Teaching


Below is the list of courses we teach each semester or that we taught in the past. Click on the specific entry for more info:




Fall 2024 - Seminar: Vision-Based Drone Flight

UZH-22MIS105



Important Dates


Most parts of the course will be taking place at the drone test facility of the Robotics and Perception Group in Winterthur.


Seminar Times: Every day from 09.09.2024 to 13.09.2024 from 9am to 5pm.

Booking period: from 14.08.2024 10:00 to 27.08.2024 24:00

Place allocation at the latest: 02.09.2024 22:00

Waiting list progression period: from 30.08.2024 12:00 to 06.09.2024 24:00

Cancellation period: from 30.08.2024 12:00 to 09.09.2024 24:00



Prerequisites and Registration


Due to the challenging nature of this seminar interested students should send their transcripts via e-mail to Leonard Bauersfeld and Angel Romero (bauersfeld@ifi.uzh.ch, roagui@ifi.uzh.ch) such that we can ensure that all prerequisites are met and the students have sufficient background knowledge to pass the course. We will reply before the initial registration opens on August 14 with a decision. In general we expect a strong background in machine learning and computer vision. Students must have attended the lecture on UZH - Vision Algorithms for Mobile Robotics (or similar courses, e.g. ETH - Computer Vision or ETH - Image Analysis and Computer Vision). Furthermore, for the background in machine learning should have at least attended UZH Advanced Topics in Artificial Intelligence (or similar). Finally, students must be comfortable programming in C++ or Python and working in a Linux-based software environment and should be familiar with a Linux shell.


Additional Info for ETH students


Registration for ETH students is done through UZH. To do so, you will first need to register to get a UZH account. Afterwards, you can login and book the course. All the instructions can be found here.

ETH students should mind the following: to be able to register for the seminar, they need to register for a UZH account before August 13 at 23:59hrs Zurich time. Once you receive the login credentials (it may take a few days), you can finally book the course until August 27 at 23:59.



Goal of the Course


Autonomous drones are ubiquitous across different industries, from inspection and mapping to search and rescue applications. In those scenarios, vision-based autonomy is vital as a drone must be able to perceive and react to its environment. This task is very challenging because the real world is inherent-ly unpredictable and difficult to capture in mathematical models. Combined with the strict real-time requirements when controlling a drone, these requirements make deep-learning solutions the ideal choice for percep-tion and control. In this course, the students will get hands-on experience in autonomous vision-based flight. We consider a Holly-wood-inspired setting where a camera drone needs to track an "actor drone." The students will design a perception module to detect and track other drones and train a reinforcement learning (RL) controller to track the actor drone. The developed pipeline will be de-ployed in the real world on physical hard-ware. For the control task, the students will be pro-vided with an RL environment in which they can integrate their controllers. Similarly, the students will have a software framework to integrate their work into the perception mod-ule. For the final deployment, drones will be provided so that students can focus primarily on their algorithms.



Instructors


Lecture Instructors: Leonard Bauersfeld and Angel Romero.



Spring 2024 - Foundations of Computing I

UZH-03SM22AINF05



Registration


This course is mandatory for students enrolled in the Bachelor's degree in informatics at the University of Zurich. Registration is done through UZH. The course can be booked from January 17, 2024, at 10:00 am until March 12, 2024, at 23:59 hrs Zurich time. NB. If you fail to book the course, you won't be able to take the exam..



Exam cancelation


At UZH, registration for a course or cancelation of an examination is carried out directly in the module booking tool. Booking a module also automatically registers the student for the corresponding examination. Once the module booking deadline (March 12, 2024, 23:59) has passed, absolutely no further registrations or cancelations can be processed. Cancelations at a later date due to illness are only possible if a petition is submitted within five business days from the exam, accompanied by a properly dated original doctor's attestation. More info here.



Goal of the Course


This course introduces mathematical tools used in computer science (from database management to artificial intelligence, machine learning, data analysis and visualization, cryptography and security, computer graphics, computer vision, and image processing). In particular, the course will teach the following topics: propositional logic, digital logic circuits, induction and recursion, convolution, relations, modular arithmetic with application to cryptography, graphs, and trees.



