Student Projects

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, ...).

  • 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.

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  • Event cameras such as the Dynamic and Active Pixel Vision Sensor (DAVIS) are recent sensors with large potential for high-speed and high dynamic range robotic applications.

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  • MPC for high speed trajectory tracking

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  • In this project we want to explore the possibility of active sensing using an event camera, where the task is to do asynchronous 3D reconstruction.

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  • Develop a planning framework for very fast or even time-optimal quadrotor trajectories.

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  • Develop an embedded low level flight controller for quadrotors on existing hardware.

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  • The goal of this project is explore ways to adapt existing deep learning algorithms to handle sparse asynchronous data from events.

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  • The goal of this project is to explore new algorithms for processing events within a deep-learning context.

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  • Explore an unknown space in 3D, relying only on visual-inertial odometry (with drift) and basic place recognition (but no loop closure/map optimization).

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  • Work on decentralized SLAM that requires a minimum of data exchange between robots. Focus will be on Map Optimization / Bundle Adjustment

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  • This project focuses on the object tracking, (i.e., target following) on nano-UAVs (few centimeters in size).

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  • Exploit sparsity of data flow in spiking neural network simulations

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  • An exploration of optimization methods for spiking neural networks

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  • In this project, we aim to build a self-supervised depth estimation and segmentation algorithm by embedding classic computer vision principles (e.g. brightness constancy) into a neural network.

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  • During this project, we will develop machine learning based techniques to let a (real) drone learn to fly nimbly through gaps and gates, while minimizing the risk of critical failures and collisions.

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  • Explore machine learning based approaches for deblurring of images with events

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  • Create an autonomous car dataset to assess the utility of event cameras in driving scenarios

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  • Use machine learning to condense visual observations of places into minimal representations that allow for 6dof relative pose estimation.

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  • The goal of this project is to study the impact of the morphology assumed by a quadrotor able to change its shape in-flight on the performance of its propulsion system.

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  • To prevent a collision with an obstacle or an incoming object, a robot needs to detect them as fast as possible and execute a safe maneuver to avoid them. The higher the relative speed between the robot and the object, the more critical the role of perception latency becomes. The goal of this project is to extend our previous work (http://rpg.ifi.uzh.ch/docs/RAL19_Falanga.pdf) on the analysis of the role of perception latency in high speed sense and avoid, in order to consider different robot dynamics and multiple scenarios, such as cases including dynamic obstacles.

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  • Learn depth from RGB frames and sparse depth information.

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