Difference between revisions of "ME/CS 132a, Winter 2011"
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** You will need the data in [http://www.cds.caltech.edu/~murray/courses/me132wi11/hw02_material.zip this zip file].  ** You will need the data in [http://www.cds.caltech.edu/~murray/courses/me132wi11/hw02_material.zip this zip file].  
** ...and the data in the documentation for [http://www.vision.caltech.edu/bouguetj/calib_doc/ the Matlab Calibration Toolbox].  ** ...and the data in the documentation for [http://www.vision.caltech.edu/bouguetj/calib_doc/ the Matlab Calibration Toolbox].  
+  ** For a very brief walkthrough of SVD and its application in leastsquares solution of linear systems, see [http://www.cds.caltech.edu/~murray/courses/me132wi11/svd.pdf here].  
__NOTOC__  __NOTOC__ 
Revision as of 04:31, 28 January 2011
Advanced Robotics: Navigation and Vision 
Instructors

Teaching Assistants (me132tas@caltech.edu)
Course Mailing List: me132students@caltech.edu (sign up) 
Announcements
 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.
 An FAQ page has been created for HW #1.
 The TA office hours will be on Monday from 56:30pm, 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.
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 weeklong 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 weeklong 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 23 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:
 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 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)  Lowlevel 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 (weeklong lab 1)  
10 Feb (Th)  No class (weeklong 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 (weeklong 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)
 You will need the data in this zip file.
 ...and the data in the documentation for the Matlab Calibration Toolbox.
 For a very brief walkthrough of SVD and its application in leastsquares solution of linear systems, see here.