用合成数据训练轻量模型，实现高精度低功耗的花朵姿态估计。
FloPE: Flower Pose Estimation for Precision Pollination
- 用3D高斯点云生成逼真合成数据，支持知识蒸馏。
- 误差仅0.6厘米、19.14度，计算开销极低。
- 适合资源受限的机器人授粉系统，实测成功率78.75%。
本文提出一种实时花朵姿态估计框架FloPE，适用于计算资源受限的机器人授粉系统。由于自然传粉者数量下降，机器人授粉被提议以保障全球粮食安全。然而，授粉中的花朵姿态估计面临自然变异、花簇干扰及高精度要求（因花朵脆弱）等挑战。该方法利用3D高斯点云生成具有精确姿态标注的逼真合成数据集，实现从大容量教师模型到轻量学生模型的知识蒸馏，提升推理效率。在单臂与多臂机器人平台上均进行了评估，平均姿态误差达0.6厘米和19.14度，计算成本低。实验验证了其有效性，授粉成功率最高达78.75%，优于现有技术。
原文摘要 · Abstract (English)
This study presents Flower Pose Estimation (FloPE), a real-time flower pose estimation framework for computationally constrained robotic pollination systems. Robotic pollination has been proposed to supplement natural pollination to ensure global food security due to the decreased population of natural pollinators. However, flower pose estimation for pollination is challenging due to natural variability, flower clusters, and high accuracy demands due to the flowers' fragility when pollinating. This method leverages 3D Gaussian Splatting to generate photorealistic synthetic datasets with precise pose annotations, enabling effective knowledge distillation from a high-capacity teacher model to a lightweight student model for efficient inference. The approach was evaluated on both single and multi-arm robotic platforms, achieving a mean pose estimation error of 0.6 cm and 19.14 degrees within a low computational cost. Our experiments validate the effectiveness of FloPE, achieving up to 78.75% pollination success rate and outperforming prior robotic pollination techniques.
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