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

arXiv:2404.17718v1 Announce Type: cross
Abstract: 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 …

abstract accuracy arxiv autonomous cs.ai cs.cv cs.lg cs.ro error gps lidar navigation noise robots semantic sim type vision