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Robot Vision Systems

Course goals

This course will address the topic of implementing robot vision systems using ROS and OpenCV with a special focus on visual computing for robotics. That means, we will use ROS and OpenCV as platforms for implementing own computational schemes and estimation methods for own vision models and visual representations for robotic applications. Combining those schemes and methods with high-level tools provided by ROS and OpenCV is intended to be done in the project part of the course, whereas the lectures concentrate on the fundamental data structures, primarily for dense and sparse matrices, how to manipulate and extend those for your own purposes, how to efficiently use these in ROS nodes and nodelets providing topics and services.


Course participants are expected to have a solid background in mathematics (linear algebra, numerical methods), signal/image processing, computer vision and C++ programming. Bring your own laptop with internet access and admin rights to install software. Camera supported by Ubuntu 14.04. Access to robotic hardware (if you want to go beyond simulated robots).

Course organization

The course will consist of

  • lectures, where core features of OpenCV 3 rc1 and ROS will be presented
  • seminars, where the course participants are supposed to prepare presentations of selected topics
  • exercises, where the participants should bring their own laptop and will go through essential steps
  • a project, where the participants are supposed to implement an example application

This course will be based on the OpenCV course given 2012-2013. Compared to that course, we will extend the contents and the number of credits (6+3 instead of 3+3). The course will give 9hp credits (=6 weeks full time work) if the student conduct successfully a project work, is present at least at 80% of the lectures, exercises and seminars, and gives at least one own presentation at the seminar. Without the project work, but otherwise the same rules as above, the course will give 6hp. If the course is only attended 'listen-only', no credit points will be given. Student who have participated in the OpenCV course 2012-2013, may attend the second (ROS) half only, giving 6hp (3hp without project).

Instead of making a manual install of Eclipse, OpenCV, and boost, we will look into the option of using Ceemple. We are also looking into various options of Python packages, e.g. Python(x,y) and WinPython (as well as corresponding Linux variants). We will use OpenCV 3.0 rc1. We are further aiming at using ROS Indigo in a VirtualBox running Ubuntu 14.04 LTS (unless you are running Ubuntu natively) or possibly ROS Jade.

Preliminary schedule

The course will be given during the end of the spring term 2015 and the beginning of the autumn term 2015 . All lectures, exercises and seminars will take place in 'Algoritmen' ( map ). The schedule below might be subject to updates.

Seminar topics

Participants might think about which topic they would like to present in the seminar. The topics are typically required C++ topics that are essential to programming in OpenCV, but are not part of OpenCV. If there are more course participants than topics in this list, we will extend the list appropriately. We will assign seminar topics during the first lecture. Each seminar should start with a presentation of about 20 minutes, including examples and time for discussion. This allows us to fit 4 seminar talks into a 2-hours block.

  1. classes (fundamentals): Giulia
  2. classes (templates and namespaces): Mattias
  3. inheritance and virtual methods: Martin
  4. documentation with Doxygen: Amanda
  5. vectors in STL (with iterators): Tommaso
  6. C++11: Andreas
  7. Ceres solver: Mikael
  8. Cmake: Gustav


Python assignments

OpenCV download

OpenCV documentation

OpenCV online tutorials

C++ online tutorial

Eclipse download

STL short tutorial and links

Python downloads and documentation

differences to Matlab

Contact information

  • Michael Felsberg, michael.felsberg@liu.se


  • Finally, the website is updated!

Senast uppdaterad: 2016-09-05