Inhalte

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.

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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 .

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Furthermore, students will get to know different ways to model robot motion and measuerments of various types of sensors.

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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.

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?bungen

Es erscheint w?chentlich ein ?bungsblatt zu den behandelten Vorlesungsinhalten. Jedes ?bungsblatt wird in der Globalübung?der folgenden Woche besprochen. Es gibt keine Abgabe / Korrektur von ?bungsbl?ttern.

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Turnus

Achtung: In Zukunft wird Probabilistic Robotics nur unregelm??ig?im Sommersemester gelesen.

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Literatur

• Sebastian Thrun, Wolfram Burgard, Dieter Fox.?Probabilistic Robotics. MIT Press. (http://www.probabilistic-robotics.org/). Mandatory to read chapters 1 - 8

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