Visual Servoing in Orchard Settings

TL;DR

This paper introduces a robust visual servoing framework using a novel feature tracking algorithm for precise sensor and end effector positioning in orchard environments, enabling applications like fruit inspection and pesticide application.

Contribution

A new visual servoing approach with a robust feature tracking algorithm tailored for challenging orchard conditions, demonstrating reliable convergence in real-world experiments.

Findings

Converges under environmental disturbances like wind and lighting changes

Achieves accurate positioning from diverse initial conditions

Enables applications such as automated fruit inspection and pesticide spraying

Abstract

We present a general framework for accurate positioning of sensors and end effectors in farm settings using a camera mounted on a robotic manipulator. Our main contribution is a visual servoing approach based on a new and robust feature tracking algorithm. Results from field experiments performed at an apple orchard demonstrate that our approach converges to a given termination criterion even under environmental influences such as strong winds, varying illumination conditions and partial occlusion of the target object. Further, we show experimentally that the system converges to the desired view for a wide range of initial conditions. This approach opens possibilities for new applications such as automated fruit inspection, fruit picking or precise pesticide application.