Course detail

Advanced Methods for Mapping and Self-localization in Robotics

FEKT-MPC-MAPAcad. year: 2025/2026

The course focuses on navigation in mobile robotics, with an emphasis on self-localization and planning. Students will be introduced to the necessary foundations of probability theory and algorithms for mobile robot motion control, localization using particle and Kalman filters, trajectory planning, and the basic principles of SLAM – Simultaneous Localization and Mapping. The individual algorithms will be implemented and tested during independent laboratory exercises in a simple MATLAB simulation environment. At the end of the course, students will apply these algorithms to complete a project addressing a complex task involving localization, planning, and control of a mobile robot.

Language of instruction

Czech

Number of ECTS credits

Mode of study

Not applicable.

Entry knowledge

The course requires knowledge at the level of mandatory undergraduate subjects, with particular emphasis on topics such as matrix calculus, probability, and control theory. Familiarity with the MATLAB environment, which is used for exercises, is expected. A general understanding of robotics-related issues, which can be acquired through courses such as BPC-RBM, BPC-PRP, and MPC-RBT, is advantageous. Students should have sufficient language skills to comprehend study materials in English.

Laboratory work requires valid certification as a "osoba poučená," which students must obtain before the start of the course. Information regarding this certification can be found in the Dean's Directive on Seznámení studentů s bezpečnostními předpisy.

Rules for evaluation and completion of the course

Points can be earned in the following categories:

  • Laboratory Assessment: Up to 50 points can be earned from laboratory assignments completed during the semester (no minimum required).
  • Project: Up to 50 points can be awarded for the final project submitted after the lectures have concluded (a minimum of 20 points is required to successfully complete the course).

Lectures are optional but recommended. Attendance in laboratory sessions is mandatory and necessary to earn the credit. Completing laboratory exercises requires the submission of assignments assigned by the instructor. Absences, if properly excused, can be made up after consulting with the instructor.

Aims

The goal of the course is to introduce students to the main challenges and issues in mobile robot navigation and to familiarize them with key algorithms for self-localization, path planning, and robot control, which they will also learn to implement and optimize. Graduates of the course will be able to select an appropriate localization algorithm based on the target application and available sensors, implement it, and fine-tune its parameters. Similarly, they will be capable of working with trajectory planning and motion control algorithms, optimizing and adapting them for specific platforms and tasks. Graduates will thus acquire essential knowledge for designing autonomous mobile robots.

Study aids

Not applicable.

Prerequisites and corequisites

Basic literature

Not applicable.

Recommended reading

THRUN, Sebastian, Wolfram BURGARD a Dieter FOX, 2005. Probabilistic Robotics. 1st edition. Cambridge, Mass: The MIT Press. ISBN 978-0-262-20162-9. (EN)

Classification of course in study plans

  • Programme MPC-KAM Master's 2 year of study, summer semester, compulsory-optional

Type of course unit

 

Lecture

14 hod., optionally

Teacher / Lecturer

Syllabus

1. Introduction to the course and basic concepts.
2. Probability, sensor modeling, and mapping.
3. Motion control and kinematics.
4. Particle filter.
5. Kalman filter and EKF.
6. Path planning.
7. SLAM – Simultaneous Localization and Mapping.  

Laboratory exercise

14 hod., compulsory

Teacher / Lecturer

Syllabus

1. Introduction to navigation problems.
2. Uncertainties and probability.
3. Motion control.
4. Particle filter.
5. Kalman filter and EKF.
6. Path planning.
7. Independent project work.