PeAR WPI

Welcome to RBE595-F02-ST: Hands-On Autonomous Aerial Robotics course at WPI. This course is taught by Prof. Nitin J. Sanket. Please sign up for Piazza if you haven't done so already.

All the class announcements will be made through Piazza. Please use Piazza to contact TAs. Please do NOT contact the TA's or the Instructor via email unless it's an emergency, and do NOT contact the TA's on any social media platform such as Facebook or WhatsApp (please respect their privacy) regarding course content. If you want to have a chat about our research, feel free to reach out to Prof. Sanket after class.

All the student reports will be released publicly online to enable better learning experience for others. We will be also announce top three submissions for each announcement on the website. All the projects and homeworks are to be submitted using Canvas. If you find any errors/typo in the course github website, please edit the markdown '.md' file and send a 'pull request'. If you don't know how to use pull request, please check out this tutorial, alternatively post on Piazza.

Project 0 (P0) has been released on Aug 10, 2025 and is due on Aug 24, 2025. The knowledge from P0 is a pre-requisite for the class.

What is this course about?

This is an advanced course on autonomy stack for aerial robots. A unique aspect of this course is its hands-on aspect. Our goal is to train the students to develop real-time perception, planning, control and AI algorithms for the realization of autonomous aerial systems. The course follows a series of projects that will finally build upto an autonomous drone race on an obstacle course in which the students will compete.

This course will explore rigid body transformations, attitude estimation, Bayesian filter, Linear and Unscented Kalman filters, Path and Motion planning, Deep learning, sim2real, Camera models, Gaussian Mixture Models, Image Processing, Visual feature detection and tracking, Projective Geometry, Optical Flow, Stereopsis, Quadrotor Dynamics and Controls, Structure from Motion/SLAM.

Team

Prof. Nitin J. Sanket

Instructor
he/him/his

Deepak Singh

Teaching Assistant
he/him/his

Pre-requisities

Programming proficiency in Python 3, experience with Linux and completion of P0 is a hard pre-requisite. We assume that the students are proficient with basic Linear Algebra, calculus and machine learning to the level of an undergraduate class. Experience working on quadrotors, ROS and Linux is a big plus.

Office Hours

Deepak Singh: Mondays and Thursdays 10:00AM to 12:00PM, TBD.
Prof. Nitin J. Sanket: By Appointment Only, UH250E.

Expectations

This course is fast-paced and has high-expectations of time and effort commitment from students. The students are expected to complete projects on time since the concepts build on top of each other and it is very easy to fall behind in this course. Please understand the concepts well, ask questions, use Piazza heavily and come to office hours. Help your peers out, but do not plagiarize.

Software Environment

We will use Python 3 as the programming platform throughout this course along with packages from OpenCV, PyTorch, TensorFlow, Numpy, Scikit and Matplotlib.

Assignments

All projects are to be done in groups of THREE. However, we encourage you to discuss with your peers and not cheat. For clarifications, post a private post on Piazza. For further details, read the Collaboration Policy and Honor Code below. We are pro ChatGPT (and other LLMs) in this course as long as the chat prompts are included in the report and complete assignment is not plagiarized.

Assignment Name With Link	Deadline	Grade Percentage	Student Outputs
P0: Alohomora!	Tuesday, Aug 26, 2025 at 11:59:59 PM (Individual Submissions)	5
P1: Magical Filtering! P1a: Magic Madgwick Filter for Attitude Estimation P1b: Non-stinky Unscented Kalman Filter for Attitude Estimation	P1a: Tuesday, Sept 02, 2025 at 11:59:59 PM (Group Submissions) P1b: Tuesday, Sept 16, 2025 at 11:59:59 PM (Group Submissions)	16
P2: Path Following! P2a: Tree Planning Through The Trees! P2b: Fly through trees!	P2a: Tuesday, Sept 30, 2025 at 11:59:59 PM (Group Submissions) P2b: Tuesday, Oct 07, 2025 at 11:59:59 PM (Group Submissions)	17
P3: Mini Drone Race!	Saturday, Nov 01, 2025 at 11:59:59 PM (Group Submissions)	19
P4: Navigating The Unknown!	Friday, Nov 14, 2025 at 11:59:59 PM (Group Submissions)	17
P5: The Final Race!	Thursday, Dec 12, 2025 at 7:59:59 AM (Group Submissions)	21
In-class Quizzes (5)	Announced in class	5	NA

