The City College of New York
Department of Electrical Engineering
G3300: Advanced Mobile ROBOTICS
Spring 2024
This course is an in-depth study of state-of-the-art technologies and methods of mobile robotics. The course consists of two components: lectures on theory, and course projects. Lectures will draw from textbooks and current research literature with several reading discussion classes. In project component of this class, students will do computer simulation of SLAM algorithms and/or implement algorithms on mobile devices or robot platforms at the CCNY Robotics Lab.
You can download the G5501 lecture notes about the mobile robotics (starting from week 10) from the course website:
https://ccny-ros-pkg.github.io/prof/G5501-F23.htm
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Description: |
Graduate level course, 3 credits,
will be offered in spring semester every year. |
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Prerequisites: |
G5501 (Introduction to
ROBOTICS), |
Lecture Time: |
Tue.
6:30-9:15pm
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Instructor |
Prof. John (Jizhong) Xiao |
Location: |
Shepard Hall 73 |
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Office: |
Steinman Hall, Room T-534 |
Office Hours: |
Tue. 3:00-6:00pm or by appointment |
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E-mail: |
jxiao@ccny.cuny.edu |
Tel: |
212-650-7268 |
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Website: |
CCNY Robotics Lab: http://robotics.ccny.cuny.edu/ Dr. Xiao's Personal website: https://ccny-ros-pkg.github.io/prof/jxiao.html |
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1. Introduction to AI
Robotics, Robin R. Murphy, The MIT Press, 2000, ISBN 0-262-13383-0.
2. Introduction to Autonomous
Mobile Robots, Roland Siegwart, Illah R. Nourbakhsh, The MIT Press, 2004, ISBN 0-262-19502-X.
3. Papers from current research
literature.
You can find these books (new or
used) from Amazon.com.
Mathematic Background for Probabilistic Robotics, Mobile Robot Simultaneous Localization and Mapping (SLAM)
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Week and date |
Lecture
notes |
Homework |
Notes |
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Week 2 (Jan. 30) |
Syllabus/Introduction/Review: ppt format,
Reading Assignment 1) Measurement
and Correction of Systematic Odometry Errors in Mobile Robots, Johann
Borenstein, Liqiang Feng,
IEEE Transaction on Robotics and Automation, Vol. 12, No. 6, Dec. 1996. 2) A
calibration method for odometry of mobile robots based on the least-squares
technique: theory and experimental validation |
HWK1 Due date: Feb 6 Hint: You
can either use "ode45" function in Matlab
to solve the differential equation and embed in each sample step or write
your own C/C++ program and use small step (0.001) integration to iteratively
calculate the trajectory. I need a homework report
consisting of the answers, plots, and Matlab code
together. You can show me the animation after the class to gain extra credit. |
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Week 3 (Feb. 6) |
Reading Assignment: Chapter 1 and
2 of the textbook, Probabilistic Robotics
I. Mathematic
Background for Bayes Filters. Additional
Reading for Homework: Probability-Basics,
Markov
Chain, Entropy Rate of Markov
Chain,
and Entropy, |
HWK2:
Exercises 1, 2 and 3 (a), (b) on pp36-37 of the textbook Hint:
You can use Matlab function %y
= sample(x) returns y sampled from distribution x You
can also use C++ programming, Srand ( time(NULL));
// seed random number generator HWK2
Due date: Feb. 20 |
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Week
4 (Feb. 12) |
College
closed |
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Week 4 (Feb. 13) |
Reading Assignment: Chapter 3 and
7 of the textbook Probabilistic
Robotics II: Mathematic
Background for Gaussians and Kalman Filter |
HWK3:
Section 2.8 Exercise 4 in the textbook, (p37~38) HWK3 Due date: March 5 |
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Week 5 (Feb. 19) |
President's Day College closed |
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Week 5 (Feb 20) |
Reading
Assignment: Chapter 6 of the textbook. Robot
Perception Model |
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HWK 4:
Section 3.8 Exercise 1 and 2 in the textbook, (p81~82), Due date: March 12 |
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Week 6 (Feb 27) |
Reading
Assignment: Chapter 5 and 7 of the textbook. Robot
Motion and EKF-Localization |
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Week 7 (Mar 5) |
Reading
Assignment: Chapter 10 of the textbook. SLAM |
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Week 8 (Mar 12) |
Homework
review: |
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Week 9 (Mar 19) |
Mid-term
Exam: Time: 6:30~9:00pm, Closed book, One-page
cheat sheet allowed |
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Week 10 (Mar 26) |
Reading
Assignment: Chapter 4 and Chapter 8 of the textbook. Particle
Filter |
Project 1,
with hint to derive F and G matrix, Project-1
Solution, Due date: April 16 |
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Week 11 (April 2) |
Reading
Assignment: Chapter 13 of the textbook. Fast
SLAM, |
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Week 12 (April 9 |
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Final Project, Due date: May 22, |
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Week 13 (April 16 |
Project
practice/Paper reading (Visual Inertia Odometry,VIO) |
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Week
14-15(April 22~ April 30) |
Spring
recession |
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Week 16 (May 7) |
Paper reading/presentation (ORB-SLAM, code
architecture), (Quaternion
representation of 3D pose) |
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Week
17 (May 14) |
No class May 13~17,
ICRA2024 conference |
You
will get 80 if you can run the ORB-SLAM existing code. You can earn extra
points if you improve the code. |
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Week
18 (May 16~ 22) |
May 16 (Last
day of Classes) Final
Exam Week May
22, (End
of Spring Term) |
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Final
Project Due date: May 22, 2024 Special request
for grace period will be granted case by case. |
For these students who haven't finished the final project, INC grades will be given. The grade will be changed after you submit the final project report and show me the demo. If you have any special request or have dispute on the grade, please email me as soon as possible.
Homework
20%
Mid-term Exam
30%
Project
1
20%
Final Project
30%
A: 90~100; B: 80~90; C: 70~80; D: 60~70; F: under 60