FloPE: Flower Pose Estimation for Precision Pollination
Journal:
arXiv
Published Date:
Mar 8, 2025
Abstract
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.
