Programming proficiency in Python 2.7 is a soft pre-requisite. We assume that the students are proficient with basic Linear Algebra and calculus to the level of an undergraduate class. Experience working on quadrotors, ROS and Linux is a big plus.

• Nitin J Sanket: By Appointment
• Chahat Deep Singh: By Appointment

This is a hands-on course, centered around five projects. The tentative grading breakdown is as follows:

• Project 1: 22%
• Project 2: 10%
• Project 3: 20%
• Project 4: 22%
• Project 5: 26%

Note that the course is going to be dynamically adapted based on student performance. Hence, the course structure and grading breakdown can change during the course based on instructor discretion.

Software Environment

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We will use Python 2.7 as the programming platform throughout this course along with packages from ROS, OpenCV, Numpy, Scikit and Matplotlib. We officially support Ubuntu 16.04 only. Feel free to use VirtualMachine if you have a Mac/Windows system (We'll have a class on this to help students setup). Use of third party code is generally not allowed in this course unless specifically stated so.

No.	Tutorials	Link	Video	Excercise
1.	Introduction to Python	Link	–	–
2.	Object Oriented Programming in Python	Link	Video	Ex.1
3.	Introduction to ROS	Link	–	–
4.	Introduction to OpenCV	Link	–	–

Assignments

All assignments are to be done in groups of 3. However, we encourage you to discuss with your peers and not cheat. For further details, read the Collaboration Policy and Honor Code below. For each project, the test sets will be released on Piazza 24 hours before the deadline.

The Assignments are as follows:

No.	Assignment	Link	Test set	Student Outputs
1.a.	"Magic" Madgwick Filter for Attitude Estimation	Link	✔	Link
1.b	Non-stinky Unscented Kalman Filter for Attitude Estimation	Link	✔	Link
2.	Trajectory Following on the PRG Husky	Link	–	Link
3.a.	Mini Drone Race	Link	–	Link
3.b.	Circular Bullseye	Link	–	Link
4.a.	Stereo Pose Estimation	Link	–	Link
4.b.	Avoid the wall and find the bridge	Link	–	Link
5.	The Final Race!	Link

Lectures

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Class No.	Topic	Videos	Slides
1.	Introduction	Link	pdf
2.a	Rigid Body Transformations and Rotations	Link	pdf
2.b	IMU Basics, Attitude Estimation using CF and Madgwick	Link	pdf
3.a	Quadrotor Hardware	Link	pdf
3.b	Hacking Bebop, DIY Research Drone	Link	pdf
4	Bayes Filter, Kalman Filter	Link	pdf
5	Extended Kalman Filter and Unscented Kalman Filter	Link	pdf
6	Quadrotor Dynamics	Link	pdf
7	Quadrotor Controls	Link	pdf
8	Cameras, Image Formation and GMM	Link	pdf
9	Fundamentals of Image Processing	Link	pdf
10	Features in Computer Vision	Link	pdf
11	Camera Models	Link	pdf
12	Projective Transformations	Link	pdf
13	Structure from Motion I: Epipolar Geometry	Link	pdf
14.a	Structure from Motion II	Link	pdf
14.b	Structure from Motion - Supplementary	Link	pdf
15	Optical Flow	Link	pdf
16	Stereopsis	Link	pdf
17	GTSAM	In Class	pdf
18	Introduction to Deep Learning	In Class	pdf

Guest Lectures

1	Flow Sensing For Small UAV Control	pdf
2	Lightweight Inertial Navigation System For Micro Air Vehicle Applications	pdf
3	Introduction to PX4 Autopilot	pdf
4	Advanced Topics in Nonlinear Control	pdf
5	Achieving Autonomy with Betaflight based Flight Controllers	pdf

Late Policy

This course moves quickly, and concepts tend to build on top of each other. Therefore it's very important to keep up with the material. To encourage this, late assignments are penalized for 25% per day after the due date. But life is unpredictable; we all need a break sometimes. So, we allow you six late days, to spend on any assignment(s) except the final project. You may submit an assignment late (after the due date) using a late day without any penalty. Think of a late day as pushing the deadline back by a day. So, to get full credit on a 2-days-late assignment, you'd need to use two late days. Late days can only be spent as full days (i.e., you can't use only half a late day for an assignment you submit 12 hrs late). If you are using a late day, mention it in the title of your submission as "USING X LATE DAY(S)" and post a comment on ELMS about the usage of a late day. We expect you to keep track of number of late days you have remaining and notify us of usage of one or more late days. We will default for penalty if we don't see the mention of late day usage as a comment on ELMS on that particular assignment. If you are using a late day, mention it in the title of your submission as "USING X LATE DAY(S)"

Collaboration Policy and Honor Code

Collaboration is encouraged, but one should know the difference between collaboration and cheating. Cheating is prohibited and will carry serious consequences. Cheating may be defined as using or attempting to use unauthorized assistance, material, or study aids in academic work or examinations. Some examples of cheating are: collaborating on an take-home exam or homework unless explicitly allowed; copying homework; handing in someone else's work as your own; and plagiarism. You are welcome to collaborate with your peers on Piazza and in person. However it's important that the work you submit is an expression of your understanding, and not merely something you copied from a peer. So, we place strict limits on collaboration: Firstly, you must clearly cite your collaborators by name at the top of your report. This includes Piazza posts reference. You may not share or copy each other's code. You can discuss how your code works, and the concepts it implements, but you can't just show someone your code. You may use free and publicly available sources, such as books, journal and conference publications, and web pages, as research material for your answers. (You will not lose points for using external sources.) You may not use any service that involves payment, and you must clearly and explicitly cite all outside sources and materials that you made use of. We consider the use of uncited external sources as portraying someone else's work as your own, and as such it is a violation of the University's policies on academic dishonesty. Instances will be dealt with harshly and typically result in a failing course grade. Unless otherwise specified, you should assume that that the UMD Code of Academic Integrity applies. Unless otherwise specified, you should assume that that the UMD Code of Academic Integrity applies.

Reference Books

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All concepts will be covered in class lecture, and in the lecture notes. However, we also recommend the following books as good references:

• Robotics, Vision and Control, Peter Corke, 2011.
• Multiple view geometry in computer vision, Cambridge university press, Richard Hartley, and Andre Zisserman, 2003.
• Computer Vision: Algorithms and Applications, Springer, Richard Szeliski, 2010.
• Digital Image Processing, Prentice Hall, Rafael Gonzalez, and Richard Woods, 2008.

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