Intelligent Mobile Robotics

Jacky Baltes <jacky@cs.umanitoba.ca>

Important Updates

  1. Assignment 2 is now available. Tue 27th Oct 2015.
  2. Added Readme file to vision module with build and run instructions. Sat Oct 10 14:07:39 CDT 2015.
  3. Assignment 1 is now available. Mon  5 Oct 2015 12:21:08 CDT
  4. Reading assignment for Tue., Sept 29th 2015 : Fu, Gonzales, and Lee - Perspective Transformation 
  5. The updated ROASS document is available here.
  6. Page opened. Thu 17 Sep 2015 13:45:22 CDT

Content

This course covers a variety of topics in intelligent mobile robotics. Intelligent mobile robotics is a wide field that covers many areas of computer science, including robotics, control theory, computer vision, artificial intelligence, and machine learning. The various topics are grounded in challenging real world problems (e.g., robotic soccer) and will use real physical robots. Therefore, all topics are covered with specific emphasis on real-time performance.

Lecture Slides

The following lecture slides are available.

  1. Images
  1. Images and Affine Transformations
  2. Optical Illusions
  1. Affine Transformations
  1. Affine Transformations - Translation
  2. Affine Transformations - Rotation
  3. Addition Theorem for Sine and Cosine
  1. Rotation as Shearing
  2. Perspective Distortion
  3. Camera Calibration (Linear Model)
  1. Linear Distance Transform
  2. Moore-Penrose Pseudo Inverse
  1. Integral Images
  2. Iterative Closest Point Algorithm
  1. Singular Value Decomposition
  1. Hover Ball

Sample Code

Here is some sample code

  1. Affine Transformations
  2. Moore Penrose Pseudo Inverse
  3. Linear Distance Transform in Python
  4. Madmax Video Server
  5. Tsai, Zhang 1D, and Zhang 2D Camera Calibrations implemented in ANSI C
  6. My own version of the Tsai calibration for coplanar calibration. Also included image_to_world and world_to_image coordinate routines.
  7. Iterative Closest Point (ICP) algorithm in python.

Assignments

  1. Camera Calibration using Tsai’s planar method
  2. Local Environment Map for Obstacle Run