15 citations · 42 across the 15 of their papers we have counts for
15 papers
Self-supervised Domain Adaptation for Visual 3D Pose Estimation of Nano-drone Racing Gates by Enforcing Geometric Consistency
Nicholas Carlotti, Michele Antonazzi, Elia Cereda +4
We consider the task of visually estimating the relative pose of a drone racing gate in front of a nano-quadrotor, using a convolutional neural network pre-trained on simulated dat…
Tiny-DroNeRF: Tiny Neural Radiance Fields aboard Federated Learning-enabled Nano-drones
Ilenia Carboni, Elia Cereda, Lorenzo Lamberti +3
Sub-30g nano-sized aerial robots can leverage their agility and form factor to autonomously explore cluttered and narrow environments, like in industrial inspection and search and…
NanoCockpit: Performance-optimized Application Framework for AI-based Autonomous Nanorobotics
Elia Cereda, Alessandro Giusti, Daniele Palossi
Autonomous nano-drones, powered by vision-based tiny machine learning (TinyML) models, are a novel technology gaining momentum thanks to their broad applicability and pushing scien…
Multi-modal On-Device Learning for Monocular Depth Estimation on Ultra-low-power MCUs
Davide Nadalini, Manuele Rusci, Elia Cereda +3
Monocular depth estimation (MDE) plays a crucial role in enabling spatially-aware applications in Ultra-low-power (ULP) Internet-of-Things (IoT) platforms. However, the limited num…
Nonlinear System Identification Nano-drone Benchmark
Riccardo Busetto, Elia Cereda, Marco Forgione +3
We introduce a benchmark for system identification based on 75k real-world samples from the Crazyflie 2.1 Brushless nano-quadrotor, a sub-50g aerial vehicle widely adopted in robot…
Training on the Fly: On-device Self-supervised Learning aboard Nano-drones within 20 mW
Elia Cereda, Alessandro Giusti, Daniele Palossi
Miniaturized cyber-physical systems (CPSes) powered by tiny machine learning (TinyML), such as nano-drones, are becoming an increasingly attractive technology. Their small form fac…
Fusing Multi-sensor Input with State Information on TinyML Brains for Autonomous Nano-drones
Luca Crupi, Elia Cereda, Daniele Palossi
Autonomous nano-drones (~10 cm in diameter), thanks to their ultra-low power TinyML-based brains, are capable of coping with real-world environments. However, due to their simplifi…
On-device Self-supervised Learning of Visual Perception Tasks aboard Hardware-limited Nano-quadrotors
Elia Cereda, Manuele Rusci, Alessandro Giusti +1
Sub-\SI{50}{\gram} nano-drones are gaining momentum in both academia and industry. Their most compelling applications rely on onboard deep learning models for perception despite se…
A Sim-to-Real Deep Learning-based Framework for Autonomous Nano-drone Racing
Lorenzo Lamberti, Elia Cereda, Gabriele Abbate +7
Autonomous drone racing competitions are a proxy to improve unmanned aerial vehicles' perception, planning, and control skills. The recent emergence of autonomous nano-sized drone…
Sim-to-Real Vision-depth Fusion CNNs for Robust Pose Estimation Aboard Autonomous Nano-quadcopter
Luca Crupi, Elia Cereda, Alessandro Giusti +1
Nano-quadcopters are versatile platforms attracting the interest of both academia and industry. Their tiny form factor, i.e., 10 cm diameter, makes them particularly useful in…
Secure Deep Learning-based Distributed Intelligence on Pocket-sized Drones
Elia Cereda, Alessandro Giusti, Daniele Palossi
Palm-sized nano-drones are an appealing class of edge nodes, but their limited computational resources prevent running large deep-learning models onboard. Adopting an edge-fog comp…
Ultra-low Power Deep Learning-based Monocular Relative Localization Onboard Nano-quadrotors
Stefano Bonato, Stefano Carlo Lambertenghi, Elia Cereda +2
Precise relative localization is a crucial functional block for swarm robotics. This work presents a novel autonomous end-to-end system that addresses the monocular relative locali…
Vision-State Fusion: Improving Deep Neural Networks for Autonomous Robotics
Elia Cereda, Stefano Bonato, Mirko Nava +2
Vision-based deep learning perception fulfills a paramount role in robotics, facilitating solutions to many challenging scenarios, such as acrobatic maneuvers of autonomous unmanne…
Training Lightweight CNNs for Human-Nanodrone Proximity Interaction from Small Datasets using Background Randomization
Marco Ferri, Dario Mantegazza, Elia Cereda +5
We consider the task of visually estimating the pose of a human from images acquired by a nearby nano-drone; in this context, we propose a data augmentation approach based on synth…
Learning Relative Interactions through Imitation
Giorgia Adorni, Elia Cereda
In this project we trained a neural network to perform specific interactions between a robot and objects in the environment, through imitation learning. In particular, we tackle th…