Course detail

Advanced Methods for Mapping and Self-localization in Robotics

FEKT-MPC-MAPAcad. year: 2020/2021

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.

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.

Recommended optional programme components

Not applicable.

Recommended or required reading

J. Borenstein, H.R. Everett, and L. Feng: Where am I? Sensors and Methods for Mobile Robot Positioning. (EN)
Mohanta Jagadish Chandra: Introduction to Mobile Robots Navigation. ISBN: 3659680648 (EN)

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.

Language of instruction

Czech

Work placements

Not applicable.

Course curriculum

1. The concept of self-localization, navigation, mapping. Reference systems. Number of degrees of freedom.
2. Self-localization and navigation I: Odometry, inertial self-localization, global satellite navigation systems.
3. Self-localization and navigation II: Navigation with proximity sensors - ultrasound sensors, lidars. Self-localization and navigation without map and with known map.
4. 2D mapping. Robot evidence grids. Vectorization. Geometry maps.
5. 3D mapping I: Robot evidence maps - extension to 3D. Indoor mapping.
6. 3D mapping II: Outdoor mapping. Multispectral mapping. Environmental mapping.
7. SLAM - simultaneous localization and mapping. 2D and 3D approach, problems, state-of-the-art.

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.

Classification of course in study plans

  • Programme MPC-KAM Master's, 2. year of study, summer semester, 3 credits, compulsory-optional

Type of course unit

 

Lecture

22 hours, optionally

Teacher / Lecturer

Laboratory exercise

4 hours, compulsory

Teacher / Lecturer

eLearning