SUMMARY
Over the years there have been a number of different computational methodsthat allow for the identification of outliers. Methods for robust estimation are knownin the set of M-estimates methods (derived from the method of MaximumLikelihood Estimation) or in the set of R-estimation methods (robust estimationbased on the application of some rank test). There are also algorithms that are notclassified in any of these groups but these methods are also resistant to gross errors,for example, in M-split estimation. Another proposal, which can be used to detectoutliers in the process of transformation of coordinates, where the coordinates ofsome points may be affected by gross errors, can be a method called RANSACalgorithm (Random Sample and Consensus). The authors present a study that wasperformed in the process of 2D transformation parameter estimation usingRANSAC algorithm to detect points that have coordinates with outliers. Thecalculations were performed in three scenarios on the real geodetic network.Selected coordinates were burdened with simulated values of errors to confirm theefficiency of the proposed method.