基于树冠最高点和地面树干中心的森林影像点云配准方法  

Forest point cloud registration using the tree top and the ground-level tree center

作　　者：刘金成 郭义军 曾嘉 陈卓 王海明 于强 LIU Jincheng;GUO Yijun;ZENG Jia;CHEN Zhuo;WANG Haiming;YU Qiang(College of Natural Resources and Environment,Northwest A&F University,Yangling 712100,China;College of Soil and Water Conservation Science and Engineering,Northwest A&F University,Yangling 712100,China)

机构地区：[1]西北农林科技大学资源环境学院,杨凌712100 [2]西北农林科技大学水土保持科学与工程学院,杨凌712100

出　　处：《农业工程学报》2024年第15期127-134,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(32001249、32371875)。

摘　　要：为了弥补无人机和地面不同观测视角导致的森林信息缺失以及不同平台摄影测量影像点云难以高效配准的问题,该研究基于无人机和地面摄影测量构建森林三维影像点云数据,以提取的无人机树冠最高点和地面树干中心为关键特征点,参考由粗到精的配准思路,借助特征点在3D和2D的映射关系以及改进的模拟退火算法和迭代最近点算法,提出了一种适合于无人机和地面不同观测平台的森林影像点云配准框架。测试结果表明,相较于银杏树和楸树而言,杨树对关键点匹配影响较大(杨树1.13 m>楸树0.75 m>银杏树0.52 m),不同树种对粗配准影响较小(杨树0.13 m>银杏树0.08 m>楸树0.07 m);所提方法在不同树种组成的6块典型样地上表现出色,平均精配准误差分为0.06 m,有效实现了无人机和地面平台影像点云的精细化配准。研究结果可为森林资源调查、森林三维重建以及影像点云的推广应用提供有力的支撑。Unmanned aerial vehicle(UAV)and ground-based photogrammetry have been essential to the forest resource surveys,due to the low cost,high efficiency and scalability.However,the complex and heterogeneous nature of forest environments can often lead to the UAV platforms with the less understory information.While the ground platforms can frequently miss the canopy details.The information gaps can be caused by different observation perspectives from UAVs and ground platforms.Some challenges are also remained to efficiently register photogrammetric point clouds.In this study,a point cloud registration was proposed for forest images,according to three-dimensional(3D)point cloud from UAV and ground photogrammetry.Key points of feature were then extracted using coarse-to-fine registration,including the tree top and the ground-level tree center.The specific procedures were as follows:1)Image Point Cloud Acquisition.In UAV platforms,DJI Phantom 4 RTK UAV were used to capture plot images via oblique photogrammetry.In ground platforms,a"simulated flight path"was employed to obtain the plot images.3D reconstruction was implemented to generate 3D point cloud data.2)Feature Point Extraction.Single tree segmentation was combined with AABB bounding boxes and Euclidean clustering.The highest points of individual UAV tree canopies were extracted as feature points.Euclidean clustering was used on ground image point clouds.Single trees were then segmented to extract the ground-level tree center as ground feature points.3)Coarse Registration.Feature points were mapped from 3D to 2D.Transformation relationships were calculated using improved simulated annealing,partial/full point pair transformation and scale adjustment.The coarse registration was achieved in the original point cloud.4)Fine Registration.Precise registration was achieved to reduced further error using iterative closest point.A series of tests were performed on six sample plots with different tree species.The results demonstrated the following:1)The high accuracy and relia

关 键 词：无人机 点云 森林 树冠 地面摄影测量 

分 类 号：S771.8[农业科学—森林工程]