摘要
构建精确三维模型无损获取植物表型特征信息,对研究农作物的精准化种植、可视化管理和智能化操控具有实际意义。针对当前植物三维建模过程点云数据量大、配准精度不高的问题,该研究提出基于轻量化处理的迭代最近点算法(Iterative Closest Point,ICP)点云配准优化方法。首先以人机交互算法对获取的植株点云图像进行背景滤除,然后利用高效近邻搜索算法判别点云的离群点进行噪声去噪;通过引入辅助特征坐标参数解决ICP算法配准过程中易陷于局部最优解的问题,获取多组点云的精确配准;最后提出体素化网格法,在保证点云三维形态特征的前提下有效滤除冗余点云数据点。试验结果表明,单株和多植株的精简配准效果良好,表型清晰明显,细节易区分,精简后的植株点云冗余数据减少96.90%~97.35%。精简后的植物点云表型可有效重构植株的形态特征,单植株的株高误差为0.20%~0.45%,冠幅误差为0.17%~0.47%,多植株的株高误差为0.25%~0.60%,冠幅误差为0.42%~0.80%。优化后的ICP算法实现滴水莲点云数据精准融合时间为124.3s,较暴力算法提升26.75%,冗余点云数据精简96.90%~97.20%,为植物表型的三维建模轻量化处理提供参考。
An accurate three-dimensional(3D)model has been one of the most practical significance to acquire the plant phenotypic traits without damage,particularly for the accurate planting,visual management,and intelligent control of crops.However,the existing plant 3D model cannot fully meet the rapid and accurate requirement of modern agriculture,due to its low accuracy of point cloud registration and a large amount of data during modeling.In this study,a classical iterative closest point(ICP)registration of point clouds was proposed for the plant 3D modeling using lightweight processing.The original data of point clouds was firstly sampled to determine the initial corresponding point set,where the wrong corresponding points were removed as well.The optimal coordinate transformation was then calculated by the least square method,indicating a better accuracy of stitching.Nevertheless,the operation speed and the convergence to the global optimum depended mainly on the given initial transformation and the generation relation.Therefore,the ICP was then optimized to construct a three-dimensional plant model with fewer data,in order to avoid the ICP falling into the local extremum.The specific procedure of optimization was as follows.Firstly,the background of the plant image from the point cloud was filtered by the human-computer interaction,where the outlier noise of the point cloud was identified to denoise using the efficient nearest neighbor search.The auxiliary feature coordinate parameters were also introduced to establish the feature points.Secondly,the initial transformation matrix of ICP was obtained by manual intervention.An initial solution was then provided to avoid the local optimal solution in the accurate registration for the multiple groups of point clouds.Finally,a three-dimensional voxel grid was created to approximately display the points in the voxel using the center of gravity of all points in each voxel.All points in the voxel were represented by a center of gravity,thereby effectively filtering out the redundant data points in the point clouds.The results show that better performance of registration was achieved for the plant point cloud,where the clear and outstanding phenotype was easy to be distinguished after ICP optimization.Both single and multiple plants presented an excellent registration,clear phenotype,and distinguishable details.The redundant data of the plant point cloud was also reduced by 96.90%-97.35%after simplification.The simplified point cloud of plant phenotype can be widely expected to effectively reconstruct the morphological traits of plants.Specifically,the plant height error of single plant was 0.20%-0.45%,and the crown width error was 0.17%-0.47%,whereas,the plant height error of multiple plants was 0.25%-0.60%,and the crown width error was 0.42%-0.80%.Consequently,the optimized ICP can achieve the precision fusion of point clouds in 124.3 s,which was 26.75%higher than the traditional in 169.7s,indicating an effective reduction in the redundancy data.The finding can also provide a strong reference for the lightweight processing on the 3D modeling of plant phenotype.
作者
陆健强
兰玉彬
毋志云
梁效
常虎虎
邓小玲
吴泽锦
唐亚战
Lu Jianqiang;Lan Yubin;Wu Zhiyun;Liang Xiao;Chang Huhu;Deng Xiaoling;Wu Zejin;Tang Yazhan(College of Electronic Engineering(College of Artificial Intelligence),South China Agricultural University,Guangzhou 510642,China;National Precision Agriculture International Joint Research Center of Aerial Application Technology,Guangzhou 510642,China;Guangdong Laboratory of Lingnan Modern Agriculture,Guangzhou 510642)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2022年第2期183-191,共9页
Transactions of the Chinese Society of Agricultural Engineering
基金
广东省重点领域研发计划资助(2019B020214003)
广东高校重点领域人工智能专项(2019KZDZX1012,2019KZDZX1001)
广州市重点领域专项(202103000090)。
关键词
植物
表型
三维模型
轻量化处理
点云配准
ICP算法
体素化网格法
plant
phenotype
3D modelling
lightweight processing
point cloud registration
ICP algorithm
voxelized grid
