基于层级定位与GLOMAP的温室隐藏番茄检测视觉SLAM系统及其在机器人采摘中的应用

中文摘要 AI 辅助

温室内的先进作物监测正成为研究中心的主要目标之一。高性能传感器，如激光雷达或立体相机，传统上被用于此目的，但这些传感器通常成本较高。本研究提出了一种使用单目相机的视觉SLAM系统，该系统显著更具成本效益，并专门针对农业应用（如温室番茄作物测绘）进行了定制。测试在位于Agroconnect实验温室中的真实番茄串上进行。开发了一个ROS 2 Humble节点，运行在机器人上以捕获这些作物的图像，随后将图像存储用于离线处理。为了生成温室中作物的三维映射模型，将基于运动恢复结构的GLOMAP映射器与层级定位工具箱集成。这一初始映射为未来更先进的算法分析生长模式和优化农业管理奠定了基础。该系统利用基于由粗到精策略的层级定位范式：首先执行全局检索以生成位置假设，然后在识别出的候选区域内结合局部特征。结果表明，系统正确识别了番茄簇，并正确表征了被遮挡且传统视觉技术无法触及的番茄。重建的三维模型进一步与果实大小、质心位置和朝向的手动地面真值测量进行了验证，确认了所提出的低成本单目管线的几何精度。

英文摘要

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.

发表机构

• Universidad de Almería（阿尔梅里亚大学）

机构由 AI 辅助整理，请以论文原文为准。