CS 3630
Introduction to Robotics and Perception

      Georgia Institute of Technology

    All dates in the future are subject to change as the semester evolves.
    Readings below refer to books listed on the course Resources page

Date Topic Notes  
Aug. 21 Course Overview

Aug. 23 Introduction to Image Processing: grayscale images, filtering, image features, Histogram of Oriented Gradients (HOG) features Read: [Corke] 12.2-12.4; [SNS] 4.3, 4.4
For each of these, the course will focus mainly on the sections cited above, but you can learn lots of useful and interesting things by reading the entire chapters.

Aug. 28 Supervised Learning: inductive learning, cross-validation, k-nearest neighbors

Aug. 30 Support vector machines, parameter optimization, classification valuation metrics

Sep. 4 Quiz 1 (in class)
After the quiz, collect robots
Topics for Quiz 1 include material presented in lecture Aug 21-30, and the assigned reading materials.

Sep. 6 Cozmo check. Bring your robots and laptop to class.  

Sep. 11 RANSAC, Robot Programming  Read: [Corke] 14.2.3 The RANSAC algorithm is introduced here for the task of estimating the fundamental matrix, but you don't need to understand the fundamental matrix to understand RANSAC.

Sep. 13 Coordinate Transformations, SO(n), SE(n), composition of transformations Read: [Corke] Chapter 2. You can skip Sections, and the quaternion stuff in Section 2.2.2, but be aware that quaternions are pretty cool.
I also quite like the presentation of this material in Chapter 2 of [SHV].

Sep. 18 Quiz 2
In-class demos
The quiz will focus on material from the Sep 11 and 13 lectures, but could also include questions dealing with material covered in previous lectures.

Sep. 20 Probability for State Estimation Read:Chapter 2 of [TBF]. This chapter gives an excellent coverage of probability theory and the development of the Bayes filter.

Sep. 25 Particle Filters Read:[Corke] Section 6.5
[SNS] Sections 5.1-5.5, and Section 5.6 excluding Section 5.6.8 (which we will cover later)

Sep. 27 Particle Filters (cont)

Oct. 2 Quiz 3  

Oct. 4 Guest Lecture Frank Dellaert will talk about very cool stuff.

Oct. 9 Fall Recess -- No lecture  

Oct. 11 Kalman filtering Read: [SNS] Section 5.6.8
[Corke] Appendix H (a somewhat more dense treatment)

Oct. 16 Kalman Filtering (cont)  

Oct. 18 Quiz 4
Graph Search
The quiz will cover the Kalman filtering lectures.
Read: Various search algorithms are described in [SNS] Section 6.3.1

Oct. 23 BUG Algorithms Read: The BUG algorithms are described in [SNS] Section 6.4.1, and in [Choset] Chapter 2.

Oct. 25 Motion Planning in the Plane: Visibility Graphs, Generalized Voronoi Diagrams, Cell Decompositions Read: These algorithms are described in [SNS] Section 6.3, and in [Choset] Sections 5.1, 5.2 and 6.1.

Oct. 30 Quiz 5
The Configuration Space
The quiz will cover only graph search.
Read: A light intro to configuration spaces is given in Section 6.3 of [SNS]. A more mathematical treatment is given in [Choset] Chapter 3.

Nov. 1 Sampling-based methods for path planning Read: [SNS] Section has a terse description of RRTs
[Choset] Sections 7.1 and 7.2 give a more detailed treatment of PRM and RRTs.

Nov. 6 Lab 4 Demo  

Nov. 8 Quiz 6 This quiz will cover BUG algorithms, the visibility graph, Generalized Voronoi Diagrams, and Cell Decomposition.

Nov. 13 Artificial Potential Fields Read: [SNS] Section 6.3.2 gives a nice overview of the method.
[Choset] Chapter 4 gives a more detailed treament, including grid-based implementations.

Nov. 15 Lab 5 Demo  

Nov. 20  

Nov. 22 Thanksgiving  

Nov. 27 Quiz 7 The quiz will cover configuration space and sampling-based planners  

Nov. 29 Quiz 8 The quiz will cover artificial potential field planning methods

Dec. 4 Lab 6 Demo