MOBILE ROBOTICS: METHODS & ALGORITHMS - WINTER 2022
University of Michigan - NA 568/EECS 568/ROB 530
For slides, lecture notes, and example codes, see https://github.com/UMich-CURLY-teaching/UMich-ROB-530-public
Recorded Lectures
- Playlist of the lectures on YouTube: https://www.youtube.com/watch?v=pH4Pkmey2_E&list=PLdMorpQLjeXmbFaVku4JdjmQByHHqTd1F
Course description
Theory and application of probabilistic and geometric techniques for autonomous mobile robotics. This course presents and critically examines contemporary algorithms for robot perception. Topics include Bayesian filtering; stochastic representations of the environment; motion and sensor models for mobile robots; algorithms for mapping, localization; application to autonomous marine, ground, and air vehicles.
Class Goals
Learn the math and algorithms underneath state-of-the-art robotic systems. The majority of these techniques are heavily based on geometric and probabilistic reasoning---an area with extensive applicability in modern robotics. An intended side-effect of the course is to strengthen your expertise in this area.
- Implement, and experiment with these algorithms.
- Be able to understand research papers in the field of robotics.
- Try out some ideas/extensions of your own.
Note: the focus of the course is on math and algorithms. We will not study the mechanical or electrical design of robots.
Textbook
We will use the combination of the following two books:
- Probabilistic Robotics S. Thurn, W. Burgard, and D. Fox MIT Press, Cambridge, MA, September 2010. ISBN-13: 978-0-262-20162-9, Third Printing
Errata for the third printing can be found on the book's website: http://www.probabilistic-robotics.org. It is strongly recommended that you annotate your text copy with the errata corrections.
- State Estimation for Robotics Timothy D. Barfoot, University of Toronto, 2021