Difference between revisions of "ME/CS 132a, Winter 2012"

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(Lecture Notes)
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|align="center" | 1
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|5 Jan (Th)
|5 Jan (Th)
|Course Overview, Illumination, Radiometry,
 and a (Very Brief) Introduction to the
 Physics of Remote Sensing
|[http://www.cds.caltech.edu/~stsuei/me132_2012/me132a_winter2012_lecture1.pdf Course Overview, Illumination, Radiometry,
 and a (Very Brief) Introduction to the
 Physics of Remote Sensing]
|Larry Matthies
|Larry Matthies

Revision as of 18:05, 9 January 2012

Advanced Robotics: Navigation and Vision


  • Larry Matthies (coordinator), lhm@jpl.nasa.gov
  • Roland Brockers, Adnan Ansar, Yang Cheng, Nick Hudson, Tom Howard, Yoshi Kuwata, Jeremy Ma
  • Lectures: Tue/Thu, 2:30-4 pm, 308 FIR
  • Office hours: TBA

Teaching Assistants (me132-tas@caltech.edu)

  • Stephanie Tsuei
  • Office hours: TBA

Course Mailing List: me132-students@caltech.edu (sign up)


  • First lecture on 1/5

Course Information


There are no formal prerequisites for the course. ME 115 ab (Introduction to Kinematics and Robotics) is recommended but not necessary. 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 can choose to turn in a hard copy in class or send an electronic copy to Stephanie Tsuei (stsuei at caltech.edu). If you are unable attend the lecture, contact the TAs to find an alternative way to turn in your homework.
  • 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

There are two required textbooks:

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

Additionally, there is an optional textbook that is available as a free download

Lecture Notes

Week Date Topic Instructor
1 5 Jan (Th) Course Overview, Illumination, Radiometry,
 and a (Very Brief) Introduction to the
 Physics of Remote Sensing Larry Matthies
2 10 Jan (Tu) Cameras and Calibration Larry Matthies
12 Jan (Th) Radiometry, Reflectance, and Color Larry Matthies
3 17 Jan (Tu) Low Level Image Processing Larry Matthies
19 Jan (Th) Feature Detection and Matching Roland Brockers
4 24 Jan (Tu) Stereo Vision Roland Brockers
26 Jan (Th) Tracking and Other Detection Yang Cheng
5 31 Jan (Tu) Structure from motion and visual odometry Adnan Ansar
2 Feb (Th) Overview of Range Sensors, Introduction to Lab 1 Jeremy Ma
6 7 Feb (Tu) No Class (Lab 1)
9 Feb (Th) No Class (Lab 1)
7 14 Feb (Tu) Introduction to Estimation Nick Hudson
16 Feb (Th) Linear Kalman Filter Nick Hudson
8 21 Feb (Tu) Extended Kalman Filter Nick Hudson
23 Feb (Th) Particle Filter, Unscented Kalman Filter Nick Hudson
9 28 Feb (Tu) Mapping, Introduction to Lab 2 Jeremy Ma
1 Mar (Th) Buffer Lecture Jeremy Ma
10 6 Mar (Tu) No class (Lab 2)
8 Mar (Th) No class (Lab 2)


  • HW 1