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 the project is to implement a Machine Learning algorithm that can recover the camera motion from a single blurry image.

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  • 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.

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  • In robotics, the inertial measurement unit (IMU) is a commonly used sensor and provides the motion information of the correct scale. This project aims to implement a deep learning algorithm that can estimate the scene depth from images and the IMU measurements.

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  • The project aims to develop a robust and adaptive multi-camera visual odometry pipeline based on the existing framework in our lab.

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  • Specific work will include the theoretical analysis of the visual odometry pipeline and validation by simulation/experiments. It is also possible to perform the study for different camera configurations.

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  • In human environments, windows and doors represent thresholds between spaces. This project deals with designing a system to robustly detect windows/doors/thresholds that a quadrotor could fly through, using cameras and range sensors.

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  • The project aims to develop algorithms for robust feature tracking/matching in dynamic environments.

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  • The project aim to develop algorithms to initialize monocular visual odometry from small motion.

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  • The project aims to develop loop detection and closing algorithms suitable for embedded systems.

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  • The project aims to develop fast visual-inertial odometry algorithms that can execute in real-time on embedded systems.

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  • The goal of this master thesis is to develop and implement an algorithm for visual localization for event-based cameras. This algorithm will make use of novel features derived from events that will be developed as part of the thesis. In addition, the algorithm will be integrated with a state-of-the-art SLAM system for event-based cameras to build a prototypical localization system.

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  • The majority of the quadrotors available on the market rely on a fixed mechanical structure, which cannot be changed while flying. The goal of this project is to enable quadrotors to change their morphology while they are airborne, guaranteeing stability during the entire flight.

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  • This projects aims at implementing and evaluating state-of-the-art algorithms for time to contact using a Neuromorphic Event Camera. Such algorithms can be applied to obstacle avoidance with quadrotors.

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  • The goal of this project is to analyze the impact of the sampling frequency on the tracking performance of a quadrotor flying an high-speed trajectory.

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  • In this project, we aim at designing a collision free trajectory generation method using visual information provided by an onboard monocular or stereo camera.

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  • In this project, we aim at designing quadrotor trajectories to fly through the predefined waypoints in the time optimal manner.

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  • In this project, we aim at designing a robust quadrotor controller which can reject external disturbances and enhance overall quadrotor control accuracy.

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  • Camera calibration is a paramount pre-processing stage of many robotic vision applications such as 3D reconstruction, obstacle avoidance and ego-motion estimation. The goal of this project is to develop a user-friendly, single and multi-camera calibration toolbox adapted to our robotic system.

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  • Hand-Eye calibration is a paramount pre-processing stage of many robotic and augmented reality applications. The goal of this project is to develop a user-friendly hand-eye calibration toolbox integrated with our robotic system.

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  • Model Predictive Control allows to execute fast and agile maneuvers with quadrotors in a robust and safe way. It is extremely versatile and covers many control scenarios and goals, and could even integrate planning like obstacle avoidance. This project aims at implementing MPC for quadrotors.

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  • Aerodynamic effects on a quadrotor become more prominent with higher velocities. This project aims at setting up high-speed experiments for quadrotors using a tether and collect data to model and control the aerodynamic effects precisely.

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  • The optical flow of an image sequence holds some information about approaching obstacles. This could possibly be used to avoid obstacle during quadrotor flight maneuvers in a reactive fashion, especially in fast maneuvers.

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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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  • 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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  • The project aims to develop machine learning based techniques that will enable a drone to learn flying by looking at an other robot flying.

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  • In this project, we aim at using a vision based technique to land precisely in a designated place by a drone sender or recipient.

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  • The goal of this project is to explore the possibilities that continuous structure from motion (SfM) has to offer for event cameras.

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  • 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.

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  • 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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  • 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.

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  • The goal of this project is to use a stereo pair of event cameras to obtain a 3D reconstruction of a scene.

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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. The goal of this project is to extend and improve an existing event camera simulator developed in our lab.

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  • 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.

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