用合成数据训练模型，一次检测即可识别草莓的6自由度位姿和3D尺寸。
Single-Shot 6DoF Pose and 3D Size Estimation for Robotic Strawberry Harvesting
- 基于仿真数据与领域随机化训练，实现高精度位姿与尺寸估计。
- 在模拟数据上达到84.77%的3D IoU平均精度，实测表现稳定。
- 处理遮挡能力强，推理速度达60帧/秒，适合实际采摘机器人使用。
本研究提出一种深度学习方法，用于同时估计草莓的6自由度位姿与3D尺寸，以显著提升机器人采摘效率。模型在Ignition Gazebo模拟器中自动生成的合成草莓数据集上训练，特别关注草莓的固有对称性。通过领域随机化技术，模型在模拟数据集上实现了84.77%的3D交并比（IoU）平均精度（AP）。实测评估表明，尽管训练数据为合成数据，该模型在真实场景下仍具备可行性。模型对遮挡具有强鲁棒性，即使被其他草莓或叶片遮挡也能保持准确检测能力。此外，模型推理速度高达60帧每秒（FPS），满足实时应用需求。
原文摘要 · Abstract (English)
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).
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