ME/CS 132a, Winter 2012

Advanced Robotics: Navigation and Vision

Instructors 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, 306 FIR Office hours: After class/by appointment

Teaching Assistants (me132-tas@caltech.edu) Scott Livingston (slivingston@caltech.edu) Stephanie Tsuei (stsuei@caltech.edu) Office hours: Mon, 4-6pm, SFL 2-2 Course Mailing List: me132-students@caltech.edu (sign up)

Announcements

• Sign up for a lab 2 time slot
• TA office hours will be held on Mondays from 4-6pm in SFL 2-2.
• Jan 12: Please sign up for the mailing list and vote on a time for office hours before 10pm tonight!
• First lecture on 1/5

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 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.
  • Chapter 1 (Caltech access only)
• 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

• Richard Szeliski, Computer Vision: Algorithms and Applications, Springer, 2010.

Supplementary Reading

• P. Hebert, N. Hudson, J. Ma, J.W. Burdick (2011). Fusion of Stereo Vision, Force-Torque, and Joint Sensors for Estimation of In-Hand Object Location. Proc. of the IEEE Int'l Conf. on Robotics and Automation (ICRA), pp. 5935-5941. [pdf]
• P. Hebert, N. Hudson, J. Ma, T. Howard, T. Fuchs, M. Bajracharya, J.W. Burdick (2012). Combined Shape, Appearance and Silhouette for Simultaneous Manipulator and Object Tracking. Proc. of the IEEE Int'l Conf. on Robotics and Automation (ICRA), to appear. [pdf]

Lecture Notes

Week	Date	Topic	Reading	Instructor
1	5 Jan (Th)	Course Overview, Illumination, Radiometry, and a (Very Brief) Introduction to the Physics of Remote Sensing	Forsyth 2.1, 3.1, 3.2	Larry Matthies
2	10 Jan (Tu)	Cameras and Calibration	Forsyth Ch. 1	Larry Matthies
	12 Jan (Th)	Radiometry, Reflectance, and Color	Forsyth 3.3, 3.4, 3.5	Larry Matthies
3	17 Jan (Tu)	Low Level Image Processing	Forsyth 4.1, 4.2, 4.5	Roland Brockers
	19 Jan (Th)	Feature Detection and Matching	Forsyth ch 5	Roland Brockers
4	24 Jan (Tu)	Stereo Vision	Forsyth ch 7	Roland Brockers
	26 Jan (Th)	Tracking and Outlier Detection	Forsyth 10.4, 11	Yang Cheng
5	31 Jan (Tu)	Structure from motion and visual odometry	Forsyth ch 8	Adnan Ansar
	2 Feb (Th)	Overview of Range Sensors, Introduction to Lab 1	Forsyth ch 14	Jeremy Ma
6	7 Feb (Tu)	No Class (Lab 1)
	9 Feb (Th)	No Class (Lab 1)
7	14 Feb (Tu)	Introduction to Estimation	Thrun 1, 2	Nick Hudson
	16 Feb (Th)	Linear Kalman Filter, Proof of Kalman Filter	Thrun 3.2	Nick Hudson
8	21 Feb (Tu)	Extended Kalman Filter, Unscented Kalman Filter	Thrun 3.3	Nick Hudson
	23 Feb (Th)	Particle Filter	Thrun 3.4	Nick Hudson
9	28 Feb (Tu)	Vision and Space Systems		Yang Cheng
	1 Mar (Th)	Mapping, Introduction to Lab 2	Thrun 9	Jeremy Ma
10	6 Mar (Tu)	No class (Lab 2)
	8 Mar (Th)	No class (Lab 2)

Homework

Please pay attention to the implementation guidelines when writing code for homework.

• HW 1 (due Tuesday, Jan. 17)
• HW 2 (due Tuesday, Jan. 24)
  • Images you'll need
• HW 3 (due Tuesday, Jan. 31)
  • Datasets
• HW 4 (due Tuesday, Feb. 7)
• Lab 1
  • Additional Instructions
  • Sign Up Page
  • Materials
• HW 5 (due Tuesday, Feb. 21)
  • Sample Code (Feel free to translate to another language - it's not that much)
• HW 6 (due Tuesday, Feb. 28)
  • Materials
• Lab 2
  • Materials
  • Sign-Up Page