Robots are increasingly used in tomato greenhouses to automate
labour-intensive tasks such as selective harvesting and de-leafing. To perform
these tasks, robots must be able to accurately and efficiently perceive the
plant nodes that need to be cut, despite the high levels of occlusion from
other plant parts. We formulate this problem as a local next-best-view (NBV)
planning task where the robot has to plan an efficient set of camera viewpoints
to overcome occlusion and improve the quality of perception. Our formulation
focuses on quickly improving the perception accuracy of a single target node to
maximise its chances of being cut. Previous methods of NBV planning mostly
focused on global view planning and used random sampling of candidate
viewpoints for exploration, which could suffer from high computational costs,
ineffective view selection due to poor candidates, or non-smooth trajectories
due to inefficient sampling. We propose a gradient-based NBV planner using
differential ray sampling, which directly estimates the local gradient
direction for viewpoint planning to overcome occlusion and improve perception.
Through simulation experiments, we showed that our planner can handle
occlusions and improve the 3D reconstruction and position estimation of nodes
equally well as a sampling-based NBV planner, while taking ten times less
computation and generating 28% more efficient trajectories.

机器人在番茄大棚中越来越多地用于自动化劳动密集型任务，但面临着植物其他部分的遮挡引起的感知困难。我们提出了一种基于梯度的下一最佳观测点规划方法，通过差分射线采样直接估计视点规划的局部梯度方向，旨在克服遮挡问题并提高感知质量，在模拟实验中证明了该方法的有效性。

基于梯度的本地最佳视角规划提高对目标植物节点的感知

Gradient-based Local Next-best-view Planning for Improved Perception of  Targeted Plant Nodes

Motivated by the advances in 3D sensing technology and the spreading of
low-cost robotic platforms, 3D object reconstruction has become a common task
in many areas. Nevertheless, the selection of the optimal sensor pose that
maximizes the reconstructed surface is a problem that remains open. It is known
in the literature as the next-best-view planning problem. In this paper, we
propose a novel next-best-view planning scheme based on supervised deep
learning. The scheme contains an algorithm for automatic generation of datasets
and an original three-dimensional convolutional neural network (3D-CNN) used to
learn the next-best-view. Unlike previous work where the problem is addressed
as a search, the trained 3D-CNN directly predicts the sensor pose. We present a
comparison of the proposed network against a similar net, and we present
several experiments of the reconstruction of unknown objects validating the
effectiveness of the proposed scheme.

本文提出了一种基于监督深度学习的最佳视角规划方案，该方案包括自动生成数据集的算法和原始三维卷积神经网络 (3D-CNN)，用于学习下一个最佳视角，并与类似网络进行了比较，并进行了多个实验验证了该方案的有效性，以实现对不知名对象的重建。