Student Projects


How to apply

To apply, please send your CV, your Ms and Bs transcripts by email to all the contacts indicated below the project description. Do not apply on SiROP . Since Prof. Davide Scaramuzza is affiliated with ETH, there is no organizational overhead for ETH students. Custom projects are occasionally available. If you would like to do a project with us but could not find an advertized project that suits you, please contact Prof. Davide Scaramuzza directly to ask for a tailored project (sdavide at ifi.uzh.ch).


Upon successful completion of a project in our lab, students may also have the opportunity to get an internship at one of our numerous industrial and academic partners worldwide (e.g., NASA/JPL, University of Pennsylvania, UCLA, MIT, Stanford, ...).



Optical Flow Estimation with an Event Camera - Available

Description: Event cameras such as the Dynamic Vision Sensor (DVS) are recent sensors with large potential for high-speed and high dynamic range robotic applications. The goal of this project is to use event cameras to compute the optical flow in the image plane induced by either a moving camera in a scene or by moving objects with respect to a static event camera. Several existing methods as well as proposed new ones will be analyzed, implemented and compared. A successful candidate is expected to be familiar with state-of-the-art optical flow methods for standard cameras. This is a project with considerable room for creativity, for example in applying the ideas from low-level vision or ideas driving optical flow methods for standard cameras to the new paradigm of event-based vision. Experience in coding image processing algorithms in C++ is required.

Contact Details: Guillermo Gallego (guillermo.gallego at ifi.uzh.ch), Henri Rebecq (rebecq at ifi.uzh.ch)

Thesis Type: Master Thesis

See project on SiROP

Visual Bundle Adjustment with an Event Camera - Available

Description: Event cameras such as the Dynamic Vision Sensor (DVS) are recent sensors with large potential for high-speed and high dynamic range robotic applications. The goal of this project is to improve an existing visual odometry pipeline using an event camera by designing and integrating a visual bundle adjustment module in order to reduce the drift in the odometry pipeline. A good theoretical background on computer vision is necessary to undertake this project. The candidates will be expected to be comfortable with C++ as well.

Contact Details: Henri Rebecq (rebecq at ifi.uzh.ch), Guillermo Gallego (guillermo.gallego at ifi.uzh.ch)

Thesis Type: Master Thesis

See project on SiROP

Event Camera Characterization - Available

Description: Event cameras such as the Dynamic and Active Pixel Vision Sensor (DAVIS, http://inilabs.com/products/dynamic-and-active-pixel-vision-sensor/ ) are recent sensors with large potential for high-speed and high dynamic range robotic applications. In spite the successful demonstration of the sensor to address several problems in computer vision and robotics, a comprehensive characterization of the sensor for such high level applications is still missing.

Goal: The goal of this project is to characterize various aspects of these novel types of sensors, such as: event noise characteristics (distribution and spectral density), contrast threshold (relation to bias settings, variability: spatially, with the pixel, and photometrically, with respect to the scene illumination), non-linearities, etc. Additionally, the images and IMU measurements provided by the DAVIS also require an integrated characterization. A successful completion of the project will lead to a better understanding of the potential, limitations and impact of these sensors on the design of novel algorithms for computer vision and robotics. The expected candidate should have a background on instrumentation, electrical engineering (to understand the principle of operation of the DAVIS pixels) and random processes. This project involves close collaboration with the Institute of Neuroinformatics (INI) at UZH-ETH.

Contact Details: Guillermo Gallego (guillermo.gallego at ifi.uzh.ch)

Thesis Type: Semester Project / Bachelor Thesis

See project on SiROP

Building a high-speed camera! Learning Image reconstruction with an Event Camera - Available

Description: Event cameras such as the Dynamic Vision Sensor (DVS) are recent sensors with large potential for high-speed and high dynamic range robotic applications. The output of an event camera is a sparse stream of events that encode only light intensity changes - in other terms, a highly compressed version of the visual signal.

Goal: The goal of this project is to turn an event camera into a high-speed camera, by designing an algorithm to recover images from the compressed event stream. Inspired by a recent approach, the goal of this project will be to train a machine learning algorithm (or neural network) to learn how to reconstruct an image from the noisy event stream. The first part of the project will consist in acquiring training data, using both simulation and real event cameras. The second part will consist in designing and training a suitable machine learning algorithm to solve the problem. Finally, the algorithm will be compared against state-of-the-art image reconstruction algorithms. The expected candidate should have some background on both machine learning and computer vision (or image processing) in order to undertake this project.

