Enhancing visual autonomous navigation in row-based crops with effective synthetic data generation
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| Pubblicato in: | Precision Agriculture vol. 25, no. 6 (Dec 2024), p. 2881 |
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| Autore principale: | |
| Altri autori: | , , , |
| Pubblicazione: |
Springer Nature B.V.
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| Accesso online: | Citation/Abstract Full Text - PDF |
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| 001 | 3129053302 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1385-2256 | ||
| 022 | |a 1573-1618 | ||
| 024 | 7 | |a 10.1007/s11119-024-10157-6 |2 doi | |
| 035 | |a 3129053302 | ||
| 045 | 2 | |b d20241201 |b d20241231 | |
| 084 | |a 108509 |2 nlm | ||
| 100 | 1 | |a Martini, Mauro |u Politecnico di Torino, Department of Electronics and Telecommunications, Turin, Italy (GRID:grid.4800.c) (ISNI:0000 0004 1937 0343) | |
| 245 | 1 | |a Enhancing visual autonomous navigation in row-based crops with effective synthetic data generation | |
| 260 | |b Springer Nature B.V. |c Dec 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a IntroductionService robotics is recently enhancing precision agriculture enabling many automated processes based on efficient autonomous navigation solutions. However, data generation and in-field validation campaigns hinder the progress of large-scale autonomous platforms. Simulated environments and deep visual perception are spreading as successful tools to speed up the development of robust navigation with low-cost RGB-D cameras.Materials and methodsIn this context, the contribution of this work resides in a complete framework to fully exploit synthetic data for a robust visual control of mobile robots. A wide realistic multi-crops dataset is accurately generated to train deep semantic segmentation networks and enabling robust performance in challenging real-world conditions. An automatic parametric approach enables an easy customization of virtual field geometry and features for a fast reliable evaluation of navigation algorithms.Results and conclusionThe high quality of the generated synthetic dataset is demonstrated by an extensive experimentation with real crops images and benchmarking the resulting robot navigation both in virtual and real fields with relevant metrics. | |
| 653 | |a Robotics | ||
| 653 | |a Datasets | ||
| 653 | |a Visual perception | ||
| 653 | |a Crops | ||
| 653 | |a Robust control | ||
| 653 | |a Image segmentation | ||
| 653 | |a Visual fields | ||
| 653 | |a Image processing | ||
| 653 | |a Autonomous navigation | ||
| 653 | |a Semantic segmentation | ||
| 653 | |a Virtual networks | ||
| 653 | |a Image quality | ||
| 653 | |a Visual perception driven algorithms | ||
| 653 | |a Visual control | ||
| 653 | |a Robot control | ||
| 653 | |a Synthetic data | ||
| 653 | |a Deep learning | ||
| 653 | |a Food | ||
| 653 | |a Robots | ||
| 653 | |a Localization | ||
| 653 | |a Agriculture | ||
| 653 | |a Simulation | ||
| 653 | |a 3-D graphics | ||
| 653 | |a Farming | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Algorithms | ||
| 653 | |a Semantics | ||
| 653 | |a Precision agriculture | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Ambrosio, Marco |u Politecnico di Torino, Department of Electronics and Telecommunications, Turin, Italy (GRID:grid.4800.c) (ISNI:0000 0004 1937 0343) | |
| 700 | 1 | |a Navone, Alessandro |u Politecnico di Torino, Department of Electronics and Telecommunications, Turin, Italy (GRID:grid.4800.c) (ISNI:0000 0004 1937 0343) | |
| 700 | 1 | |a Tuberga, Brenno |u Politecnico di Torino, Department of Electronics and Telecommunications, Turin, Italy (GRID:grid.4800.c) (ISNI:0000 0004 1937 0343) | |
| 700 | 1 | |a Chiaberge, Marcello |u Politecnico di Torino, Department of Electronics and Telecommunications, Turin, Italy (GRID:grid.4800.c) (ISNI:0000 0004 1937 0343) | |
| 773 | 0 | |t Precision Agriculture |g vol. 25, no. 6 (Dec 2024), p. 2881 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3129053302/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3129053302/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |