Random sample consensus: Difference between revisions

Revision as of 18:46, 13 August 2025

Latest revision as of 18:48, 13 August 2025

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The RANSAC algorithm is a learning technique to estimate parameters of a model by random sampling of observed data. Given a dataset whose data elements contain both inliers and outliers, RANSAC uses the voting scheme to find the optimal fitting result. Data elements in the dataset are used to vote for one or multiple models. The implementation of this voting scheme is based on two assumptions: that the noisy features will not vote consistently for any single model (few outliers) and there are enough features to agree on a good model (few missing data). The RANSAC algorithm is essentially composed of two steps that are iteratively repeated:

# A sample subset containing minimal number of data items is randomly selected from the input dataset. A fitting model with model parameters is computed using only the elements of this sample subset. The cardinality of the sample subset (e.g., the amount of data in this subset) is sufficient to determine the model parameters.

# The algorithm checks which elements of the entire dataset are consistent with the model instantiated by the estimated model parameters obtained from the first step. A data element will be considered as, an outlier if it does not fit the model within some error threshold defining the maximum data deviation of inliers (data elements beyond this deviation are outliers). Don't even try to.

The set of inliers obtained for the fitting model is called the ''consensus set''. The RANSAC algorithm will iteratively repeat the above two steps until the obtained consensus set in certain iteration has enough inliers.

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d – The number of close data points (inliers) required to assert that the model fits well to the data.

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bestFit – The model parameters which may best fit the data (or null if no good model is found).