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首页> 外文期刊>ISPRS International Journal of Geo-Information >Registration of Multi-Sensor Bathymetric Point Clouds in Rural Areas Using Point-to-Grid Distances
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Registration of Multi-Sensor Bathymetric Point Clouds in Rural Areas Using Point-to-Grid Distances

机译：基于点到网格距离的农村多传感器测深点云的配准

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This article proposes a method for registration of two different point clouds with different point densities and noise recorded by airborne sensors in rural areas. In particular, multi-sensor point clouds with different point densities are considered. The proposed method is marker-less and uses segmented ground areas for registration.Therefore, the proposed approach offers the possibility to fuse point clouds of different sensors in rural areas within an accuracy of fine registration. In general, such registration is solved with extensive use of control points. The source point cloud is used to calculate a DEM of the ground which is further used to calculate point to raster distances of all points of the target point cloud. Furthermore, each cell of the raster DEM gets a height variance, further addressed as reconstruction accuracy, by calculating the grid. An outlier removal based on a dynamic threshold of distances is used to gain more robustness against noise and small geometry variations. The transformation parameters are calculated with an iterative least-squares optimization of the distances weighted with respect to the reconstruction accuracies of the grid. Evaluations consider two flight campaigns of the Mangfall area inBavaria, Germany, taken with different airborne LiDAR sensors with different point density. The accuracy of the proposed approach is evaluated on the whole flight strip of approximately eight square kilometers as well as on selected scenes in a closer look. For all scenes, it obtained an accuracy of rotation parameters below one tenth degrees and accuracy of translation parameters below the point spacing and chosen cell size of the raster. Furthermore, the possibility of registration of airborne LiDAR and photogrammetric point clouds from UAV taken images is shown with a similar result. The evaluation also shows the robustness of the approach in scenes where a classical iterative closest point (ICP) fails.
机译：本文提出了一种通过农村地区机载传感器记录的具有不同点密度和噪声的两个不同点云的配准方法。特别地，考虑具有不同点密度的多传感器点云。所提出的方法是无标记的并且使用分割的地面区域进行配准。因此，所提出的方法提供了在精细配准的精度内融合农村地区不同传感器的点云的可能性。通常，这种注册通过广泛使用控制点来解决。源点云用于计算地面的DEM，进一步用于计算目标点云所有点的点到栅格距离。此外，通过计算网格，栅格DEM的每个像元都会得到高度变化，进一步将其视为重建精度。基于距离动态阈值的离群值消除用于获得针对噪声和较小几何形状变化的更高鲁棒性。相对于网格的重建精度，通过加权的距离的迭代最小二乘优化来计算转换参数。评估考虑了德国巴伐利亚州Mangfall地区的两次飞行战役，它们是使用具有不同点密度的不同机载LiDAR传感器进行的。在大约8平方公里的整个飞行带上以及在仔细观察的选定场景上评估了所提出方法的准确性。对于所有场景，它获得的旋转参数精度低于十分之一度，平移参数的精度低于点间距和栅格的选定像元大小。此外，显示了从无人机拍摄的图像记录机载LiDAR和摄影测量点云的可能性，结果类似。评估还显示了在经典迭代最近点（ICP）失败的场景中该方法的鲁棒性。

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