Leaving the Lines Behind: Vision-Based Crop Row Exit for Agricultural Robot Navigation

TL;DR

This paper presents a vision-based method for agricultural robots to exit crop rows using RGB images for navigation and depth images to estimate distance, enabling effective transition from crop to headland areas.

Contribution

It introduces a novel vision-only approach for crop row exit that combines RGB feature matching with depth-based distance estimation, which is less explored in existing frameworks.

Findings

Successfully navigates crop row exit with 50 cm accuracy

Works across diverse soil and vegetation conditions

Enables complete transition from crop row to headland

Abstract

Usage of purely vision based solutions for row switching is not well explored in existing vision based crop row navigation frameworks. This method only uses RGB images for local feature matching based visual feedback to exit crop row. Depth images were used at crop row end to estimate the navigation distance within headland. The algorithm was tested on diverse headland areas with soil and vegetation. The proposed method could reach the end of the crop row and then navigate into the headland completely leaving behind the crop row with an error margin of 50 cm.