论文/arXiv 2609.18738·1 个月前

面向机器人采摘的主动感知：地中海温室中簇内隐藏番茄的3D重建与定位

针对温室簇生番茄遮挡难题，提出基于NBV主动感知的3D重建与定位流程，并在经真实条件验证的仿真中评估。

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原文链接：Active perception for robotic harvesting: 3D reconstruction and localisation of tomatoes hidden within clusters in a Mediterranean greenhouse（arXiv 2609.18738 · 发布日期：2026-09-16）

Active perception for robotic harvesting: 3D reconstruction and localisation of tomatoes hidden within clusters in a Mediterranean greenhouse

针对温室簇生番茄遮挡难题，提出基于NBV主动感知的3D重建与定位流程，并在经真实条件验证的仿真中评估。

核心要点

• 动机是固定传感器难以发现簇生蔬菜可见表面后方的隐藏果实。
• 方法串联NBV点云采集、SOR滤波、RG分割、DBSCAN恢复和3D位姿估计。
• 在不同遮挡水平场景中，精度超过90%，平均召回率82.8%，mIoU为80.7%。
• 质心估计RMSE为4.2 mm，显示面向自主采摘的技术可行性和高精度。

验证范围与局限：验证在经真实条件严格验证的仿真框架中进行，覆盖不同遮挡水平的多个场景。

英文摘要 (Abstract)

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.

作者：Fernando Cañadas-Aránega, Rowan Border, Jose C. Moreno, Jose Luis Blanco-Claraco

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1. arXiv 2609.18738：Active perception for robotic harvesting: 3D reconstruction and localisation of tomatoes hidden within clusters in a Mediterranean greenhouse访问 2026-10-21