Visual Servoing in Orchard Settings
Nicolai H\"ani, Volkan Isler

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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
