Difference between revisions of "ME/CS 132b, Spring 2013"
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== Course Information == | == Course Information == | ||
=== Prerequisites === | === Prerequisites === | ||
There are no formal prerequisites for the course | There are no formal prerequisites for the course, other than ME/CS 132(a). Some of the required background material will be reviewed during the first weeks of lecture. The theory part of ME/CS 132(b) is largely independent of the material in ME/CS 132(a), but students are expected to be able to use the experimental lab equipment introduced in the first quarter of the course, and are expected to be able to apply the sensor processing and mapping techniques learned in the first quarter. The greater emphasis on a final project in this quarter will require a good comfort level with computer programming in at least one of the following languages: C, Python, or MATLAB. | ||
=== Grading === | === Grading === | ||
Revision as of 20:37, 1 April 2013
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Advanced Robotics: Navigation and Vision |
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Instructors
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Teaching Assistants (me132-tas@caltech.edu)
Course Mailing List: me132-students@caltech.edu (sign up) |
Announcements
- First lecture on 4/2 at 2:30 pm in Room 306 Thomas.
Course Information
Prerequisites
There are no formal prerequisites for the course, other than ME/CS 132(a). Some of the required background material will be reviewed during the first weeks of lecture. The theory part of ME/CS 132(b) is largely independent of the material in ME/CS 132(a), but students are expected to be able to use the experimental lab equipment introduced in the first quarter of the course, and are expected to be able to apply the sensor processing and mapping techniques learned in the first quarter. The greater emphasis on a final project in this quarter will require a good comfort level with computer programming in at least one of the following languages: C, Python, or MATLAB.
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 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.
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.
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 me132-tas@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 me132-tas@caltech.edu
