# Ultra-low Power Deep Learning-based Monocular Relative Localization Onboard Nano-quadrotors

arXiv: https://arxiv.org/abs/2303.01940 · HTML: https://mlsanity.com/paper/2303.01940/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 3 Mar 2023.

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

- [PACT: Parameterized Clipping Activation for Quantized Neural Networks](https://mlsanity.com/paper/1805.06085/) (2018)
- [DORY: Automatic End-to-End Deployment of Real-World DNNs on Low-Cost IoT MCUs](https://mlsanity.com/paper/2008.07127/) (2020)
- [The Robotarium: A remotely accessible swarm robotics research testbed](https://mlsanity.com/paper/1609.04730/) (2016)
- [Fully Onboard AI-powered Human-Drone Pose Estimation on Ultra-low Power Autonomous Flying Nano-UAVs](https://mlsanity.com/paper/2103.10873/) (2021)
- [An Open Source and Open Hardware Deep Learning-powered Visual Navigation Engine for Autonomous Nano-UAVs](https://mlsanity.com/paper/1905.04166/) (2019)
- [Drone Detection Using Depth Maps](https://mlsanity.com/paper/1808.00259/) (2018)
- [Self-supervised Monocular Multi-robot Relative Localization with Efficient Deep Neural Networks](https://mlsanity.com/paper/2105.12797/) (2021)
- [Technical Report: NEMO DNN Quantization for Deployment Model](https://mlsanity.com/paper/2004.05930/) (2020)
- [Learning-based Bias Correction for Ultra-wideband Localization of Resource-constrained Mobile Robots](https://mlsanity.com/paper/2003.09371/) (2020)

## Built on by (most cited first, 1 of 1)

- [High-throughput Visual Nano-drone to Nano-drone Relative Localization using Onboard Fully Convolutional Networks](https://mlsanity.com/paper/2402.13756/) (2024)

## Abstract (arXiv)

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 localization, through deep neural networks (DNNs), of two peer nano-drones, i.e., sub-40g of weight and sub-100mW processing power. To cope with the ultra-constrained nano-drone platform, we propose a vertically-integrated framework, from the dataset collection to the final in-field deployment, including dataset augmentation, quantization, and system optimizations. Experimental results show that our DNN can precisely localize a 10cm-size target nano-drone by employing only low-resolution monochrome images, up to ~2m distance. On a disjoint testing dataset our model yields a mean R2 score of 0.42 and a root mean square error of 18cm, which results in a mean in-field prediction error of 15cm and in a closed-loop control error of 17cm, over a ~60s-flight test. Ultimately, the proposed system improves the State-of-the-Art by showing long-endurance tracking performance (up to 2min continuous tracking), generalization capabilities being deployed in a never-seen-before environment, and requiring a minimal power consumption of 95mW for an onboard real-time inference-rate of 48Hz.