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, ...).



Multi-agent Drone Racing via Self-play and Reinforcement Learning - Available

Description: Drone racing requires human pilots to not only complete a given race track in minimum-time, but also to compete with other pilots through strategic blocking, or to overtake opponents during extreme maneuvers. Single-player RL allows autonomous agents to achieve near-time-optimal performance in time trial racing. While being highly competitive in this setting, such training strategy can not generalize to the multi-agent scenario. An important step towards artificial general intelligence (AGI) is versatility -- the capability of discovering novel skills via self-play and self-supervised autocurriculum. In this project, we tackle multi-agent drone racing via self-play and reinforcement learning.

Goal: Create a multi-agent drone racing system that can discover novel racing skills and compete against each other. Applicants should have strong experience in C++ and python programming. Reinforcement learning and robotics background are required.

Contact Details: Yunlong Song (song (at) ifi (dot) uzh (dot) ch), Elia Kaufmann (ekaufmann (at) ifi (dot) uzh (dot) ch)

Thesis Type: Master Thesis

See project on SiROP

Vision-based Dynamic Obstacle Avoidance - Available

Description: Dynamic obstacle avoidance is a grand challenge in vision-based drone navigation. The classical mapping-planning-control pipeline might have difficulties when facing dynamic objects. Learning-based systems, such as end-to-end neural network policies, are gaining popularity in robotics for dynamic objects, due to their powerful performance and versatility in handling high-dimensional state representations. Particularly, deep reinforcement learning allows for optimizing neural network policies via trial-and-error, forgoing the need for demonstrations.

Goal: The goal is to develop an autonomous vision-based navigation system that can avoid dynamic obstacles using deep reinforcement learning. Applicants should have strong experience in C++ and python programming. Reinforcement learning and robotics background are required.

Contact Details: Yunlong Song (song (at) ifi (dot) uzh (dot) ch), Antonio Loquercio (loquercio (at) ifi (dot) uzh (dot) ch)

Thesis Type: Semester Project / Master Thesis

See project on SiROP

Deep reinforcement learning for collaborative aerial transportation - Available

Description: Collaborative object transportation using micro aerial vehicles (MAVs) is a promising drone technique. It is challenging from a control perspective, since multiple MAVs are mechanically coupled, imposing hard kinematic constraints. Traditional model-based methods often require linearization of the nonlinear problem which restrains the performance such as transporting speed and the payload. The goal of his project aims at exploring the possibility of using the deep reinforcement learning approach to obtain a centralized control policy for collaborative aerial transportation, which is more efficient than the state-of-the-art methods. The policy will be trained in a simulation environment and then transferred to real-life experiments. Applications should have strong experience in C++, Python. Applicants with reinforcement learning and flight control background are favored.

Goal: The goal of this project is to use deep reinforcement learning on collaborative aerial transportation. The method needs to be validated in real flight tests.

Contact Details: Sihao Sun (sun at ifi.uzh.ch), Yunlong Song (song at ifi.uzh.ch)

Thesis Type: Semester Project / Master Thesis

See project on SiROP

3D reconstruction with event cameras - Available

Description: Event cameras are bio-inspired sensors that offer several advantages, such as low latency, high-speed and high dynamic range, to tackle challenging scenarios in computer vision. Research on structure from motion and multi-view stereo with images has produced many compelling results, in particular accurate camera tracking and sparse reconstruction. Active sensors with standard cameras like Kinect have been used for dense scene reconstructions. Accurate and efficient reconstructions using event-camera setups is still an unexplored topic. This project will focus on solving the problem of 3D reconstruction using active perception with event cameras​ .

Goal: The goal is to develop a system for accurate mapping of complex and arbitrary scenes using depth acquired by an event camera setup. We seek a highly motivated student with the following minimum qualifications: - Excellent coding skills in Python and C++ - At least one course in computer vision (multiple view geometry) - Strong work ethic - Excellent communication and teamwork skills Preferred qualifications: - Experience with machine learning Contact for more details.

Contact Details: Manasi Muglikar, muglikar (at) ifi (dot) uzh (dot) ch

Thesis Type: Semester Project / Master Thesis

See project on SiROP

Events and Lidar For Autonomous Driving - Available

Description: Billions of dollars are spent each year to bring autonomous vehicles closer to reality. One of the remaining challenges is the design of reliable algorithms that work in a diverse set of environments and scenarios. At the core of this problem is the choice of sensor setup. Ideally, there is a certain redundancy in the setup while each sensor should also excel at a certain task. Sampling-based sensors (e.g. LIDAR, standard cameras, etc.) are today's essential building blocks of autonomous vehicles. However, they typically oversample far-away structure (e.g. building 200 meters away) and undersample close structure (e.g. fast bike crossing in front of the car). Thus, they enforce a trade-off between sampling frequency and computational budget. Unlike sampling-based sensors, event cameras capture change in their field-of-view with precise timing and do not record redundant information. As a result, they are well suited for highly dynamic scenarios such as driving on roads. We are currently building a large-scale dataset with high-resolution event cameras and 128-beam Lidar that is targeting object detection and tracking. This project builds on top of already existing hardware that we have built and tested in the last year. In this project, it will be extended with state-of-the-art sensors.

