Automatic classifying and identifying method for road line point cloud based on three-dimensional laser reflection strength

Abstract

The invention relates to a method for automatic classification and recognition of road marking point clouds based on three-dimensional laser reflection intensity. The operation steps are: using a laser scanner to obtain point cloud data, and according to the attitude and position information of the POS system in each scanning circle of the point cloud data , calculate the equation of the POS plane for each scanning circle; calculate the angle between the three-dimensional coordinate points in the scanning circle and the POS center and the POS plane, compare the angle with the angle threshold, and judge the point greater than the angle threshold as the road surface point; calculate the initial judgment again as the distance from the road surface point to the POS plane of the scanning circle where the point is located, take the height value H installed when the POS system is installed as the known value, and compare the difference between the calculated distance value and the height value H, and the difference is less than The point with the threshold value is the road surface point; the intensity value of the second determination as the road surface point is counted, and the point with the intensity value greater than the threshold value is determined as the road marking line. The method has the advantages of rapidity, high efficiency, and convenience for repeated troubleshooting.

Description

technical field [0001] The invention relates to an automatic classification and recognition method for road marking point clouds based on three-dimensional laser reflection intensity, and belongs to the technical fields of photogrammetry and three-dimensional laser. Background technique [0002] Road markings are one of the important symbols of the urban environment and are of great significance to urban traffic planning, order maintenance and management. The collection and identification of road markings based on the traditional surveying and mapping methods based on total stations include the traditional operation method of geodetic step-by-step collection based on total stations, and the selective extraction based on aerial remote sensing images. However, the former has a huge amount of field collection and low work efficiency. The latter has higher requirements on the spatial resolution of image data, while the acquisition conditions of high-resolution aerial image data ...

AI Technical Summary

Problems solved by technology

The collection and identification of road markings based on the traditional surveying and mapping methods based on total stations include the traditional operation method of geodetic step-by-step collection based on total stations, and the selective extraction based on aerial remote sensing images. However, the former has a huge amount of field collection and low work efficiency. The latter has higher requirements on the spatial resolution of image data, while the acquisition conditions of high-resolution aerial image data are relatively strict and difficult to obtain

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