Perception

RANSAC

RANSAC (random sample consensus) is a robust estimation algorithm introduced by Fischler and Bolles in 1981 that fits models to outlier-contaminated data by repeatedly sampling minimal subsets, hypothesizing a model from each, and keeping the hypothesis with the largest inlier consensus. In robotics it underpins plane extraction from point clouds, feature-based pose and homography estimation, and geometric verification of loop-closure candidates.

Why it matters for physical AI

Real sensor data is full of gross outliers from mismatches, reflections, and dynamic objects, and RANSAC-style hypothesize-and-verify remains the default defense wherever geometric models are fit to it.