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arXiv Open Access 2024

Lessons from Deploying CropFollow++: Under-Canopy Agricultural Navigation with Keypoints

Arun N. Sivakumar Mateus V. Gasparino Michael McGuire Vitor A. H. Higuti M. Ugur Akcal +1 lainnya
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Abstrak

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.

Topik & Kata Kunci

cs.RO cs.AI cs.CV cs.LG

Penulis (6)

A

Arun N. Sivakumar

M

Mateus V. Gasparino

M

Michael McGuire

V

Vitor A. H. Higuti

M

M. Ugur Akcal

G

Girish Chowdhary

Format Sitasi

APA MLA BibTeX
Sivakumar, A.N., Gasparino, M.V., McGuire, M., Higuti, V.A.H., Akcal, M.U., Chowdhary, G. (2024). Lessons from Deploying CropFollow++: Under-Canopy Agricultural Navigation with Keypoints. https://arxiv.org/abs/2404.17718

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2024
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arXiv
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