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Advancing Augmented Reality Helmets for motorcyclists and racecars: Independence through Self-Localization
Advancing Augmented Reality Helmets for motorcyclists and racecars: Independence through Self-Localization
Keywords: Localization, Autonomous Driving
Augmented reality (AR) helmets represent a significant advancement in automotive technology for motorcyclists and racecars, offering drivers essential information while maintaining focus on the road. These systems can project 3D navigation arrows
anchored on the road or the optimal race-trajectory on the track. Check out this video
(https://www.youtube.com/watch?v=JYwFaNrGHrY) for how the system performs in
Zurich. Current systems rely on vehicle data for localization, limiting their flexibility and
performance. This project aims to develop a robust state estimation framework that enables
AR helmets to localize independently of vehicle data. By leveraging onboard visual and
inertial sensors, we seek to enhance the helmet's ability to accurately determine its position
relative to the vehicle.
This thesis is done in collaboration with Aegis Rider (https://aegisrider.com/)
Augmented reality (AR) helmets represent a significant advancement in automotive technology for motorcyclists and racecars, offering drivers essential information while maintaining focus on the road. These systems can project 3D navigation arrows anchored on the road or the optimal race-trajectory on the track. Check out this video (https://www.youtube.com/watch?v=JYwFaNrGHrY) for how the system performs in Zurich. Current systems rely on vehicle data for localization, limiting their flexibility and performance. This project aims to develop a robust state estimation framework that enables AR helmets to localize independently of vehicle data. By leveraging onboard visual and inertial sensors, we seek to enhance the helmet's ability to accurately determine its position relative to the vehicle. This thesis is done in collaboration with Aegis Rider (https://aegisrider.com/)
Our objective is to design a state-of-the-art state estimation framework that localizes
the AR helmet relative to the vehicle without any vehicle data and is suitable for deployment
on mobile computing platforms. We prioritize achieving minimal latency while ensuring
precise localization using only helmet-mounted visual and inertial sensors. We look for students with strong programming (C++ preferred), computer vision (ideally have taken Prof. Scaramuzza's class), and robotic background. Hardware experience (running code on robotic platforms) is preferred.
Our objective is to design a state-of-the-art state estimation framework that localizes the AR helmet relative to the vehicle without any vehicle data and is suitable for deployment on mobile computing platforms. We prioritize achieving minimal latency while ensuring precise localization using only helmet-mounted visual and inertial sensors. We look for students with strong programming (C++ preferred), computer vision (ideally have taken Prof. Scaramuzza's class), and robotic background. Hardware experience (running code on robotic platforms) is preferred.
Giovanni Cioffi (cioffi@ifi.uzh.ch), Simon Hecker (simon@aegisrider.com)
Giovanni Cioffi (cioffi@ifi.uzh.ch), Simon Hecker (simon@aegisrider.com)