Modul


Allgemeine Informationen
Pose Estimation for Mapping, VR & AR-Tracking
Pose Estimation for Mapping, VR & AR-Tracking
MI142
Prof. Dr. Woelk, Felix (felix.woelk@haw-kiel.de)
Dr. Köser, Kevin (kkoeser@geomar.de)
Prof. Dr. Woelk, Felix (felix.woelk@haw-kiel.de)
Wintersemester 2022/23
1 Semester
In der Regel im Wintersemester
Englisch
Studiengänge und Art des Moduls (gemäß Prüfungsordnung)
Studiengang Vertiefungsrichtung Schwerpunkt Modulart Fachsemester
M.Sc. - MIE - Information Engineering (PO 2022, V3) Wahlmodul
M.Eng. - MET - Elektrische Technologien (PO 2017, V3) Kommunikationstechnik und Embedded Systems Wahlmodul

Kompetenzen / Lernergebnisse
Kompetenzbereiche: Wissen und Verstehen; Einsatz, Anwendung und Erzeugung von Wissen; Kommunikation und Kooperation; Wissenschaftliches Selbstverständnis/Professionalität.
Students understand the basic theories and algorithms underlying pose estimation systems. Such systems are used in a variety of algorithms and applications, f.e. in mapping algorithms (SLAM) or tracking systems for augmented and virtual reality. Students can judge the possibilities and limitations of theses algorithms and systems.
Students can choose and use the right building blocks from software libraries to tailer specific pose estimation applications.
Students are able to collaborate in a team and present their work.
Students are able work independently on complex assignments.
Angaben zum Inhalt
The module covers the fundamental technical and theoretical building blocks of a pose estimation system for mapping, augmented or virtual reality system:
- camera models
- lens distortion
- camera calibration
- pose estimation
- marker detection
- feature detection
- feature description
- handling of outliers
- camera tracking
- triangulation

The basic building blocks of a tracking system based on OpenCV using python will be implemented in the lab.
"Multiple View geometry", Richard Hartley and Andrew Zisserman, Cambridge, 2003
"Computer Vision: Algorithms and Applications", Richard Szeliski, Springer, 2011
More literature will be given in the first lecture
Lehrformen der Lehrveranstaltungen
Lehrform SWS
Labor 2
Lehrvortrag 2
Arbeitsaufwand
4 SWS
5,0 Leistungspunkte
48 Stunden
102 Stunden
Modulprüfung
Prüfungsform Dauer Gewichtung wird angerechnet gem. § 11 Absatz 2 PVO Benotet Anmerkung
Übung 50 %
Klausur 120 Minuten 50 %
Sonstiges
- Good programming skills in one language using object oriented paradigm
- Knowledge in mathematics, particularly in linear algebra
Lab exercises (Übung) and written exam (Klausur) must be taken within the same term, no transferal of test performance to following terms.