Difference between revisions of "ME/CS 132a, Winter 2015"

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| colspan=2 align=center |
<font color='blue' size='+2'>Advanced Robotics: Navigation and Vision</font>
+
<font color='blue' size='+2'>Introduction to Vision-based Robot Navigation</font>
 
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</td><td width=5%></td><td width=45%>
 
</td><td width=5%></td><td width=45%>
 
'''Teaching Assistants'''  (me132-tas@caltech.edu)
 
'''Teaching Assistants'''  (me132-tas@caltech.edu)
* Lu Li plus TBD
+
* Lu Li, Zheng Li
 
* Office hours:  
 
* Office hours:  
 
** TBD
 
** TBD
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== Announcements ==
 
== Announcements ==
* First lecture on 1/6.  
+
* First lecture on 1/6
 +
TA office hour:
 +
Lu Li: Monday 4-5pm in ANB104 (luli20101234@gmail.com)
 +
 
 +
Zheng Li: Wednesday 4-5pm ANB104  (zli@caltech.edu)
 +
 
 +
Questions about the course, please send an email to me132-students@caltech.edu.
  
 
== Course Information ==
 
== Course Information ==
Line 34: Line 40:
 
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'''.  
 
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.
+
* '''Homework''': Homework is usually due in one week after it is assigned. You need to turn in a hard copy in class or send an electronic copy to luli20101234@gmail.com. If you are unable attend the lecture, contact the TAs to find an alternative way to turn in your homework.
 +
The first assignment is due on Jan 15th, Thursday. Please hand in your hard copy to the lecturer in class. The electronic version with all files required should be sent to luli20101234@gmail.com. If you have any questions about homework, please send an email to me132-students@caltech.edu.
 +
 
 +
 
 
* '''Labs''': Students will form groups of 2-3 people and perform lab experiments together. Detail of this will be announced later in the course.
 
* '''Labs''': Students will form groups of 2-3 people and perform lab experiments together. Detail of this will be announced later in the course.
  
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=== Course Texts ===
 
=== Course Texts ===
There are two required textbooks:
+
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.
** [http://www.cds.caltech.edu/~murray/courses/me132-wi12/caltech/Forsyth_1st_edition_ch1.pdf Chapter 1] (Caltech access only)
+
* 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.
Additionally, there is an optional textbook that is available as a free download
 
* Richard Szeliski, [http://szeliski.org/Book/ ''Computer Vision: Algorithms and Applications''], Springer, 2010.
 
  
 
== Lecture Notes ==  
 
== Lecture Notes ==  
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|-
 
|-
 
|rowspan=2 align="center" | 1
 
|rowspan=2 align="center" | 1
|8 Jan (Tu)
+
|6 Jan (Tu)
|[http://www.cds.caltech.edu/~yhhuang/lectures/lecture1.pdf Course Overview, Illumination, Radiometry]
+
| Introduction, Illumination, Imagers
|Forsyth 2.1, 3.1, 3.2
+
|
 
|Larry Matthies
 
|Larry Matthies
 
|-
 
|-
|10 Jan (Th)
+
|8 Jan (Th)
|[http://www.cds.caltech.edu/~yhhuang/lectures/lecture2.pdf Cameras and Calibration]
+
| Reflectance, Optics, Calibration, Range Sensors
|Forsyth Ch. 1
+
|Forsyth Ch. 1, 2.1
 
|Larry Matthies
 
|Larry Matthies
 
|-
 
|-
 
|rowspan=2 align="center" | 2
 
|rowspan=2 align="center" | 2
|15 Jan (Tu)
+
|13 Jan (Tu)
|[http://www.cds.caltech.edu/~yhhuang/lectures/lecture3.pdf Radiometry, Reflectance, and Color]
+
|Low Level Image Processing
|Forsyth 3.3, 3.4, 3.5
 
|Larry Matthies
 
|-
 
|17 Jan (Th)
 
|[http://www.cds.caltech.edu/~yhhuang/lectures/lecture4.pdf Low Level Image Processing]
 
 
|Forsyth 4.1, 4.2, 4.5
 
|Forsyth 4.1, 4.2, 4.5
 
|Roland Brockers
 
|Roland Brockers
 
|-
 
|-
|rowspan=2 align="center" | 3
+
|15 Jan (Th)
|22 Jan (Tu)
+
| Feature Detection and Matching
|[http://www.cds.caltech.edu/~yhhuang/lectures/lecture5.pdf Feature Detection and Matching]
 
