基于车载三维激光雷达的玉米点云数据滤波算法
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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。
In order to support phenotypic parameter measurement and digital plant related research,the obtained maize point cloud data collected by 3 D light detection and ranging( Li DAR) 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 12 th leaf,Jingnongke728 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 12 000 to 1 700. 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 1 700 to 1 400,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 1 400 to 1 300,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.
In order to support phenotypic parameter measurement and digital plant related research,the obtained maize point cloud data collected by 3 D light detection and ranging( Li DAR) 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 12 th leaf,Jingnongke728 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 12 000 to 1 700. 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 1 700 to 1 400,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 1 400 to 1 300,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.
引文
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[15]HONG T,LUO X,CHEN H,et al.A ground-based Li DAR and imaging spectrometer synchronous experiment on vegetation[J].ISPRS Annals of Photogrammetry,Remote Sensing and Spatial Information Sciences,2017,IV-2/W4:219-225.
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[17]苏伟,赵晓凤,张明政,等.基于机载激光雷达点云的飞行障碍物提取方法研究[J/OL].农业机械学报,2017,48(增刊):79-85,97.SU Wei,ZHAO Xiaofeng,ZHANG Mingzheng,et al.Extracting flying obstacles using airborne Li DAR point cloud data[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2017,48(Supp.):79-85,97.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=2017s013&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2017.S0.013.(in Chinese)
[18]马鼎,李斌兵.黄土沟壑区切沟植被的激光点云滤波及地形构建[J].农业工程学报,2013,29(15):162-171.MA Ding,LI Binbing.Vegetation filtering in gully region of loess plateau based on laser scanning point cloud's intensity attenuation model and its terrain construct[J].Transactions of the CSAE,2013,29(15):162-171.(in Chinese)
[19]胡永杰,程朋根,陈晓勇,等.机载激光雷达点云滤波算法分析与比较[J].测绘科学技术学报,2015,32(1):72-77.HU Yongjie,CHENG Penggen,CHEN Xiaoyong,et al.The analysis and comparison of airborne Li DAR point cloud filter algorithms[J].Journal of Geomatics Science and Technology,2015,32(1):72-77.(in Chinese)
[20]万剑华,黄荣刚,周行,等.基于曲率统计的Li DAR点云二次滤波方法[J].中国石油大学学报(自然科学版),2013,37(1):56-60.WAN Jianhua,HUANG Ronggang,ZHOU Hang,et al.A secondary filter method of Li DAR point cloud based on curvature statistics[J].Journal of China University of Petroleum(Edition of Natural Science),2013,37(1):56-60.(in Chinese)
[21]李峰.机载Li DAR点云的滤波分类研究[D].北京:中国矿业大学(北京),2013.LI Feng.A research on filtering and classification of airborne Li DAR point clouds[J].Beijing:China University of Mining and Technology(Beijing),2013.(in Chinese)
[22]吴军,李伟,彭智勇,等.融合形态学灰度重建与三角网分层加密的Li DAR点云滤波[J].武汉大学学报(信息科学版),2014,39(11):1298-1303.WU Jun,LI Wei,PENG Zhiyong,et al.Integrating morphological grayscale reconstruction and TIN models for high-quality filtering of airborne Li DAR points[J].Geomatics and Information Science of Wuhan University,2014,39(11):1298-1303.(in Chinese)
[23]EHLERT D,HEISIG M.Sources of angle-dependent errors in terrestrial laser scanner-based crop stand measurement[J].Computers and Electronics in Agriculture,2013,93:10-16.
[24]EHLERT D,HEISIG M,ADAMEK R.Suitability of a laser rangefinder to characterize winter wheat[J].Precision Agriculture,2010,11(6):650-663.
[25]彭博.基于路面高精三维图像的裂缝自动识别与分类算法[D].成都:西南交通大学,2014.PENG Bo.Automatic crack detection and classification algorithms based on high resolution 3D pavement images[D].Chengdu:Southwest Jiaotong University,2014.(in Chinese)
[26]RUSU R B,MARTON Z C,BLODOW N,et al.Towards 3D point cloud based object maps for household environments[J].Robotics and Autonomous Systems,2008,56(11):927-941.
