Difference between revisions of "ME/CS 132a, Winter 2015"
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=== Course Texts ===  === Course Texts ===  
We will not closely follow any single textbook. Good references for the course material include:  
* David A. Forsyth and Jean Ponce, ''Computer Vision: A Modern Approach'' (2nd Edition), Prentice Hall, 2011.  * David A. Forsyth and Jean Ponce, ''Computer Vision: A Modern Approach'' (2nd Edition), Prentice Hall, 2011.  
*  * Richard Szeliski, [http://szeliski.org/Book/ ''Computer Vision: Algorithms and Applications''], Springer, 2010.  
* Sebastian Thrun, Wolfram Burgard, and Dieter Fox, ''Probabilistic robotics'', MIT Press, 2005.  * Sebastian Thrun, Wolfram Burgard, and Dieter Fox, ''Probabilistic robotics'', MIT Press, 2005.  
== Lecture Notes ==  == Lecture Notes == 
Revision as of 11:31, 6 January 2015
Introduction to Visionbased Robot Navigation 
Instructors

Teaching Assistants (me132tas@caltech.edu)
Course Mailing List: me132students@caltech.edu (sign up) 
Announcements
 First lecture on 1/6.
Course Information
Prerequisites
There are no formal prerequisites for the course. 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 Yifei Huang (yifei.huang 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 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
We will not closely follow any single textbook. Good references for the course material include:
 David A. Forsyth and Jean Ponce, Computer Vision: A Modern Approach (2nd Edition), Prentice Hall, 2011.
 Richard Szeliski, Computer Vision: Algorithms and Applications, Springer, 2010.
 Sebastian Thrun, Wolfram Burgard, and Dieter Fox, Probabilistic robotics, MIT Press, 2005.
Lecture Notes
Week  Date  Topic  Reading  Instructor 
1  8 Jan (Tu)  Course Overview, Illumination, Radiometry  Forsyth 2.1, 3.1, 3.2  Larry Matthies 
10 Jan (Th)  Cameras and Calibration  Forsyth Ch. 1  Larry Matthies  
2  15 Jan (Tu)  Radiometry, Reflectance, and Color  Forsyth 3.3, 3.4, 3.5  Larry Matthies 
17 Jan (Th)  Low Level Image Processing  Forsyth 4.1, 4.2, 4.5  Roland Brockers  
3  22 Jan (Tu)  Feature Detection and Matching  Forsyth ch 5  Roland Brockers 
24 Jan (Th)  Stereo Vision  Forsyth ch 7  Roland Brockers  
4  29 Jan (Tu)  Tracking and Outlier Detection  Forsyth 10.4, 11  Yang Cheng 
31 Jan (Th)  Structure from motion and visual odometry  Forsyth ch 8  Adnan Ansar  
5  5 Feb (Tu)  Overview of Range Sensors, Introduction to Lab 1  Forsyth ch 14  Jeremy Ma 
7 Feb (Th)  No Class (Lab 1)  
6  12 Feb (Tu)  No Class (Lab 1)  
14 Feb (Th)  Introduction to Estimation, Notes on Estimation  Thrun 1, 2  Paul Hebert  
7  19 Feb (Tu)  Linear Kalman Filter, Notes on Kalman Filters, Car example, Moon Lander example  Thrun 3.2  Nick Hudson 
21 Feb (Th)  Extended Kalman Filter, EKF example, UKF example  Thrun 3.3  Nick Hudson  
8  26 Feb (Tu)  Particle Filter and Unscented Kalman Filter, Particle Filter Notes  Thrun 3.4  Nick Hudson 
28 Feb (Th)  Vision and Space Systems  Yang Cheng  
9  5 Mar (Tu)  Examples, Intro to Mapping, Paper on LS3 Robot Paper on Mapping  Thrun 9  Jeremy Ma 
7 Mar (Th)  Occupancy Grid Maps, Intro to Lab 2  
10  12 Mar (Tu)  No class (Lab 2) 
Homework
Please pay attention to the implementation guidelines when writing code for homework.
 Homework 1  Due: Tue Jan 22 at 11:59pm (Solutions )
 Ch. 1.2 of Forsyth (2nd Edition)  equations for problem 3
 Homework 2  Due: Tue Jan 29 at 11:59pm (Solutions )
 Homework 3  Due: Thurs Feb 7 at 11:59pm (Solutions )
 Lab 1: Due: Fri Feb 15 at 11:59pm  email to me132tas@caltech.edu
 Homework 4  Due: Thurs Feb 21 at 11:59pm (Solutions )
 Homework 5  Due: Tue Mar 5 at 11:59pm  turn in set to Ilya (ilyanep@caltech.edu)
 Lab 2: Due: Fri Mar 15 at 11:59pm  email to me132tas@caltech.edu