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Hierarchical Registration Method for Airborne and Vehicle LiDAR Point Cloud

机译:机载LiDAR点云的分层配准方法

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A new hierarchical method for the automatic registration of airborne and vehicle light detection and ranging (LiDAR) data is proposed, using three-dimensional (3D) road networks and 3D building contours. Firstly, 3D road networks are extracted from airborne LiDAR data and then registered with vehicle trajectory lines. During the registration of airborne road networks and vehicle trajectory lines, a network matching rate is introduced for the determination of reliable transformation matrix. Then, the RIMM (reversed iterative mathematic morphological) method and a height value accumulation method are employed to extract 3D building contours from airborne and vehicle LiDAR data, respectively. The Rodriguez matrix and collinearity equation are used for the determination of conjugate building contours. Based on this, a rule is defined to determine reliable conjugate contours, which are finally used for the fine registration of airborne and vehicle LiDAR data. The experiments show that the coarse registration method with 3D road networks can contribute to a reliable initial registration result, and the fine registration using 3D building contours obtains a final registration result with high reliability and geometric accuracy.
机译:提出了一种使用三维(3D)道路网络和3D建筑轮廓自动注册机载和车辆光检测和测距(LiDAR)数据的新分层方法。首先,从机载LiDAR数据中提取3D道路网络,然后将其与车辆轨迹线对齐。在空中道路网络和车辆轨迹线的配准过程中,引入网络匹配率以确定可靠的转换矩阵。然后,采用RIMM(逆向迭代数学形态学)方法和高度值累积方法分别从机载和车辆LiDAR数据中提取3D建筑轮廓。 Rodriguez矩阵和共线性方程式用于确定共轭建筑轮廓。基于此,定义规则以确定可靠的共轭轮廓,最后将其用于机载和车辆LiDAR数据的精细配准。实验表明,采用3D道路网的粗注册方法可以为可靠的初始注册结果做出贡献,而使用3D建筑轮廓进行的精细注册可以获得具有高可靠性和几何精度的最终注册结果。

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