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Hierarchical extraction of pole-like objects from scene point clouds

Abstract. We present a hierarchical extraction algorithm to extract pole-like objects (PLOs) from scene point clouds. First, the point clouds are divided into a set of data blocks along the x- and y-axes after computing the dimensionality structure of each point. An effective height constrained voxel-based segmentation algorithm is proposed to segment the scene point clouds. The adjacent voxels with similar heights are grouped into an individual object based on the spatial proximity and height information. The objects consisting of a smaller number of voxels and most of the linear points are extracted and regarded as the pole-like candidates (PLCs). Then a Euclidean distance clustering algorithm is adopted to segment the PLCs and remove the floating and short segments. Next, each PLC is divided along the z-axis to extract the vertical structure. The straightness of the vertical structure is computed to remove the false PLOs. Finally, a collection of characteristics, such as point distribution and size, are applied to classify the PLOs into a street light pole, high-mast light, beacon light, and single pole. The experimental results demonstrate that our method can extract PLOs quickly and effectively.