localization

The chicken-and-egg problem of robot autonomy: to localise you need a map; to build a map you need a pose. Covers front-end matching, back-end pose-graph optimisation, loop closure, and visual vs LiDAR SLAM systems.

Fuse noisy sensors over time with provably optimal estimates. Covers the state and noise model, predict and update equations with the Kalman gain, why it's optimal for linear-Gaussian systems, and the Extended KF for nonlinear robots.