Time and location


All lectures and exercises occur on Tuesdays and Thursdays from 16:15 to 18:00 hrs. Please have a look at the course agenda on the official UZH catalog for the exact times and rooms.



Instructors


Lecture Instructor: Prof. Dr. Davide Scaramuzza.

Exercise Instructors: Noam Szyfer and Serafin Schoch.

Office hours of Prof. Scaramuzza: Every Friday from 16:00 to 18:00 on ZOOM or in person (please book your slot by email by using "FOC Office Hours" as the email subject).



Course Program, Slides, Video Recordings, and Student Forum


The syllabus of the course, slides, additional reading materials, and all video recordings of the lectures and exercises will be made available within the course online platform OLAT. Please use the forum therein to ask the instructors questions.



Exam


The written exam will take place on June 25, 2024 from 14:00 to 17:00 hrs Zurich time as a Bring Your Own Device (BYOD) online exam on-site. The exam will be closed book, which means you won't be allowed to use any slides, cheat sheets, book, etc. To take the exam, you must bring your own laptop (Windows or MacOS). The exam cannot be taken on tablets. To take the exam, you must install the Safe Exam Browser (SEB) in advance and take an infrastructure test and a mock exam using your individual technical equipment. Exact information on the required SEB version and other details will be provided to you during the semester. The mandatory functional test will take place from 09.04.2024 to 19.04.2024. The mock exam will be held on-site on 23.04.204 from 18:15 to 20:00 hrs. Attendance in the mock exam is mandatory. Students who have not attended the mock exam will not be admitted to the final exam and fail the course. If you cannot attend the mock exam due to illness or accident, you must email support@oec.uzh.ch with a doctor's note.



Fall 2023 - Vision Algorithms for Mobile Robotics

UZH-DINF2039 / ETH-151-0632-00L



Registration for ETH students


This course is a core course of the ETH Master in Robotics, Systems and Control. Registration for ETH students is done through UZH. To do so, you will first need to register to get a UZH account. Afterwards, you can login and book the course. All the instructions can be found here.

ETH students should mind the following deadlines: the deadline to register for a UZH account is September 15 at 23:59hrs Zurich time. Once you receive the login credentials (it may take a few days), you can finally book the course until October 10 at 23:59. NB. If you fail to book the course, you won't be able to take the exam..



Exam cancelation


At UZH, registration for a course or cancelation of an examination is carried out directly in the module booking tool. Booking a module also automatically registers the student for the corresponding examination. Once the module booking deadline (October 10 23:59) has passed, absolutely no further registrations or cancelations can be processed. Cancelations at a later date due to illness are only possible if a petition is submitted within five business days from the exam, accompanied by a properly dated original doctor's attestation. More info here.



Goal of the Course


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 image formation, filtering, feature extraction, multiple view geometry, dense reconstruction, tracking, image retrieval, event-based vision, visual-inertial odometry, Simultaneous Localization And Mapping (SLAM), and some basics of deep learning. Specifically, we will see the algorithms behind Apple ARKit, Google Visual Positioning Service, Microsoft Hololens, Magic Leap, Oculus Quest and Oculus Insight, the Mars Perseverance rover, and the Mars helicopter. Basic knowledge of algebra, geometry, and matrix calculus (product, inversion, eigenvalue decomposition) is required.



Time and location


All lectures and exercises will be taking place on-site in the UZH City campus (next to UZH and ETH main buildings) in SOC-F-106 (located on floor F) and will also be video recorded. If the front door of the building is locked, please use the side door (located on the right side of the building).


Lectures: every Thursday from 8:00 to 9:45 am. Please notice that the lecture starts at 8 o'clock and not 8:15!


Exercises: every Thursday from 12:15 to 13:45.


Please check out the course agenda below for the exact schedule.



Instructors


Lecture Instructor: Prof. Dr. Davide Scaramuzza.

Exercise Instructors: Leonard Bauersfeld and Jiaxu Xing.

