In this course, we will introduce the basic concepts for autonomous navigation with quadrotors, including topics such as probabilistic state estimation, linear control, and path planning.
This is a past/archived course. At this time, you can only explore this course in a self-paced fashion. Certain features of this course may not be active, but many people enjoy watching the videos and working with the materials. Make sure to check for reruns of this course.
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 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 including topics such as probabilistic state estimation, linear control, and path planning. You will learn how to infer the position of the quadrotor from its sensor readings, how to navigate along a series of waypoints, and how to plan collision free trajectories. The course consists of a series of weekly lecture videos that we be interleaved by interactive quizzes and hands-on programming tasks. The programming exercises will require you to write small code snippets in Python to make a quadrotor fly in simulation.
This course is intended for graduate students in computer science, electrical engineering or mechanical engineering. The course is based on the TUM lecture “Visual Navigation for Flying Robots” which received the TUM TeachInf best lecture award in 2012 and 2013. The course website from last year (including lecture videos and course syllabus) can be found here: http://vision.in.tum.de/teaching/ss2013/visnav2013
Jürgen Sturm is a postdoctoral researcher in the Computer Vision group at the Technische Universität München. His major research interests lie in dense localization and 3D reconstruction for micro aerial vehicles. In 2011, he obtained his PhD from the Autonomous Intelligent Systems lab headed by Prof. Wolfram Burgard at the University of Freiburg. He won several awards including the ECCAI best dissertation award in 2011 and the TUM Teach Inf best lecture award for his course "Visual Navigation for Flying Robots" in 2012 and 2013.
Daniel Cremers received Bachelor degrees in Mathematics (1994) and Physics (1994), and a Master's degree in Theoretical Physics (1997) from the University of Heidelberg. In 2002 he obtained a PhD in Computer Science from the University of Mannheim, Germany. Subsequently he spent two years as a postdoctoral researcher at the University of California at Los Angeles (UCLA) and one year as a permanent researcher at Siemens Corporate Research in Princeton, NJ. From 2005 until 2009 he was associate professor at the University of Bonn, Germany. Since 2009 he holds the chair for Computer Vision and Pattern Recognition at the Technische Universität München. His publications received several awards, including the award of Best Paper of the Year 2003 by the Int. Pattern Recognition Society and the 2005 UCLA Chancellor's Award for Postdoctoral Research. In December 2010 the magazine Capital listed Prof. Cremers among "Germany's Top 40 Researchers Below 40".
Christian Kerl is a PhD student in the Computer Vision group at the Technische Universität München. His main research interests, are visual SLAM and 3D reconstruction using RGB-D cameras, either mounted on a quadrotor or handheld. In 2012, he obtained his Master's degree in Robotics from the Technische Universität München.
Julian Tatsch is a master student in computer science at the Technische Universität München. He supports the creation of the interactive exercises.
Jonas Jelten is a bachelor student in computer science at the Technische Universität München. He supports the creation of the interactive exercises.
Benjamin Strobel is a master student in computer science at the Technische Universität München. He is in charge of the transcriptions of the video lectures and helped with the setup instructions for the real Parrot AR.Drone.