Enhancing visual autonomous navigation in row-based crops with effective synthetic data generation
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| 發表在: | Precision Agriculture vol. 25, no. 6 (Dec 2024), p. 2881 |
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| 主要作者: | |
| 其他作者: | , , , |
| 出版: |
Springer Nature B.V.
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| 主題: | |
| 在線閱讀: | Citation/Abstract Full Text - PDF |
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| Resumen: | 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. |
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| ISSN: | 1385-2256 1573-1618 |
| DOI: | 10.1007/s11119-024-10157-6 |
| Fuente: | ABI/INFORM Global |