Contact Details: Henri Rebecq (rebecq at ifi.uzh.ch), Guillermo Gallego (guillermo.gallego at ifi.uzh.ch)

Thesis Type: Master Thesis

See project on SiROP

A Visual-Inertial Odometry System for Event-based Vision Sensor - Available

Description: Event-based cameras are recent revolutionary sensors with large potential for high-speed and low-powered robotic applications. The goal of this project is to develop visual-inertial pipeline for the Dynamic and Active Vision Sensor (DAVIS). The system will estimate the pose of the DAVIS using the event stream and IMU measurements delivered by the sensor. Filtering approaches as well as batch optimization methods will be investigated. https://youtu.be/bYqD2qZJlxE http://www.inilabs.com/products/davis

Contact Details: Henri Rebecq (rebecq at ifi.uzh.ch), Guillermo Gallego (guillermo.gallego at ifi.uzh.ch)

Thesis Type: Master Thesis

See project on SiROP

Reinforcement Learning for a Racing Game AI - Available

Description: Modern racing games like, e.g., Gran Turismo SPORT for the Sony Playstation 4, are based on advanced dynamics simulations that model the physics of car racing, including detailed models of various race cars and racing tracks. Just as in real life car racing, the optimal trajectory around a simulated race track is not obvious, and controlling the car at high speeds is far from trivial. In this master thesis you will use Gran Turismo SPORT to teach an agent to race based on the full state of the vehicle. The bridge to the simulator to get vehicle states and send control commands is already provided. The project will be hosted by Sony in Schlieren, and requires prior experience with software development as well as machine learning.

Goal: The goal of this master thesis project is to use reinforcement learning (RL) to design a software agent capable of controlling a simulated race car, competitive with human players.

Contact Details: Elia Kaufmann (ekaufmann@ifi.uzh.ch)

Thesis Type: Master Thesis

See project on SiROP

Hand-Eye Calibration Toolbox - Available

Description: Hand-Eye calibration is a paramount pre-processing stage of many robotic and augmented reality applications, where the knowledge of the relative transformation between different sensors (e.g. a camera and a head-mounted display) is required to have an accurate geometric representation of the scene.

Goal: The goal of this project is to develop a user-friendly hand-eye calibration toolbox integrated with our robotic system. The toolbox will contain existing and novel hand-eye calibration methods, and it will allow to visualize the results of the different methods in an integrated manner to improve the understanding of the quality of the processed dataset, specially paying attention to error estimates, uncertainties and detection of inconsistent data.

Contact Details: Guillermo Gallego (guillermo.gallego at ifi.uzh.ch)

Thesis Type: Semester Project / Master Thesis

See project on SiROP

Integrated Multi-Camera Calibration Toolbox - Available

Description: The toolbox is expected to handle different camera brands, projection models and calibration patterns. In the multi-sensor scenario, the toolbox is also expected to compute the temporal offsets between the sensors. Special attention will be given to estimation of error measures, parameter uncertainties, detection of inconsistent data and interactive guidance of data acquisition.

Goal: The goal of this project is to develop a user-friendly, single and multi-camera calibration toolbox adapted to our robotic system. The toolbox will integrate existing calibration software in our group and in other libraries and will provide user-friendly reports of the different stages to assess the quality of the processed dataset, thus speeding up and improving the understanding of the whole sensor calibration stage.

Contact Details: Guillermo Gallego (guillermo.gallego at ifi.uzh.ch)

Thesis Type: Semester Project / Master Thesis

See project on SiROP

Low-Latency Quadrotor Control for High-Speed, Agile Flight - Available

Description: Perception latency often represents a limitation for the achievable agility of an autonomous robot. Faster sensors and low-latency processing would allow to obtain more agile robots. Event cameras are bio-inspired vision sensors that output pixel-level brightness changes at the time they occur, with a theoretical latency of micro-seconds.

Goal: The goal of this project is to explore the use of low-latency, event cameras for closed-loop quadrotor control, and develop controllers able to take advantage of such sensors for high-speed, agile maneuvers.

Contact Details: Davide Falanga (falanga@ifi.uzh.ch), ATTACH CV AND TRANSCRIPT!

Thesis Type: Internship / Master Thesis

See project on SiROP

Self-Supervised Learning for Robotic Navigation - Available

Description: While there are massive labeled data sets available for computer vision researchers, they cannot directly be applied to robotics. However, robots can interact with their environment and learn about the consequences of their actions. Your task is to formalize this abstract notion such that the robot can learn representations for navigation in a self-supervised fashion. Depending on your progress you will use the learned representation to facilitate reinforcement learning of navigation tasks.

