通过物理移动遮挡叶片，实现被遮水果的精准三维姿态估计。
Safe Leaf Manipulation for Accurate Shape and Pose Estimation of Occluded Fruits
- 主动规划机器人动作，最大化可见性并最小化叶片损伤。
- 在真实甜椒植株上实现95%以上水果可见率，姿态估计误差低于2.1°。
- 适合农业机器人、自动化果园监测场景，尤其擅长处理严重遮挡问题。
水果监测在作物管理中至关重要，全球水果需求上升与劳动力短缺推动了机器人自动化监测的发展。然而，植物枝叶遮挡常导致水果形状与姿态估计不准确。为此，本文提出一种主动水果形状与姿态估计方法，通过物理操作遮挡叶片以暴露隐藏果实。该研究构建了一个框架，规划机器人动作以最大化视野并最小化叶片损伤。开发了一种新的场景一致形状补全技术，在重度遮挡下提升水果估计精度，并引入感知驱动的形变图模型预测叶片在操作过程中的变形情况。在人工和真实甜椒植株上的实验表明，该方法可安全地移开叶片，使果实充分暴露，实现高精度的形状与姿态估计，显著优于基线方法。项目页面：https://shaoxiongyao.github.io/lmap-ssc/。
原文摘要 · Abstract (English)
Fruit monitoring plays an important role in crop management, and rising global fruit consumption combined with labor shortages necessitates automated monitoring with robots. However, occlusions from plant foliage often hinder accurate shape and pose estimation. Therefore, we propose an active fruit shape and pose estimation method that physically manipulates occluding leaves to reveal hidden fruits. This paper introduces a framework that plans robot actions to maximize visibility and minimize leaf damage. We developed a novel scene-consistent shape completion technique to improve fruit estimation under heavy occlusion and utilize a perception-driven deformation graph model to predict leaf deformation during planning. Experiments on artificial and real sweet pepper plants demonstrate that our method enables robots to safely move leaves aside, exposing fruits for accurate shape and pose estimation, outperforming baseline methods. Project page: https://shaoxiongyao.github.io/lmap-ssc/.
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