Davide Scaramuzza: Catalogue data in Autumn Semester 2022

Name Prof. Dr. Davide Scaramuzza
Address
University of Zurich
Andreasstrasse 15 / AND 2.10
Robotics and Perception Group
8050 Zürich
SWITZERLAND
Telephone044 635 24 09
E-maildavide.scaramuzza@mavt.ethz.ch
URLhttp://rpg.ifi.uzh.ch/people_scaramuzza.html
DepartmentMechanical and Process Engineering
RelationshipLecturer

NumberTitleECTSHoursLecturers
151-0632-00LVision Algorithms for Mobile Robotics (University of Zurich) Information
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH as an incoming student.
UZH Module Code: DINF2039

Mind the enrolment deadlines at UZH:
Link
6 credits2V + 2UD. Scaramuzza
AbstractFor a robot to be autonomous, it has to perceive and understand the world around it. This course introduces you to the key computer vision algorithms used in mobile robotics, such as feature extraction, structure from motion, dense reconstruction, tracking, image retrieval, event-based vision, and visual-inertial odometry (the algorithms behind Hololens, Oculus Quest, and the NASA Mars rovers).
ObjectiveLearn the fundamental computer vision algorithms used in mobile robotics, in particular: filtering, feature extraction, structure from motion, multiple view geometry, dense reconstruction, tracking, image retrieval, event-based vision, and visual-inertial odometry and Simultaneous Localization And Mapping (SLAM) (the algorithms behind Hololens, Facebook-Oculus Quest, and the NASA Mars rovers).
ContentEach lecture will be followed by a lab session where you will learn to implement a building block of a visual odometry algorithm in Matlab. By the end of the course, you will integrate all these building blocks into a working visual odometry algorithm.
Lecture notesLecture slides will be made available on the course official website: http://rpg.ifi.uzh.ch/teaching.html
Literature[1] Computer Vision: Algorithms and Applications, by Richard Szeliski, Springer, 2010.
[2] Robotics Vision and Control: Fundamental Algorithms, by Peter Corke 2011.
[3] An Invitation to 3D Vision, by Y. Ma, S. Soatto, J. Kosecka, S.S. Sastry.
[4] Multiple view Geometry, by R. Hartley and A. Zisserman.
[5] Introduction to autonomous mobile robots 2nd Edition, by R. Siegwart, I.R. Nourbakhsh, and D. Scaramuzza, February, 2011
Prerequisites / NoticeFundamentals of algebra, geomertry, matrix calculus, and Matlab programming.

Note: If you are interested in taking UZH courses, you must register as an incoming mobility student at UZH. For details, see as follows:

UZH course enrollment for ETH student at University of Zurich (UZH) > Mobility within Switzerland – Incoming > Module Mobility: The easiest way to take individual modules/courses to supplement your studies at your home university is with module mobility. This option is not available to students who have dropped out of their home university or have been definitely excluded or banned from the relevant a program > Application and Deadlines: Applications are submitted via the UZH application portal (https://www.uzh.ch/cmsssl/en/studies/application/chmobilityin.html)
Step-by-step guidelines on how ETH students can register for this course, are given on the official course website: https://rpg.ifi.uzh.ch/teaching.html

ATTENTION: When you book the course at UZH, you are automatically registered for the exam at UZH and you can unregister until the October deadline. After registering for the course, you as an ETH student need to check out your **UZH email account** to receive the relelated information from the lecturer.
227-1039-00LBasics of Instrumentation, Measurement, and Analysis (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH as an incoming student.
UZH Module Code: INI502

Mind the enrolment deadlines at UZH:
Link

Registration in this class requires the permission of the instructors. Class size will be limited to available lab spots.
Preference is given to students that require this class as part of their major.
4 credits9SS.‑C. Liu, T. Delbrück, R. Hahnloser, G. Indiveri, V. Mante, P. Pyk, D. Scaramuzza, W. von der Behrens
AbstractExperimental data are always as good as the instrumentation and measurement, but never any better. This course provides the very basics of instrumentation relevant to neurophysiology and neuromorphic engineering, it consists of two parts: a common introductory part involving analog signals and their acquisition (Part I), and a more specialized second part (Part II).
ObjectiveThe goal of Part I is to provide a general introduction to the signal acquisition process. Students are familiarized with basic lab equipment such as oscilloscopes, function generators, and data acquisition devices. Different electrical signals are generated, visualized, filtered, digitized, and analyzed using Matlab (Mathworks Inc.) or Labview (National Instruments).

In Part II, the students are divided into small groups to work on individual measurement projects according to availability and interest. Students single-handedly solve a measurement task, making use of their basic knowledge acquired in the first part. Various signal sources will be provided.
Prerequisites / NoticeFor each part, students must hand in a written report and present a live demonstration of their measurement setup to the respective supervisor. The supervisor of Part I is the teaching assistant, and the supervisor of Part II is task specific. Admission to Part II is conditional on completion of Part I (report + live demonstration).

Reports must contain detailed descriptions of the measurement goal, the measurement procedure, and the measurement outcome. Either confidence or significance of measurements must be provided. Acquisition and analysis software must be documented.