In this study, we introduce a deep-learning approach for determining both the 6DoF pose and 3D size of strawberries, aiming to significantly augment robotic harvesting efficiency. Our model was trained on a synthetic strawberry dataset, which is automatically generated within the Ignition Gazebo simulator, with a specific focus on the inherent symmetry exhibited by strawberries. By leveraging domain randomization techniques, the model demonstrated exceptional performance, achieving an 84.77\% average precision (AP) of 3D Intersection over Union (IoU) scores on the simulated dataset. Empirical evaluations, conducted by testing our model on real-world datasets, underscored the model's viability for real-world strawberry harvesting scenarios, even though its training was based on synthetic data. The model also exhibited robust occlusion handling abilities, maintaining accurate detection capabilities even when strawberries were obscured by other strawberries or foliage. Additionally, the model showcased remarkably swift inference speeds, reaching up to 60 frames per second (FPS).

翻译：本研究提出一种深度学习方法，用于同时确定草莓的六自由度位姿和三维尺寸，旨在显著提升机器人采摘效率。我们的模型在合成草莓数据集上进行训练，该数据集通过Ignition Gazebo仿真器自动生成，特别关注草莓固有的对称特性。通过采用域随机化技术，该模型在仿真数据集上表现出卓越性能，三维交并比平均精度达到84.77%。通过在真实数据集上进行实证评估，验证了该模型在实际草莓采摘场景中的可行性，尽管其训练完全基于合成数据。该模型还展现出强大的遮挡处理能力，即使草莓被其他果实或枝叶遮挡时仍能保持精确检测性能。此外，模型推理速度极快，最高可达每秒60帧。