Class Slides

Class Number	Class Date	Slides/Resources	Class Topic(s)
1	Aug 21 2025	Slides, Video:	Introduction, Logistics And What is a Robot?
2	Aug 25 2025	Slides, Video:	Rigid Body Transformations, Rotations and Velocities
3	Aug 28 2025	Slides	IMU Basics, Attitude Estimation using CF and Madgwick, Bayes Filter
4	Sept 04 2025		Kalman Filter, EKF, UKF
5	Sept 08 2025		Quadrotor Hardware
6	Sept 11 2025		Quadrotor Dynamics And Linear Controls
7	Sept 15 2025		Graph and Sampling based planners
8	Sept 18 2025		Trajectory and Motion Planning
9	Sept 22 2025		Deep Learning and Neural Networks
10	Sept 25 2025		NN Tuning, GANs, Bayesian NNs
11	Sept 29 2025		Simulation for Data Generation and Sim2Real
12	Oct 02 2025		Cameras And Image Formation
13	Oct 06 2025		Fundamentals of Image Processing
14	Oct 09 2025		Image Features and Descriptors
15	Oct 20 2025		Camera Models And Projective Transformations
16	Oct 23 2025		Epipolar Geometry and Homography
17	Oct 27 2025		Triangulation and PnP
18	Oct 30 2025		Factor Graphs for Fusion
19	Nov 03 2025		Optical Flow and Velocities
20	Nov 06 2025		Detection And Segmentation
21	Nov 10 2025		Learned Depth: Monocular and Stereo
22	Nov 13 2025		Visual Inertial Odometry
23	Nov 17 2025		Reinforcement Learning
24	Nov 20 2025		Guest Lecture (TBD)
25	Nov 24 2025		Guest Lecture (TBD)
26	Dec 01 2025		Guest Lecture (TBD)
27	Dec 04 2025		Guest Lecture (TBD)
28	Dec 11 2025		Summary, Recap and Conclusions

Syllabus

Rigid body transformations, attitude estimation, Bayesian filters, linear and unscented Kalman filters, camera models, Gaussian mixture models, image processing, visual feature detection and tracking, projective geometry, optical flow, stereopsis, quadrotor dynamics and controls, and structure from motion/SLAM.

Piazza and ELMS

If you haven't done so already, register yourself on Piazza with your wpi email. We'll be using Piazza for all announcements and discussions. Please use Piazza to contact the instructor/TAs (Feel free to use private posts on Piazza to contact me/TAs). Please do NOT contact the instructor/TAs via email unless it's an emergency, and do NOT contact the instructor/TAs on any social media platform such as Facebook or WhatsApp (please respect our privacy) regarding course content. If you want to have a chat about research, chat with Prof. Sanket after class.

All the assignments will be released on this website page.

All the assignments are to be submitted using Canvas. If you find any errors/typo in the course website, please post on Piazza.

Submission Policy

The submissions are made through Canvas (unless otherwise specified) with the name pk_DirID.zip for projects (where k is the assignment number, for e.g., for project 0, this would be p0_DirID.zip). Here, DirID is your directory ID, i.e., the first part of your wpi email address. For e.g., if your wpi email address is ABCD@wpi.edu, then your DirID is ABCD. Keep your submissions professional, grammatically correct without spelling mistakes. Do not use slangs and chat shorthands on your submissions. You'll get 25% grade penalty for not following the submission guidelines.

Late Submission Policy

This course moves quickly, and concepts will 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 seven late days, to spend on any assignment(s) except the quizzes and the final project (P5). 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 Canvas 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. Again, 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 HIGHLY 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 WPI Code of Academic Integrity applies. Unless otherwise specified, you should assume that that the WPI Code of Academic Integrity applies.

Reference Books

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.
Computer vision: A Modern Approach, Prentice Hall, David Forsyth, and Jean Ponce, 2002.
Digital Image Processing, Prentice Hall, Rafael Gonzalez, and Richard Woods, 2008.
Deep Learning Book, MIT Press, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016.

Furthermore, refer to the slides from courses in the acknowledgement section.

Acknowledgements

This course is developed by being inspired by adapting some of the best parts of each of the courses at multiple universities and these resources are linked below and you are encouraged to look at their content to learn from them as well. The goal of this course is to be the best hands-on aerial robotics course in the world.