RANSACmenetelmiä

RANSAC, which stands for Random Sample Consensus, is an iterative method to estimate parameters of a mathematical model from an observed data set that contains outliers. The core idea is to select a small subset of the data points, fit a model to this subset, and then evaluate how well this model fits the entire data set. This process is repeated multiple times, and the model that best fits the consensus of the data is chosen.

The algorithm works by repeatedly performing the following steps: randomly select a minimal subset of data points required to estimate the model parameters. For example, to fit a line, two points are sufficient. To fit a plane, three points are needed. Calculate the model parameters using this minimal subset. Then, determine the set of inliers, which are data points that are close to the model, typically within a predefined threshold. Count the number of inliers. After a sufficient number of iterations, the model with the largest number of inliers is considered the best fit.

RANSAC is particularly useful when the data is expected to contain a significant number of outliers, which can heavily distort the results of traditional fitting methods like least squares. It is widely used in computer vision for tasks such as line detection, plane fitting, and object recognition, as well as in other fields like robotics and geostatistics. The effectiveness of RANSAC depends on parameters such as the number of iterations and the inlier threshold, which need to be chosen appropriately for the specific problem.