Direkt zum Inhalt springen
Computer Vision Group
TUM School of Computation, Information and Technology
Technical University of Munich

Technical University of Munich



Practical Course: Vision-based Navigation IN2106 (6h SWS / 10 ECTS)

WS 2021, TU München

Lecturers: Jason Chui, Simon Klenk

Please direct questions to visnav_ws2021@vision.in.tum.de

  • 2021-12-20: Our next meeting will be on 10.01.2022
  • 2021-11-29: Please fill the google sheet in the material link for the phase2
  • 2021-10-18: The first lecture is on 25.10.2021 in Seminarraum 01.06.011
  • 2021-08-06: We have sent an email to those students who have been matched to the course. If you have been matched to the course and have not yet received the email from us, please send us an email via visnav_ws2021@vision.in.tum.de
  • 2021-07-07: The recording of the pre-meeting on 05.07.2021 has been uploaded. link
  • 2021-06-24: Places are assigned through TUM matching system. Please see http://docmatching.in.tum.de/ and http://docmatching.in.tum.de/index.php/schedule for the general procedure and for important dates. Besides signing up in the matching system, you are required to send information about your prior experience before the end of the matching deadline (20.07.2021) via email, so we can verify prerequisites. Please consult the pre-meeting slides for instructions on what information to send. (Please wait for the pre-meeting before asking questions about this.)
  • 2021-06-24: The pre-meeting will be held online on 05.07.2021 at 2pm.
  • 2021-06-24: Launch of this website. Tentative information, expect updates. Dead links will be functional when you need them.

The course may take place in the presence.

Time & Date
  • Lecture & exercises (assignment phase): Mondays 2pm to 4pm. Tutor sessions 4pm to 6pm.
  • Individual weekly meetings (project phase): Fixed 30min time slot for each project/group, preferably Mondays between 2pm and 6pm. Arranged after the assignment phase.
  • Project presentations: 31.01.2022, 2pm
  • Project report due: 31.03.2022
  • Maybe: Use of computer labs for exercise and project work.

To participate in the course you need to fulfill the following requirements:

  • Good knowledge of the C/C++ language is essential
  • Good knowledge of 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:
    • Computer Vision I: Variational Methods
    • Computer Vision II: Multiple View Geometry
    • Similar lectures can also be accepted, please contact us.
Pre-meeting and Registration

Places are assigned through TUM matching system. Please see http://docmatching.in.tum.de/ for the general procedure and for important dates (matching registration deadline is 16.02.2021).

TUMOnline course entry: Vision-based Navigation (IN2106)

Pre-meeting for more information about the course content and procedure will be held online on 05.07.2021 at 2pm. Attendance to the pre-meeting is not required for participation in the course, but registration through the matching system is. You can find details about the registration procedure in the pre-meeting slides(ss21) or video recording (tba).

You are required to send information about your prior experience to verify prerequisites before the end of the matching deadline (20.07.2021). Please consult the pre-meeting slides for instructions on what information to send.

Course Description

Vision-based localization, mapping, and navigation have 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, tracking of handheld devices, or 3D reconstruction. The investigated algorithms may include, visual odometry, structure from motion, simultaneous localization and mapping with monocular, stereo, or RGB-D cameras, (semi-)dense 3D reconstruction.

Number of participants: max. 12

Course Layout
  • Lecture & Exercise : up to 2 hours per week recorded video lecture; 2 hours per week tutored Q&A and exercise session, Mondays from 4pm to 6pm. There are 5 lectures & exercise sessions. Each week, the exercise for the following week will be announced and has to be handed in online by each student individually within 2 weeks. Attendance to lecture and tutor sessions are voluntary but highly encouraged.
  • Project : After the initial 5 weeks, students should form groups of 1-2. Each group will be assigned to a project. Students can work on their own 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 12 minutes long + 3 minutes for questions. The presentation should comprise 5-10 slides to explain the project goals and results 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.

The final grade will be determined by both the programming assignments and the project.

For grading the programming assignments we consider completeness and timely submission (not so many things such as code quality, as long as it works). Note that you have to complete all exercise sheets to pass the course, even if you miss a submission deadline.

In the project phase, the main focus is on your implementation, on the presentation and the report, but we will also consider how you approach the problem, how you engage with your tutors, and how you manage your time.

Tutor Sessions

We will start with general announcements, then do a common Q&A session for the latest lecture as well as the current and the previous exercise sheet. The usefulness for everyone depends on you asking questions.

Afterward, you are encouraged to work on the exercises. The call will remain open and you can talk to us about any issues that come up. While you can also work on the exercises in your own time, we encourage you to make use of the tutor sessions as much as possible, as most questions – that might hold you up otherwise – can usually be resolved quickly during the tutor session.

In case you cannot attend, you may also send us questions (on the latest lecture as well as the current and the previous exercise sheet) to visnav_ws2021@vision.in.tum.de and we will try to address them in the Q&A session. Please send us the questions at least one day in advance, if possible.


After the lecture and assignment phase is completed, students work in groups of 1-2 people on a more open-ended project. We will present some example projects, but you may also suggest your own.

See the projects slides (ss21) for more details.


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

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

Less relevant, but still helpful:

Selected publications:

Rechte Seite

Informatik IX
Computer Vision Group

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

Follow us on:



CVPR 2023

We have six papers accepted to CVPR 2023.


NeurIPS 2022

We have two papers accepted to NeurIPS 2022.


WACV 2023

We have two papers accepted at WACV 2023.


Fulbright PULSE podcast on Prof. Cremers went online on Apple Podcasts and Spotify.


MCML Kick-Off

On July 27th, we are organizing the Kick-Off of the Munich Center for Machine Learning in the Bavarian Academy of Sciences.