Active perception for fruit mapping and harvesting is a difficult task since occlusions occur frequently and the location as well as size of fruits change over time. State-of-the-art viewpoint planning approaches utilize computationally expensive ray casting operations to find good viewpoints aiming at maximizing information gain and covering the fruits in the scene. In this paper, we present a novel viewpoint planning approach that explicitly uses information about the predicted fruit shapes to compute targeted viewpoints that observe as yet unobserved parts of the fruits. Furthermore, we formulate the concept of viewpoint dissimilarity to reduce the sampling space for more efficient selection of useful, dissimilar viewpoints. Our simulation experiments with a UR5e arm equipped with an RGB-D sensor provide a quantitative demonstration of the efficacy of our iterative next best view planning method based on shape completion. In comparative experiments with a state-of-the-art viewpoint planner, we demonstrate improvement not only in the estimation of the fruit sizes, but also in their reconstruction, while significantly reducing the planning time. Finally, we show the viability of our approach for mapping sweet peppers plants with a real robotic system in a commercial glasshouse.

翻译：果实测绘与收获的主动感知是一项艰巨任务，因为遮挡频繁发生，且果实的位置与大小随时间变化。现有最先进的视角规划方法利用计算成本高昂的光线投射操作寻找最优视角，旨在最大化信息增益并覆盖场景中的果实。本文提出一种新颖的视角规划方法，通过显式利用预测的果实形状信息，计算能够观测到果实尚未被观测部分的针对性视角。此外，我们提出视角相异度概念，以缩减采样空间，从而更高效地选择有用且相异的视角。搭载RGB-D传感器的UR5e机械臂仿真实验定量展示了我们基于形状补全的迭代式下一最佳视角规划方法的有效性。在与最先进视角规划器的对比实验中，我们证明该方法不仅改善了果实尺寸的估计精度，还提升了果实重建效果，同时显著降低了规划时间。最后，我们在商业温室中通过真实机器人系统展示了该方法对甜椒植株测绘的可行性。