Vehicular point cloud clustering method and system

Abstract

The invention provides a vehicular point cloud clustering method and system. The vehicular point cloud clustering method comprises carrying out de-noising and filtering on point clouds, removing scattered noise points on the point clouds, carrying out filtering processing on the point clouds, and distinguishing between ground points and non-ground points; segmenting the point clouds to generate super voxels, segmenting the non-ground points by means of a spatial clustering algorithm based on the density to generate the super voxels, and guaranteeing that different surface features do not adhere to each other; clustering the point clouds which have spatial context linkage, analyzing features and the spatial context linkage of the super voxels, carrying out region growing on the super voxels by integrating multifactor weights, and completing point cloud clustering. According to the invention, the problem that during the vehicular point cloud clustering process, over-segmentation or insufficient segmentation occurs is improved, the demand of obtaining 3d spatial information quickly is satisfied, and the vehicular point cloud clustering method and system have an important market value.

Description

technical field [0001] The invention relates to the technical field of vehicle-mounted laser scanning data processing, in particular to a vehicle-mounted point cloud clustering method and system combined with spatial context association. Background technique [0002] The point cloud data acquired by the vehicle-borne laser scanning system (Vehicle-borne Laser Scanning, VLS) has the characteristics of high density, high precision, and fast acquisition. At present, scholars at home and abroad have conducted a lot of research on the application of point cloud data, among which vehicle-mounted point cloud clustering is an important part of VLS data processing and information extraction, and is the premise and key link to realize automatic recognition of ground objects. [0003] The current common point cloud clustering methods can be divided into partition clustering, hierarchical clustering, grid clustering and density clustering. The division clustering method depends on the ...

AI Technical Summary

Problems solved by technology

However, urban streetscapes are densely populated with vertically distributed features, and the adhesion between features is serious. In this case, density clustering that only considers the spatial distance between points will cause serious under-segmentation or over-segmentation. Happening

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