Precision agriculture is a fast-growing field that aims at introducing affordable and effective automation into agricultural processes. Nowadays, algorithmic solutions for navigation in vineyards require expensive sensors and high computational workloads that preclude large-scale applicability of autonomous robotic platforms in real business case scenarios. From this perspective, our novel proposed control leverages the latest advancement in machine perception and edge AI techniques to achieve highly affordable and reliable navigation inside vineyard rows with low computational and power consumption. Indeed, using a custom-trained segmentation network and a low-range RGB-D camera, we are able to take advantage of the semantic information of the environment to produce smooth trajectories and stable control in different vineyards scenarios. Moreover, the segmentation maps generated by the control algorithm itself could be directly exploited as filters for a vegetative assessment of the crop status. Extensive experimentations and evaluations against real-world data and simulated environments demonstrated the effectiveness and intrinsic robustness of our methodology.
Deep Semantic Segmentation at the Edge for Autonomous Navigation in Vineyard Rows / Aghi, Diego; Cerrato, Simone; Mazzia, Vittorio; Chiaberge, Marcello. - ELETTRONICO. - (2021), pp. 3421-3428. (Intervento presentato al convegno 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) tenutosi a Prague, Czech Republic nel 27 Sept.-1 Oct. 2021) [10.1109/IROS51168.2021.9635969].
Deep Semantic Segmentation at the Edge for Autonomous Navigation in Vineyard Rows
Cerrato, Simone;Mazzia, Vittorio;Chiaberge, Marcello
2021
Abstract
Precision agriculture is a fast-growing field that aims at introducing affordable and effective automation into agricultural processes. Nowadays, algorithmic solutions for navigation in vineyards require expensive sensors and high computational workloads that preclude large-scale applicability of autonomous robotic platforms in real business case scenarios. From this perspective, our novel proposed control leverages the latest advancement in machine perception and edge AI techniques to achieve highly affordable and reliable navigation inside vineyard rows with low computational and power consumption. Indeed, using a custom-trained segmentation network and a low-range RGB-D camera, we are able to take advantage of the semantic information of the environment to produce smooth trajectories and stable control in different vineyards scenarios. Moreover, the segmentation maps generated by the control algorithm itself could be directly exploited as filters for a vegetative assessment of the crop status. Extensive experimentations and evaluations against real-world data and simulated environments demonstrated the effectiveness and intrinsic robustness of our methodology.File | Dimensione | Formato | |
---|---|---|---|
IROS_2021.pdf
accesso aperto
Descrizione: Post-print
Tipologia:
2. Post-print / Author's Accepted Manuscript
Licenza:
PUBBLICO - Tutti i diritti riservati
Dimensione
5.34 MB
Formato
Adobe PDF
|
5.34 MB | Adobe PDF | Visualizza/Apri |
Mazzia-Deep semantic.pdf
non disponibili
Tipologia:
2a Post-print versione editoriale / Version of Record
Licenza:
Non Pubblico - Accesso privato/ristretto
Dimensione
5.87 MB
Formato
Adobe PDF
|
5.87 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11583/2946672