RANSAC is an abbreviation for "RANdom SAmple Consensus". It is an iterative method to estimate parameters of a mathematical model from a set of observed data which contains outliers. It is a non-deterministic algorithm in the sense that it produces a reasonable result only with a certain probability, with this probability increasing as more iterations are allowed.
The input to the RANSAC algorithm is a set of observed data values, a parameterized model which can explain or be fitted to the observations, and some confidence parameters.
RANSAC achieves its goal by iteratively selecting a random subset of the original data. These data are hypothetical inliers and this hypothesis is then tested as follows: A model is fitted to the hypothetical inliers, i.e. all free parameters of the model are reconstructed from the inliers.
All other data are then tested against the fitted model and, if a point fits well to the estimated model, also considered as a hypothetical inlier. The estimated model is reasonably good if sufficiently many points have been classified as hypothetical inliers.
The model is reestimated from all hypothetical inliers, because it has only been estimated from the initial set of hypothetical inliers.
Finally, the model is evaluated by estimating the error of the inliers relative to the model.
This procedure is repeated a fixed number of times, each time producing either a model which is rejected because too few points are classified as inliers or a refined model together with a corresponding error measure. In the latter case, we keep the refined model if its error is lower than the last saved model.
Source: Wikipedia
