We propose a 3D ray cloud based method for estimating the canopy density of vineyards and orchards that have been scanned by a vehicle-mounted mobile 3D spinning LiDAR (AgScan3D). The method is composed of two parts. Firstly, the AgScan3D data is processed through a Continuous-Time SLAM algorithm into a globally registered 3D ray cloud. The global ray cloud is a canonical data format (a digital twin) from which we can compare vineyard snapshots over multiple times within a season and across seasons. Secondly, the vineyard rows are automatically extracted from the ray cloud and a novel density calculation is performed to estimate the maximum likelihood canopy densities of the vineyard. This combination of digital twinning, together with the accurate extraction of canopy structure information, allows entire vineyards to be analysed and compared, across the growing season and from year to year. The proposed method is evaluated both in simulation and field experiments. Field experiments were performed at four sites, which varied in vineyard structure and vine management, over two growing seasons, resulting in a total traversal of 160 kilometres, 42.4 scanned hectares of vines with a combined total of approximately 93000 of scanned vines. The field datasets and ray cloud library will be released publicly.

中文翻译：

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使用 3D 旋转激光雷达 SLAM 估计多年生园艺作物的冠层密度

我们提出了一种基于 3D 射线云的方法，用于估计由车载移动 3D 旋转 LiDAR (AgScan3D) 扫描的葡萄园和果园的冠层密度。该方法由两部分组成。首先，AgScan3D 数据通过连续时间 SLAM 算法处理成全局注册的 3D 射线云。全球射线云是一种规范的数据格式（数字孪生），我们可以从中比较一个季节内和跨季节多次的葡萄园快照。其次，从射线云中自动提取葡萄园行，并执行新的密度计算来估计葡萄园的最大似​​然冠层密度。这种数字孪生结合准确提取树冠结构信息，可以分析和比较整个葡萄园，整个生长季节和年复一年。所提出的方法在模拟和现场实验中都得到了评估。田间试验在四个地点进行，这些地点的葡萄园结构和葡萄藤管理各不相同，超过两个生长季节，总共遍历了 160 公里，扫描了 42.4 公顷的葡萄藤，总共扫描了大约 93000 株葡萄藤。现场数据集和射线云库将公开发布。