地面激光三维扫描中球面标靶自动检测方法
|
摘要
提出了一种从地面激光点云数据中提取球面标靶目标的有效方法。该方法首先在单站数据的栅格结构中利用邻域距离突变提取遮挡边界点,同时对其进行空间聚类;然后,利用随机采样一致性方法,在各聚类结点中的二维栅格结构中探测近圆结构,同时,根据点到扫描中心距离和扫描角差估算圆半径和圆的一致集数,在通过估算半径约束的圆形区域所对应的三维点集中探测球体模型;最后,通过预设球体半径和估算球面点数约束的球体模型,作为最终球面标靶模型。实验结果表明,该方法能够在1 min之内完成千万级点云数据中的球面标靶探测工作。
An efficient method for extracting spherical targets from point cloud in terrestrial laser scanning is proposed in this paper. The method is as follows, firstly occlusion boundary points are extracted via the distance mutation between the neighbors in the single station's raster structure, and spatially clustered. Then, the random sample consensus method is used to detect near-circular structures in the two-dimensional raster structure for each clustering node, meanwhile, the near-circular structure's radius and consensus set number are estimated according to the distance from the detected center point to scanner center and the scan angle interruption, and the spherical models are detected in point cloud subset whose circular region can pass radius and point number threshold value. Finally, these spherical models are chosen as the final results. The experimental results show that the proposed method can effectively detect spherical targets in more than 10 million point clouds within one minute.
An efficient method for extracting spherical targets from point cloud in terrestrial laser scanning is proposed in this paper. The method is as follows, firstly occlusion boundary points are extracted via the distance mutation between the neighbors in the single station's raster structure, and spatially clustered. Then, the random sample consensus method is used to detect near-circular structures in the two-dimensional raster structure for each clustering node, meanwhile, the near-circular structure's radius and consensus set number are estimated according to the distance from the detected center point to scanner center and the scan angle interruption, and the spherical models are detected in point cloud subset whose circular region can pass radius and point number threshold value. Finally, these spherical models are chosen as the final results. The experimental results show that the proposed method can effectively detect spherical targets in more than 10 million point clouds within one minute.
引文
[1] 浦石,李京伟,郭四清.融合语义特征与GPS位置的地面激光点云拼接方法[J].测绘学报,2014,43(5):545-550
[2] Yang B,Dong Z,Liang F,et al.Automatic Registration of Large-Scale Urban Scene Point Clouds Based on Semantic Feature Points [J].ISPRS Journal of Photogrammetry & Remote Sensing,2016,113:43-58
[3] Yun D,Kim S,Heo H,et al.Automated Registration of Multi-view Point Clouds Using Sphere Targets [J].Advanced Engineering Informatics,2015,29(4):930-939
[4] 王晏民,石宏斌.应用三维线特征自动配准建筑物点云数据[J].测绘通报,2014(9):23-25
[5] 贾东峰,程效军,刘燕萍.球标靶在点云数据配准中的应用分析[J].工程勘察,2011,39(9):64-68
[6] 蓝秋萍,田青红,李子宽,等.利用球目标的多测站点云自动配准[J].测绘地理信息,2018,43(6):91-94
[7] Liang Y B,Zhan Q M,Che E Z,et al.Automatic Registration of Terrestrial Laser Scanning Data Using Precisely Located Artificial Planar Targets[J].IEEE Geoscience & Remote Sensing Letters,2013,11(1):69-73
[8] 李庆,雍小龙,黄先锋,等.结合点云和影像的标靶自动识别与中心提取[J].测绘地理信息,2015,40(3):21-25
[9] Becerik-Gerber B,Jazizadeh F,Kavulya G,et al.Assessment of Target Types and Layouts in 3D Laser Scanning for Registration Accuracy [J].Automation in Construction,2011,20(5):649-658
[10] Franaszek M,Cheok G S,Witzgall C.Fast Automatic Registration of Range Images from 3D Imaging Systems Using Sphere Targets[J].Automation in Construction,2009,18(3):265-274
[11] He B,Lin Z,Li Y F.An Automatic Registration Algorithm for the Scattered Point Clouds Based on The Curvature Feature[J].Optics & Laser Technology,2013,46(1):53-60
[12] 石宏斌,殷义程,袁曼飞.点云数据的多几何面片特征自动识别[J].测绘通报,2017(2):6-9
[13] 张立朔,程效军,谭凯.基于强度图像的标靶球自动提取与参数拟合[J].工程勘察,2014,42(12):65-69
[14] 方伟,黄先锋,张帆,等.依据点云强度校正的壁画纠正[J].测绘学报,2015,44(5):541-547
[15] 王晏民,郭明.大规模点云数据的二维与三维混合索引方法[J].测绘学报,2012,41(4):605-612
[2] Yang B,Dong Z,Liang F,et al.Automatic Registration of Large-Scale Urban Scene Point Clouds Based on Semantic Feature Points [J].ISPRS Journal of Photogrammetry & Remote Sensing,2016,113:43-58
[3] Yun D,Kim S,Heo H,et al.Automated Registration of Multi-view Point Clouds Using Sphere Targets [J].Advanced Engineering Informatics,2015,29(4):930-939
[4] 王晏民,石宏斌.应用三维线特征自动配准建筑物点云数据[J].测绘通报,2014(9):23-25
[5] 贾东峰,程效军,刘燕萍.球标靶在点云数据配准中的应用分析[J].工程勘察,2011,39(9):64-68
[6] 蓝秋萍,田青红,李子宽,等.利用球目标的多测站点云自动配准[J].测绘地理信息,2018,43(6):91-94
[7] Liang Y B,Zhan Q M,Che E Z,et al.Automatic Registration of Terrestrial Laser Scanning Data Using Precisely Located Artificial Planar Targets[J].IEEE Geoscience & Remote Sensing Letters,2013,11(1):69-73
[8] 李庆,雍小龙,黄先锋,等.结合点云和影像的标靶自动识别与中心提取[J].测绘地理信息,2015,40(3):21-25
[9] Becerik-Gerber B,Jazizadeh F,Kavulya G,et al.Assessment of Target Types and Layouts in 3D Laser Scanning for Registration Accuracy [J].Automation in Construction,2011,20(5):649-658
[10] Franaszek M,Cheok G S,Witzgall C.Fast Automatic Registration of Range Images from 3D Imaging Systems Using Sphere Targets[J].Automation in Construction,2009,18(3):265-274
[11] He B,Lin Z,Li Y F.An Automatic Registration Algorithm for the Scattered Point Clouds Based on The Curvature Feature[J].Optics & Laser Technology,2013,46(1):53-60
[12] 石宏斌,殷义程,袁曼飞.点云数据的多几何面片特征自动识别[J].测绘通报,2017(2):6-9
[13] 张立朔,程效军,谭凯.基于强度图像的标靶球自动提取与参数拟合[J].工程勘察,2014,42(12):65-69
[14] 方伟,黄先锋,张帆,等.依据点云强度校正的壁画纠正[J].测绘学报,2015,44(5):541-547
[15] 王晏民,郭明.大规模点云数据的二维与三维混合索引方法[J].测绘学报,2012,41(4):605-612