Introduction to Computer Vision & Robotics

Details

In this course, concepts important for robotic applications will be taught as e.g.:

  • Sensors and actuators
  • Robot control
  • Movement generation methods
  • Path planning
  • Learning algorithms in robotics
  • Image filtering
  • Line, Circle & Feature Detection
  • Image segmentation techniques

Module: B.Phy.5668 Credits: 3 CP

Link to StudIP

Organizers:

Tomas Kulvicius, tkulvic@gwdg.de, E.01.104

Schedule

The lecture will be held via BBB weekly on wednesdays 10:15 - 11:45. Link to the BBB event: see StudIP link above.

The 1st exam (written, in person) will take place on 16.02.2022 at 15:00 in HS1 and HS2.

The 2nd exam (written, in person) will take place on 06.04.2022 at 15:00 in HS5.

Lecture 1
27.10.2021
Introduction of course and tutors
Lecture 2
03.11.2021
R1. Introduction to Robotics. Sensors and actuators
Presentation
Video
Kalman Filter Demo
Lecture 3
10.11.2021
R2. Robot kinematics and control
Presentation
Video
Denavit-Hartenberg Convention
Control Demo
Kinematics Demo
Lecture 4
17.11.2021
CV1. Thresholding, Filtering & Connected Components
Presentation
Video
Jupyter Notebook
Lecture 5
24.11.2021
CV2. Bilateral Filtering, Morphological Operators & Edge Detection
Presentation
Video
Jupyter Notebook
Lecture 6
01.12.2021
CV3. Corner Detection & Non-Local Filtering
Presentation
Video
Jupyter Notebook
Corner Detection
Non local Means
Lecture 7
08.12.2021
R3. Path planning algorithms
Video Part 1
Video Part 2
Presentation
Papers
Dijkstra Demo
Lecture 8
15.12.2021
R4. Movement generation methods
Presentation
Video
DMP Demo
Lecture 9
22.12.2021
CV4. Line/Circle Detection, Template Matching & Feature Detection
Presentation
Video
Jupyter Notebook
Lecture 10
12.01.2022
CV5. Face Detection, Pedestrian Tracking
Presentation
Video
Jupyter Notebook
Lecture 11
19.01.2022
CV6. Segmentation & Computer Vision in 3D
Presentation
Video
Jupyter Notebook
Lecture 12
26.01.2022
R5. Learning algorithms in robotics I: Supervised and unsupervised learning
Presentation
Video
Correlation Learning Demo
Lecture 13
02.02.2022
R6. Learning algorithms in robotics II: Reinforcement learning
Presentation
Video
PI2 Demo
Q-Learning Demo
RL Tutorial
Lecture 14
09.02.2022
Q&A

Computational Neuroscience Group