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WS 15/16: Probabilistic Robotics

From Multimedia Computing Lab - University of Augsburg

Registration is now open at Digicampus


Instructors: Prof. Dr. Rainer Lienhart, Dan Zecha
Time Lecture: Tuesday, 10:00 - 11:30, Room: 1057 N, starts Oct 13thth
Time Exercise: Tuesday, 08:15 - 09:45, Room: 1057 N. starts: Oct 27th
Credits: 2 + 2 SWS, Schein: yes, LP 5
Exam: February 23rd 2016, 10:00 - 11:30, Room: 2045N
Multimedia Teilbereiche: Multimedia Methoden, Multimedia Anwendungen, Systemnahe Grundlagen von Multimedia


  • [03.09.2015] The date and room for the exam have been set.
  • [29.07.2015] Registration is now open at Digicampus
  • [29.07.2015] Page creation.


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 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 Fridays (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. MIT Press. (
Mandatory to read chapters 1 - 8