'''Random sample consensus''' ('''RANSAC''') is an [[iterative method]] to estimate perimeterparameters of a mathematical model from a set of observed data that contains [[outliers]], when outliers are to be accorded no influence on the values of the estimates. Therefore, it also can be interpreted as an outlier detection method.<ref>Data Fitting and Uncertainty, T. Strutz, Springer Vieweg (2nd edition, 2016)</ref> 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 algorithm was first published by Fischler and Bolles at [[SRI International]] in 1981. They used RANSAC to solve the Location Determination Problem (LDP), where the goal is to determine the points in the space that project onto an image into a set of landmarks with known locations.
RANSAC uses [[Cross-validation (statistics)#Repeated random sub-sampling validation|repeated random sub-sampling]].<ref>Cantzler, H. "Random sample consensus (ransac)." ''Institute for Perception, Action and Behaviour, Division of Informatics, University of Edinburgh'' (1981).
