张漫,苗艳龙,仇瑞承,季宇寒,李寒,李民赞.基于车载三维激光雷达的玉米点云数据滤波算法[J].农业机械学报,2019,50(4):170-178.
ZHANG Man,MIAO Yanlong,QIU Ruicheng,JI Yuhan,LI Han,LI Minzan.Maize Point Cloud Data Filtering Algorithm Based on Vehicle 3D LiDAR[J].Transactions of the Chinese Society for Agricultural Machinery,2019,50(4):170-178.
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基于车载三维激光雷达的玉米点云数据滤波算法   [下载全文]
Maize Point Cloud Data Filtering Algorithm Based on Vehicle 3D LiDAR   [Download Pdf][in English]
投稿时间:2018-12-05  
DOI:10.6041/j.issn.1000-1298.2019.04.019
中文关键词:  玉米  激光雷达  点云  表型  滤波
基金项目:国家自然科学基金项目(31571570)、国家重点研发计划项目(2017YFD0700400-2017YFD0700403)和北京农业信息技术研究中心开放课题项目(KF2018W002)
作者单位
张漫 中国农业大学 
苗艳龙 中国农业大学 
仇瑞承 中国农业大学 
季宇寒 中国农业大学 
李寒 中国农业大学 
李民赞 中国农业大学 
中文摘要:为支持表型参数测量和数字植物相关研究,对车载三维激光雷达获取的玉米点云数据进行分析处理,提出了一种基于统计分析的两次滤波算法。以大喇叭口期的京农科728和农大84玉米为研究对象,使用VLP-16型三维激光雷达采集田间玉米点云数据;对点云数据进行直通滤波预处理,去除无关点后,进行第1次点云数据滤波处理,设置精确率和召回率阈值,选取参数组合;再对点云进行第2次滤波处理,确定精确率和召回率最优组合(110,0.9)、(6,1.2),边际组合(100,1.0)、(6,1.2)和(110,0.8)、(5,0.9),共3组参数组合;以3组验证集数据进行测试,结果表明:最优组合性能最优,可在京农科728和农大84玉米点云数据滤波中通用
ZHANG Man  MIAO Yanlong  QIU Ruicheng  JI Yuhan  LI Han  LI Minzan
China Agricultural University,China Agricultural University,China Agricultural University,China Agricultural University,China Agricultural University and China Agricultural University
Key Words:maize  LiDAR  point cloud  phenotyping  filtering
Abstract:In order to support phenotypic parameter measurement and digital plant related research,the obtained maize point cloud data collected by 3D light detection and ranging (LiDAR) were analyzed and processed. The filtering algorithm of maize point cloud data was carried out, and a two times filtering algorithm based on statistical analysis was proposed. The vegetative stages of the 12th leaf, Jingnongke 728 and Nongda 84 maize were used as research objects, and VLP-16 was used to collect field maize point cloud data. Firstly, the point cloud data was subjected to pass filtering processing to remove extraneous points. The number of point clouds was reduced from 12000 to 1700. Secondly, the point cloud data was subjected to the first filtered process, and the precision and recall threshold were set. The average number of point clouds was reduced from 1700 to 1400, and 300 outliers were removed. Then, the point cloud was subjected to the second filtered process. The optimal combination and marginal combinations of precision and recall were determined. The optimal combination was (110,0.9) and (6,1.2). The marginal combinations were (100,1.0), (6,1.2) and (110,0.8), (5,0.9), a total of three combinations of parameters. The average number of point clouds was reduced from 1400 to 1300, and 100 outliers were removed. Finally, the three sets of verification set data were tested. The results showed that the optimal combination performance was optimal, which can be used to Jingnongke 728 and Nongda 84.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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