On-device Self-supervised Learning of Visual Perception Tasks aboard Hardware-limited Nano-quadrotors
Elia Cereda,Manuele Rusci,Alessandro Giusti,Daniele Palossi,Elia Cereda,Manuele Rusci,Alessandro Giusti,Daniele Palossi
Sub-50g nano-drones are gaining momentum in both academia and industry. Their most compelling applications rely on onboard deep learning models for perception despite severe hardware constraints (i.e., sub-100mW processor). When deployed in unknown environments not represented in the training data, these models often underperform due to domain shift. To cope with this fundamental problem, we propo...
