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, ...).

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

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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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  • The project aims to develop techniques based on machine learning to have maximal knowledge transfer between simulated and real world on a navigation task.

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

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  • Event cameras such as the Dynamic Vision Sensor (DVS) are recent sensors with large potential for high-speed and high dynamic range robotic applications, such as fast obstacle avoidance. In particular, event cameras can be used to track features or objects in the blind time between two frames which makes it possible to react quickly to changes in the scene. In this project we want to deploy a feature tracking algorithm on a resource constrained platform such as a drone. Applicants should have a strong background in C++ programming and low-level vision. Experience with embedded programming is a plus.

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  • Develop an event-based vision front-end for high-speed, HDR feature tracking

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  • Eye-tracking study in human drone racing pilots

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  • Develop a Research-Grade Flight Simulator for Human-Piloted Drone Racing

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  • Analysis of 3D flight trajectories from human-piloted drone racing.

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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 techniques for modelling an event camera.

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  • The goal of this project is to explore a learning-based 3D reconstruction method with an event camera.

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  • At the Robotics & Perception Group research lab we are developing a custom carrier board for NVIDIA’s Jetson TX2 (running on Ubuntu). We have an existing, but not yet fully functional prototype of this connector board.

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  • Reinforcement Learning for Drone Racing

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  • Recent work has shown that it is possible to learn temporally and geometrically aligned keypoints given only videos.

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  • In the course of this master thesis, you will help us to improve our current surgery planning methods by developing an approach to predict the reposition of the fragment and the pose of the cutting planes defining the bone wedge.

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  • Collect a dataset of high-speed maneuvers in the optitrack and identify the quadrotor platform. Use this model to create a fast and accurate quadrotor simulation. Verify the simulator by comparing it to real-world data.

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  • The future of lightweight VIO systems. Embedded Semi-Direct Visual Inertial Odometry (SVO) algorithm in CortexAX processors.

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  • High-quality Power-line dataset for Autonomous Drone Inspection (PADI).

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  • Perception Aware Model Predictive Control (MPC) for Power Line Tracking

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  • An exploration of optimization methods for spiking neural networks

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  • Combine the complementary information from standard and event cameras to enhance images and video.

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