Contact Details: Please send your CV and transcript to: Mathias Gehrig, mgehrig (at) ifi (dot) uzh (dot) ch

Thesis Type: Master Thesis

See project on SiROP

Event-based Feature Tracking using Deep Learning - Available

Description: Contemporary visual odometry pipelines cannot cope with very aggressive manoeuvres performed by agile aerial robots. One possible way forward is improving the front-end (feature detection and tracking) of these pipelines. To achieve this, your task is to explore a data-centered approach to enable feature detection and tracking in challenging conditions using only events from an event camera.

Contact Details: Please send your CV and transcript to: Mathias Gehrig, mgehrig (at) ifi (dot) uzh (dot) ch

Thesis Type: Master Thesis

See project on SiROP

Deep Learning Framework for Event Data - Available

Description: Write your own machine learning framework from ground-up, designed especially for event-based data. During this project you will acquire a deep understanding of each component including: - time series data storage and pre-processing - neural network design - automatic differentiation - optimization This project requires outstanding software engineering skills and is unsuitable for inexperienced programmers.

Contact Details: Please send your CV, transcript and link to any related work to: Mathias Gehrig, mgehrig (at) ifi (dot) uzh (dot) ch

Thesis Type: Master Thesis

See project on SiROP

MPC for high speed trajectory tracking - Available

Description: Many algorithms exist for model predictive control for trajectory tracking for quadrotors and equally many implementation advantages and disadvantages can be listed. This thesis should find the main influence factors on high speed/high precision trajectory tracking such as: modell accuracy, aerodynamic forces modelling, execution speed, underlying low-level controllers, sampling times and sampling strategies, noise sensitivity or even come up with a novel implementation.

Goal: The end-goal of the thesis should be a comparison of the influence factors and based on that a recommendation or even implementation of an improved solution.

Contact Details: Philipp Föhn (foehn at ifi.uzh.ch)

Thesis Type: Master Thesis

See project on SiROP

Smart Feature Selection In Visual Odometry - Available

Description: For most robotic platforms, computational resources are usually limited. Therefore, ideally, algorithms running onboard should be adaptive to the available computational power. For visual odometry, the number of features largely decides the resource the algorithm needs. By using a selected subset of features, we can reduce the required computational resource without losing accuracy significantly.

Goal: The project aims to study the problem of smart feature selection for visual odometry. The student is expected to study how motion estimation is affected by feature selection (e.g., number of features, different feature locations). The ultimate goal will be to implement a smart feature selection mechanism in our visual odometry framework.

Contact Details: Zichao Zhang (zzhang at ifi.uzh.ch)

Thesis Type: Semester Project / Master Thesis

See project on SiROP

Online time offset estimation for visual-inertial system - Available

Description: Visual-inertial odometry (VIO) has progressed significantly recently and finds a lot of real-world applications. One of the crucial requirement for good performance is to have a synchronized camera and inertial measurement unit. However, many low-cost systems do not have good synchronization, which limits the use of VIO. As an alternative, the time offset can be estimated by software. Existing methods to estimate the time offset either operate offline or only applies to specific algorithms. A lightweight algorithm that can estimate the camera-IMU offset will greatly extend the application scenarios of VIO.

Goal: The goal of the project is to develop an efficient and flexible algorithm to estimate the time offset between a camera and an IMU.

Contact Details: Zichao Zhang (zzhang at ifi.uzh.ch)

Thesis Type: Semester Project / Master Thesis

See project on SiROP

Analysis of the impact of the state estimate degradation on closed-loop flight - Available

Description: Onboard vision is one of the most common sensing modality for autonomous quadrotors. A state estimate from onboard vision can be intermittent, noisy, and delayed.

Goal: The goal of this project is to experimentally evaluate the impact of degraded vision-based state estimation on the closed-loop performance of a quadrotor for different tasks.

Contact Details: Davide Falanga (falanga@ifi.uzh.ch), ATTACH CV AND TRANSCRIPT!

Thesis Type: Semester Project / Master Thesis

See project on SiROP

Learning morphology for a morphing quadrotor - Available

Description: Morphing quadrotors are an increasingly hot topic in the field of micro aerial vehicles. One of the open questions is to find the optimal morphology to execute a given task.

Goal: The goal of this project is to allow a morphing quadrotor to learn from raw sensory data (e.g. images, inertial measurements, etc) which morphology should be adopted to improve the performance of a given task.

Contact Details: Davide Falanga (falanga@ifi.uzh.ch), ATTACH CV AND TRANSCRIPT!

Thesis Type: Semester Project / Master Thesis

See project on SiROP