Course Autonomous Mobile Robots

Bachelor Artificial Intelligence

This is the information for Spring 2025

This year the course will be given again by Arnoud Visser, together Shaodi You.In the period 2017-2024 the course was given by Herke van Hoof and Shaodi You. The information of the year 2016 is also still available.


The description is available in the course catalogue with code AUMR6Y. The course is a free choice in the Bachelor Artificial Intelligence Curriculum.


This course gives an introduction in the fundamentals of mobile robotics, spanning the mechanical, motor, sensory, perceptual, and cognitive layers the field comprises. The focus will be on the mechanisms that allow a mobile robot to move through a real world environment to perform its tasks. It synthesizes material from the fields of kinematics, control theory, signal analysis, computer vision, information theory, artificial intelligence and probability theory.

This course is based on the book 'Introduction to Autonomous Mobile Robots', from Prof. Dr. Roland Yves Siegwart, Prof. Dr. Illah R. Nourbakhsh and Prof. Dr. Davide Scaramuzza. The assignments are all based on the Matlab environment. This YouTube lectures give a short introduction to the Matlab environment: the workspace, variables, vectors, colon operator, matrices, concatenating, matrix initialization.


The official schedule should be found at mytimetable or datanose. Chapter 1-4 of the book will be introduced by Arnoud Visser. Chapter 5-6 will be covered by Shaodi You.

For the assignments not only a solution is expected, but also a rational. The experiments performed to solve the given problem should be described in a lab report, which will be graded based on the following criteria.

Week 44: Chapter 1 & 2 - Introduction & Locomotion
   Solve OpenLoop steering assignment, including RWTH Toolbox Installation Instructions.

Week 44: Chapter 3 - Kinematics

Week 45: Chapter 4.1 Sensors for Mobile Robots

Week 45: Chapter 4.2 Fundamentals of Computer Vision
    Solve Assignment 3, matlab files, camera snapshots.

Week 46: Chapter 4.3-4.5 Feature Extraction

Week 46: Chapter 4.6-4.7 Place Recognition

Week 47: Partial Exam

Week 48: Chapter 5.1-5.5 The Challenge of Localization including YouTube lecture
   Solve Localization assignment, Matlab code, color picker and two locally recorded datasets (one for training and one for testing) Extra: 5 december recordings: (training set and testing set) and the 2014 Dataset
from Areg.

Week 48: Chapter 5.6 Probabilistic Map Based Localization

Week 49: Chapter 5.6.8 Kalman Filter Localization including YouTube lecture
and Kalman Filter Geometric Approach (Slides and Dutch recording)
Only slides 9-45. Note the slightly different notation for the intermediate prediction;
   x(k+1|k) by Choset et al.and x(t) by Thrun/Siegwart et al.

Week 49: Chapter 5.8 Simultaneous Localization and Mapping (part I and part II)
YouTube lecture EKF-SLAM, YouTube lecture Monocular SLAM
    Solve Assignment 4, with provided Logger, Example log, Matlab files and a locally recorded dataset (dataset and route (displacements of 15cm at the straight lines)).

Week 50: Chapter 6 - Planning and Navigation (part I and part 2)
YouTube lecture 1, lecture 2, lecture 3, lecture 4, lecture 5.

Week 51: Partial Exam


Roland Siegwart, Illah R. Nourbakhsh and Davide Scaramuzza 'Introduction to Autonomous Mobile Robots', 2nd edition, The MIT Press, 2011.

Embedding in AI curriculum

This course is supported by the following chapters of 'Artificial Intelligence - A Modern Approach' 4th edition, by Stuart Russell and Peter Norvig:
  • Chapter 12: Quantifying Uncertainty
  • Chapter 13: Probabilistic Reasoning
  • Chapter 14: Probabilistic Reasoning over Time

  • Chapter 25: Perception
  • Chapter 26: Robotics


Software toolkits

Chapter 4, section 2.6 (page 186) - Structure from Motion: Chapter 4, section 5 (page 234) - Interest Point Detectors: Chapter 5, section 8 (page 365) - Simultaneous Localization and Mapping algorithms: Bibliography (page 444) - Referenced webpages:
Last updated March 5, 2024

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