# RANSAC - It is an iterative method to estimate parameters of a mathematical model from a set of observed data - A simple example is fitting a line to a set of observations. - Outliers are points that don't "fit" the model and points that do fit are called "inliers" - Table detection - The algorithm starts by generating plane hypotheses based on three unique points. - For each plane hypothesis, distances from all points in the point cloud to the plane are computed. - The plane hypotheses are then scored based on counting the number of inlier points, e.g., distance to the plane  20mm. -  - The RANSAC algorithm is repeated for a certain number of iterations, e.g., n = 200. - Object detection - It is now possible to extract the points which lie directly above it. - By removing the table, we have a point cloud where all the objects that are on top of the table are included. - The obtained point cloud is then segmented into individual clusters Each small group of points will be treated as an object candidate. -  -