2个数量级。%In order to efficiently and accurately estimate model parameters from data contaminated by heavy outliers, fast resampling optimal sample consensus (FROSAC) algorithm is proposed. Firstly, a pre-validation step is added before model validation, and the quality of models is evaluated by the spline-based loss function. Secondly, inlier set is optimized by iteratively resampling and model validating. Then inlier set is refined gradually according to the bi-threshold. Finally the model is estimated with the op-timal inlier set. Experiments on feature matching and fundamental matrix estimation show that the proposed algorithm is high in accuracy and efficiency, and is faster than the traditional algorithms by more than two degrees of magnitude in the case that the percentage of outliers is over 50%.
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