ME/CS 132a, Winter 2015

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Introduction to Vision-based Robot Navigation


  • Larry Matthies (coordinator),
  • Roland Brockers, Brandon Rothrock, Thomas Fuchs, Stephan Weiss, Michael Ryoo
  • Lectures: Tue/Thu, 1:00-2:30 pm, 105 Annenberg
  • Office hours: After class/by appointment

Teaching Assistants (

  • Lu Li, Zheng Li
  • Office hours:
    • TBD

Course Mailing List: (sign up)


  • First lecture on 1/6.

TA office hour: Lu Li: Monday 4-5pm in ANB104

Course Information


There are no formal prerequisites for the course. Students are expected to have basic understanding of linear algebra, probability and statistics. We will review some of the required background materials during the first week of lectures. Besides these, students should have some prior programming experience and know at least one of the following languages: C, Python, or MATLAB. Depending on the background of the class, we will hold tutorials for some of the programming languages to help students get started.


There are no midterm/final exams for this course. The grade will be based on weekly homework (60%) and two week-long labs (20% each). Late homework will not be accepted without a letter from the health center or the Dean. However, you are granted a grace period of five late days throughout the entire term for weekly homework. Please email the TAs and indicate the number of late days you have used on the homework. No grace period is allowed for week-long labs.

  • Homework: Homework is usually due in one week after it is assigned. You need to turn in a hard copy in class or send an electronic copy to If you are unable attend the lecture, contact the TAs to find an alternative way to turn in your homework.

The first assignment is due on Jan 15th, Thursday. Please hand in your hard copy to the lecturer in class. The electronic version with all files required should be sent to

  • Labs: Students will form groups of 2-3 people and perform lab experiments together. Detail of this will be announced later in the course.

Collaboration Policy

Students are encouraged to discuss and collaborate with others on the homework. However, you should write your own solution to show your own understanding of the material. You should not copy other people's solution or code as part of your solution. You are allowed to consult the instructors, the TAs, and/or other students. Outside reference materials can be used except for solutions from prior years or similar courses taught at other universities. Outside materials must be cited if used.

Course Texts

We will not closely follow any single textbook. Good references for the course material include:

  • David A. Forsyth and Jean Ponce, Computer Vision: A Modern Approach (2nd Edition), Prentice Hall, 2011.
  • Richard Szeliski, Computer Vision: Algorithms and Applications, Springer, 2010.
  • Sebastian Thrun, Wolfram Burgard, and Dieter Fox, Probabilistic robotics, MIT Press, 2005.

Lecture Notes

Week Date Topic Reading Instructor
1 6 Jan (Tu) Introduction, Illumination, Imagers Larry Matthies
8 Jan (Th) Reflectance, Optics, Calibration, Range Sensors Forsyth Ch. 1, 2.1 Larry Matthies
2 13 Jan (Tu) Low Level Image Processing Forsyth 4.1, 4.2, 4.5 Roland Brockers
15 Jan (Th) Feature Detection and Matching Forsyth ch 5 Roland Brockers
3 20 Jan (Tu) Stereo Vision Forsyth ch 7 Roland Brockers
22 Jan (Th) Optical Flow TBD Brandon Rothrock
4 27 Jan (Tu) Visual Odometry and Outlier Detection TBD Brandon Rothrock
29 Jan (Th) Learning/Classification TBD Thomas Fuchs
5 3 Feb (Tu) Learning/Classification TBD Thomas Fuchs
5 Feb (Th) No Class (Larger homework 1)
6 10 Feb (Tu) No Class (Larger homework 1)
12 Feb (Th) Object/Event Recognition TBD Michael Ryoo
7 17 Feb (Tu) Object/Event Recognition TBD Michael Ryoo
19 Feb (Th) State Estimation TBD Stephan Weiss
8 24 Feb (Tu) State Estimation TBD Stephan Weiss
26 Feb (Th) State Estimation TBD Stephan Weiss
9 3 Mar (Tu) Mapping TBD Larry Matthies
5 Mar (Th) Case Studies TBD
10 10 Mar (Tu) No class (Larger homework 2)


Assignments Descriptions Files
Assignment 1 [1]