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：针对果实建图与采摘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和大小随时间变化。现有最先进的视点规划方法利用计算量庞大的光线投射操作寻找能够最大化信息增益并覆盖场景中果实的最优视点。本文提出一种新颖的视点规划方法，该方法显式利用预测的果实形状信息，计算针对性的视点以观察果实尚未被观测的部分。此外，我们提出了视点差异性的概念，用于减少采样空间，从而更高效地选择有用且差异化的视点。通过配备RGB-D传感器的UR5e机械臂仿真实验，我们定量验证了基于形状补全的迭代式下一最优视点规划方法的有效性。在与现有最先进视点规划器的对比实验中，我们不仅证明了该方法在果实尺寸估计上的改进，还展示了其在重建质量上的提升，同时显著缩短了规划时间。最后，通过在商业温室中使用真实机器人系统对甜椒植株进行建图，我们验证了所提方法的可行性。