Visual Servoing-based Navigation for Monitoring Row-Crop Fields

TL;DR

This paper presents a vision-based navigation framework for autonomous field robots in row-crop fields, enabling accurate, map-free, and GPS-independent operation suitable for precision agriculture tasks.

Contribution

The proposed approach uniquely exploits crop-row structures using only onboard camera images, eliminating the need for explicit localization or expensive GPS solutions.

Findings

Operates accurately in simulated and real environments

Runs at frame-rate on actual robots

Does not require GPS or detailed maps

Abstract

Autonomous navigation is a pre-requisite for field robots to carry out precision agriculture tasks. Typically, a robot has to navigate through a whole crop field several times during a season for monitoring the plants, for applying agrochemicals, or for performing targeted intervention actions. In this paper, we propose a framework tailored for navigation in row-crop fields by exploiting the regular crop-row structure present in the fields. Our approach uses only the images from on-board cameras without the need for performing explicit localization or maintaining a map of the field and thus can operate without expensive RTK-GPS solutions often used in agriculture automation systems. Our navigation approach allows the robot to follow the crop-rows accurately and handles the switch to the next row seamlessly within the same framework. We implemented our approach using C++ and ROS and thoroughly tested it in several simulated environments with different shapes and sizes of field. We also demonstrated the system running at frame-rate on an actual robot operating on a test row-crop field. The code and data have been published.