Navigation & SLAM

Particle Filter

A particle filter is a sequential Monte Carlo algorithm that represents the probability distribution over a robot's state with a weighted set of samples, propagating them through a motion model and reweighting them against sensor observations. Unlike Kalman filters, it handles nonlinear dynamics and multimodal beliefs, which made it the foundation of Monte Carlo Localization and the FastSLAM family of mapping algorithms.

Why it matters for physical AI

Particle filters remain a workhorse for global localization, where a robot must recover its pose from complete uncertainty, and they exemplify the belief-space reasoning needed whenever perception is ambiguous.