Localisation

Working out where the robot is when it does not already know: a belief made of samples, moved by the motion model, weighted by the measurements, and resampled into an answer.

Module U7University7 lessons

Start in the simulator
  1. U7.1 Where am I? Tracking against global localisation, and why one Gaussian is not enough for the second.
  2. U7.2 A cloud of guesses Representing a belief as a thousand samples, and what that buys over a mean and a variance.
  3. U7.3 Moving the cloud The motion update: every particle drives, and every particle's own error goes with it.
  4. U7.4 Weighing the guesses The measurement update: how likely is this reading if the robot were there?
  5. U7.5 Resampling Keeping the good guesses without losing the diversity that lets the filter recover.
  6. U7.6 Monte Carlo localisation The three steps in a loop on a real mat, with tags as the measurement.
  7. U7.7 Project: the kidnapped robot No idea where it starts. Work it out, then drive somewhere on purpose.