Learned Visual Navigation for Under-Canopy Agricultural Robots

This paper describes a system for visually guided autonomous navigation of under-canopy farm robots. Low-cost under-canopy robots can drive between crop rows under the plant canopy and accomplish tasks that are infeasible for over-the-canopy drones or larger agricultural equipment. However; autonomously navigating them under the canopy presents a number of challenges: unreliable GPS and LiDAR; high cost of sensing; challenging farm terrain; clutter due to leaves and weeds; and large variability in appearance over the season and across crop types. We address these challenges by building a modular system that leverages machine learning for robust and generalizable perception from monocular RGB images from low-cost cameras; and model predictive control for accurate control in challenging terrain. Our system; CropFollow; is able to autonomously drive 485 meters per intervention on average; outperforming a state-of-the-art LiDAR based system (286 meters per intervention) in extensive field testing spanning over 25 km.