Courses for undergraduate and PhD

Elective Courses:

Vision in Robotics: in this course the fundamental theoretical concepts in robot vision are presented. After a first stage of introduction to mathematical notions of computer vision, the course focus on solving a particular task using vision-based mobile robots. [link]

Intensive courses in the School of Computer Science:

ECI 2011 – Introduction to Mobile Robotics (Miroslav Kulich, Czech Technical University in Prague, Czech Republic) The aim of this course is to introduce the main challenges of programming mobile robots with cognitive abilities and what techniques are fundamental to address them. Techniques also modeling environment, the location of the robot in the same road and planning will be presented to deal with the processing and fusion of data provided by the sensors of the robot, as well. During the course a e-learning system called SyRoTek ( will be used to implement some of the techniques presented.[slides]
ECI 2012 – Mobile Robotics II: Simultaneous Localization and Mapping (Miroslav Kulich, Czech Technical University in Prague, Czech Republic) The aim of Mobile Robotics II is to introduce one of the hottest topics in mobile robotics – simultaneous localization and mapping (SLAM). SLAM is a technique to build a model (map) within an unknown environment by a mobile robot while at the same time keeping track of robot’s position. The course is divided into two parts. In the theoretical part, the students will learn fundamental methods of si- multaneous localization and mapping. In the practical part, the students will implement the learned methods and test them in a simulated environment as well as on real robots. The practical experiments will be done with a e-learning system SyRoTek ( developed at Czech Technical University and introduced during last ECI course. The knowledge of topics learned during last year are welcome but not necessary. [slides]
ECI 2014 – Robot Vision: Geometry and Comprehension (Javier Civera, University of Zaragoza, Spain)Information extraction from images is one of the most active areas of engineering and greater progress in recent years. The cameras have great potential as a sensor for its low cost, the wealth of information they contain, and the existence of huge databases in which to train non-parametric models. Robotics imposes additional restrictions on the general goals of computer vision, which could be defined generally as “a computer to be able to see.” The aim of this course is to present the main techniques in computer vision for robotics interest. First geometric models that allow the estimation of the 3D structure of a scene from images will be studied. In the second part of the course models for extracting semantic information from higher level will be studied such as object and scenes recognition and segmentation. [slides]