Precision Evaluation and Fusion of Topographic Data Based on UAVs and TLS Surveys of a Loess Landslide
Precision Evaluation and Fusion of Topographic Data Based on UAVs and TLS Surveys of a Loess Landslide
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DOI：
10.3389/feart.2021.801293
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发表时间：
2021-12
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影响因子：
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通讯作者：
Zhong-an Mao;Shen Hu;Ninglian Wang;Yongqing Long
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作者：
Zhong-an Mao;Shen Hu;Ninglian Wang;Yongqing Long
近年来，低成本的无人机（UAV）摄影测量和地面激光扫描（TLS）技术已成为获取滑坡地形数据的重要非接触测量方法。然而，由于无人机类型和测量中是否设置地面控制点的差异，所获得的滑坡地形数据往往存在较大的精度差异。在这项研究中，两种类型的无人机（DJI Mavic Pro和DJI Phantom 4 RTK）使用和不使用GCPs来调查黄土滑坡。获得了滑坡的无人机点云和数字表面模型（DSM）数据。在此基础上，利用地貌变化检测软件（GCD 7.0）和Cloud Compare软件中的多尺度模型间云比较（M3 C2）算法，对相同和不同无人机获取的点云和DSM数据进行对比分析和精度评价。实验结果表明，DJI Phantom 4 RTK在设置GCPs时获得的滑坡地形数据精度最高。此外，我们还使用Maptek I-Site 8，820地面激光扫描仪获得了北国滑坡的更高精度地形点云数据。但由于地形条件的限制，在TLS测量的盲区内，部分点云数据丢失。为了弥补TLS的扫描缺陷，我们使用Cloud Compare软件中的迭代最近点（ICP）算法，将DJI Phantom 4 RTK与GCPs获得的点云与TLS获得的点云进行数据融合。结果表明，数据融合后的点云不仅保留了TLS原始点云的高精度特性，而且填补了TLS数据的盲区。为无人机地质灾害调查精度评估和基于不同传感器的点云数据融合提供了一种新的视角和技术方案。
In recent years, low-cost unmanned aerial vehicles (UAVs) photogrammetry and terrestrial laser scanner (TLS) techniques have become very important non-contact measurement methods for obtaining topographic data about landslides. However, owing to the differences in the types of UAVs and whether the ground control points (GCPs) are set in the measurement, the obtained topographic data for landslides often have large precision differences. In this study, two types of UAVs (DJI Mavic Pro and DJI Phantom 4 RTK) with and without GCPs were used to survey a loess landslide. UAVs point clouds and digital surface model (DSM) data for the landslide were obtained. Based on this, we used the Geomorphic Change Detection software (GCD 7.0) and the Multiscale Model-To-Model Cloud Comparison (M3C2) algorithm in the Cloud Compare software for comparative analysis and accuracy evaluation of the different point clouds and DSM data obtained using the same and different UAVs. The experimental results show that the DJI Phantom 4 RTK obtained the highest accuracy landslide terrain data when the GCPs were set. In addition, we also used the Maptek I-Site 8,820 terrestrial laser scanner to obtain higher precision topographic point cloud data for the Beiguo landslide. However, owing to the terrain limitations, some of the point cloud data were missing in the blind area of the TLS measurement. To make up for the scanning defect of the TLS, we used the iterative closest point (ICP) algorithm in the Cloud Compare software to conduct data fusion between the point clouds obtained using the DJI Phantom 4 RTK with GCPs and the point clouds obtained using TLS. The results demonstrate that after the data fusion, the point clouds not only retained the high-precision characteristics of the original point clouds of the TLS, but also filled in the blind area of the TLS data. This study introduces a novel perspective and technical scheme for the precision evaluation of UAVs surveys and the fusion of point clouds data based on different sensors in geological hazard surveys.