Perception

Robust Estimation

Robust estimation is the family of estimation techniques that tolerate outliers and non-Gaussian noise, including M-estimators with influence-limiting loss functions such as Huber and Cauchy, RANSAC-style consensus methods, and outlier-rejecting formulations in factor-graph optimization like switchable constraints and graduated non-convexity. In robot perception it protects pose estimation, registration, and SLAM back ends from mismatched features and spurious loop closures.

Why it matters for physical AI

A single unrejected outlier can corrupt an entire map or pose estimate, so robust losses and consensus checks are what keep state estimation trustworthy on real, messy sensor data.