一种高程相关的机载LiDAR点云配准方法

针对现有配准方法难以提取大范围机载LiDAR点云特征信息的问题,提出了一种基于2片待配准机载LiDAR点云高程数据相关的点云自动配准方法。首先,将待配准点一定范围内的点云拟合局部曲面;然后,在另一点云片中确定搜索区域,利用拟合结果求解搜索区域内的点云在拟合曲面上的高程;最后,通过计算拟合高程与实际高程的相关系数,选择搜索区域内相关系数最大位置作为配准的关键点参与点云配准,反复迭代直到完成配准。文章用实际采集的机载LiDAR数据进行了实验分析,并与传统的ICP算法进行了对比。实验结果表明,该方法在配准精度上能达到较高的水准,能够满足机载LiDAR点云配准的要求。

An Airborne LiDAR Point Cloud Registration Method Based on Elevation Correlation

Because the existing registration methods are difficult to extract the feature information of large scale airborne LiDAR point clouds,an automatic point cloud registration method based on two pieces of airborne LiDAR point cloud elevation data is proposed.Firstly,the point clouds within a certain distance of the point for registration are fitted to a local surface.Then,the search area is determined in another point cloud piece and the elevation of the point clouds in the search area on the fitting surface is solved by using the fitting results.Finally,by calculating the correlation coefficient between the fitting elevation and the actual elevation,the position with the maximum correlation coefficient in the search area is selected as the key point of registration to participate in the point cloud registration,and the registration is iterated until the registration is completed.In this paper,the experimental analysis of airborne LiDAR data is performed and compared with the traditional ICP algorithm.The results show that this method can achieve a high level of accuracy,which could meet the requirements of airborne LiDAR point cloud registration.