Course Autonomous Mobile Robots
Bachelor Artificial Intelligence
This is the information of Fall 2012
The previous year a course with a slightly different focus was given (Probabilistic Robotics
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 official schedule
should be found here.
The Studio Class Room is scheduled on Monday and Thursday, from 9u00 to 13u00. The Studio Class Room will be a combination of lectures, book exercises and assignments. The course will take place in A1.30. A list of Frequent Asked Questions will be maintained. Chapter 2-4 of the book will be introduced by Toto van Inge. Chapter 1 and 5-6 will be covered by Arnoud Visser.
Students, who were not able to attend a lecture, can catch up by listing to the recordings of my (Dutch). Download Lecturnity Player and listen to lecture, synchronized with the sheets.
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
Week 46: Chapter 4.3-4.5 Feature Extraction
Week 46: Chapter 4.6-4.7 Place Recognition
Week 47: Partial Exam (exam including answers)
Week 48: Chapter 5.1-5.5 The Challenge of Localization
Solve Localization assignment, Matlab code, color picker.
Week 48: Chapter 5.6 Probabilistic Map Based Localization
Week 49: Chapter 5.6.8 Kalman Filter Localization
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
Assignment 4, with provided Logger, Example log, Matlab files.
Week 50: Chapter 6 - Planning and Navigation
Week 50: Summary, including
Week 51: Partial Exam, December 20th, 13:00-15:00, A1.04
Roland Siegwart, Illah R. Nourbakhsh and Davide Scaramuzza 'Introduction to Autonomous Mobile Robots', 2nd edition, The MIT Press, 2011.
- Monday November 29th: page 56
- Thursday November 1st: page 99
- Thursday November 8th: page 194
- Thursday November 15th: page 264
- Thursday November 29th: page 321
- Thursday December 6th: page 368
- Thursday December 13th: page 424
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 24: Perception
- Chapter 25: Robotics
The course of this year evaluated by the participants with a 7.7:
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:
- 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
- Newton Labs website
- 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 June 6, 2013
This web-page and the list of participants to this course is maintained by
University of Amsterdam