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/.

翻译：果实监测在作物管理中具有重要作用，全球水果消费量的增长与劳动力短缺问题使得机器人自动化监测成为必要。然而，植物叶片的遮挡常常阻碍果实形状与姿态的精确估计。为此，我们提出一种主动式果实形状与姿态估计方法，通过物理操控遮挡叶片以显露被隐藏的果实。本文介绍了一种规划机器人动作的框架，旨在最大化果实可见性并最小化叶片损伤。我们开发了一种新颖的场景一致性形状补全技术，以改进严重遮挡下的果实估计，并利用感知驱动的变形图模型在规划过程中预测叶片形变。在人工与真实甜椒植株上进行的实验表明，我们的方法能使机器人安全地将叶片移开，从而暴露果实以实现精确的形状与姿态估计，其性能优于基线方法。项目页面：https://shaoxiongyao.github.io/lmap-ssc/。