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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Ground Surface Recognition at Voxel Scale From Mobile Laser Scanning Data in Urban Environment
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Ground Surface Recognition at Voxel Scale From Mobile Laser Scanning Data in Urban Environment

机译：来自城市环境中的移动激光扫描数据的体素刻度的地面识别

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摘要

Ground filtering is an essential process for further classification of other features from a point cloud. This letter aims to develop an efficient method for the automatic recognition of a ground surface in urban environments from a point cloud acquired by mobile laser scanning (MLS). The MLS point cloud has large amounts of data. To decrease the calculation effort, the point clouds are first segmented into regular voxels using an octree structure. Then, voxel cloud connectivity segmentation (VCCS) is applied to generate supervoxels, which alleviate the issue of boundary cross between voxels and ground truth. Finally, several geometric features on the voxel scale are selected for a support vector machine (SVM) to label segments as a ground surface and nonground objects. For an objective comparison with other methods, the accuracy of the proposed method is evaluated by public MLS data sets. Experimental results show that all ground type objects can be well recognized with least susceptibility to scenic change.
机译：地面过滤是从点云进一步分类其他特征的重要过程。这封信旨在通过移动激光扫描（MLS）获取的点云，开发一种高效的方法，用于从城市环境中的城市环境中的地面。 MLS点云具有大量数据。为了减少计算工作，将点云首先使用Octree结构分段为常规体素。然后，应用体素云连接分割（VCCS）来生成超值，这缓解了体素与地面真理之间的边界交叉问题。最后，为支持向量机（SVM）选择了voxel刻度上的几个几何特征，以将段标记为地面和非应对象。对于与其他方法的客观比较，所提出的方法的准确性由公共MLS数据集评估。实验结果表明，所有地面型对象都可以很好地识别出对景区变化的最小敏感性。

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