Lecturers: Vladyslav Usenko, Nikolaus Demmel

Please direct questions to visnav_ws2019@vision.in.tum.de

Places have been assigned with the course matching system. Please see http://docmatching.in.tum.de/ for the general procedure and for important dates (matching registration deadline is 24.07.19).

TUMOnline course entry: https://campus.tum.de/tumonline/wbLv.wbShowLVDetail?pStpSpNr=950456854

Pre-meeting for more information about the course content and procedure was on Thursday 18.07.2019, 4pm (s.t.), seminar room 02.09.023. Attendance to the pre-meeting is not required for participation in the course. See premeeting_slides.pdf.

You are required to send information about your prior experience to verify prerequisites before the end of the matching deadline (24.07.19). Please consult the pre-meeting slides for instructions on what information to send.

Lecture & exercises (assignment phase): Mondays, lectures approx. 2pm to 4pm (starting 2:00 sharp) in 00.08.055, tutoring of exercises approx. 4pm to 6pm in 02.05.014
Tutored lab time (project phase) : Mondays from 2pm to 6pm in lab 02.05.014 (other times for free project work available, tbd)

The course starts on Monday October 21st 2019, 2:00pm (s.t.) in 00.08.055.

There will be no lecture/exercise on November 4th 2019.

The final presentations are on February 3rd 2020, 2:00pm (s.t.) in 01.09.014.

The course will take place in the seminar room 00.08.055 and the lab room 02.05.014. In the beginning phase (5 weeks), there will be introductory lectures and then programming assignments that can be worked on on the lab computers (or also on your own laptop of you wish). Programming assignment sheets will be handed out every week and should be solved individually.

In a second phase, you will work in teams of 1-2 students on a practical problem (project). For the rest of the semester, the group meets weekly with their tutors and presents and discusses their progress. At the end of the course, the teams will present their project in a talk and demonstrate their solutions. They will document their project work in a written report. Both the assignments and the project part will be graded, and together determine the final grade.

For more details see Course Layout below.

Places assigned through TUM matching system.

Requirements:

• Good knowledge of the C/C++ language and basic mathematics such as linear algebra, calculus, and numerics is required
• Participation in at least one of the following lectures of the TUM Computer Vision Group: Variational Methods for Computer Vision, Multiple View Geometry, Autonomous Navigation for Flying Robots. Similar lectures can also be accepted, please contact us.
  

Number of participants: max. 12

Vision-based localization, mapping, and navigation has recently seen tremendous progress in computer vision and robotics research. Such methods already have a strong impact on applications in fields such as robotics and augmented reality.

In this course, students will develop and implement algorithms for visual navigation and 3D-reconstruction, relevant for applications such as, autonomous navigation of wheeled robots and quadrocopters, or tracking of handheld devices. The investigated algorithms includede, e.g., visual odometry, structure from motion, simultaneous localization and mapping with monocular, stereo, or RGB-D cameras, (semi-)dense 3D reconstruction.

• Lecture & Exercise : 2 hours per week lecture session, Mondays from 2pm to 4pm. 2 hours per week tutored exercises, Mondays from 4pm to 6pm. There are 5 lecture & exercise sessions. Each week, the exercise for the following week will be announced and has do be handed in online by each student individually within 2 weeks. Students can use our lab computers in room 02.05.014. Attendance is mandatory.
• Project : After the initial 5 weeks, student should form groups of 1-2. Each group will be assigned to a project. Students can work in the lab (at any time) and consult the tutors in a weekly meeting to discuss project progress and next steps. Attendance to meetings with tutors is mandatory.
• Presentation and demo : Each group will be assigned a time slot on one of the last days of the semester to present their results, followed by a Q&A session. The presentation shall be 10 minutes long + 5 minutes for questions. The presentation should comprise 5-10 slides to explain the project goals and results in to fellow students and may include a short live demo or video.
  
• Project Report : Each group writes a report on their project work (10-12 pages, single column, single-spaced lines, 11pt font size; title page, table of content and references will not be accounted for in the page numbers). The report should summarize the project goals, what was implemented, and what results were obtained.

A good introduction to many aspects of computer vision relevant for the practical project is the following course, which has recordings on YouTube:

• Computer Vision II: Multiple View Geometry, http://vision.in.tum.de/teaching/ss2018/mvg2018

The following book also covers many aspects. You should focus on Part II and III and selected background from Part I as needed:

• Timothy D. Barfoot, "State Estimation for Robotics", July 2017, Cambridge University Press
• Free pdf available: http://asrl.utias.utoronto.ca/~tdb/bib/barfoot_ser17.pdf

Less relevant, but still helpful:

• Autonomous Navigation for Flying Robots (EdX course), http://vision.in.tum.de/teaching/ss2015/autonavx
• Computer Vision I: Variational Methods, http://vision.in.tum.de/teaching/ws2017/vmcv2017

Selected publications:

• Edward Rosten et al., "Faster and better: a machine learning approach to corner detection" (https://arxiv.org/pdf/0810.2434.pdf)
• Michael Calonder et al., "BRIEF: Binary Robust Independent Elementary Features" (https://infoscience.epfl.ch/record/149242/files/top_1.pdf
• Ethan Rublee et al., "ORB: an efficient alternative to SIFT or SURF" (http://www.willowgarage.com/sites/default/files/orb_final.pdf)
• Raúl Mur-Artal et al., "ORB-SLAM: A Versatile and Accurate Monocular SLAM System" (http://webdiis.unizar.es/~raulmur/MurMontielTardosTRO15.pdf)
• Ethan Eade, "Lie Groups for 2D and 3D Transformations" (http://ethaneade.com/lie.pdf) –> compare also chapter 7 in the Barfoot book mentioned above

Additional material can be downloaded from here.