State estimation

Two imperfect sources of the same number, and how to combine them: the complementary filter, then predict and correct, then the Kalman gain that works out the weighting for you.

Module U6University7 lessons

Start in the simulator
  1. U6.1 Two sources, one state A signal that drifts and a signal that is noisy, and why either alone is worse than both.
  2. U6.2 Predict and correct The shape every filter has: a model that guesses forward, a measurement that pulls it back.
  3. U6.3 The Kalman gain Where the weighting comes from: two variances, one line of arithmetic, no taste involved.
  4. U6.4 Q and R The two numbers you actually choose, what they mean, and how to measure them.
  5. U6.5 Covariance and the ellipse Uncertainty in two dimensions has a shape, and the shape is the useful part.
  6. U6.6 Fusing a fix Odometry prediction, a tag as the measurement, and a gain that rises as the estimate ages.
  7. U6.7 Project: navigate on the estimate Reach a target the robot cannot see, using nothing but the filter you wrote.