ME/CS 132a, Winter 2011
Advanced Robotics: Navigation and Vision
Teaching Assistants (email@example.com)
Course Mailing List: firstname.lastname@example.org (sign up)
- Homework 1 has been posted. It is due 2:30pm, 18 Jan.
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 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.
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.
There are two required textbooks, both of which are freely available online:
- Richard Szeliski, Computer Vision: Algorithms and Applications, Springer, 2010.
- Steven M. LaValle, Planning Algorithms, Cambridge University Press, 2006.
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 1 (4 Jan): Overview
- Lecture 2 (6 Jan): Illumination, Radiometry, and a (Very Brief) Introduction to the Physics of Remote Sensing
- Homework 1 (Due: 2:30pm, 18 Jan)
- You will need Chapter 1 from Forsyth's book, which is available here (Caltech access only).