用地面laser扫描仪对复杂地形进行精确的三维比较：应用于Rangitikei峡谷（N-Z）
Accurate 3D comparison of complex topography with terrestrial laser scanner: application to the Rangitikei canyon (N-Z)
摘要：
测量技术，如地面激光扫描仪最近已被用来测量表面变化，通过三维点云（PC）比较。已经追求两种类型的方法：3D跟踪的表面的同源部分计算位移场，和两个点云之间的距离计算时，同源部分不能被定义。本研究涉及第二种方法，典型的自然表面改变的侵蚀，沉积或植被之间的调查。当前的比较方法是基于最近点距离或要求至少一个PC进行网格化，当表面在所有尺度上呈现粗糙度元素时具有严重的限制。我们介绍了一种新的算法进行直接比较的点云在3D。首先在3D中以与局部表面粗糙度一致的比例估计表面法线。然后通过显式计算置信区间来测量沿法线方向的平均变化。与现有方法的比较表明，我们的方法具有更高的精度，以及更容易的工作流程，由于没有表面网格或DEM生成。该方法在一个快速侵蚀的弯曲基岩河流（Rangitikei河峡谷）的应用说明了它的能力，以处理复杂的情况下（平面和垂直表面在同一场景）的三维差异，以减少相关的不确定性点云粗糙度通过本地平均和生成的不确定性水平的三维地图。结合点云的毫米范围局部地理参考，可以在50米范围内现场常规达到低至6毫米的检测水平。我们提供了证据的自仿射行为不同的表面。我们展示了这如何影响法向量的计算，并演示了变化检测水平的缩放行为。
英文摘要：
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. We introduce a new algorithm performing a direct comparison of point clouds in 3D. Surface normals are first estimated in 3D at a scale consistent with the local surface roughness. The measurement of the mean change along the normal direction is then performed with an explicit calculation of a 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 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. Combined with mm-range local georeferencing of the point clouds, levels of detection down to 6 mm can be routinely attained in situ over ranges of 50 m. We provide evidence for the self-affine behavior of different surfaces. We show how this impacts the calculation of normal vectors and demonstrate the scaling behavior of the level of change detection.
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