In the course of this lecture, students will learn how robots can estimate their state (e.g. their pose) in a probabilistic fashion, i.e. in the face of uncertainty.
The main focus of this lecture is on the Bayes Filter algorithm which enables robots to estimate their new state after executing a control and to incorporate sensor measurements to update their belief. Various flavors of the Bayes Filter such as the Kalman Filter and the Particle Filter will be discussed in detail.
Furthermore, students will get to know different ways to model robot motion and measurements of various types of sensors.
The final chapters of the lecture will be on approaches to robot localization, i.e. the problem of the robot having to determine its position on a given map of the environment. Also, the localization problem will be discussed for situations when the robot has to generate a map itself by occupancy grid mapping or simultaneous localization and mapping (SLAM) algorithms.
