复杂采空区激光多点探测及点云拼接与精简
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摘要
针对复杂采空区激光探测中存在探测"盲区"和点云数据分布不均的问题,研究激光多点扫描和点云数据拼接与精简方法.通过多点探测避免了单次探测"盲区",加密了数据稀疏区.提出了基于公共坐标和最小二乘法的靶标矩阵转换方法,实现了多点探测点云的拼接.统计了点云密集区的分布规律;对密集散乱点云,提出了沿y轴方向分层剖分,层内数据以x和z坐标极值分区,区内每点以x值排序后依步长筛选的精简算法.大型贯通采空区验证表明:基于最小二乘法的拼接算法最优,误差范围在0.1 mm左右;数据精简率为15%~25%,确保了边界三维信息的完整性.
In view of the problems of ' blind spots' in complicated goaf detecting by using laser scanning and point cloud density distribution inhomogeneity,this article introduced multi-point laser scan and point cloud merging and compression.Multi-point scan in complicated goaf avoided ' blind spots' and densified sparse point cloud regions.The merging algorithm of point cloud data was put forward based on a common coordinate system and the least-squares principle to solve the target transformation matrix.After the distribution rule of point cloud concentration areas was analyzed,the scattered point cloud compression algorithm was proposed,in which the point cloud was divided into portions along the y direction firstly,then intralayer data were divided by the extreme values of x and z,and each point was sorted on the x value and screened on step k.Error analysis of an instance of large versed goaf shows that the merging algorithm based on the least-squares principle will achieve high precision with an error range of about 0.1 mm.The compression algorithm can achieve a compression proportion of 15%to 25%and ensure the integrity of 3D boundary information at the same time.
In view of the problems of ' blind spots' in complicated goaf detecting by using laser scanning and point cloud density distribution inhomogeneity,this article introduced multi-point laser scan and point cloud merging and compression.Multi-point scan in complicated goaf avoided ' blind spots' and densified sparse point cloud regions.The merging algorithm of point cloud data was put forward based on a common coordinate system and the least-squares principle to solve the target transformation matrix.After the distribution rule of point cloud concentration areas was analyzed,the scattered point cloud compression algorithm was proposed,in which the point cloud was divided into portions along the y direction firstly,then intralayer data were divided by the extreme values of x and z,and each point was sorted on the x value and screened on step k.Error analysis of an instance of large versed goaf shows that the merging algorithm based on the least-squares principle will achieve high precision with an error range of about 0.1 mm.The compression algorithm can achieve a compression proportion of 15%to 25%and ensure the integrity of 3D boundary information at the same time.
引文
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[14]Li B,Liu S L,Zhang L Y,et al.Bas-relief generation algorithm based on Laplace operator decomposition of 3D model.Comput Integr Manuf Syst,2011,17(5):946(李博,刘胜兰,张丽艳,等.基于三维模型的拉普拉斯分解的浮雕生成算法.计算机集成制造系统,2011,17(5):946)
[15]Luo Z Q,Liu X M,Su J H,et al.Deposit 3D modeling and application.J Cent South Univ Technol,2007,14(2):225
[16]Xiong L X,Luo Z Q,Tan L L,et al.Impact factors analysis and error correction of laser scanning for goaf.J Cent South Univ Nat Sci,2014,45(4):1244(熊立新,罗周全,谭浪浪,等.采空区激光探测精度影响因素分析及误差纠正.中南大学学报:自然科学版,2014,45(4):1244)
[17]Zhang J Y.Fuzzy comprehensive evaluation method of the foundation stability of new-buildings above worked-out areas.J Univ Sci Technol Beijing,2009,31(11):1368(张俊英.采空区地表建筑地基稳定性模糊综合评价方法.北京科技大学学报,2009,31(11):1368)
