Difference between revisions of "ME/CS 132b, Spring 2013"

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=== Grading ===
=== Grading ===
ME/CS 132(b) is primarily a project-based course.  The grade will be based on 2 homeworks (20% of total grade) and two week-long labs (10% of total grade each). Sixty percent (60%) of the grade will be based on a final project which is due on the last day of the finals period.  The final project can potentially be done in teams, with the instructor's approval.  
ME/CS 132(b) is primarily a project-based course.  The grade will be based on <b> 2 homeworks</b> (20% of total grade) and two week-long <b> labs </b> (10% of total grade each). Sixty percent (60%) of the grade will be based on a <b> final project</b> which is due on the last day of the finals period.  The final project can potentially be done in teams, with the instructor's approval.  


* '''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.
* '''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.

Revision as of 20:47, 1 April 2013

Advanced Robotics: Navigation and Vision

Instructors

  • Joel Burdick, jwb@robotics.caltech.edu
  • Lectures: Tue/Thu, 2:30-4 pm, 206 TOM
  • Office hours: After class/by appointment

Teaching Assistants (me132-tas@caltech.edu)

  • Yifei Huang (yifei.huang@caltech.edu)
  • Ilya Nepomnyashchiy (ilyanep@caltech.edu)
  • Office hours: TBD

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

ME/CS 132(b) is primarily a project-based course. The grade will be based on 2 homeworks (20% of total grade) and two week-long labs (10% of total grade each). Sixty percent (60%) of the grade will be based on a final project which is due on the last day of the finals period. The final project can potentially be done in teams, with the instructor's approval.

  • 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. The lab will consist of implementing and testing basic algorithms on a mobile robot, and demonstrating the result, as well as submitting a copy of the code underlying the lab demonstration. The one-week labs this quarter are intended to help get the students prepared for the final project.

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.
  • 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

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.