 
|Forsyth ch 5
 
|Forsyth ch 5
 
|Roland Brockers
 
|Roland Brockers
 
|-
 
|-
|24 Jan (Th)
+
|rowspan=2 align="center" | 3
|[http://www.cds.caltech.edu/~yhhuang/lectures/lecture6.pdf Stereo Vision]
+
|20 Jan (Tu)
 +
| Stereo Vision
 
|Forsyth ch 7
 
|Forsyth ch 7
 
|Roland Brockers
 
|Roland Brockers
 +
|-
 +
|22 Jan (Th)
 +
| Optical Flow
 +
| TBD
 +
|Brandon Rothrock
 
|-
 
|-
 
|rowspan=2 align="center" | 4
 
|rowspan=2 align="center" | 4
|29 Jan (Tu)
+
|27 Jan (Tu)
|[http://www.cds.caltech.edu/~yhhuang/lectures/lecture7.pdf Tracking and Outlier Detection]
+
| Visual Odometry and Outlier Detection
|Forsyth 10.4, 11
+
|TBD
|Yang Cheng
+
|Brandon Rothrock
 
|-
 
|-
|31 Jan (Th)
+
|29 Jan (Th)
|[http://www.cds.caltech.edu/~yhhuang/lectures/lecture8.pdf Structure from motion and visual odometry]
+
| Learning/Classification
|Forsyth ch 8
+
|TBD
|Adnan Ansar
+
|Thomas Fuchs
 
|-
 
|-
 
|rowspan=2 align="center" | 5
 
|rowspan=2 align="center" | 5
|5 Feb (Tu)
+
|3 Feb (Tu)
|[http://www.cds.caltech.edu/~yhhuang/lectures/lecture9.pdf Overview of Range Sensors, Introduction to Lab 1]
+
|Learning/Classification
|Forsyth ch 14
+
|TBD
|Jeremy Ma
+
|Thomas Fuchs
 
|-
 
|-
|7 Feb (Th)
+
|5 Feb (Th)
|No Class (Lab 1)
+
|No Class (Larger homework 1)
 
|
 
|
 
|
 
|
 
|-
 
|-
 
|rowspan=2 align="center" | 6
 
|rowspan=2 align="center" | 6
|12 Feb (Tu)
+
|10 Feb (Tu)
|No Class (Lab 1)
+
|No Class (Larger homework 1)
 
|
 
|
 
|
 
|
 
|-
 
|-
|14 Feb (Th)
+
|12 Feb (Th)
|Introduction to Estimation, [http://www.cds.caltech.edu/~yhhuang/lectures/notes_est.pdf Notes on Estimation]
+
|Object/Event Recognition
|Thrun 1, 2
+
|TBD
|Paul Hebert
+
|Michael Ryoo
 
|-
 
|-
 
|rowspan=2 align="center" | 7
 
|rowspan=2 align="center" | 7
|19 Feb (Tu)
+
|17 Feb (Tu)
|Linear Kalman Filter, [http://www.cds.caltech.edu/~yhhuang/lectures/notes_est_2.pdf Notes on Kalman Filters], [http://www.cds.caltech.edu/~yhhuang/lectures/auton_car_kf.m Car example], [http://www.cds.caltech.edu/~yhhuang/lectures/moon_lander_kf.m Moon Lander example]
+
|Object/Event Recognition
|Thrun 3.2
+
|TBD
|Nick Hudson
+
|Michael Ryoo
 
|-
 
|-
|21 Feb (Th)
+
|19 Feb (Th)
|[http://www.cds.caltech.edu/~yhhuang/lectures/lecture12.pdf Extended Kalman Filter], [http://www.cds.caltech.edu/~yhhuang/lectures/EKF_ackermann.m EKF example], [http://www.cds.caltech.edu/~yhhuang/lectures/UKF_ackermann.m UKF example]
+
|State Estimation
|Thrun 3.3
+
|TBD
|Nick Hudson
+
|Stephan Weiss
 
