OUTLIERS DETECTION BY RANSAC ALGORITHM IN THE TRANSFORMATION OF 2D COORDINATE FRAMES

Abstract

Over the years there have been a number of different computational methods
that allow for the identification of outliers. Methods for robust estimation are known
in the set of M-estimates methods (derived from the method of Maximum
Likelihood Estimation) or in the set of R-estimation methods (robust estimation
based on the application of some rank test). There are also algorithms that are not
classified 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 detect
outliers in the process of transformation of coordinates, where the coordinates of
some points may be affected by gross errors, can be a method called RANSAC
algorithm (Random Sample and Consensus). The authors present a study that was
performed in the process of 2D transformation parameter estimation using
RANSAC algorithm to detect points that have coordinates with outliers. The
calculations were performed in three scenarios on the real geodetic network.
Selected coordinates were burdened with simulated values of errors to confirm the
efficiency of the proposed method.