Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z)
Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z)
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DOI：
10.1016/j.isprsjprs.2013.04.009
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发表时间：
2013-08-01
影响因子：
12.7
通讯作者：
Leroux, Jerome
中科院分区：
文献类型：
--
作者：
Lague, Dimitri;Brodu, Nicolas;Leroux, Jerome
最近已使用诸如陆地激光扫描仪之类的测量技术通过3D点云（PC）比较来测量表面变化。已经采用了两种类型的方法：表面同源部分的3D跟踪以计算位移场，以及当无法定义同源部分时，两个点云之间的距离计算。这项研究涉及第二种方法，即侵蚀，沉积或调查之间的植被改变的典型自然表面。当前的比较方法基于最接近的点距离，或者当表面在各个尺度上呈现粗糙度元素时，至少需要一台PC与严重的局限性进行分离。为了解决这些问题，我们引入了一种新算法，对3D中的点云进行直接比较。该方法有两个步骤：（1）表面正常估计和3D的方向在与局部表面粗糙度一致的尺度上； （2）通过明确计算局部置信区间的平均表面变化沿正常方向进行测量。与现有方法的比较证明了我们的方法的较高准确性，并且由于没有表面网格划分或数字高程模型（DEM）的生成，因此更容易的工作流程。该方法在快速侵蚀的，蜿蜒的基岩河（Rangitikei河峡谷）中的应用说明了其在复杂情况下处理3D差异的能力（同一场景上的平坦和垂直表面），以减少与局部平均和局部平均粗糙度相关的不确定性生成不确定性水平的3D地图。我们还证明，对于高精度调查扫描仪，变更检测的总误差预算由点云注册误差和表面粗糙度主导。结合点云的MM范围局部地理发作，检测水平降低至6 mm（定义为95％的置信度），可以在50 m的范围内原位实现。我们为不同表面的自我伴随行为提供了证据。我们展示了这如何影响正常向量的计算，并证明了变化检测水平的缩放行为。该算法已在免费的开源软件包中实现。它在复杂的3D病例中运行，也可以用作更简单，更强大的替代方法，可在2D情况下进行DEM差异。 （c）2013年国际摄影和遥感学会（ISPRS）由Elsevier B.V.保留所有权利。
Surveying techniques such as terrestrial laser scanner have recently been used to measure surface changes via 3D point cloud (PC) comparison. Two types of approaches have been pursued: 3D tracking of homologous parts of the surface to compute a displacement field, and distance calculation between two point clouds when homologous parts cannot be defined. This study deals with the second approach, typical of natural surfaces altered by erosion, sedimentation or vegetation between surveys. Current comparison methods are based on a closest point distance or require at least one of the PC to be meshed with severe limitations when surfaces present roughness elements at all scales. To solve these issues, we introduce a new algorithm performing a direct comparison of point clouds in 3D. The method has two steps: (1) surface normal estimation and orientation in 3D at a scale consistent with the local surface roughness; (2) measurement of the mean surface change along the normal direction with explicit calculation of a local confidence interval. Comparison with existing methods demonstrates the higher accuracy of our approach, as well as an easier workflow due to the absence of surface meshing or Digital Elevation Model (DEM) generation. Application of the method in a rapidly eroding, meandering bedrock river (Rangitikei River canyon) illustrates its ability to handle 3D differences in complex situations (flat and vertical surfaces on the same scene), to reduce uncertainty related to point cloud roughness by local averaging and to generate 3D maps of uncertainty levels. We also demonstrate that for high precision survey scanners, the total error budget on change detection is dominated by the point clouds registration error and the surface roughness. Combined with mm-range local georeferencing of the point clouds, levels of detection down to 6 mm (defined at 95% confidence) can be routinely attained in situ over ranges of 50 m. We provide evidence for the self-affine behaviour of different surfaces. We show how this impacts the calculation of normal vectors and demonstrate the scaling behaviour of the level of change detection. The algorithm has been implemented in a freely available open source software package. It operates in complex 3D cases and can also be used as a simpler and more robust alternative to DEM differencing for the 2D cases. (c) 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.