Browse Teach Curate

Kalman Filtering for Sensor Fusion and State Estimation

The recursive Bayesian estimator that fuses noisy measurements from multiple sensors with a dynamic motion model to produce an optimal estimate of a system's state and its uncertainty. Covers the predict-update cycle, process and measurement noise covariances, and extensions for nonlinear systems, as used in navigation to combine inertial, satellite, and odometry data.

jeremy Video

The Linear Kalman Filter for State Estimation in Robotics

The Kalman filter is a recursive state estimator that fuses uncertain sensor measurements with a probabilistic motion model to estimate the evolving state of a dynamic system in real time, operating …