Obtaining 3D sensor data of complete plants or plant parts (e.g., the crop or fruit) is difficult due to their complex structure and a high degree of occlusion. However, especially for the estimation of the position and size of fruits, it is necessary to avoid occlusions as much as possible and acquire sensor information of the relevant parts. Global viewpoint planners exist that suggest a series of viewpoints to cover the regions of interest up to a certain degree, but they usually prioritize global coverage and do not emphasize the avoidance of local occlusions. On the other hand, there are approaches that aim at avoiding local occlusions, but they cannot be used in larger environments since they only reach a local maximum of coverage. In this paper, we therefore propose to combine a local, gradient-based method with global viewpoint planning to enable local occlusion avoidance while still being able to cover large areas. Our simulated experiments with a robotic arm equipped with a camera array as well as an RGB-D camera show that this combination leads to a significantly increased coverage of the regions of interest compared to just applying global coverage planning.

翻译：然而,由于植物或植物部件(如作物或水果)的结构复杂,封闭程度高,很难获得3D传感器数据,然而,特别是为了估计水果的位置和大小,必须尽量避免隔离,并获取相关部分的传感器信息。全球观点规划者提出了一系列观点,以覆盖感兴趣的区域,但通常优先考虑全球覆盖,并不强调避免地方隔离。另一方面,有些办法旨在避免地方隔离,但不能在更大的环境中使用,因为它们只达到地方覆盖的最大范围。因此,在本文件中,我们提议将一种基于梯度的本地方法与全球观点规划结合起来,以便在仍然能够覆盖大片地区的情况下避免地方隔离。我们用一个配有相机阵列的机器人臂和一台RGB-D照相机进行的模拟实验显示,这种组合导致与仅仅应用全球覆盖规划相比,对感兴趣的区域覆盖面的覆盖大大扩大。