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A Simple Sample Consensus Algorithm to Find Multiple Models

机译:查找多个模型的简单样本共识算法

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摘要

In many applications it is necessary to describe some experimental data with one or more geometric models. A naive approach to find multiple models consists on the sequential application of a robust regression estimator, such as RANSAC [2], and removing inliers each time that a model instance was detected. The quality of the final result in the sequential approach depends strongly on the order on which the models were. The MuSAC method proposed in this paper discovers several models at the same time, based on the consensus of each model. To reduce bad correspondences between data points and geometric models, this paper also introduces a novel distance for laser range sensors. We use the MuSAC algorithm to find models from 2D range images on cluttered environments with promising results.
机译:在许多应用中,必须使用一个或多个几何模型来描述一些实验数据。一种找到多个模型的幼稚方法包括顺序应用稳健的回归估计器(如RANSAC [2]),并在每次检测到模型实例时都删除内部值。顺序方法中最终结果的质量在很大程度上取决于模型的顺序。本文提出的MuSAC方法基于每个模型的共识,可以同时发现多个模型。为了减少数据点和几何模型之间的不良对应关系,本文还介绍了一种新型的激光测距传感器距离。我们使用MuSAC算法从杂乱环境中的2D距离图像中找到模型,并获得了令人满意的结果。

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