Occlusion Handling by Pushing for Enhanced Fruit Detection
Ege Gursoy, Dana Kuli\'c, Andrea Cherubini

TL;DR
This paper presents a robotic method that detects occluded fruits in orchards, estimates their hidden parts, and pushes branches to improve visibility for better fruit harvesting.
Contribution
It introduces a novel combination of deep learning, image processing, and 3D Hough transform techniques for occlusion removal in agricultural robotics.
Findings
Improved fruit visibility after occlusion clearance
Successful branch pushing demonstrated on real robot
Effective detection under different lighting and fruit types
Abstract
In agricultural robotics, effective observation and localization of fruits present challenges due to occlusions caused by other parts of the tree, such as branches and leaves. These occlusions can result in false fruit localization or impede the robot from picking the fruit. The objective of this work is to push away branches that block the fruit's view to increase their visibility. Our setup consists of an RGB-D camera and a robot arm. First, we detect the occluded fruit in the RGB image and estimate its occluded part via a deep learning generative model in the depth space. The direction to push to clear the occlusions is determined using classic image processing techniques. We then introduce a 3D extension of the 2D Hough transform to detect straight line segments in the point cloud. This extension helps detect tree branches and identify the one mainly responsible for the occlusion.…
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