Random Sample Consensus

Random Sample Consensus (RANSAC) is a method used to identify the best fit for a model within data that contains many outliers or errors. It works by repeatedly selecting small random samples from the data to estimate a model. Then, it checks how many data points agree with that model, considering them as inliers. After many iterations, the model with the highest number of inliers is chosen as the best representation of the true pattern. RANSAC is widely used in computer vision and robotics for robustly fitting models to messy, real-world data.