Ultra-low Power Deep Learning-based Monocular Relative Localization Onboard Nano-quadrotors
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2023
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866916149324677120 |
|---|---|
| author | Bonato, Stefano Lambertenghi, Stefano Carlo Cereda, Elia Giusti, Alessandro Palossi, Daniele |
| author_facet | Bonato, Stefano Lambertenghi, Stefano Carlo Cereda, Elia Giusti, Alessandro Palossi, Daniele |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_01940 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Ultra-low Power Deep Learning-based Monocular Relative Localization Onboard Nano-quadrotors Bonato, Stefano Lambertenghi, Stefano Carlo Cereda, Elia Giusti, Alessandro Palossi, Daniele Robotics Artificial Intelligence 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. |
| title | Ultra-low Power Deep Learning-based Monocular Relative Localization Onboard Nano-quadrotors |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2303.01940 |