[18]Xiong L X,Luo Z Q,Luo Z Y,et al.Data amendment of abnormal point cloud of goaf by laser scan in deep complex environment.J Northeast Univ Nat Sci,2014,35(3):438(熊立新,罗周全,罗贞焱,等.深部复杂环境下采空区激光扫描异常点云数据修正.东北大学学报:自然科学版,2014,35(3):438)
[2]Zhang H F,Cheng X J,Liu Y P.Study on improved point cloud compression algorithm with features reserved.Appl Mech Mater,2011,63(2):470
[3]Smith M,Posner I,Newman P.Adaptive compression for 3D laser data.Int J Rob Res,2011,30(7):914
[4]Friedman S,Stamos I.Online detection of repeated structures in point clouds of urban scenes for compression and registration.Int J Comput Vision,2013,102(1):112
[5]Shen H P,Da F P,Lei J Y.Research of point-clouds registration based on least-square method.J linage Graphics,2005,10(9):1112(沈海平,达飞鹏,雷家勇.基于最小二乘法的点云数据拼接研究.中国图象图形学报,2005,10(9):1112)
[6]Sheng Y H,Zhang K,Zhang K,et al.Seamless multi-station merging of terrestrial laser scanned 3D point clouds.J China Univ Min Technol,2010,29(2):233(盛业华,张卡,张凯,等.地面三维激光扫描点云的多站数据无缝拼接.中国矿业大学学报,2010,29(2):233)
[7]Zhang S S,Zheng C Z,Xiao SB,et al.Realization of point cloud data combination based on openGL function in MFC.Mack Tool Hydraul,2006(2):76(张树森,郑成志,肖胜兵,等.基于MFC环境下利用OvenGL函数实现点云数据拼接.机床与液压,2006(2):76)
[8]Wang R,Zhou M Q,Xing Y H.Reduction algorithm for 3D scattered points cloud data based on clustering plane feature.Comput Eng,2011,37(10):249(王茹,周明全,邢毓华.基于聚类平面特征的三维点云数据精简算法.计算机工程,2011,37(10):249)
[9]Zhou Y,Lei Y,Du F R,et al.Algorithm of scattered point cloud data reduction based on non-uniform subdivision.Trans Chin Soc Agric Mach,2009,40(9):193(周煜,雷雨,杜发荣,等.基于非均匀细分的散乱点云数据精简算法.农业机械学报,2009,40(9):193)
[10]Xie Z X,Xu S,Li X Y.A fine registration method for 3D point clouds in reverse engineering.China Mech Eng,2009,20(13);1577(解则晓,徐尚,李绪勇.逆向工程中三维点云数据精确拼接方法.中国机械工程,2009,20(13):1577)
[11]Cai R B,Pan G R.A new method for multi-view point cloud registration.J Tongji Univ Nat Sci,2006,34(7):913(蔡润彬,潘国荣.三维激光扫描多视点点云拼接新方法.同济大学学报:自然科学版,2006,34(7):913)
[12]Zhang J Q,Zhai R F,Zheng S Y.Automatic seamless registration of 3D multiple range views.Geomat Inf Sci Wuhan Univ,2007,32(2):100(张剑清,翟瑞芳,郑顺义.激光扫描多三维视图的全自动无缝镶嵌.武汉大学学报:信息科学版,2007,32(2):100)
[13]Wu M,Zhou L S,Wang Z D,et al.Research of multi-view registration and integration on measured point cloud data.J Nanjing Univ Aeronaut Astronaut,2003,35(5):552(吴敏,周来水,王占东,等.测量点云数据的多视拼接技术研究.南京航空航天大学学报,2003,35(5):552)
[14]Li B,Liu S L,Zhang L Y,et al.Bas-relief generation algorithm based on Laplace operator decomposition of 3D model.Comput Integr Manuf Syst,2011,17(5):946(李博,刘胜兰,张丽艳,等.基于三维模型的拉普拉斯分解的浮雕生成算法.计算机集成制造系统,2011,17(5):946)
[15]Luo Z Q,Liu X M,Su J H,et al.Deposit 3D modeling and application.J Cent South Univ Technol,2007,14(2):225
[16]Xiong L X,Luo Z Q,Tan L L,et al.Impact factors analysis and error correction of laser scanning for goaf.J Cent South Univ Nat Sci,2014,45(4):1244(熊立新,罗周全,谭浪浪,等.采空区激光探测精度影响因素分析及误差纠正.中南大学学报:自然科学版,2014,45(4):1244)
[17]Zhang J Y.Fuzzy comprehensive evaluation method of the foundation stability of new-buildings above worked-out areas.J Univ Sci Technol Beijing,2009,31(11):1368(张俊英.采空区地表建筑地基稳定性模糊综合评价方法.北京科技大学学报,2009,31(11):1368)
[18]Xiong L X,Luo Z Q,Luo Z Y,et al.Data amendment of abnormal point cloud of goaf by laser scan in deep complex environment.J Northeast Univ Nat Sci,2014,35(3):438(熊立新,罗周全,罗贞焱,等.深部复杂环境下采空区激光扫描异常点云数据修正.东北大学学报:自然科学版,2014,35(3):438)