ABSTRACT

Mobile laser scanning data can be used for effective extraction of road edge information, which is important in the domain of road maintenance and intelligent transportation. This paper proposes a road edge detection method that combines a deep learning and spatial statistics of point cloud data. Semantic segmentation using a deep neural network enables the effective extraction of point cloud fragments recognized as road. The process continues with the spatial statistical analysis of voxel features of data organized into a 3D voxel grid. Filtered voxels are clustered into spatially proximate clusters of similar shape, i.e. straight or curved edges.

中文翻译：

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基于激光雷达数据深度学习与空间统计相结合的道路边缘检测

摘要

利用移动激光扫描数据可以有效提取道路边缘信息，这在道路养护和智能交通领域具有重要意义。本文提出了一种结合深度学习和点云数据空间统计的道路边缘检测方法。使用深度神经网络的语义分割可以有效提取被识别为道路的点云片段。该过程继续对组织成 3D 体素网格的数据体素特征进行空间统计分析。过滤后的体素被聚类成形状相似的空间上邻近的簇，即直的或弯曲的边缘。