Updates
Course Overview
 Instructor: Prof. Mangal Kothari
 Class Venue: L9, Lecture Hall Complex
 Class Timings: Monday (12:00PM — 1:15PM), Tuesday (9:00AM — 10:15AM)
 Office Hours: Tuesday (4:00PM — 6:00PM) (Helicopter Lab)
 TA Hours: Monday (After the class) (L9), Friday (5:30PM — 7:30PM) (G117/Hall 1)
This 9credit graduate level course is designed to teach the essentials of robotics. The course would deal with dynamics and state estimation for various robotic systems, mainly focusing on the Kalman filter and its family. It would also cover path planning and SLAM algorithms for these systems, as well as some basic computer vision. Towards the end of the course, topics like interrobot communication and cooperative localization may be discussed, based on the time available and the feedback from the class.
Course PreRequisites
There are no formal prerequisites for the course. However, prior knowledge of dynamics, probability and statistics is necessary. The students also need to be proficient in objectoriented programming (Python/C++) and MATLAB. Familiarity with basic Linux commands, robotic hardware and ROS would be beneficial but is not expected from all students. Lectures and workshops will be held to ensure that students do not face difficulties in these areas.
NOTE: The course is being offered to advanced undergraduate and graduate students only.
Schedule
Week  Topic  Summary  

Introduction  
Week 1  07/01/2019 08/01/2019 
Course Overview and Motivation Lecture
Homework

Slides References 
Dr. Mangal Kothari 

Week 2  12/01/2019 14/01/2019(opt.) 
Introduction to ROS Lecture
Homework

Slides 
Aalap Shah 

State Estimation  
Week 2  15/01/2019 
Mathematical Background Lecture

References 
Dr. Mangal Kothari 

Week 3  21/01/2019 22/01/2019 
Mathematical Background (contd.) & Introduction to State Estimation Lecture
Homework

References

Dr. Mangal Kothari 

Week 4  28/01/2019 29/01/2019 
Gaussian Filters Lecture

References

Dr. Mangal Kothari 

Week 5  04/02/2019 05/02/2019 
Gaussian Filters Lecture

References

Dr. Mangal Kothari 

Week 6  11/02/2019 
Applying Gaussian Filters Lecture

References 
Dr. Mangal Kothari 

Path Planning  
Week 6  12/02/2019 
Path Planning Lecture
Homework

Slides References

Dr. Mangal Kothari 

MidSemester Exam  
Week 7  20/02/2019 
MidSemester Exam 

Sensing & Perception  
Week 8  04/03/2019 
Sensing Technologies for Mobile Robotics Lecture

Slides 
Aalap Shah, Dr. Mangal Kothari 

Week 8  05/03/2019 
Introduction to Computer Vision Lecture

Slides 
Harsh Sinha, Dr. Mangal Kothari 

Week 9  11/03/2019 12/03/2019 
Introduction to Computer Vision Lecture

Slides References 
Harsh Sinha, Dr. Mangal Kothari 

Simultaneous Localization & Mapping  
Week 10  25/03/2019 26/03/2019 
Cooperative Localization & SLAM Lecture

Slides 
Dr. Mangal Kothari 

Week 11  01/04/2019 02/04/2019 
Cooperative Localization & SLAM Lecture


Dr. Mangal Kothari 

Grading Policy
The tentative weightages for each component of the course are as follows:
 MidSemester Examination (10%) (Closed Notes)
 Quizzes (10%) (Unannounced)
 Homework Assignments (40%) (Individual)
 Course Project (40%) (Groups of 3)
 Minimum of 70% attendance (Compulsory)
Homework Assignments (40%)
There would be 45 homework assignments in the course. Assignments would comprise of theory as well as implementation based questions. Starter code would be provided for most programming questions. Some questions might carry forward from one assignment to another (for instance, a question in assignment 2 might require you to further build up on the code already developed in assignment 1). There might be bonus questions in each assignment carrying extra points. Submissions will be managed through Canvas.
NOTE: The use of any unfair means such as plagiarism by any student would be severely punished. For details regarding the mode of submission and penalties imposed for late submissions, compilation errors, etc., refer to this document.
Course Project (40%)
The course heavily emphasizes on the project aspect. You would be required to work in a team of 23 members. The project has to be an implementation of a recent work (from 2013 onwards) in robotics appearing in conferences like RSS, ICRA, or IROS. The source code for the same should not be publicly available. Points would be given on the basis of code readabilty, documentation, and presentation. There would be an abstract submission, a midterm evaluation and an endterm evaluation. A report along with source code for the project will have to be submitted towards the end. Again, submissions will be managed through Canvas.
Course Staff
For doubts related to assignments, send an email to ae640a@gmail.com. For sensitive issues, please email Prof. Mangal Kothari directly.
References
 Probabilistic Robotics by Sebastian Thrun, Wolfram Burgard and Dieter Fox. MIT press, 2005.
 Principles of Robot Motion: Theory, Algorithms and Implementations by Howie Choset et al.. MIT Press, 2005.
 State Estimation for Robotics by Timothy D. Barfoot
 A Gentle Introduction to ROS by Jason M. O'Kane (available online)
 ROS Wiki