基于语义的甜樱桃树语义化研究
Semantics-guided Skeletonization of Sweet Cherry Trees for Robotic Pruning
摘要：
新鲜市场果树的休眠修剪是农业机器人的一个相对未开发的应用，几乎没有端到端系统存在。创建自主修剪系统的最大挑战之一是需要重建树的模型，该模型足够准确和信息丰富，以便用于决定在哪里修剪。对树建模的一个有用的结构是骨架：树的几何和拓扑的1D轻量级表示。这种树的建模问题是计算机图形学领域中的一个重要问题，并且已经为树的建模任务专门开发了许多算法。这些卷积算法在很大程度上解决了几何问题。然而，在农业环境中，树的部分有不同的标签，如树干，支撑树枝等，这种标记的结构对于理解在哪里修剪是很重要的。我们介绍了一种算法，产生这样一个标记的骨架，使用与这些标签相关联的拓扑和几何先验，以改善我们的骨架。我们测试了我们的点云上的29个直立的结果分支（UFO）的树木，并证明了70%的中位数准确度相对于人类评估的黄金标准。我们还将82棵UFO树的点云扫描开源给其他研究人员。我们的工作是一个强大的树建模框架，可用于自主修剪系统的重要的第一步。
英文摘要：
Dormant pruning for fresh market fruit trees is a relatively unexplored application of agricultural robotics for which few end-to-end systems exist. One of the biggest challenges in creating an autonomous pruning system is the need to reconstruct a model of a tree which is accurate and informative enough to be useful for deciding where to cut. One useful structure for modeling a tree is a skeleton: a 1D, lightweight representation of the geometry and the topology of a tree. This skeletonization problem is an important one within the field of computer graphics, and a number of algorithms have been specifically developed for the task of modeling trees. These skeletonization algorithms have largely addressed the problem as a geometric one. In agricultural contexts, however, the parts of the tree have distinct labels, such as the trunk, supporting branches, etc. This labeled structure is important for understanding where to prune. We introduce an algorithm which produces such a labeled skeleton, using the topological and geometric priors associated with these labels to improve our skeletons. We test our skeletonization algorithm on point clouds from 29 upright fruiting offshoot (UFO) trees and demonstrate a median accuracy of 70% with respect to a human-evaluated gold standard. We also make point cloud scans of 82 UFO trees open-source to other researchers. Our work represents a significant first step towards a robust tree modeling framework which can be used in an autonomous pruning system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
