FloPE: Flower Pose Estimation for Precision Pollination

2025-03-08

arXiv.org

AI Summary

To address the challenge of high-precision pose estimation for robotic pollination under severe resource constraints—exacerbated by floral morphological variability, clustered occlusions, and mechanical fragility—this paper proposes a lightweight pose estimation framework synergizing differentiable 3D Gaussian Splatting (3DGS)-based synthetic data generation and knowledge distillation. We pioneer the use of differentiable 3DGS to synthesize photorealistic floral images with pixel-perfect, physically consistent pose annotations. A teacher–student distillation paradigm then transfers robust pose regression capability from a large-capacity teacher model to an efficient student network, markedly improving resilience to occlusion and non-rigid deformation. Evaluated on both single- and multi-arm robotic platforms, our method achieves a translational error of 0.6 cm and rotational error of 19.14°, yielding a pollination success rate of 78.75%—substantially outperforming state-of-the-art approaches.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Biometrics, Face, Gesture & Pose
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Estimating flower pose for robotic pollination systems
• Addressing variability and fragility in pollination accuracy
• Enabling real-time processing with low computational cost

Innovation

• 3D Gaussian Splatting for synthetic datasets
• Knowledge distillation for lightweight model
• Real-time flower pose estimation framework