Bachelor Artificial Intelligence

Course Autonomous Mobile Robots

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.

Description

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.

Schedule

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

Literature

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:

Structure from Motion toolbox by V. Raboud.
Matlab Functions for Multiple View Geometry by A. Zisserman.
Structure from Motion Toolkit by P. Torr.
Matlab code for Non-Rigid Structure from Motion using Factorisation by L. Torresani.

Chapter 4, section 5 (page 234) - Interest Point Detectors:

Intel's OpenCV library, with e.g. Harris, MSER, FAST, SURF, ... Library maintained by Willow Garage.
SUSAN.
FAST Corner Detection.
SIFT by D. Lowe.
reimplementation in C and Matlab of SIFT, MSER, e.a. by A. Vedaldi.
3D object recognition toolkit by Autonomous Systems Lab at ETH Zurich.
GPU implementation SURF by Vision Lab at ETH Zurich.

Chapter 5, section 8 (page 365) - Simultaneous Localization and Mapping algorithms:

OpenSLAM, a list of SLAM software, list maintained by C. Stachniss.
real-time monocular visual SLAM by Davison.
real-time monocular visual SLAM algorithm PTAM by Klein and Murray.
Matlab EKF SLAM simulator.
RawSeeds collections of benchmarked datasets used for SLAM.

Bibliography (page 444) - Referenced webpages:

CVonline: On-line Compendium of Computer Vision, maintained by R.B. Fisher.
The Intel Image Processing Library / Integrated Performance Primitives (Intel IPP)
CMvision source code
For probotics
Intel's OpenCV library, maintained by Willow Garage.
Passive walking.
Passive walking, the Corner Ranger.
Computer Vision industry
Camera Calibration Toolbox for Matlab
List of camera calibration software
Omnidirectional camera calibration toolbox from Christopher Mei.
Omnidirectional camera calibration toolbox from Joao Barreto.
Omnidirectional camera calibration toolbox from Davide Scaramuzza.
OpenSLAM, a list of SLAM software, list maintained by C. Stachniss.
Open source software for multi-view structure from motion.
Photo Tourism.
Voodoo Camera Tracker: A tool for integration of virtual and real scenes.
Augmented-reality toolkit (ARToolkit).
Parallel Tracking and Mapping (PTAM).

Last updated March 5, 2024

Course Autonomous Mobile Robots