# Autonomous Navigation for Flying Robots (edX)

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To follow this course, we recommend a solid background in linear algebra and 3D geometry. The programming exercises will require you to write small code snippets in Python to make a quadrotor fly in simulation.
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You will learn how to infer the position of the quadrotor from its sensor readings and how to navigate it along a trajectory.

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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:

- 3D geometry,

- probabilistic state estimation,

- visual odometry, SLAM, 3D mapping,

- linear control.

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.

What you'll learn

After successful participation of this module, students will be able to

- Understand the flight principles of quadrotors and their application potential

- Specify the pose of objects in 3D space and to perform calculations between them (e.g., compute the relative motion)

- Explain the principles of Bayesian state estimation

- Implement and apply an extended Kalman filter (EKF), and to select appropriate parameters for it

- Implement and apply a PID controller for state control, and to fine tune its parameters

- Understand and explain the principles of visual motion estimation and 3D mapping

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