Davide Scaramuzza: Katalogdaten im Herbstsemester 2019

NameHerr Prof. Dr. Davide Scaramuzza
Adresse
Autonome Systeme
ETH Zürich, LEE J 206
Leonhardstrasse 21
8092 Zürich
SWITZERLAND
E-Maildavide.scaramuzza@mavt.ethz.ch
URLhttp://rpg.ifi.uzh.ch/people_scaramuzza.html
DepartementMaschinenbau und Verfahrenstechnik
BeziehungDozent

NummerTitelECTSUmfangDozierende
151-0632-00LVision Algorithms for Mobile Robotics Information Belegung eingeschränkt - Details anzeigen
Number of participants limited to 55
Registration is on a first come, first served basis and SPACE IS LIMITED!
4 KP2V + 2UD. Scaramuzza
KurzbeschreibungFor 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, multiple view geometry, dense reconstruction, tracking, image retrieval, event-based vision, and visual-inertial odometry (the algorithms behind Google Tango, Ms Hololens, and the Mars rovers).
LernzielLearn the fundamental computer vision algorithms used in mobile robotics, in particular: feature extraction, multiple view geometry, dense reconstruction, object tracking, image retrieval, event-based vision, and visual-inertial odometry (the algorithm behind Google Tango).
InhaltEach lecture will be followed by a lab session where you will learn to implement the 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.
SkriptLecture slides will be made available on the course official website: http://rpg.ifi.uzh.ch/teaching.html
Literatur[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
Voraussetzungen / BesonderesFundamentals of algebra, geomertry, matrix calculus, and Matlab programming.
227-1039-00LBasics of Instrumentation, Measurement, and Analysis (University of Zurich) Belegung eingeschränkt - Details anzeigen
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: INI502

Mind the enrolment deadlines at UZH:
https://www.uzh.ch/cmsssl/en/studies/application/mobilitaet.html

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 KP9SS.‑C. Liu, T. Delbrück, R. Hahnloser, G. Indiveri, V. Mante, P. Pyk, D. Scaramuzza, W. von der Behrens
KurzbeschreibungExperimental 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).
LernzielThe 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.
Voraussetzungen / BesonderesFor 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.