In recent years, flying robots such as miniature helicopters or quadrotors have received a large gain in popularity. Potential applications range from aerial filming over remote visual inspection of industrial sites to automatic 3D reconstruction of buildings. Navigating a quadrotor manually requires a skilled pilot and constant concentration. Therefore, there is a strong scientific interest to develop solutions that enable quadrotors to fly autonomously and without constant human supervision. This is a challenging research problem because the payload of a quadrotor is uttermost constrained and so both the quality of the onboard sensors and the available computing power is strongly limited.
In this course, we will introduce the basic concepts for autonomous navigation for quadrotors. The following topics will be covered:
probabilistic state estimation,
visual odometry, SLAM, 3D mapping,
In particular, you will learn how to infer the position of the quadrotor from its sensor readings and how to navigate it along a trajectory.
The course consists of a series of weekly lecture videos that we be interleaved by interactive quizzes and hands-on programming tasks. For the flight experiments, we provide a browser-based quadrotor simulator which requires the students to write small code snippets in Python.
This course is intended for undergraduate and graduate students in computer science, electrical engineering or mechanical engineering. This course has been offered by TUM for the first time in summer term 2014 on EdX with more than 20.000 registered students of which 1400 passed examination. The MOOC is based on the previous TUM lecture “Visual Navigation for Flying Robots” which received the TUM TeachInf best lecture award in 2012 and 2013.
Do I need to buy a textbook?
No, all required materials will be provided within the courseware. However, if you are interested, we recommend the following additional materials:
This course is based on the TUM lecture Visual Navigation for Flying Robots. The course website contains lecture videos (from last year), additional exercises and the full syllabus: http://vision.in.tum.de/teaching/ss2013/visnav2013
Probabilistic Robotics. Sebastian Thrun, Wolfram Burgard and Dieter Fox. MIT Press, 2005.
Computer Vision: Algorithms and Applications. Richard Szeliski. Springer, 2010.
Do I need to build/own a quadrotor?
No, we provide a web-based quadrotor simulator that will allow you to test your solutions in simulation. However, we took special care that the code you will be writing will be compatible with a real Parrot Ardrone quadrotor. So if you happen to have a Parrot Ardrone quadrotor, we encourage you to try out your solutions for real.
Start your review of Autonomous Navigation for Flying Robots
Billy Lim completed this course, spending 5 hours a week on it and found the course difficulty to be hard.
This is more of a matrix-transforming course where the quadrotors only come in during programming assignments or when the lecturer gives a real-world example. However, it does a good job of explaining matrix transformations. The programming assignments, on the other hand, are a little messy & hard to understand.
José Ignacio González Cárdenas
José Ignacio González Cárdenas completed this course.
This is a great course. The math involved is a little bit hard, but I think they managed to make it well structured.
Anonymous completed this course.
It's really great course on edX, I learned a lot! Very well structured with great examples. I really recomment this course.
Nuno Goncalves completed this course, spending 8 hours a week on it and found the course difficulty to be medium.
Is a very good introduction to autonomous robot, good exercises to practice, is very well structured, i recomend this course.