Module


General information
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
Curricular relevance (according to examination regulations)
Study Subject Study Specialization Study Focus Module type Semester
M.Sc. - MIE - Information Engineering (PO 2022, V3) Wahlmodul
M.Eng. - MET - Elektrische Technologien (PO 2017, V3) Kommunikationstechnik und Embedded Systems Wahlmodul

Qualification outcome
Areas of Competence: Knowledge and Understanding; Use, application and generation of knowledge; Communication and cooperation; Scientific self-understanding / professionalism.
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.
Content information
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
Teaching formats of the courses
Teaching format SWS
Labor 2
Lehrvortrag 2
Workload
4 SWS
5,0 Credits
48 Hours
102 Hours
Module Examination
Method of Examination Duration Weighting wird angerechnet gem. § 11 Absatz 2 PVO Graded Remark
Übung 50 %
Klausur 120 Minutes 50 %
Miscellaneous
- 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.