主动感知在机器人采摘中的应用：地中海温室中簇生番茄的3D重建与定位

中文摘要 AI 辅助

在地中海温室中进行集约化农业的机器人采摘自动化，需要克服与植物几何复杂性和果实遮挡相关的重大挑战。尽管现有文献提供了针对孤立生长作物（如苹果、甜椒或桃子）的解决方案，但根本挑战在于簇生蔬菜，其中安装在机器人系统上的固定传感器无法检测到隐藏在可见表面后面的果实。为解决这一局限性，本研究提出了一种全面的流程，用于对簇内每个果实（包括严重遮挡的实例）进行3D重建和精确定位。所提出的方法分为五个连续阶段：i) 使用AgriSEE Next Best View (NBV) 主动规划器进行点云采集；ii) 通过统计离群值移除 (SOR) 进行随机噪声滤波；iii) 使用区域生长 (RG) 进行表面分类和分割；iv) 通过基于密度的噪声应用空间聚类 (DBSCAN) 隔离和恢复被遮挡的果实；以及v) 3D姿态估计（位置和方向）。该方法提取完整的簇几何结构，确保可靠识别部分隐藏的番茄。在模拟框架内对多种遮挡程度不同的场景进行了评估，该框架经过严格验证并与真实世界条件相符，系统实现了超过90%的精度、82.8%的平均召回率以及80.7%的平均交并比 (mIoU)。此外，它在质心估计中表现出高重复性，均方根误差 (RMSE) 仅为4.2毫米，验证了其在自主采摘操作中的技术可行性和高精度。

英文摘要

Automating robotic harvesting in intensive agriculture within Mediterranean greenhouses requires overcoming significant challenges related to the geometric complexity of plants and occluded fruits. Although existing literature offers solutions targeting crops that grow in isolation (e.g., apples, sweet peppers, or peaches), the fundamental challenge lies in cluster-growing vegetables, where fixed sensors mounted on robotic systems fail to detect fruits hidden behind the visible surface. To address this limitation, this study presents a comprehensive pipeline for the 3D reconstruction and precise localization of each fruit within a cluster, including heavily occluded instances. The proposed methodology is structured into five sequential stages: i) point cloud acquisition using the AgriSEE Next Best View (NBV) active planner; ii) stochastic noise filtering via Statistical Outlier Removal (SOR); iii) surface classification and segmentation using Region Growing (RG); iv) isolation and recovery of occluded fruits through Density-Based Spatial Clustering of Applications with Noise (DBSCAN); and v) 3D pose estimation (position and orientation). This approach extracts the complete cluster geometry, ensuring the reliable identification of partially hidden tomatoes. Evaluated across multiple scenarios with varying occlusion levels within a simulation framework rigorously validated against real-world conditions, the system achieves a precision exceeding 90\%, an average recall of 82.8\%, and a mean Intersection over Union (mIoU) of 80.7\%. Furthermore, it demonstrates high repeatability in centroid estimation with a Root Mean Square Error (RMSE) of merely 4.2~mm, verifying its technical feasibility and high accuracy for autonomous harvesting operations.

发表机构

• Universidad de Almería（阿尔梅里亚大学）
• University of Cyprus（塞浦路斯大学）

机构由 AI 辅助整理，请以论文原文为准。