From the 1 of 5 linked papers with an AI index.
31 citations · 31 across the 2 of their papers we have counts for
5 papers
Thinking Fast, Thinking Slow: Adaptive Multimodal Transformer-based Sensor Fusion for Depth Estimation on Ultra-low-power MCUs
Luca Crupi, Lorenzo Lamberti, Giovanni Badaracco +3
Artificial intelligence (AI)-based multimodal sensor fusion is a relevant topic gaining ever more traction across ultra-low-power (ULP) embedded and cyber-physical systems, as it i…
Improving Autonomous Nano-drones Performance via Automated End-to-End Optimization and Deployment of DNNs
Vlad Niculescu, Lorenzo Lamberti, Francesco Conti +2
The paper presents an automated workflow to train, optimize, and deploy a vision-based CNN (PULP‑Dronet) on an ultra‑low‑power multicore SoC for autonomous navigation of sub‑10 cm…
Low-Power License Plate Detection and Recognition on a RISC-V Multi-Core MCU-Based Vision System
Lorenzo Lamberti, Manuele Rusci, Marco Fariselli +2
In this paper, we present the first (to the best of our knowledge) demonstration of a low-power MCU-based edge device for Automatic License Plate Recognition (ALPR). The design lev…
TinyDEVO: Deep Event-based Visual Odometry on Ultra-low-power Multi-core Microcontrollers
Alessandro Marchei, Lorenzo Lamberti, Daniele Palossi +1
A key task in embedded vision is visual odometry (VO), which estimates camera motion from visual sensors, and it is a core component in many embedded power-constrained systems, fro…
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…