A Point Cloud Completion Approach for the Grasping of Partially Occluded Objects and Its Applications in Robotic Strawberry Harvesting
Ali Abouzeid, Malak Mansour, Chengsong Hu, Dezhen Song

TL;DR
This paper introduces a comprehensive point cloud completion and grasp planning framework for robotic strawberry harvesting, significantly improving grasp success rates and safety in occluded, unstructured environments.
Contribution
It presents a novel end-to-end approach combining point cloud denoising, segmentation, completion, and collision-aware planning tailored for robotic fruit picking.
Findings
Achieved a low Chamfer Distance of 1.10 mm in shape reconstruction.
Improved grasp success rate to 79.17%.
Reduced obstacle hit rate from 43.33% to 13.95%.
Abstract
In robotic fruit picking applications, managing object occlusion in unstructured settings poses a substantial challenge for designing grasping algorithms. Using strawberry harvesting as a case study, we present an end-to-end framework for effective object detection, segmentation, and grasp planning to tackle this issue caused by partially occluded objects. Our strategy begins with point cloud denoising and segmentation to accurately locate fruits. To compensate for incomplete scans due to occlusion, we apply a point cloud completion model to create a dense 3D reconstruction of the strawberries. The target selection focuses on ripe strawberries while categorizing others as obstacles, followed by converting the refined point cloud into an occupancy map for collision-aware motion planning. Our experimental results demonstrate high shape reconstruction accuracy, with the lowest Chamfer…
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Taxonomy
TopicsImage Processing and 3D Reconstruction · Smart Agriculture and AI