[27]麦春艳,郑立华,孙红,等.基于RGB-D相机的果树三维重构与果实识别定位[J/OL].农业机械学报,2015,46(增刊):35-40.MAI Chunyan,ZHENG Lihua,SUN Hong,et al.Research on 3D reconstruction of fruit tree and fruit recognition and location method based on RGB-D camera[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2015,46(Supp.):35-40.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=2015S006&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2015.S0.006.(in Chinese)
[2]GROΒKINSKY D K,SVENSGAARD J,CHRISTENSEN S,et al.Plant phenomics and the need for physiological phenotyping across scales to narrow the genotype-to-phenotype knowledge gap[J].Journal of Experimental Botany,2015,66(18):5429-5440.
[3]赵春江,陆声链,郭新宇,等.数字植物及其技术体系探讨[J].中国农业科学,2010,43(10):2023-2030.ZHAO Chunjiang,LU Shenglian,GUO Xinyu,et al.Exploration of digital plant and its technology system[J].Scientia Agricultural Sinica,2010,43(10):2023-2030.(in Chinese)
[4]王传宇,赵明,阎建河,等.基于双目立体视觉技术的玉米叶片三维重建[J].农业工程学报,2010,26(4):198-202.WANG Chuanyu,ZHAO Ming,YAN Jianhe,et al.Three-dimensional reconstruction of maize leaves based on binocular stereovision system[J].Transactions of the CSAE,2010,26(4):198-202.(in Chinese)
[5]王传宇,郭新宇,吴升,等.基于计算机视觉的玉米果穗三维重建方法[J/OL].农业机械学报,2014,45(9):274-279.WANG Chuanyu,GUO Xinyu,WU Sheng,et al.Three dimensional reconstruction of maize ear based on computer vision[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2014,45(9):274-279.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20140944&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2014.09.044.(in Chinese)
[6]韩东,杨贵军,杨浩,等.基于立体视觉的玉米雄穗三维信息提取[J].农业工程学报,2018,34(11):166-173.HAN Dong,YANG Guijun,YANG Hao,et al.Three dimensional information extraction from maize tassel based on stereoscopic vision[J].Transactions of the CSAE,2018,34(11):166-173.(in Chinese)
[7]SONG Y,GLASBEY C A,POLDER G,et al.Non-destructive automatic leaf area measurements by combining stereo and timeof-flight images[J].IET Computer Vision,2014,8(5):391-403.
[8]仇瑞承,苗艳龙,季宇寒,等.基于RGB-D相机的单株玉米株高测量方法[J/OL].农业机械学报,2017,48(增刊):211-219.QIU Ruicheng,MIAO Yanlong,JI Yuhan,et al.Measurement of individual maize height based on RGB-D camera[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2017,48(Supp.):211-219.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=2017s034&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2017.S0.034.(in Chinese)
[9]何东健,邵小宁,王丹,等.Kinect获取植物三维点云数据的去噪方法[J/OL].农业机械学报,2016,47(1):331-336.HE Dongjian,SHAO Xiaoning,WANG Dan,et al.Denoising method of 3-D point cloud data of plants obtained by Kinect[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2016,47(1):331-336.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=20160145&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2016.01.045(in Chinese)
[10]夏春华,施滢,尹文庆.基于TOF深度传感的植物三维点云数据获取与去噪方法[J].农业工程学报,2018,34(6):168-174.XIA Chunhua,SHI Ying,YIN Wenqing.Obtaining and denoising method of three-dimensional point cloud data of plants based on TOF depth sensor[J].Transactions of the CSAE,2018,34(6):168-174.(in Chinese)
[11]郭庆华,吴芳芳,庞树鑫,等.Crop 3D-基于激光雷达技术的作物高通量三维表型测量平台[J].中国科学:生命科学,2016,46(10):1210-1221.GUO Qinghua,WU Fangfang,PANG Shuxin,et al.Crop 3D:a platform based on Li DAR for 3D high-throughput crop phenotyping[J].Science China Life Sciences,2016,46(10):1210-1221.(in Chinese)
[12]LIN Y.Li DAR:an important tool for next-generation phenotyping technology of high potential for plant phenomics?[J].Computers and Electronics in Agriculture,2015,119:61-73.
[13]张伟洁,刘刚,郭彩玲,等.基于三维点云的苹果树叶片三维重建研究[J/OL].农业机械学报,2017,48(增刊):103-109.ZHANG Weijie,LIU Gang,GUO Cailing,et al.Apple tree leaf three-dimensional reconstruction based on point cloud[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2017,48(Supp.):103-109.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=2017s017&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2017.S0.017.(in Chinese)
[14]王红丽.基于地面Li DAR的水稻生物量高精度反演[D].乌鲁木齐:新疆大学,2017.WANG Hongli.Based on Li DAR ground rice biomass and high precision of inversion[D].Urumqi:Xinjiang University,2017.(in Chinese)
[15]HONG T,LUO X,CHEN H,et al.A ground-based Li DAR and imaging spectrometer synchronous experiment on vegetation[J].ISPRS Annals of Photogrammetry,Remote Sensing and Spatial Information Sciences,2017,IV-2/W4:219-225.
