Course detail

Advanced Methods for Mapping and Self-localization in Robotics

FEKT-MPC-MAPAcad. year: 2022/2023

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

Mode of study

Not applicable.

Guarantor

Ing. Lukáš Kopečný, Ph.D.

Department

Department of Control and Instrumentation (UAMT)

Learning outcomes of the course unit

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.

Prerequisites

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

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations.
Teaching methods include lectures and one laboratory or home project, that the student elaborates during the semester.

Assesment methods and criteria linked to learning outcomes

Requirements for completion of a course are specified by a regulation issued by the lecturer responsible for the course and updated for every year.

Course curriculum

1. Self-localization, navigation and mapping general overview. Reference coordinate systems. Sensors for data acquisition in real environment.
2. Self-localization – self-localization and probability theoretical overview.
3. Kalman filter – theory, principle of operation, implementation.
4. Particle filter – theory, principle of operation, implementation.
5. Path planning – algorithms, path optimization.
6. Navigation – motion control, PID controller.
7. Simultaneous localization and mapping (SLAM) – implementation options, current state of the art.

Work placements

Not applicable.

Aims

To acquaint students with the current state of knowledge in the field of autonomous mapping, navigation, and self-localization in mobile robotics.

Specification of controlled education, way of implementation and compensation for absences

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.

Recommended optional programme components

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. Lukáš Kopečný, Ph.D.
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. Adam Ligocki, Ph.D.
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.