Autonomous Navigation for Flying Robots's Full Profile
About this Course Note - This is an Archived course 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
May 06, 2014
Days of the Week:
Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday
- Level of Difficulty: Beginner
- Size: Massive Open Online Course
- Instructors: Benjamin Strobel, Jonas Jelten, Julian Tatsch, Christian Kerl, Daniel Cremers, Jürgen Sturm
- Cost: Free
- Institution: EdX
- Topics: Computer Science, Python, Electrical Engineering, General Engineering, Mechanical Engineering
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