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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2304.08988 |
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| _version_ | 1866910342495338496 |
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| author | Navone, Alessandro Martini, Mauro Ostuni, Andrea Angarano, Simone Chiaberge, Marcello |
| author_facet | Navone, Alessandro Martini, Mauro Ostuni, Andrea Angarano, Simone Chiaberge, Marcello |
| contents | Segmentation-based autonomous navigation has recently been proposed as a promising methodology to guide robotic platforms through crop rows without requiring precise GPS localization. However, existing methods are limited to scenarios where the centre of the row can be identified thanks to the sharp distinction between the plants and the sky. However, GPS signal obstruction mainly occurs in the case of tall, dense vegetation, such as high tree rows and orchards. In this work, we extend the segmentation-based robotic guidance to those scenarios where canopies and branches occlude the sky and hinder the usage of GPS and previous methods, increasing the overall robustness and adaptability of the control algorithm. Extensive experimentation on several realistic simulated tree fields and vineyards demonstrates the competitive advantages of the proposed solution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_08988 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Autonomous Navigation in Rows of Trees and High Crops with Deep Semantic Segmentation Navone, Alessandro Martini, Mauro Ostuni, Andrea Angarano, Simone Chiaberge, Marcello Robotics Segmentation-based autonomous navigation has recently been proposed as a promising methodology to guide robotic platforms through crop rows without requiring precise GPS localization. However, existing methods are limited to scenarios where the centre of the row can be identified thanks to the sharp distinction between the plants and the sky. However, GPS signal obstruction mainly occurs in the case of tall, dense vegetation, such as high tree rows and orchards. In this work, we extend the segmentation-based robotic guidance to those scenarios where canopies and branches occlude the sky and hinder the usage of GPS and previous methods, increasing the overall robustness and adaptability of the control algorithm. Extensive experimentation on several realistic simulated tree fields and vineyards demonstrates the competitive advantages of the proposed solution. |
| title | Autonomous Navigation in Rows of Trees and High Crops with Deep Semantic Segmentation |
| topic | Robotics |
| url | https://arxiv.org/abs/2304.08988 |