Road boundary point automatic extracting and vectorizing method based on on-vehicle laser scanning data

Abstract

The invention discloses a road boundary point automatic extracting and vectorizing method based on on-vehicle laser scanning data. The method comprises the steps of a first step, calculating the characteristic of each laser footpoint in three-dimensional laser point cloud data; a second step, according to the characteristic of each laser footpoint, classifying the laser footpoints for obtaining road boundary points and non-road-boundary points by means of a naive Bayes classifier, and marking the obtained road boundary points as initial road boundary points; a third step, establishing a KD tree by means of all initial road boundary points, and respectively calculating the directional characteristic of each initial road boundary point; a fourth step, according to the directional characteristic of the initial road boundary point, clustering the initial road boundary points by means of the KD tree; and a fifth step, calculating the characteristic of each clustering area, eliminating the clustering areas which do not satisfy a preset condition, and obtaining a road boundary point extracting result. The road boundary point automatic extracting and vectorizing method improves automatic degree and production efficiency in point cloud data processing. Furthermore the road boundary point automatic extracting and vectorizing method has advantages of simple operation, easy realization and high practical value.

Description

technical field [0001] The invention belongs to the technical field of laser scanning data intelligence, and more specifically relates to an automatic extraction and vectorization method of road ridge points based on vehicle-mounted laser scanning data. Background technique [0002] Road boundary information is one of the important components of basic geographic information. Accurate and high-precision road information plays an important role in urban planning, traffic control, and emergency response (Yang Bisheng, 2013). At the same time, the road boundary is also an important information for autonomous navigation of unmanned vehicles. It distinguishes the road area from the surrounding environment and provides feasible areas for unmanned vehicles. Semi-automatic or automatic extraction of road information based on optical images has always been a research hotspot in the field of photogrammetry and remote sensing. However, the road extraction results based on optical image...

AI Technical Summary

Problems solved by technology

[0004] In general, there are still problems in quickly and accurately extracting road sill points from large-scale laser scanning data: (1) It is seriously affected by factors such as noise, point density changes, and occlusion, resulting in low accuracy of road sill extraction ; (2) It is only suitable for road sill extraction in certain scenarios such as urban areas, expressways, and national highways, lacking wide applicability, and low degree of automation in actual production, etc.

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