利用结构特征的点云快速配准算法
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摘要
为提高三维激光扫描点云的配准精度以及效率,解决数据点缺失、点云散乱时的配准问题,结合点云的全局和局部结构特征的不变特性,提出基于全局结构特征的初始配准算法和利用局部结构特征的快速精确配准算法。首先,给出全局结构特征的定义,并阐明初始配准方法,证明在点云样本集缺失数据时初始配准算法的有效性;然后,给定一种空间区域的划分方式,并找出划分的空间区域中两个点云的对应点;最后,通过找出的有限个对应点实现点云的精确配准。在仿真和实验数据处理时,该精确配准算法能够有效地完成缺失、散乱点云的精确、快速配准,且在效率和精度上比其他几种算法具有明显优势。
To improve the registration of point clouds scanned by 3 Dlaser in terms of accuracy and efficiency,and solve the registration problems when data points are missing and out of order,based on the invariant characteristics of global and local structure features of point clouds,an initial registration algorithm using global structure features and a fast and accurate registration algorithm using local structure features are proposed.First,the global structure feature and the initial registration method are defined.The validity of the initial registration is strictly proved when the data point is lost.Then,we propose a way to partition the spatial region and find out the corresponding points of the two point clouds in the spatial region.Finally,the two clouds achieve precise registration through the corresponding points found.In the processes of simulation and experiment,the proposed algorithm can effectively perform accurate and rapid registration of missing and scattered point clouds.It has obvious advantages in efficiency and accuracy than other algorithms.
To improve the registration of point clouds scanned by 3 Dlaser in terms of accuracy and efficiency,and solve the registration problems when data points are missing and out of order,based on the invariant characteristics of global and local structure features of point clouds,an initial registration algorithm using global structure features and a fast and accurate registration algorithm using local structure features are proposed.First,the global structure feature and the initial registration method are defined.The validity of the initial registration is strictly proved when the data point is lost.Then,we propose a way to partition the spatial region and find out the corresponding points of the two point clouds in the spatial region.Finally,the two clouds achieve precise registration through the corresponding points found.In the processes of simulation and experiment,the proposed algorithm can effectively perform accurate and rapid registration of missing and scattered point clouds.It has obvious advantages in efficiency and accuracy than other algorithms.
引文
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[14] Bae K H.Evaluation of the convergence region of an automated registration method for 3Dlaser scanner point clouds[J].Sensors,2009,9(1):355-375.
[15] Ying S H,Peng J G,Du S Y,et al.A scale stretch method based on ICP for 3D data registration[J].IEEE Transactions on Automation Science and Engineering,2009,6(3):559-565.
[2] Han X,Zan N,Zhen Z,et al.Registration of point cloud data based on PSO[C].AASRI International Conference on Circuits and Systems,2015.
[3] Hai V P,Lech M,Nguyen T D.Registration of 3D range images using particle swarm optimization[C].Asian Computing Science Conference,2004:223-235.
[4] Ji S J,Ren Y C,Ji Z,et al.An improved method for registration of point cloud[J].Optik-International Journal for Light and Electron Optics,2017,140:451-458.
[5] Yan S J,Zhou Y F,Peng F Y,et al.Research on the localisation of the workpieces with large sculptured surfaces in NC machining[J].The International Journal of Advanced Manufacturing Technology,2004,23(5/6):429-435.
[6] Huang A W,Sullivan J M,Kulkarni P,et al.Automatic 3D image registration usingvoxel similarity measurements based on a genetic algorithm[J].Proceedings of SPIE,2006,6144:614430.
[7] He Y,Liang B,Yang J,et al.An iterative closest points algorithm for registration of 3Dlaser scanner point clouds with geometric features[J].Sensors,2017,17(8):1862.
[8] Zeng F X,Li L,Diao X P.Iterative closest point algorithm registration based on curvature features[J].Laser&Optoelectronics Progress,2017,54(1):011003.曾繁轩,李亮,刁鑫鹏.基于曲率特征的迭代最近点算法配准研究[J].激光与光电子学进展,2017,54(1):011003.
[9] Ge B,Peng B, Tian Q.Registration of threedimensional point-cloud data based on curvature map[J].Journal of Tianjin University,2013,46(2):174-180.
[10] Jiang J,Cheng J,Chen X L.Registration for 3D point cloud using angular-invariant feature[J].Neurocomputing,2009,72(16):3839-3844.
[11] Zhang Z,Xu H L,Yin H.A fast point cloud registration algorithm based on key point selection[J].Laser&Optoelectronics Progress,2017,54(12):121001.张哲,许宏丽,尹辉.一种基于关键点选择的快速点云配准算法[J].激光与光电子学进展,2017,54(12):121001.
[12] Cheng L,Tong L H,Li M C,et al.Semi-automatic registration of airborne and terrestrial laser scanning data using building corner matching with boundaries as reliability check[J].Remote Sensing,2013,5(12):6260-6283.
[13] Sharp G C,Lee S W,Wehe D K.ICP registration using invariant features[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(1):90-102.
[14] Bae K H.Evaluation of the convergence region of an automated registration method for 3Dlaser scanner point clouds[J].Sensors,2009,9(1):355-375.
[15] Ying S H,Peng J G,Du S Y,et al.A scale stretch method based on ICP for 3D data registration[J].IEEE Transactions on Automation Science and Engineering,2009,6(3):559-565.