Lessons from Deploying CropFollow++: Under-Canopy Agricultural Navigation with Keypoints
<|reference_start|>Lessons from Deploying CropFollow++: Under-Canopy Agricultural Navigation with Keypoints: 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.<|reference_end|>
@article{sivakumar2024lessons,
    title={Lessons from Deploying CropFollow++: Under-Canopy Agricultural
  Navigation with Keypoints},
    author={Arun N. Sivakumar, Mateus V. Gasparino, Michael McGuire, Vitor A. H.
  Higuti, M. Ugur Akcal, Girish Chowdhary},
    journal={arXiv preprint arXiv:2404.17718},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.17718},
    primaryClass={cs.RO cs.AI cs.CV cs.LG}
}