Course Probabilistic Robotics

Master Artificial Intelligence

This is the information of Fall 2017

This course was previously given in the Bachelor Articial Intelligence (Fall 2011). The latest information (current year) can be found here.


The description is available in the course catalogue of the UvA The course is a constrained choice in the Master Artificial Intelligence Curriculum. More details about the organization of the course can be found in the Course Manual.


Probabilistic robotics is a subfield of robotics concerned with the perception and control part. It relies on statistical techniques for representing information and making decisions. By doing so, it accommodates the uncertainty that arises in most contemporary robotics applications.

This course is based on the book 'Probabilistic Robotics', from Sebastian Thrun, Wolfram Burgard and Dieter Fox. The book concentrates on the algorithms, and only offers a limited number of exercises. Their suggestion is to accompany the book with a number of practical, hands-on assignments for each chapter. The assignments of this course are designed to understand the basic problems concerning mobile robotics.


The course will take place in a studio classroom setting. A list of Frequent Asked Questions will be maintained.

Students, who were not able to attend a lecture, can catch up by listing to the recordings of Burghard's lectures (English).

The assignments are based on the Octave or Matlab environment. This YouTube lectures give a short introduction to some essentials of the Matlab environment: the workspace, variables, vectors, colon operator, matrices, concatenating, matrix initialization. Also note Octave cheat sheet.

Week 36: Chapter 1 - Introduction & Robot Paradigms
    Recording Robot Paradigms
    Solve Exercise 2.8.1, 2.8.2 & 2.8.3 and 2.8.4.

Week 36: Chapter 2 State Estimation & Chapter 3.2 - Gaussian Filters - Kalman Filter
    Recordings State Estimation part 1 and part 2 & discussed Burghard's lecture
    Discussed solutions of assignments 2.8.1, 2.8.2 & 2.8.3 and 2.8.4

Week 37: Chapter 3.3 - 3.5 Extended Kalman Filters & Geometric Approach
    Recording Extended Kalman Filter
    Assignment 3.8.1 and 3.8.2.

Week 37: Chapter 4 - Nonparametric Filters: Discrete and Particle Filters
    Recordings Discrete Filters and Particle Filters (part 1 & part 2)
    Assignment 4.6.1 and 4.6.4.

Week 38: Chapter 5 - Wheeled Locomotion & Robot Motion Models
    Recordings Wheeled Locomotion (part 1, part 2, part 3, part 4) and Motion Models (part 1, part 2, part 3).
                   Motion model Assignment.

Week 38: Chapter 6 - Sensors & Robot Perception Models
    Recordings Sensors (part 1) and Sensor Models (part 1, part 2).
    Stanley's RaceDay movie and Stanley's Tech lecture recording (Dutch)

Week 39: Chapter 9 - Mapping with known poses
    Recordings (part 1, part 2, part 3, part 4).

               EKF - SLAM Assignment, including first, second and third dataset.

Week 39: Chapter 10 - SLAM .
    montemerlo-fastslam-aaai video, corresponding to AAAI'03 paper
    Recordings (part 1, part 2).
    animation of raw data of Intel Research Lab, animation of CMU's Wean Hall

Week 40: Monday no lecture - conflict with Delft Workshop on Robot Learning

Week 40: Chapter 13 - The FastSLAM Algorithm (landmark based and grid based)
    Recordings (part 1, part 2).

Week 41: Chapter 11 - GraphSLAM and Chapter 12 - SEIF SLAM.
    video GroundHog, video mine map, video GraphSLAM with Segbot

       FastSLAM with Known Data Association Assignment.

Week 41: Chapter 17 - Exploration and 3D Mapping
    Recordings (exploration and 3D mapping).
    video CMU 1999 multi-robot exploration, video exploration with limited communication

Week 42: Final - Sebastian Thrun's TED talk.

Week 43: Exam ('open book') of Chapter 1-13 & 17 of the book, Thursday October 26, 9:00-12:00, A1.02.

Week 2: Reexam ('open book'), Thursday January 11, 9:00-12:00, room A1.06


Sebastian Thrun, Wolfram Burgard and Dieter Fox, Probabilistic Robotics, The MIT Press, 2005.

Reading guide

  • Week 36: until section 3.3 - page 54
  • Week 37: until chapter 4 - page 116
  • Week 38: until chapter 6 - page 187
  • Week 39: until chapter 10 - page 335
  • Week 40: chapter 13 - page 437-483
  • Week 41: chapter 11,12,17 - page 336-436 and 569-605

Embedding in AI curriculum

This course is supported by the following chapters of 'Artificial Intelligence - A Modern Approach' 3rd edition, by Stuart Russell and Peter Norvig:
  • Chapter 13: Quantifying Uncertainty
  • Chapter 14: Probabilistic Reasoning
  • Chapter 15: Probabilistic Reasoning over Time

  • Chapter 25: Robotics


The course was evaluated by the participants with a 6.0:


Last updated November 15, 2017

o This web-page and the list of participants to this course is maintained by Arnoud Visser (
Faculty of Science
University of Amsterdam

6 visitors in August 2007