Event-based cameras offer significant benefits in difficult robotic scenarios characterized by high-dynamic range and rapid motion. These are precisely the challenges faced by spacecraft during landings on celestial bodies like Mars or the Moon, where sudden light changes, fast dynamics relative to the surface, and the need for quick reaction times can overwhelm vision-based navigation systems relying on standard cameras. In this work, we aim to design novel spacecraft navigation methods for the descent and landing phases, exploiting the power efficiency and sparsity of event cameras. Particular effort will be dedicated to developing a lightweight frontend, utilizing asynchronous convolutional and graph neural networks to effectively harness the sparsity of event data, ensuring efficient and reliable processing during these critical phases. The project is in collaboration with European Space Agency at the European Space Research and Technology Centre (ESTEC) in Noordwijk (NL).
Event-based cameras offer significant benefits in difficult robotic scenarios characterized by high-dynamic range and rapid motion. These are precisely the challenges faced by spacecraft during landings on celestial bodies like Mars or the Moon, where sudden light changes, fast dynamics relative to the surface, and the need for quick reaction times can overwhelm vision-based navigation systems relying on standard cameras. In this work, we aim to design novel spacecraft navigation methods for the descent and landing phases, exploiting the power efficiency and sparsity of event cameras. Particular effort will be dedicated to developing a lightweight frontend, utilizing asynchronous convolutional and graph neural networks to effectively harness the sparsity of event data, ensuring efficient and reliable processing during these critical phases. The project is in collaboration with European Space Agency at the European Space Research and Technology Centre (ESTEC) in Noordwijk (NL).
Investigate the use of asynchronous neural networks (either regular or spiking) for building an efficient frontend system capable of processing event-based data in real-time. Experiments will be conducted both pre-recorded dataset as well as on data collected during the project. We look for students with strong programming (Pyhton/Matlab) and computer vision backgrounds. Additionally, knowledge in machine learning frameworks (pytorch, tensorflow) is required.
Investigate the use of asynchronous neural networks (either regular or spiking) for building an efficient frontend system capable of processing event-based data in real-time. Experiments will be conducted both pre-recorded dataset as well as on data collected during the project. We look for students with strong programming (Pyhton/Matlab) and computer vision backgrounds. Additionally, knowledge in machine learning frameworks (pytorch, tensorflow) is required.
Interested candidates should send their CV, transcripts (bachelor and master) to: Roberto Pellerito (rpellerito@ifi.uzh.ch), Marco Cannici (cannici@ifi.uzh.ch) and Davide Scaramuzza (sdavide@ifi.uzh.ch).
Interested candidates should send their CV, transcripts (bachelor and master) to: Roberto Pellerito (rpellerito@ifi.uzh.ch), Marco Cannici (cannici@ifi.uzh.ch) and Davide Scaramuzza (sdavide@ifi.uzh.ch).
