ME/CS 132a, Winter 2015: Difference between revisions
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== Lecture Notes ==
== Lecture Notes ==
== Homework ==
== Homework ==
Revision as of 03:59, 8 January 2015
Introduction to Vision-based Robot Navigation
Teaching Assistants (firstname.lastname@example.org)
Course Mailing List: email@example.com (sign up)
- First lecture on 1/6.
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.
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 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 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.
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.
|1||6 Jan (Tu)||Course Overview, Illumination, Radiometry||Forsyth 2.1, 3.1, 3.2||Larry Matthies|
|8 Jan (Th)||Cameras and Calibration||Forsyth Ch. 1||Larry Matthies|
|2||13 Jan (Tu)||Radiometry, Reflectance, and Color||Forsyth 3.3, 3.4, 3.5||Larry Matthies|
|15 Jan (Th)||Low Level Image Processing||Forsyth 4.1, 4.2, 4.5||Roland Brockers|
|3||20 Jan (Tu)||Feature Detection and Matching||Forsyth ch 5||Roland Brockers|
|22 Jan (Th)||Stereo Vision||Forsyth ch 7||Roland Brockers|
|4||27 Jan (Tu)||Tracking and Outlier Detection||Forsyth 10.4, 11||Yang Cheng|
|29 Jan (Th)||Structure from motion and visual odometry||Forsyth ch 8||Adnan Ansar|
|5||3 Feb (Tu)||Overview of Range Sensors, Introduction to Lab 1||Forsyth ch 14||Jeremy Ma|
|5 Feb (Th)||No Class (Lab 1)|
|6||10 Feb (Tu)||No Class (Lab 1)|
|12 Feb (Th)||Introduction to Estimation, Notes on Estimation||Thrun 1, 2||Paul Hebert|
|7||17 Feb (Tu)||Linear Kalman Filter, Notes on Kalman Filters, Car example, Moon Lander example||Thrun 3.2||Nick Hudson|
|19 Feb (Th)||Extended Kalman Filter, EKF example, UKF example||Thrun 3.3||Nick Hudson|
|8||24 Feb (Tu)||Particle Filter and Unscented Kalman Filter, Particle Filter Notes||Thrun 3.4||Nick Hudson|
|26 Feb (Th)||Vision and Space Systems||Yang Cheng|
|9||3 Mar (Tu)||Examples, Intro to Mapping, Paper on LS3 Robot Paper on Mapping||Thrun 9||Jeremy Ma|
|5 Mar (Th)||Occupancy Grid Maps, Intro to Lab 2|
|10||10 Mar (Tu)||No class (Lab 2)|