A Strip Adjustment Method of UAV-Borne LiDAR Point Cloud Based on DEM Features for Mountainous Area.
A Strip Adjustment Method of UAV-Borne LiDAR Point Cloud Based on DEM Features for Mountainous Area.
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基于DEM特征的山区无人机机载LiDAR点云带状平差方法
DOI：
10.3390/s21082782
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发表时间：
2021-04-15
期刊：
影响因子：
--
通讯作者：
Yang B
中科院分区：
文献类型：
--
作者：
Chen Z;Li J;Yang B
由于无人机激光扫描（ULS）中低精度定位定向系统（POS）的轨迹误差，相邻LiDAR（Light Detection and Ranging）条带之间往往存在误差。条带平差是消除这些差异的有效方法。但现有的条带平差方法在山区人工地物少的情况下难以推广应用。为此，本文提出了一种考虑地形特征的两两配准方法--数字高程模型-迭代最近点法（DEM-ICP）。首先，DEM-ICP过滤点云以去除非地面点。其次，地面点插值生成连续的DEM。最后，一个点到平面ICP算法进行注册的相邻DEM与重叠区域。在DEM-ICP之后利用基于图的优化来估计校正参数并实现所有条带之间的全局一致性。实验进行了使用ULS在山区收集的8条，以评估所提出的方法。所有数据的平均均方根误差（RMSE）小于0.4米后，拟议的条带调整，这是只有0.015米高于手动注册的结果（地面实况）。此外，侧面点云的平面拟合精度提高了4.2倍，从1.565到0.375米，证明了所提出的方法的鲁棒性和准确性。
Due to the trajectory error of the low-precision position and orientation system (POS) used in unmanned aerial laser scanning (ULS), discrepancies usually exist between adjacent LiDAR (Light Detection and Ranging) strips. Strip adjustment is an effective way to eliminate these discrepancies. However, it is difficult to apply existing strip adjustment methods in mountainous areas with few artificial objects. Thus, digital elevation model-iterative closest point (DEM-ICP), a pair-wise registration method that takes topography features into account, is proposed in this paper. First, DEM-ICP filters the point clouds to remove the non-ground points. Second, the ground points are interpolated to generate continuous DEMs. Finally, a point-to-plane ICP algorithm is performed to register the adjacent DEMs with the overlapping area. A graph-based optimization is utilized following DEM-ICP to estimate the correction parameters and achieve global consistency between all strips. Experiments were carried out using eight strips collected by ULS in mountainous areas to evaluate the proposed method. The average root-mean-square error (RMSE) of all data was less than 0.4 m after the proposed strip adjustment, which was only 0.015 m higher than the result of manual registration (ground truth). In addition, the plane fitting accuracy of lateral point clouds was improved 4.2-fold, from 1.565 to 0.375 m, demonstrating the robustness and accuracy of the proposed method.
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