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, ...).

  • Develop a decentralized Bundle Adjustment Method that requires a minimum of data exchange between robots.

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  • Try to handle failures in april/aruco tag detection using deep learning.

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  • Title says it all

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  • Make a full simulation of a decentralized multi-quadrotor SLAM system, in preparation for real world experiments.

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  • Evaluate, from a very practical perspective, how robots can communicate in a multi-robot field experiment.

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  • The project aims to develop camera control algorithms that account for the scene brightness and camera motion at the same time.

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  • The project aims to benchmark different camera control algorithms and create related tools.

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  • The project aims to develop an algorithm to estimate the time offset between a camera and an IMU.

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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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  • 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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  • 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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  • Teach and repeat, but try to be as fast as possible on repeat. Goal is to deploy this on a quad.

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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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  • 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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  • 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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  • 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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