Vehicle-mounted laser pavement point cloud thinning method capable of reserving spatial characteristics

Abstract

The invention discloses a vehicle-mounted laser pavement point cloud thinning method capable of reserving spatial characteristics. Firstly, preprocessing such as denoising and missing interpolation iscarried out on road surface point cloud; establishing a multi-scale grid for the pavement point cloud data, constructing a spatial hybrid index in combination with a KD tree, and quickly constructinga k neighborhood topological relation of data points; performing weighted principal component analysis and least square surface fitting on k neighborhoods of the data points, calculating a feature description subset, constructing a multi-feature description model by utilizing the feature description subset, performing pavement roughness analysis, and adaptively extracting pavement internal damagefeature points; extracting global edge feature points of the pavement by utilizing a distribution uniformity constraint method of the point cloud; and finally, for a non-feature region, performing secondary thinning by using a uniform grid gravity center method, and fusing the point cloud after secondary thinning with the feature points to complete thinning.

Description

technical field [0001] The invention relates to a vehicle-mounted laser road surface point cloud extraction method for preserving spatial characteristics, and relates to the technical field of vehicle-mounted mobile measurement data road surface disease detection and data reduction. Background technique [0002] With the rapid development of highway transportation, my country's highways have reached a high degree of coverage. In the future, the maintenance of the built roads will be one of the priorities. Due to the influence of extreme conditions such as rain, snow, high temperature, and overloading, the local road surface is deformed, forming various road surface damages such as cracks, looseness, wrapping, and pit sinking. Pavement damage reduces the overall smoothness of the pavement, affects the performance of the pavement, and brings great traffic safety hazards. [0003] At present, the traditional two-dimensional pavement measurement technology measurement and manu...

AI Technical Summary

Problems solved by technology

High-precision, high-density road surface point cloud data is helpful for the fine expression of road surface terrain features, but there is data redundancy in smooth areas, which is not conducive to data display and 3D reconstruction. Therefore, it is necessary to strategically thin out the road surface point cloud

[0004] In the process of realizing the present invention, the inventor found that the existing methods have the following deficiencies: the speed of constructing the k-neighborhood topology relationship for massive road surface point clouds is relatively slow; when the road surface point cloud is thinned out, the spatial characteristics of the road surface are ignored or the recognition method is single. As a result, part of the feature information is lost, or a large area of ​​point cloud holes is generated, and the data after thinning cannot accurately express the road surface information

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