Lessons from Deploying CropFollow++: Under-Canopy Agricultural Navigation with Keypoints
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916226805006336 |
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| author | Sivakumar, Arun N. Gasparino, Mateus V. McGuire, Michael Higuti, Vitor A. H. Akcal, M. Ugur Chowdhary, Girish |
| author_facet | Sivakumar, Arun N. Gasparino, Mateus V. McGuire, Michael Higuti, Vitor A. H. Akcal, M. Ugur Chowdhary, Girish |
| contents | We present a vision-based navigation system for under-canopy agricultural robots using semantic keypoints. Autonomous under-canopy navigation is challenging due to the tight spacing between the crop rows ($\sim 0.75$ m), degradation in RTK-GPS accuracy due to multipath error, and noise in LiDAR measurements from the excessive clutter. Our system, CropFollow++, introduces modular and interpretable perception architecture with a learned semantic keypoint representation. We deployed CropFollow++ in multiple under-canopy cover crop planting robots on a large scale (25 km in total) in various field conditions and we discuss the key lessons learned from this. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_17718 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Lessons from Deploying CropFollow++: Under-Canopy Agricultural Navigation with Keypoints Sivakumar, Arun N. Gasparino, Mateus V. McGuire, Michael Higuti, Vitor A. H. Akcal, M. Ugur Chowdhary, Girish Robotics Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning We present a vision-based navigation system for under-canopy agricultural robots using semantic keypoints. Autonomous under-canopy navigation is challenging due to the tight spacing between the crop rows ($\sim 0.75$ m), degradation in RTK-GPS accuracy due to multipath error, and noise in LiDAR measurements from the excessive clutter. Our system, CropFollow++, introduces modular and interpretable perception architecture with a learned semantic keypoint representation. We deployed CropFollow++ in multiple under-canopy cover crop planting robots on a large scale (25 km in total) in various field conditions and we discuss the key lessons learned from this. |
| title | Lessons from Deploying CropFollow++: Under-Canopy Agricultural Navigation with Keypoints |
| topic | Robotics Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2404.17718 |