Goal: In this project, we explore the utility of event cameras in an autonomous car scenario. In order to achieve this, a high-quality driving dataset with state-of-the-art Lidar and event cameras will be created. Depending on the progress, the prospective student will work on building novel 3D object detection pipelines on the dataset. We seek a highly motivated student with the following minimum qualifications: - Experience with programming microcontrollers or motivation to acquire it quickly - Excellent coding skills in Python and C++ - At least one course in computer vision (multiple view geometry) - Strong work ethic - Excellent communication and teamwork skills Preferred qualifications: - Background in robotics and experience with ROS - Experience with machine learning - Experience with event-based vision Contact us for more details.

Contact Details: Mathias Gehrig (mgehrig at ifi.uzh.ch); Daniel Gehrig (dgehrig at ifi.uzh.ch) Please add CV + transcripts (Bachelor and Master)

Thesis Type: Master Thesis

See project on SiROP

Sensor Fusion For Stereo Depth Estimation - Available

Description: Stereo vision is the cornerstone of depth estimation for robotics applications. Nowadays, it is used in commercial drones (e.g. Skydio), advanced driver-assistance systems, and robotic navigation in general. However, its performance degrades in challenging illumination conditions, and the error increases with larger depth. In this project, we investigate the possibility to enhance stereo depth estimation with additional sensory modalities such as event cameras and Lidar. While event cameras provide additional information through their high dynamic range and unmatched temporal resolution, Lidar provides highly accurate depth measurements even for far-away objects. This project is aimed at augmenting the traditional stereo vision problem with our combination of choice for unmatched speed, robustness, and accuracy for depth estimation.

Goal: The goal of this project is to boost depth estimation from stereo matching with additional sensor modalities such as event cameras or Lidar. The approach will specifically exploit the characteristics of the different sensor modalities to achieve robust performance and generalization. The proposed framework will be thoroughly tested in autonomous driving scenarios in simulation and real-world. Requirements: - Strong self-motivation and curiosity for tackling research challenges - Excellent programming skills - At least one course or project in both the area of computer vision and deep learning. - Outstanding academic record is preferred but may be compensated by a strong background related to this project.

Contact Details: Mathias Gehrig, mgehrig (at) ifi.uzh.ch Send transcripts (bachelor & master) and CV

Thesis Type: Semester Project / Master Thesis

See project on SiROP

Perception Aware Model Predictive Control for Autonomous Power Line Tracking - Available

Description: Classical power line inspection and maintenance are dangerous, costly and time consuming. Drones could mitigate the risk for humans and minimize the cost for the direct benefit of the power line infrastructure. Coupling perception and path planning with control has become increasingly popular in aerial vehicles. This project will continue to investigate vision-based navigation tightly couple approaches of perception and control to satisfy the drone dynamics and compute feasible trajectories with respect to the input saturation. This involves to further development the research on perception aware Model Predictive Control (MPC) for quadrotors, solving the challenging aspects of a power line inspection scenario. A perception aware MPC approach would ideally improve the aerial vehicle's behavior during an approaching maneuver.

Goal: Pursue research on a unified control and planning approach that integrates the action and perception objectives. This work involves a final demonstration to accomplish field testing results in challenging conditions (e.g.: HDR, high speed).

Contact Details: Javier Hidalgo-Carrió (jhidalgocarrio@ifi.uzh.ch)/Giovanni Cioffi (cioffi@ifi.uzh.ch)

Thesis Type: Semester Project / Bachelor Thesis / Master Thesis

See project on SiROP

Motion-Tracking Camera Rig - Available

Description: Filming drones at high speeds during agile maneuvers is very challenging. To get the most detailed footage, one needs to precisely follow the vehicle during every turn. Commercial gimbals are – despite their high cost of several thousand dollars – not suited for this task since they are too slow and can’t be controlled externally. The position of the drone is readily available from an external Vicon motion capture system.

Goal: The goal of this project is to is to develop a camera mount that can track our quadcopters at speeds up to 20 m/s. A student applying for this project should have good coding skills and C++ and/or Python. Knowledge in CAD design and experience in photography or videography is a bonus but not required.

Contact Details: Leonard Bauersfeld (bauersfeld (at) ifi.uzh.ch) Christian Pfeiffer (cpfeiffe (at) ifi.uzh.ch)

Thesis Type: Semester Project

See project on SiROP

Deep learning based motion estimation from events - Available

Description: Optical flow estimation is the mainstay of dynamic scene understanding in robotics and computer vision. It finds application in SLAM, dynamic obstacle detection, computational photography, and beyond. However, extracting the optical flow from frames is hard due to the discrete nature of frame-based acquisition. Instead, events from an event camera indirectly provide information about optical flow in continuous time. Hence, the intuition is that event cameras are the ideal sensors for optical flow estimation. In this project, you will dig deep into optical flow estimation from events. We will make use of recent innovations in neural network architectures and insights of event camera models to push the state-of-the-art in the field. Contact us for more details.

Goal: The goal of this project is to develop a deep learning based method for dense optical flow estimation from events. Strong background in computer vision and machine learning required.

Contact Details: Mathias Gehrig, mgehrig (at) ifi (dot) uzh (dot) ch

Thesis Type: Semester Project / Master Thesis

See project on SiROP