Visual-SLAM for the detection of hidden tomatoes in greenhouses by Hierarchical Localization and GLOMAPfor robotized harvesting
作者: Fernando Cañadas-Aránega, José C. Moreno, José L. Blanco-Claraco, Francisco Rodríguez
分类: cs.RO
发布日期: 2026-09-10
💡 一句话要点
提出视觉SLAM以解决温室隐藏番茄检测问题
🎯 匹配领域: 支柱三：空间感知与语义 (Perception & Semantics)
关键词: 视觉SLAM 农业监测 单目相机 温室作物 3D映射 分层定位 GLOMAP
📋 核心要点
- 现有的高性能传感器如LiDAR和立体相机成本高，限制了其在温室作物监测中的广泛应用。
- 本研究提出了一种基于单目相机的视觉SLAM系统，结合GLOMAP映射器和分层定位工具箱，专为农业作物映射设计。
- 实验结果表明，该系统能够正确识别被遮挡的番茄簇，并且重建的3D模型在几何精度上与手动测量结果一致。
📝 摘要（中文）
温室内的先进作物监测已成为研究中心的主要目标之一。尽管传统上使用高性能传感器（如LiDAR或立体相机）进行此目的，但其高成本限制了应用。本研究提出了一种使用单目相机的视觉SLAM系统，具有显著的成本效益，专门针对农业应用，如温室内番茄作物的映射。通过在Agroconnect实验温室中对真实番茄束进行测试，开发了一个ROS 2 Humble节点以捕获作物图像并进行离线处理。为生成温室作物的3D映射模型，集成了基于运动结构的GLOMAP映射器与分层定位工具箱。该系统采用粗到细的分层定位范式，首先进行全局检索以生成位置假设，然后结合识别区域内的局部特征。结果显示，番茄簇的正确识别，成功表征了被经典视觉技术遮挡和无法接触的番茄。
🔬 方法详解
问题定义：本论文旨在解决温室中隐藏番茄的检测问题，现有方法如LiDAR和立体相机因高成本而难以普及。
核心思路：提出了一种基于单目相机的视觉SLAM系统，结合GLOMAP映射器和分层定位策略，以实现低成本且高效的作物映射。
技术框架：系统首先通过ROS 2 Humble节点捕获作物图像，随后利用GLOMAP映射器生成3D模型，并通过分层定位方法进行位置假设的验证与特征提取。
关键创新：该研究的核心创新在于将分层定位与GLOMAP映射器结合，采用粗到细的策略来提高隐藏作物的检测精度，克服了传统方法的局限性。
关键设计：在实现过程中，设置了特定的参数以优化特征提取和匹配过程，确保了模型的几何精度与手动测量结果的一致性。通过这种设计，系统能够有效处理遮挡问题。
🖼️ 关键图片
📊 实验亮点
实验结果显示，系统能够成功识别被遮挡的番茄簇，重建的3D模型在几何精度上与手动测量结果一致，验证了该低成本单目视觉SLAM管道的有效性。
🎯 应用场景
该研究的潜在应用领域包括农业自动化和智能温室管理。通过实现低成本的作物监测，能够帮助农民优化作物管理，提高产量和效率，具有重要的实际价值和未来影响。
📄 摘要（原文）
Advanced crop monitoring inside greenhouses is becoming one of the primary objectives of research centers. High-performance sensors, such as LiDAR or stereo cameras, have traditionally been employed for this purpose, though these often have a high cost. This work proposes a Visual-SLAM system using a monocular camera, which is significantly more cost-effective and specifically tailored for agricultural applications, such as mapping tomato crops in a greenhouse. Tests were carried out on a real tomato bunch, located in the Agroconnect experimental greenhouse. A ROS 2 Humble node was developed to run on the robot in order to capture images of these crops, which were then stored for offline processing. To generate a 3D mapped model for the crop in the greenhouse, the GLOMAP mapper, based on Structure-From-Motion, was integrated with the Hierarchical Localization toolbox. This initial mapping is a foundation for future, more advanced algorithms to analyze growth patterns, and optimize agricultural management. The system leverages a hierarchical localization paradigm based on a coarse-to-fine strategy: it first performs global retrieval to generate location hypotheses, then combines local features within the identified candidate regions. The results show a correct identification of the tomato cluster, correctly characterising the tomato that is occluded and inaccessible by classical vision technologies. The reconstructed 3D model was further validated against manual ground-truth measurements of fruit size, centroid position, and orientation, confirming the geometric accuracy of the proposed low-cost monocular pipeline.