Office hours of Prof. Scaramuzza: Every Thursday from 16:00 to 18:00 on ZOOM or in person (please book your slot by email by using "VAMR Office Hours" as the email subject).



Video Recordings and Student Forum


All video recordings of the lectures and exercises will be made available within the course online platform OLAT. Please use the forum therein to ask the instructors questions.



Course Program, Slides, and Additional Reading Material


Please notice that this is a tentative schedule and that the effective content of the lecture may change from week to week.


Date

Lecture and Exercise description

Slides, exercises, and additional reading material

21.09.2023 Lecture 01 - Introduction to Computer Vision and Visual Odometry
Exercise: Camera Notation Tutorial.
Lecture Slides
Exercise Lecture Notes
Additional reading on Visual Odometry
Linear Algebra Primer from Stanford University
Linear Algebra interactive tool
Camera notation tutorial
28.09.2023 Lecture 02 - Image Formation: perspective projection and camera models
Exercise 01- Augmented reality wireframe cube
Lecture Slides
Exercise 01 vectorization code
Matlab primer
Matlab primer code
Python Tutorial
Exercise 01
Solutions 01 (Matlab/Python/Numerical)
05.10.2023 Lecture 03 - Camera Calibration
Exercise 02 - PnP problem
Lecture Slides (updated 12.10.2023, see changes here)
Additional reading on P3P and PnP problems
Exercise 02
Notes on SVD for DLT
Solutions 02 (Matlab/Python/Numerical)
12.10.2023 Lecture 03 continued
Lecture 04 - Filtering & Edge detection
Exercise session replaced by continuation of Lecture 4.
Lecture Slides
Numerical Exercise Lecture 04
Numerical Exercise Lecture 04 Solution
19.10.2023 Lecture 05 - Point Feature Detectors, Part 1
Exercise 03 - Harris detector + descriptor + matching
Lecture Slides
Exercise 03
Solutions 03 (Matlab/Python/Numerical)
26.10.2023 Lecture 06 - Point Feature Detectors, Part 2
Exercise 04 - SIFT detector + descriptor + matching
Lecture Slides
Additional reading on feature detection
Exercise 04
Notes on Convolution 04
Solutions 04 (Matlab/Python/Numerical)
02.11.2023 Lecture 07 - Multiple-view Geometry 1
Exercise 05 - Stereo vision: rectification, epipolar matching, disparity, triangulation
Lecture Slides
Additional reading on stereo image rectification
Exercise 05
Solutions 05
09.11.2023 Lecture 08 - Multiple-view Geometry 2
Exercise 06 - Eight-Point Algorithm
Lecture Slides
Additional reading on 2-view geometry
Exercise 06
Solutions 06
16.11.2023 Lecture 09 - Multiple-view Geometry 3
Exercise 07 - P3P algorithm and RANSAC
Lecture Slides
Additional reading on RANSAC
Exercise 07
Solutions 07
23.11.2023 Lecture 10 - Multiple-view Geometry 4
Continuation of Lecture 10 + Exercise session on Intermediate VO Integration
Lecture Slides
Additional reading on open-source VO algorithms
Numerical Exercise 10
Numerical Exercise 10 Solution
30.11.2023 Lecture 11
1st hour: seminar by Dr. Jeff Delaune from NASA-JPL: "Vision-Based Navigation for Mars Helicopters."
2nd hour: Optical Flow and KLT Tracking
Exercise 08 - Lucas-Kanade tracker
Lecture Slides
Additional reading on KLT
Exercise 08
Solutions 08
07.12.2023 Lecture 12a (1st hour) - Place Recognition
Lecture 12b (2nd hour) - Dense 3D Reconstruction
Lecture 12c (3rd and 4th hour, replaces exercise) - Deep Learning Tutorial
Optional Exercise on Place Recogntion
Lecture Slides on Place Recognition
Lecture Slides on Dense 3D Reconstruction (updated 07.12.2023, see changes here)
Lecture Slides on Deep Learning
Optional Exercise on Place Recogntion
Numerical Exercise 12
Numerical Exercise Lecture 12 Solution
Additional reading on dense 3D reconstruction
Additional reading on Bag-of-Words-based place recognition
14.12.2023 Lecture 13 - Visual inertial fusion
Exercise 09 - Bundle Adjustment
Lecture Slides
Additional reading on VIO
Exercise 09
Solutions 09
21.12.2023 Lecture 14 - Event-based vision
Exercise session: Final VO Integration
Lecture Slides
Additional reading on event-based vision
11.01.2024 Written exam: from 08:00 to 10:00 am. More details will be given during the semester.


