Davide Scaramuzza: Catalogue data in Autumn Semester 2019
|Name||Prof. Dr. Davide Scaramuzza|
University of Zurich
Andreasstrasse 15 / AND 2.10
Robotics and Perception Group
|Telephone||044 635 24 09|
|Department||Mechanical and Process Engineering|
|151-0632-00L||Vision Algorithms for Mobile Robotics |
Number of participants limited to 55
Registration is on a first come, first served basis and SPACE IS LIMITED!
|4 credits||2V + 2U||D. Scaramuzza|
|Abstract||For 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).|
|Objective||Learn 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).|
|Content||Each 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.|
|Lecture notes||Lecture slides will be made available on the course official website: http://rpg.ifi.uzh.ch/teaching.html|
|Literature|| Computer Vision: Algorithms and Applications, by Richard Szeliski, Springer, 2010. |
 Robotics Vision and Control: Fundamental Algorithms, by Peter Corke 2011.
 An Invitation to 3D Vision, by Y. Ma, S. Soatto, J. Kosecka, S.S. Sastry.
 Multiple view Geometry, by R. Hartley and A. Zisserman.
 Introduction to autonomous mobile robots 2nd Edition, by R. Siegwart, I.R. Nourbakhsh, and D. Scaramuzza, February, 2011
|Prerequisites / Notice||Fundamentals of algebra, geomertry, matrix calculus, and Matlab programming.|
|227-1039-00L||Basics of Instrumentation, Measurement, and Analysis (University of Zurich) |
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:
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 credits||9S||S.‑C. Liu, T. Delbrück, R. Hahnloser, G. Indiveri, V. Mante, P. Pyk, D. Scaramuzza, W. von der Behrens|
|Abstract||Experimental 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).|
|Objective||The 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 / Notice||For 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.