The City College of New York
Department of Electrical Engineering
G3300: Advanced Mobile ROBOTICS
Spring 2022
This course is an indepth study of stateoftheart 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://ccnyrospkg.github.io/prof/G5501F21.htm
Description: 
Graduate level course, 3 credits,
will be offered in spring semester every year. 

Prerequisites: 
G5501 (Introduction to
ROBOTICS), 
Lecture Time: 
Tue.
6:309:15pm

Instructor 
Prof. John (Jizhong) Xiao 
Location: 
Shepard Hall 73 and Zoom meeting 
Office: 
Steinman Hall, Room T534 
Office Hours: 
Tue. 3:006:00pm or by appointment 
Email: 
jxiao@ccny.cuny.edu 
Tel: 
2126507268 
Website: 
CCNY Robotics Lab: http://robotics.ccny.cuny.edu/ Dr. Xiao's Personal website: https://ccnyrospkg.github.io/prof/jxiao.html 
1. Introduction to AI
Robotics, Robin R. Murphy, The MIT Press, 2000, ISBN 0262133830.
2. Introduction to Autonomous
Mobile Robots, Roland Siegwart, Illah
R. Nourbakhsh, The MIT Press, 2004, ISBN
026219502X.
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)
Week and date 
Lecture
notes 
Homework 
Comment 
Week 1 (Feb. 1) 
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 leastsquares technique: theory and experimental validation 
HWK1 Due date: Feb 15 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. 

Week 2 (Feb. 8) 
CLASS FOLLOW FRIDAY SCHEDLE 

Week 3 (Feb. 11~13) 
College closed 


Week 3 (Feb. 15) 
Reading Assignment: Chapter 1 and 2 of the textbook, Probabilistic Robotics I. Mathematic
Background for Bayes Filters. Additional
Reading for Homework: ProbabilityBasics,
Markov
Chain, Entropy Rate of Markov
Chain, and Entropy, 
HWK2:
Exercises 1, 2 and 3 (a), (b) on pp3637 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: March 1 

Feb. 21
President's Day Week 4 (Feb 22) 
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 8 

Week 5 (March 1) 
Reading
Assignment: Chapter 6 of the textbook. Robot
Perception Model 

HWK 4: Section
3.8 Exercise 1 and 2 in the textbook, (p81~82), Due date: March 15 
Week 6 (March 8) 
Reading
Assignment: Chapter 5 and 7 of the textbook. Robot
Motion and EKFLocalization 

Week 7 (March 15) 
Reading
Assignment: Chapter 10 of the textbook. SLAM 

Week 8 (March. 22) 
Homework
review: HWK solutions 

Week 9 (March 29) 
Midterm
Exam: Time: 6:30~9:00pm, Closed book, Onepage cheat sheet allowed 


Week 10 (April 5) 
Reading
Assignment: Chapter 4 and Chapter 8 of the textbook. Particle
Filter 
Project 1,
with hint to derive F and G matrix, Project1
Solution, Due date: April 26 

Week 11 (April 12) 
Reading Assignment: Chapter 13 of the textbook. Fast
SLAM, 


Week 12 (April 15~ April 22) 
Spring recession 


Week 12 (April
19) 
College Closed 


Week 13 (April 26) 
Final Project, Due date: May 24, 

Week 14 (May 3) 
Project practice/ Paper reading/presentation (Visual
Inertia Odometry, VIO) 


Week 15 (May 10) 
Project practice/ Paper reading/presentation (Quaternion representation of 3D pose) 

Week 16 (May 17) 
Project
practice/ Paper reading/presentation (ORBSLAM, code architecture) 

You will get 80 if you can run the ORBSLAM existing code.
You can earn extra points if you improve the code. 
Week 16 Final Exam Week (May 18~May 24) 
May 17 (Last day of
Classes) 

Final Project Due date: May 24, 2022 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%
Midterm Exam
30%
Project
1
20%
Final Project
30%
A: 90~100; B: 80~90; C: 70~80; D: 60~70; F: under 60