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Chapter 1

Kalman Filters

Recursive Bayesian state estimation, motion and sensor models, the Kalman & Extended Kalman filters, and EKF-SLAM.

  1. 1.1
    Recursive Bayes Filter

    The probabilistic foundation: belief, prediction, and correction.

  2. 1.2
    Motion & Sensor Models

    Odometry, velocity, range-bearing and other models used inside filters.

  3. 1.3
    Kalman & Extended Kalman Filters

    Linear KF, linearization via Jacobians, the EKF prediction/update cycle.

  4. 1.4
    EKF-SLAM

    Estimating the robot pose and landmark map jointly with an EKF.