ME/CS 132a, Winter 2011

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Advanced Robotics: Navigation and Vision

Instructors

  • Larry Matthies (coordinator), lhm@jpl.nasa.gov
  • Roland Brockers, Brian Williams, Adnan Ansar, Yang Cheng, Nick Hudson, Tom Howard, Yoshi Kuwata, Jeremy Ma
  • Lectures: Tue/Thu, 2:30-4 pm, 306 TOM
  • Office hours: Tue/Thu, 4-5 pm, 303 TOM (by appointment only)

Teaching Assistants (me132-tas@caltech.edu)

  • Andrea Censi, Shuo Han
  • Office hours: Mon, 5-6:30 pm, 114 STL

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

Announcements

  • 03 Feb 2011: Before midnight tonight, sign up for an ME/CS 132a user account via this page. You will receive a note once the account has been set up.
  • The solution to HW #1 has been posted (Caltech/JPL access only).
  • The due date of HW #2 has been extended. It is now due 2:30pm, 3 Feb.
  • HW #1 has been graded. It is in Box H next to the mail slots in Steele.
  • HW #2 has been posted. It is due 2:30pm, 1 Feb.
  • The location of TA office hours has been changed to 114 Steele.

Course Information

Prerequisites

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.

Grading

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 Andrea Censi (andrea at cds.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, both of which are freely available online:

Other optional reference materials (books are on reserve at SFL):

  • David A. Forsyth and Jean Ponce, Computer Vision: A Modern Approach, Prentice Hall, 2003.
  • Sebastian Thrun, Wolfram Burgard, and Dieter Fox, Probabilistic robotics, MIT Press, 2005.

Lecture Notes

Week Date Topic Instructor
1 4 Jan (Tu) Overview Larry Matthies
6 Jan (Th) Illumination, Radiometry,
 and a (Very Brief) Introduction to the
 Physics of Remote Sensing Larry Matthies
2 11 Jan (Tu) Cameras Larry Matthies
13 Jan (Th) Camera Calibration Adnan Ansar
3 18 Jan (Tu) Feature Detection and Matching Roland Brockers
20 Jan (Th) Feature Quality Assessment Yang Cheng
4 25 Jan (Tu) Egomotion Estimation Adnan Ansar
27 Jan (Th) Low-level Image Processing Larry Matthies
5 1 Feb (Tu) Stereo Vision Roland Brockers
3 Feb (Th) Overview of range sensors Jeremy Ma
6 8 Feb (Tu) No class (week-long lab 1)
10 Feb (Th) No class (week-long lab 1)
7 15 Feb (Tu) Introduction to estimation TBD
17 Feb (Th) Linear Kalman filter TBD
8 22 Feb (Tu) Extended Kalman filter TBD
24 Feb (Th) Particle filters and the UKF TBD
9 1 Mar (Tu) Simultaneous localization and mapping (SLAM) TBD
3 Mar (Th) Issues in SLAM TBD
10 8 Mar (Tu) No class (week-long lab 2)
10 Mar (Th) No class (study period)

Homework

  • Homework 1 (Due: 2:30pm, 18 Jan), (FAQ, Solution)
    • You will need Chapter 1 from Forsyth's book, which is available here (Caltech/JPL access only).
  • Homework 2 (Due: 2:30pm, 3 Feb), (FAQ, Solution)
  • Lab Assignment 1 [link]. The lab assignment is divided into two components:
    • Robot component
      • Download the file (including the tutorial code as well as reference/test images)
      • Be sure to sign up for an account here
      • Refer to the exercises on [this page] to learn how to use player/stage remotely
    • Vision component
      • Download the [file] (including the Matlab SIFT feature extraction and the reference/test images] first

Older Announcements

  • An FAQ page has been created for HW #1.
  • The TA office hours will be on Monday from 5-6:30pm, at 301 Thomas. Send your UID to Shuo Han (hanshuo at caltech) if you need access to Thomas after hours.
  • The instructors' office hours have been changed to "by appointment only". You can send the instructor email before class, or directly come to the instructor before/after class to schedule an office hour.
  • HW #1 has been posted. It is due 2:30pm, 18 Jan.