|-
 
|-
 
|rowspan=2 align="center" | 8
 
|rowspan=2 align="center" | 8
|26 Feb (Tu)
+
|24 Feb (Tu)
|[http://www.cds.caltech.edu/~yhhuang/lectures/lecture13.pdf Particle Filter and Unscented Kalman Filter], [http://www.cds.caltech.edu/~yhhuang/lectures/ParticleFilterTutorial.pdf Particle Filter Notes]
+
|State Estimation
|Thrun 3.4
+
|TBD
|Nick Hudson
+
|Stephan Weiss
 
|-
 
|-
|28 Feb (Th)
+
|26 Feb (Th)
|Vision and Space Systems
+
|State Estimation
|
+
|TBD
|Yang Cheng
+
|Stephan Weiss
 
|-
 
|-
 
|rowspan=2 align="center" | 9
 
|rowspan=2 align="center" | 9
|5 Mar (Tu)
+
|3 Mar (Tu)
|[http://www.cds.caltech.edu/~yhhuang/lectures/lecture14.pdf Examples, Intro to Mapping], [http://www.cds.caltech.edu/~yhhuang/lectures/icra2012.pdf Paper on LS3 Robot] [http://www.cds.caltech.edu/~yhhuang/lectures/icra2012_ws.pdf Paper on Mapping]
+
|Mapping
|Thrun 9
+
|TBD
|Jeremy Ma
+
|Larry Matthies
 
|-
 
|-
|7 Mar (Th)
+
|5 Mar (Th)
|[http://www.cds.caltech.edu/~yhhuang/lectures/lecture15.pdf Occupancy Grid Maps, Intro to Lab 2]
+
|Case Studies
|
 
 
|
 
|
 +
|TBD
 
|-
 
|-
 
|align="center" | 10
 
|align="center" | 10
|12 Mar (Tu)
+
|10 Mar (Tu)
|No class (Lab 2)
+
|No class (Larger homework 2)
 
|
 
|
 
|
 
|
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== Homework ==
 
== Homework ==
Please pay attention to the [http://www.cds.caltech.edu/~yhhuang/homework/implementation_guidelines.pdf implementation guidelines] when writing code for homework.
+
{| border=1 width=85% align="center"
 
+
|Assignments
*[http://www.cds.caltech.edu/~yhhuang/homework/homework1.pdf Homework 1] - Due: Tue Jan 22 at 11:59pm ([http://www.cds.caltech.edu/~yhhuang/solutions/Solution1.pdf Solutions ])
+
|Descriptions
**[http://www.cds.caltech.edu/~yhhuang/homework/Forsyth1.2_2ndEd.pdf Ch. 1.2 of Forsyth (2nd Edition)] - equations for problem 3
+
|Files
*[http://www.cds.caltech.edu/~yhhuang/homework/homework2.pdf Homework 2] - Due: Tue Jan 29 at 11:59pm ([http://www.cds.caltech.edu/~yhhuang/solutions/Solution2.zip Solutions ])
+
|-
**[http://www.cds.caltech.edu/~yhhuang/homework/homework2_images.zip Homework 2 Images]
+
|align="center" |Assignment 1
*[http://www.cds.caltech.edu/~yhhuang/homework/homework3.pdf Homework 3] - Due: Thurs Feb 7 at 11:59pm ([http://www.cds.caltech.edu/~yhhuang/solutions/Solution3.zip Solutions ])
+
|
**[http://www.cds.caltech.edu/~yhhuang/homework/homework3_data.zip Homework 3 Data]
+
|[https://www.cds.caltech.edu/~murray/wiki/images/6/68/Assignment_1_MECS132A.pdf]
*Lab 1: Due: Fri Feb 15 at 11:59pm - email to me132-tas@caltech.edu
+
|}
**[http://www.cds.caltech.edu/~yhhuang/homework/lab01.pdf Lab Description]
 
**[http://www.cds.caltech.edu/~yhhuang/homework/lab01_material.zip Lab Material]
 
**[https://www.cds.caltech.edu/~murray/wiki/index.php/ME/CS_132a,_Winter_2013,_Lab_1_Sign-Up Sign up page]
 
**[https://www.cds.caltech.edu/~murray/wiki/index.php/ME/CS_132a,_Winter_2013,_Lab_1 Additional Instructions]
 
*[http://www.cds.caltech.edu/~yhhuang/homework/homework4.pdf Homework 4] - Due: Thurs Feb 21 at 11:59pm ([http://www.cds.caltech.edu/~yhhuang/solutions/Solution4.m Solutions ])
 