[16]王安.基于地面Li DAR的冬小麦生长参数提取研究[D].南京:南京大学,2013.WANG An.Extraction of growth parameters of winter wheat based on Terrestrial Li DAR data[D].Nanjing:Nanjing University,2013.(in Chinese)
[17]苏伟,赵晓凤,张明政,等.基于机载激光雷达点云的飞行障碍物提取方法研究[J/OL].农业机械学报,2017,48(增刊):79-85,97.SU Wei,ZHAO Xiaofeng,ZHANG Mingzheng,et al.Extracting flying obstacles using airborne Li DAR point cloud data[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2017,48(Supp.):79-85,97.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=2017s013&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2017.S0.013.(in Chinese)
[18]马鼎,李斌兵.黄土沟壑区切沟植被的激光点云滤波及地形构建[J].农业工程学报,2013,29(15):162-171.MA Ding,LI Binbing.Vegetation filtering in gully region of loess plateau based on laser scanning point cloud's intensity attenuation model and its terrain construct[J].Transactions of the CSAE,2013,29(15):162-171.(in Chinese)
[19]胡永杰,程朋根,陈晓勇,等.机载激光雷达点云滤波算法分析与比较[J].测绘科学技术学报,2015,32(1):72-77.HU Yongjie,CHENG Penggen,CHEN Xiaoyong,et al.The analysis and comparison of airborne Li DAR point cloud filter algorithms[J].Journal of Geomatics Science and Technology,2015,32(1):72-77.(in Chinese)
[20]万剑华,黄荣刚,周行,等.基于曲率统计的Li DAR点云二次滤波方法[J].中国石油大学学报(自然科学版),2013,37(1):56-60.WAN Jianhua,HUANG Ronggang,ZHOU Hang,et al.A secondary filter method of Li DAR point cloud based on curvature statistics[J].Journal of China University of Petroleum(Edition of Natural Science),2013,37(1):56-60.(in Chinese)
[21]李峰.机载Li DAR点云的滤波分类研究[D].北京:中国矿业大学(北京),2013.LI Feng.A research on filtering and classification of airborne Li DAR point clouds[J].Beijing:China University of Mining and Technology(Beijing),2013.(in Chinese)
[22]吴军,李伟,彭智勇,等.融合形态学灰度重建与三角网分层加密的Li DAR点云滤波[J].武汉大学学报(信息科学版),2014,39(11):1298-1303.WU Jun,LI Wei,PENG Zhiyong,et al.Integrating morphological grayscale reconstruction and TIN models for high-quality filtering of airborne Li DAR points[J].Geomatics and Information Science of Wuhan University,2014,39(11):1298-1303.(in Chinese)
[23]EHLERT D,HEISIG M.Sources of angle-dependent errors in terrestrial laser scanner-based crop stand measurement[J].Computers and Electronics in Agriculture,2013,93:10-16.
[24]EHLERT D,HEISIG M,ADAMEK R.Suitability of a laser rangefinder to characterize winter wheat[J].Precision Agriculture,2010,11(6):650-663.
[25]彭博.基于路面高精三维图像的裂缝自动识别与分类算法[D].成都:西南交通大学,2014.PENG Bo.Automatic crack detection and classification algorithms based on high resolution 3D pavement images[D].Chengdu:Southwest Jiaotong University,2014.(in Chinese)
[26]RUSU R B,MARTON Z C,BLODOW N,et al.Towards 3D point cloud based object maps for household environments[J].Robotics and Autonomous Systems,2008,56(11):927-941.
[27]麦春艳,郑立华,孙红,等.基于RGB-D相机的果树三维重构与果实识别定位[J/OL].农业机械学报,2015,46(增刊):35-40.MAI Chunyan,ZHENG Lihua,SUN Hong,et al.Research on 3D reconstruction of fruit tree and fruit recognition and location method based on RGB-D camera[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2015,46(Supp.):35-40.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?flag=1&file_no=2015S006&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2015.S0.006.(in Chinese)
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