通过几何感知学习实现机器人荔枝采摘的精确切割点估计
Accurate Cutting-point Estimation for Robotic Lychee Harvesting through Geometry-aware Learning
AI总结：
本文提出 Fcaf3d-lychee 网络，通过引入 SE 注意力模块增强三维点云特征提取，实现荔枝采摘点精确定位，F1 分数达 88.57%，实际定位误差小于 1.5 厘米。
摘要：
在非结构化果园环境中准确识别荔枝采摘点并获取其坐标位置，对于荔枝采摘机器人的成功至关重要。然而，传统的基于二维（2D）图像的目标检测方法往往因枝条、叶片和果实复杂的几何结构而难以奏效，导致荔枝采摘点判断错误。在本研究中，我们提出了一种专门用于荔枝采摘点精确定位的 Fcaf3d-lychee 网络模型。使用微软的 Azure Kinect DK 飞行时间（TOF）相机通过多视角拼接获取自然环境中荔枝采摘点的点云数据。我们在全卷积无锚三维目标检测（Fcaf3d）模型中加入了一个挤压-激励（SE）模块，该模块利用人类视觉注意力机制来改进荔枝采摘点的特征提取。训练后的网络模型在荔枝采摘位置测试集上进行了评估，取得了令人印象深刻的 88.57% 的 F1 分数，显著优于现有模型。随后在真实荔枝园环境中对采摘点进行三维（3D）位置检测，即使在不同程度的遮挡下也取得了高精度。荔枝采摘点的定位误差在所有方向上均在 1.5 厘米以内，证明了该模型的鲁棒性和泛化能力。
英文摘要：
Accurately identifying lychee-picking points in unstructured orchard environments and obtaining their coordinate locations is critical to the success of lychee-picking robots. However, traditional two-dimensional (2D) image-based object detection methods often struggle due to the complex geometric structures of branches, leaves and fruits, leading to incorrect determination of lychee picking points. In this study, we propose a Fcaf3d-lychee network model specifically designed for the accurate localisation of lychee picking points. Point cloud data of lychee picking points in natural environments are acquired using Microsoft's Azure Kinect DK time-of-flight (TOF) camera through multi-view stitching. We augment the Fully Convolutional Anchor-Free 3D Object Detection (Fcaf3d) model with a squeeze-and-excitation(SE) module, which exploits human visual attention mechanisms for improved feature extraction of lychee picking points. The trained network model is evaluated on a test set of lychee-picking locations and achieves an impressive F1 score of 88.57%, significantly outperforming existing models. Subsequent three-dimensional (3D) position detection of picking points in real lychee orchard environments yields high accuracy, even under varying degrees of occlusion. Localisation errors of lychee picking points are within 1.5 cm in all directions, demonstrating the robustness and generality of the model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
