# High-throughput Visual Nano-drone to Nano-drone Relative Localization using Onboard Fully Convolutional Networks

arXiv: https://arxiv.org/abs/2402.13756 · HTML: https://mlsanity.com/paper/2402.13756/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 21 Feb 2024.

## Builds on (most cited first, 9 of 9)

- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [DORY: Automatic End-to-End Deployment of Real-World DNNs on Low-Cost IoT MCUs](https://mlsanity.com/paper/2008.07127/) (2020)
- [Fully Onboard AI-powered Human-Drone Pose Estimation on Ultra-low Power Autonomous Flying Nano-UAVs](https://mlsanity.com/paper/2103.10873/) (2021)
- [RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks](https://mlsanity.com/paper/2206.06637/) (2022)
- [Self-supervised Monocular Multi-robot Relative Localization with Efficient Deep Neural Networks](https://mlsanity.com/paper/2105.12797/) (2021)
- [Virtual Omnidirectional Perception for Downwash Prediction within a Team of Nano Multirotors Flying in Close Proximity](https://mlsanity.com/paper/2303.03898/) (2023)
- [Land & Localize: An Infrastructure-free and Scalable Nano-Drones Swarm with UWB-based Localization](https://mlsanity.com/paper/2307.10255/) (2023)
- [Ultra-low Power Deep Learning-based Monocular Relative Localization Onboard Nano-quadrotors](https://mlsanity.com/paper/2303.01940/) (2023)
- [Drone Formation for Efficient Swarm Energy Consumption](https://mlsanity.com/paper/2303.09694/) (2023)

## Built on by (most cited first, 2 of 2)

- [A Map-free Deep Learning-based Framework for Gate-to-Gate Monocular Visual Navigation aboard Miniaturized Aerial Vehicles](https://mlsanity.com/paper/2503.05251/) (2025)
- [Adaptive AI: Energy Efficient Multi-exit TinyML on Intelligent Vision Systems at the Edge](https://mlsanity.com/paper/2609.11939/) (2026)

## Abstract (arXiv)

Relative drone-to-drone localization is a fundamental building block for any swarm operations. We address this task in the context of miniaturized nano-drones, i.e., 10cm in diameter, which show an ever-growing interest due to novel use cases enabled by their reduced form factor. The price for their versatility comes with limited onboard resources, i.e., sensors, processing units, and memory, which limits the complexity of the onboard algorithms. A traditional solution to overcome these limitations is represented by lightweight deep learning models directly deployed aboard nano-drones. This work tackles the challenging relative pose estimation between nano-drones using only a gray-scale low-resolution camera and an ultra-low-power System-on-Chip (SoC) hosted onboard. We present a vertically integrated system based on a novel vision-based fully convolutional neural network (FCNN), which runs at 39Hz within 101mW onboard a Crazyflie nano-drone extended with the GWT GAP8 SoC. We compare our FCNN against three State-of-the-Art (SoA) systems. Considering the best-performing SoA approach, our model results in an R-squared improvement from 32 to 47% on the horizontal image coordinate and from 18 to 55% on the vertical image coordinate, on a real-world dataset of 30k images. Finally, our in-field tests show a reduction of the average tracking error of 37% compared to a previous SoA work and an endurance performance up to the entire battery lifetime of 4 minutes.