In this paper, we present a next-best-view planning approach to autonomously size apple fruitlets. State-of-the-art viewpoint planners in agriculture are designed to size large and more sparsely populated fruit. They rely on lower resolution maps and sizing methods that do not generalize to smaller fruit sizes. To overcome these limitations, our method combines viewpoint sampling around semantically labeled regions of interest, along with an attention-guided information gain mechanism to more strategically select viewpoints that target the small fruits' volume. Additionally, we integrate a dual-map representation of the environment that is able to both speed up expensive ray casting operations and maintain the high occupancy resolution required to informatively plan around the fruit. When sizing, a robust estimation and graph clustering approach is introduced to associate fruit detections across images. Through simulated experiments, we demonstrate that our viewpoint planner improves sizing accuracy compared to state of the art and ablations. We also provide quantitative results on data collected by a real robotic system in the field.

翻译：本文提出了一种最优视角规划方法，用于实现苹果幼果的自主尺寸测量。当前农业领域最先进的视角规划器主要针对尺寸较大且分布较稀疏的果实进行设计。这些方法依赖于较低分辨率的地图及尺寸测量技术，无法推广至更小尺寸的果实。为克服这些限制，本文方法结合了基于语义标注感兴趣区域的视角采样，以及一种注意力引导的信息增益机制，从而更策略性地选择以小型果实体积为观测目标的视角。此外，我们集成了一种环境双地图表示方法，该方法既能加速计算代价高昂的光线投射操作，又能保持规划所需的高分辨率占据地图，以支持围绕果实的有效路径规划。在尺寸测量环节，我们引入了一种鲁棒估计与图聚类方法，用于关联跨图像的果实检测结果。通过仿真实验，我们证明相较于现有最优方法及消融实验，本文提出的视角规划器显著提升了尺寸测量的精度。同时，我们也提供了基于真实田间机器人系统采集数据的定量评估结果。