Pose Estimation of Sweet Pepper through Symmetry Axis Detection.

Abstract

The space pose of fruits is necessary for accurate detachment in automatic harvesting. This study presents a novel pose estimation method for sweet pepper detachment. In this method, the normal to the local plane at each point in the sweet-pepper point cloud was first calculated. The point cloud was separated by a number of candidate planes, and the scores of each plane were then separately calculated using the scoring strategy. The plane with the lowest score was selected as the symmetry plane of the point cloud. The symmetry axis could be finally calculated from the selected symmetry plane, and the pose of sweet pepper in the space was obtained using the symmetry axis. The performance of the proposed method was evaluated by simulated and sweet-pepper cloud dataset tests. In the simulated test, the average angle error between the calculated symmetry and real axes was approximately 6.5°. In the sweet-pepper cloud dataset test, the average error was approximately 7.4° when the peduncle was removed. When the peduncle of sweet pepper was complete, the average error was approximately 6.9°. These results suggested that the proposed method was suitable for pose estimation of sweet peppers and could be adjusted for use with other fruits and vegetables.

Plant phenotyping relevance

甜ピーマン果実の三次元姿勢を点群から推定する手法を開発し、シミュレーションおよび実データで誤差検証している。収穫対象の単なる検出ではなく、果実器官の姿勢という再利用可能な形態状態を定量化しているため、中心的なフェノタイピング手法に該当する。

Code and data availability

The paper tests its symmetry-axis pose estimation on a sweet-pepper point cloud dataset reported by Sa et al. (cited prior work, not a public deposit by these authors) and on simulated ellipsoid point clouds. No public dataset URL, code, model, or supplement availability is stated anywhere in the supplied blocks; the P

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