**[http://www.cds.caltech.edu/~yhhuang/homework/ME132_DB.m Homework 4 matlab code]
 
*[http://www.cds.caltech.edu/~yhhuang/homework/homework5.pdf Homework 5] - Due: Tue Mar 5 at 11:59pm - turn in set to Ilya (ilyanep@caltech.edu)
 
**[http://www.cds.caltech.edu/~yhhuang/homework/homework5_material.zip Homework 5 material]
 
*Lab 2: Due: Fri Mar 15 at 11:59pm - email to me132-tas@caltech.edu
 
**[http://www.cds.caltech.edu/~yhhuang/homework/lab02.pdf Lab2 Description]
 
**[http://www.cds.caltech.edu/~yhhuang/homework/lab02_material.zip Lab Material]
 
**[https://www.cds.caltech.edu/~murray/wiki/index.php/ME/CS_132a,_Winter_2013,_Lab_2_Sign-Up Sign up page]
 
**[http://www.cds.caltech.edu/~yhhuang/homework/LMSTechnicalDescription.pdf Laser Scanner Spec Sheet]
 
**[http://www.cds.caltech.edu/~yhhuang/homework/forward_model_thrun.pdf Forward Sensor Models (for 3-person group)]
 

Latest revision as of 08:07, 14 January 2015

Introduction to Vision-based Robot Navigation

Instructors

  • Larry Matthies (coordinator), lhm@jpl.nasa.gov
  • Roland Brockers, Brandon Rothrock, Thomas Fuchs, Stephan Weiss, Michael Ryoo
  • Lectures: Tue/Thu, 1:00-2:30 pm, 105 Annenberg
  • Office hours: After class/by appointment

Teaching Assistants (me132-tas@caltech.edu)

  • Lu Li, Zheng Li
  • Office hours:
    • TBD

Course Mailing List: me132-students@caltech.edu (sign up)


Announcements

  • First lecture on 1/6.

TA office hour: Lu Li: Monday 4-5pm in ANB104 (luli20101234@gmail.com)

Zheng Li: Wednesday 4-5pm ANB104 (zli@caltech.edu)

Questions about the course, please send an email to me132-students@caltech.edu.

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 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 need to turn in a hard copy in class or send an electronic copy to luli20101234@gmail.com. If you are unable attend the lecture, contact the TAs to find an alternative way to turn in your homework.

The first assignment is due on Jan 15th, Thursday. Please hand in your hard copy to the lecturer in class. The electronic version with all files required should be sent to luli20101234@gmail.com. If you have any questions about homework, please send an email to me132-students@caltech.edu.


  • 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

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 6 Jan (Tu) Introduction, Illumination, Imagers Larry Matthies
8 Jan (Th) Reflectance, Optics, Calibration, Range Sensors Forsyth Ch. 1, 2.1 Larry Matthies
2 13 Jan (Tu) Low Level Image Processing Forsyth 4.1, 4.2, 4.5 Roland Brockers
15 Jan (Th) Feature Detection and Matching Forsyth ch 5 Roland Brockers
3 20 Jan (Tu) Stereo Vision Forsyth ch 7 Roland Brockers
22 Jan (Th) Optical Flow TBD Brandon Rothrock
4 27 Jan (Tu) Visual Odometry and Outlier Detection TBD Brandon Rothrock
29 Jan (Th) Learning/Classification TBD Thomas Fuchs
5 3 Feb (Tu) Learning/Classification TBD Thomas Fuchs
5 Feb (Th) No Class (Larger homework 1)
6 10 Feb (Tu) No Class (Larger homework 1)
12 Feb (Th) Object/Event Recognition TBD Michael Ryoo
7 17 Feb (Tu) Object/Event Recognition TBD Michael Ryoo
19 Feb (Th) State Estimation TBD Stephan Weiss
8 24 Feb (Tu) State Estimation TBD Stephan Weiss
26 Feb (Th) State Estimation TBD Stephan Weiss
9 3 Mar (Tu) Mapping TBD Larry Matthies
5 Mar (Th) Case Studies TBD
10 10 Mar (Tu) No class (Larger homework 2)

Homework

Assignments Descriptions Files
Assignment 1 [1]