Learned Visual Navigation for Under-Canopy Agricultural Robots

TL;DR

This paper presents CropFollow, a low-cost, visually guided autonomous navigation system for under-canopy farm robots, overcoming GPS and sensing challenges to achieve superior performance over LiDAR-based methods.

Contribution

The paper introduces a modular machine learning-based perception system combined with model predictive control for robust under-canopy navigation, outperforming existing LiDAR-based approaches.

Findings

Average of 485 meters per intervention, surpassing LiDAR system at 286 meters

Successfully navigated over 25 km of field testing

Robust perception from monocular RGB images across variable conditions

Abstract

We describe 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.