Exam


The written exam will take place on January 11th, 2024 from 08:00 to 10:00 am. More details will be given during the semester.



Exercises


You will be required to bring your own laptop to the exercise session. This year, for the second time, students will have the chance to solve the exercises in Python or Matlab. However, the exercise statements and material presented during the exercise sessions will only cover the implementations in Matlab. Additionally to the Matlab solutions, we will upload Python scripts as template solutions for the exercise statements. You will need to have Matlab or Python already pre-installed on your machine for the exercises. To install Matlab, follow the links below according to your affiliation.

  • ETH students: Download from here
  • UZH students: Download from here; Info on how to setup the license can be found there.
Please install all the toolboxes included in the license.



Optional Mini Project and Grading


Students taking the final exam have the opportunity to submit a mini-project to increase their final grade. Notice that the mini project is optional, that is, not mandatory. Depending on the result of the mini-project, the student will be rewarded with a grade increase of up to 0.5 on the top of the grade of the written exam. However, bear in mind that the mini-project can be quite time-consuming. Students who want to submit a mini project should work in a group of minimum 2 and max 4 persons. The deadline for the mini-project is Sunday, 07.01.2024, 23:59:59, and it can be submitted via e-mail to the assistants. Mini project specifications and files can be found in the table below.


DescriptionLink (size)
Project Specification vo_project_statement.pdf
FAQ Frequently Asked Questions
Parking garage dataset (easy) parking.zip (208.3 MB)
KITTI 05 dataset (hard) kitti05.zip (1.4 GB)
Malaga 07 dataset (hard) malaga-urban-dataset-extract-07.zip (2.4 GB)
Matlab script to load datasets main.m


Recommended Textbooks

(All available in the Swisscovery catalogue)

Unfortunately, a single textbook that covers all the material seen in class does not exist yet. However, the topics covered in class can be found in the books listed below. At the end of each lecture's slide deck, you will find a list of chapters and books where the topics can be found.

  • Computer Vision: Algorithms and Applications, by Richard Szeliski, Springer, 2nd Edition, 2021. The PDF of the book can be freely downloaded from the author's webpage. Although the author is working on a 2nd edition, this is still under progress. Thus, in the lecture nodes we will reference chapters of the 1st edition.
  • Chapter 4 of "Autonomous Mobile Robots", by R. Siegwart, I.R. Nourbakhsh, D. Scaramuzza. PDF
  • Robotics, Vision and Control: Fundamental Algorithms, 2nd Ed., by Peter Corke 2017. The PDF of the book can be freely downloaded (only with ETH VPN) from the author's webpage.
  • An Invitation to 3D Vision, by Y. Ma, S. Soatto, J. Kosecka, S.S. Sastry.
  • Multiple view Geometry, by R. Hartley and A. Zisserman.




Spring 2007-2015 - Autonomous Mobile Robots


From 2007 to 2015, Prof. Davide Scaramuzza co-lectured the course Autonomous Mobile Robots along with Prof. Roland Siegwart. The old Power Point slides and videos can be found here. The lectures of the course inspired the second edition of the omonym book Autonomous Mobile Robots below. The course continues to be taught by Prof. Siegwart at the following link and is also available as an MOOC (Massive Open Online Course) on the popular online platform edX.



Reference book

R. Siegwart, I.R. Nourbakhsh, and D. Scaramuzza

Introduction to autonomous mobile robots 2nd Edition (hardback)

A Bradford Book, The MIT Press, ISBN: 978-0-262-01535-6, February, 2011

The book can be bought during the first lecture or on Amazon.

MIT Website Buy