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WS 11/12: Probabilistic Robotics

From Multimedia Computing Lab - University of Augsburg

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Instructors: Prof. Dr. Rainer Lienhart, Christian Ries
Time Lecture: Thursday, 10:00 - 11:30, Room: 1021 N
Time Exercise: Wednesday, 8:15 - 9:45, Room: 1054 N
Credits: 2 + 2 SWS, Schein: yes, LP 5
Exam: Thursday, Feb 23rd, 2012, 10:00 - 11:30, Room: 1058N
Multimedia Teilbereiche: Multimedia Methoden, Multimedia Anwendungen, Systemnahe Grundlagen von Multimedia
All important dates: ics Calendar File



In the course of this lecture students will get to know 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 measuerments 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.

Exercise and Exam

  • There will be an exercise sheet every week
  • Solutions will be discussed during exercises on Wednesdays (no handing-in or revision of written solutions)
  • Students are encouraged to present their solutions
  • No admission requirements for the exam

Important Comments

  • Vorlesung wird auf Deutsch gehalten (trotz der englischsprachigen Folien und Literatur)
  • All reading notes are relevant for the exams independent on how thoroughly they have been discussed during the lecture. Thus read and study them carefully.


Sebastian Thrun, Wolfram Burgard, Dieter Fox. Probabilistic Robotics. Springer Verlag. (
Mandatory to read chapters 1 - 8


Date Content Slides Exercise
20.10.2011 Introduction PDF
27.10.2011 Lec 02: Introduction to Probabilistic Robotics PDF PDF-Ex1
Lec 03: Recursive State Estimation PDF PDF-Ex2

PDF-Ex3 - Ex3_Matlab

Lec 04: Gaussian Filters PDF PDF-Ex4



Lec 05: Nonparametric Filters PDF PDF-Ex7
Lec 06: Robot Motion PDF PDF-Ex8
Lec 07: Robot Perception PDF PDF-Ex9
Lec 08: Mobile Robot Localization: Markow and Gaussian PDF
Lec 09: Mobile Robot Localization: Grid and Monte Carlo PDF PDF-Ex10
Lec 10: Occupancy Grid Mapping PDF
Lec 11: SLAM PDF