Fruit localisation is a crucial step in developing a robotic fruit-harvesting system. This paper aims to improve the localisation accuracy of fruits in 3D space. In the machine vision system of a harvesting robot, in a single view the visible area of a target is often incomplete and therefore, cannot be directly used to accurately determine the target location. A 3D shape completion method is proposed that can be used on the partially visible images of strawberries obtained from a single view. This method proposed a given number of symmetric plane candidates based on the assumption that the targets are symmetrical, which is normally true for fruits such as such apples, citrus fruits and strawberries. Corresponding rating rules were proposed to select the optimal symmetry to be used for the shape completion. The algorithm was then tested on reconstructed point clouds and implemented on a strawberry harvester equipped with a Red Green Blue-Depth (RGB-D) camera. The evaluation on reconstructed strawberry data showed that the intersection over union (IoU) and centre deviation between the results obtained by this method and ground truth were 0.77 and 6.9 mm, respectively, whilst those of the unprocessed partial data were 0.56 and 14.1 mm. The evaluation results of the strawberry data captured with the RGB-D camera showed that the IoU and centre deviation between the results obtained by this method and ground truth were 0.61 and 5.7 mm, respectively, whilst those of the unprocessed partial data were 0.47 and 8.9 mm.

中文翻译：

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用于收获机器人水果定位的基于对称的 3D 形状完成

水果定位是开发机器人水果收获系统的关键一步。本文旨在提高水果在 3D 空间中的定位精度。在收割机器人的机器视觉系统中，在单一视图中，目标的可见区域往往是不完整的，因此不能直接用于准确确定目标位置。提出了一种 3D 形状补全方法，可用于从单一视图获得的草莓部分可见图像。该方法基于目标是对称的假设提出了给定数量的对称平面候选，这通常适用于苹果、柑橘类水果和草莓等水果。提出了相应的评级规则来选择用于形状完成的最佳对称性。然后在重建的点云上测试该算法，并在配备红绿蓝深度 (RGB-D) 相机的草莓收割机上实施。对重构草莓数据的评估表明，该方法获得的结果与ground truth的交集（IoU）和中心偏差分别为0.77和6.9 mm，而未处理的部分数据的交集为0.56和14.1 mm。RGB-D相机采集到的草莓数据的评估结果表明，该方法获得的结果与ground truth的IoU和中心偏差分别为0.61和5.7 mm，而未处理的部分数据的IoU和中心偏差分别为0.47和8.9毫米。对重构草莓数据的评估表明，该方法获得的结果与ground truth的交集（IoU）和中心偏差分别为0.77和6.9 mm，而未处理的部分数据的交集为0.56和14.1 mm。RGB-D相机采集的草莓数据的评估结果表明，该方法获得的结果与ground truth的IoU和中心偏差分别为0.61和5.7 mm，而未处理的部分数据的IoU和中心偏差分别为0.47和8.9毫米。对重构草莓数据的评估表明，该方法获得的结果与ground truth的交集（IoU）和中心偏差分别为0.77和6.9 mm，而未处理的部分数据的交集为0.56和14.1 mm。RGB-D相机采集的草莓数据的评估结果表明，该方法获得的结果与ground truth的IoU和中心偏差分别为0.61和5.7 mm，而未处理的部分数据的IoU和中心偏差分别为0.47和8.9毫米。