Search Results for author: Daniele Palossi

Found 10 papers, 2 papers with code

Adaptive Deep Learning for Efficient Visual Pose Estimation aboard Ultra-low-power Nano-drones

no code implementations26 Jan 2024 Beatrice Alessandra Motetti, Luca Crupi, Mustafa Omer Mohammed Elamin Elshaigi, Matteo Risso, Daniele Jahier Pagliari, Daniele Palossi, Alessio Burrello

Sub-10cm diameter nano-drones are gaining momentum thanks to their applicability in scenarios prevented to bigger flying drones, such as in narrow environments and close to humans.

Computational Efficiency Pose Estimation

A Heterogeneous RISC-V based SoC for Secure Nano-UAV Navigation

no code implementations7 Jan 2024 Luca Valente, Alessandro Nadalini, Asif Veeran, Mattia Sinigaglia, Bruno Sa, Nils Wistoff, Yvan Tortorella, Simone Benatti, Rafail Psiakis, Ari Kulmala, Baker Mohammad, Sandro Pinto, Daniele Palossi, Luca Benini, Davide Rossi

To the best of the authors' knowledge, it is the first silicon prototype of a ULP SoC coupling the RV64 and RV32 cores in a heterogeneous host+accelerator architecture fully based on the RISC-V ISA.

Secure Deep Learning-based Distributed Intelligence on Pocket-sized Drones

no code implementations4 Jul 2023 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.

Pose Estimation

Deep Neural Network Architecture Search for Accurate Visual Pose Estimation aboard Nano-UAVs

no code implementations3 Mar 2023 Elia Cereda, Luca Crupi, Matteo Risso, Alessio Burrello, Luca Benini, Alessandro Giusti, Daniele Jahier Pagliari, Daniele Palossi

In this work, we leverage a novel neural architecture search (NAS) technique to automatically identify several Pareto-optimal convolutional neural networks (CNNs) for a visual pose estimation task.

Neural Architecture Search Pose Estimation

Training Lightweight CNNs for Human-Nanodrone Proximity Interaction from Small Datasets using Background Randomization

no code implementations27 Oct 2021 Marco Ferri, Dario Mantegazza, Elia Cereda, Nicky Zimmerman, Luca M. Gambardella, Daniele Palossi, Jérôme Guzzi, Alessandro Giusti

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 synthetic background substitution to learn a lightweight CNN model from a small real-world training set.

Data Augmentation

An Open Source and Open Hardware Deep Learning-powered Visual Navigation Engine for Autonomous Nano-UAVs

2 code implementations10 May 2019 Daniele Palossi, Francesco Conti, Luca Benini

Nano-size unmanned aerial vehicles (UAVs), with few centimeters of diameter and sub-10 Watts of total power budget, have so far been considered incapable of running sophisticated visual-based autonomous navigation software without external aid from base-stations, ad-hoc local positioning infrastructure, and powerful external computation servers.

Autonomous Navigation Visual Navigation

A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones

3 code implementations4 May 2018 Daniele Palossi, Antonio Loquercio, Francesco Conti, Eric Flamand, Davide Scaramuzza, Luca Benini

As part of our general methodology we discuss the software mapping techniques that enable the state-of-the-art deep convolutional neural network presented in [1] to be fully executed on-board within a strict 6 fps real-time constraint with no compromise in terms of flight results, while all processing is done with only 64 mW on average.

Autonomous Navigation Visual Navigation

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