Basics of Mobile Robotics: Uncertainties

This lecture covers the basics of mobile robotics, focusing on the uncertainties involved in localization. Starting with the Bayes filter, the instructor explains the discrete Bayes filter and particle filters. Moving on to the Kalman Filter, the lecture delves into Gaussian representations, sensor fusion, and the Extended Kalman Filter. Practical examples include 1D and 2D localization scenarios, sensor fusion using accelerometers and gyroscopes, and the application of the Extended Kalman Filter in orientation estimation.