Papers
18
Total Citations
402
H-Index
10
About
Daniele Palossi is a pioneering researcher at the intersection of embedded systems, artificial intelligence, and autonomous robotics, with a particular focus on enabling intelligent navigation in ultra-constrained nano-drone platforms. His most celebrated contribution, "A 64-mW DNN-Based Visual Navigation Engine for Autonomous Nano-Drones" (2019, 185 citations), demonstrated that deep neural networks could be deployed on miniaturized aerial robots within severe power budgets — a breakthrough that reshaped expectations for edge AI in robotics. Alongside open-source hardware companions and automated end-to-end DNN optimization pipelines, Palossi has systematically pushed the boundaries of what sub-10cm, sub-10W platforms can autonomously achieve. His research portfolio spans swarm localization using Ultra-Wideband technology, monocular relative localization between peer nano-drones, vision-state fusion for robust deep learning perception, and secure heterogeneous RISC-V SoCs for UAV navigation. By consistently addressing the tension between computational demands and extreme energy constraints, Palossi has helped transform nano-drones from remote-controlled novelties into genuinely autonomous agents. With over 350 cumulative citations and a growing body of open-hardware contributions, his work serves as essential reading for researchers pursuing intelligence at the absolute edge of embedded computing.
Research Focus
Key Achievements
Top Papers
- 1A 64-mW DNN-Based Visual Navigation Engine for Autonomous Nano-Drones185 citations · 2019
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- 6A Heterogeneous RISC-V Based SoC for Secure Nano-UAV Navigation13 citations · 2024
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- 9Vision-state Fusion: Improving Deep Neural Networks for Autonomous Robotics11 citations · 2024
- 10Fünfiiber-Drone: A Modular Open-Platform 18-grams Autonomous Nano-Drone11 citations · 2021