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Citrus pose estimation under complex orchard environment for robotic harvesting

文献类型： 外文期刊

作者： Zhang, Guanming 1 ; Li, Li 1 ; Zhang, Yunfeng 1 ; Liang, Jiyuan 1 ; Chun, Changpin 3 ;

作者机构： 1.Southwest Univ, Coll Engn & Technol, Chongqing 400715, Peoples R China

2.Chongqing Key Lab Agr Equipment Hilly Area, Chongqing 400715, Peoples R China

3.Southwest Univ, Chinese Acad Agr Sci, Citrus Res Inst, Chongqing 400700, Peoples R China

关键词： Citrus; Loss minimization; Robotic harvesting; Pose estimation; RANSAC-LM

期刊名称：EUROPEAN JOURNAL OF AGRONOMY （ 影响因子：5.5； 五年影响因子：5.9 ）

ISSN： 1161-0301

年卷期： 2025 年 162 卷

页码：

收录情况： SCI

摘要： 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 degrees to 2.43 degrees. 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.

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