Course detail

Advanced Methods for Mapping and Self-localization in Robotics

FEKT-MPC-MAPAcad. year: 2023/2024

The concept of self-localization, navigation, mapping. Reference systems. Number of degrees of freedom. Self-localization and navigation - Odometry, inertial self-localization, global satellite navigation systems, navigation with proximity sensors - ultrasound sensors, lidars. Self-localization and navigation without map and with known map.
2D mapping - Robot evidence grids. Vectorization. Geometry maps. Indoor and outdoor 3D mapping. Multispectral mapping. Environmental mapping.
SLAM - simultaneous localization and mapping. 2D and 3D approach, problems, state-of-the-art.

Language of instruction

Czech

Number of ECTS credits

3

Mode of study

Not applicable.

Guarantor

Ing. Petr Gábrlík, Ph.D.

Department

Department of Control and Instrumentation (UAMT)

Entry knowledge

The subject knowledge on the Bachelor´s degree level is requested.

Rules for evaluation and completion of the course

The content and forms of instruction in the evaluated course are specified by a regulation issued by the lecturer responsible for the course and updated for every academic year.

Aims

To acquaint students with the current state of knowledge in the field of autonomous mapping, navigation, and self-localization in mobile robotics.
Succesful student of the course should be able to:
- Terms self-localization, navigation and mapping.
- Instrumentations and methods for indoor and outdoor localization and navigation.
- Methods for 2D and 3D map building, including multispectral and environmental maps.
- Basics of SLAM (Simultaneous localization and mapping) methods.

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

Ing. Tomáš Lázna, Ph.D.
Ing. Petr Gábrlík, Ph.D.
Ing. Adam Ligocki, Ph.D.

Syllabus

1. Úvod do předmětu a základních pojmů.
2. Pravděpodobnost, model senzoru a mapování.
3. Řízení pohybu a kinematika.
4. Částicový filtr.
5. Kalmánův filtr.
6. Plánování trajektorie.
7. SLAM – Simultánní lokalizace a mapování.

Laboratory exercise

12 hod., compulsory

Teacher / Lecturer

Ing. Tomáš Lázna, Ph.D.
Ing. Petr Gábrlík, Ph.D.

Syllabus

1. –
2. Model senzoru.
3. Řízení pohybu.
4. Částicový filtr.
5. Kalmánův filtr.
6. Plánování trajektorie.
7. Samostatná práce na projektu.