This course is an introduction to Kalman Filtering. It begins with review of important background topics, then develops and explores the algorithms for linear and nonlinear Kalman Filtering. The last part of the course requires students to collect data with a noisy IMU and write their own Kalman Filters to estimate the ground truth.
The class is organized roughly as follows:
Topic Outline: Kalman Short Course Outline
Excellent Beginner tutorial: https://home.wlu.edu/~levys/kalman_tutorial/
Probabilistic Robotics: http://www.probabilistic-robotics.org/