NBV-SC：基于形状补全的果实测绘与重建的下一最佳视角规划
NBV-SC: Next Best View Planning based on Shape Completion for Fruit Mapping and Reconstruction
AI总结：
提出基于形状补全的下一最佳视角规划方法，利用预测果实形状和视角差异性减少采样，提升果实尺寸估计与重建效率，并在仿真和真实温室中验证。
摘要：
主动感知在果实测绘和采摘中是一项困难的任务，因为遮挡频繁发生，且果实的位置和大小随时间变化。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来寻找好的视角，旨在最大化信息增益并覆盖场景中的果实。在本文中，我们提出了一种新颖的视角规划方法，该方法明确利用预测的果实形状信息来计算针对性的视角，以观察果实尚未被观察到的部分。此外，我们提出了视角差异性的概念，以减少采样空间，从而更高效地选择有用且不同的视角。我们使用配备RGB-D传感器的UR5e机械臂进行的仿真实验，定量证明了我们基于形状补全的迭代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，我们展示了在果实尺寸估计和重建方面的改进，同时显著减少了规划时间。最后，我们展示了该方法在商业玻璃温室中利用真实机器人系统对甜椒植株进行测绘的可行性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
