The growth poses of citrus on trees are diverse. To ensure minimal loss during citrus harvesting, accurately estimating the pose of citrus is particularly important. To solve this problem, this research developed a real-time citrus pose estimation system based on neural networks and point cloud processing algorithms. Specifically, this method uses neural networks to identify citrus. After constructing the citrus point cloud, it is input into the Random Sample Consensus with Levenberg-Marquardt (RANSAC-LM) point cloud processing algorithm to obtain the citrus coordinates. Combined with citrus growth information, the pose is output. By analyzing the distribution of citrus poses, citrus poses convenient for end- effector harvesting are defined. To enhance the camera's ability to obtain information about citrus, a camera observation model is constructed to dynamically adjust the camera position. Through experiments, the appropriate deep learning target detection framework YOLO V5 is selected for citrus object detection. The precision (P), recall rate (R), and mean average precision (mAP) are 92.3 %, 79.1 %, and 88.5 % respectively. This network can handle detection tasks in real orchard environments. The original Random Sample Consensus (RANSAC) is improved by using the Levenberg-Marquardt (LM) nonlinear optimization method. Experimental results show that RANSAC-LM reduces the citrus center coordinate precision error from (0.2, 0.2, 2.3) mm to (0.1, 0.2, 1.4) mm, reduces the accuracy Spherical Error Probable (SEP) from 2.77 to 1.61, and finally reduces the citrus pose error from 5.72° to 2.43°. The efficiency of the proposed citrus pose estimation algorithm is 0.24 s. Deployed on a citrus picking robot, it verifies the feasibility of the algorithm and provides a new solution for the pose estimation problem of citrus harvesting robots.

中文翻译：

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复杂果园环境下柑橘姿态估计机器人采摘


柑橘在树木上的生长姿势多种多样。为了确保柑橘收获过程中的损失最小，准确估计柑橘的形态尤为重要。为了解决这个问题，本研究开发了一种基于神经网络和点云处理算法的实时柑橘姿态估计系统。具体来说，这种方法使用神经网络来识别柑橘。构建柑橘点云后，将其输入到 Random Sample Consensus with Levenberg-Marquardt （RANSAC-LM） 点云处理算法中，以获得柑橘坐标。结合柑橘生长信息，输出姿势。通过分析柑橘姿态的分布，定义了便于末端执行器收获的柑橘姿态。为了增强相机获取柑橘信息的能力，构建了相机观测模型来动态调整相机位置。通过实验，选择合适的深度学习目标检测框架 YOLO V5 进行柑橘类对象检测。准确率 （P） 、召回率 （R） 和平均准确率均值 （mAP） 分别为 92.3 % 、 79.1 % 和 88.5 % 。该网络可以处理真实果园环境中的检测任务。通过使用 Levenberg-Marquardt （LM） 非线性优化方法改进了原始随机样本一致性 （RANSAC）。实验结果表明，RANSAC-LM将柑橘中心坐标精度误差从（0.2、0.2、2.3）mm降低到（0.1、0.2、1.4）mm，将球面误差概率精度（SEP）从2.77降低到1.61，最终将柑橘姿态误差从5.72°降低到2.43°。所提出的柑橘姿态估计算法的效率为 0.24 s。 部署在柑橘采摘机器人上，验证了算法的可行性，为柑橘采摘机器人的姿态估计问题提供了新的解决方案。