Railway track semi-automatic detection method based on integration of reflection intensity and geometric features

Abstract

The present invention discloses a railway track semi-automatic detection method based on the integration of the reflection intensity and geometric features. The method comprises the steps of 1, respectively calculating the local geometric features of each laser footprint in laser scanning data, wherein the local geometric features are composed of the main direction and the normal vector of the neighborhood point distribution of the laser footprint; 2, conducting the point cloud clustering based on the reflection intensity and the local geometric features and extracting the point cloud of a railway track. According to the technical scheme of the invention, the semi-automatic extraction for the point cloud data of the railway track can be quickly and robustly achieved. Therefore, the extraction accuracy, the extraction efficiency and the automation degree of the point cloud data of the railroad track is improved. Moreover, the method is simple and easy to implement.

Description

technical field [0001] The invention belongs to the technical field of intelligent processing of laser scanning data, and in particular relates to a semi-automatic detection method of a railway track which combines reflection intensity and geometric features. Background technique [0002] Railway is an important infrastructure of the country and plays a key role in the comprehensive transportation system. At present, in railway track surveying, conventional surveying and mapping techniques mainly use ground surveying instruments such as total stations and levels, or optical aerial photogrammetry, close-range photogrammetry and other means. These methods have a large workload in the field, low degree of automation, and low work efficiency. , The long production cycle is difficult to meet the requirements of the current railway survey for the extraction of railway thematic elements, the cycle of drawing and the accuracy. Vehicle-mounted and airborne laser scanning technology ...

AI Technical Summary

Problems solved by technology

[0004] In general, there are still problems in quickly and accurately extracting railway tracks from large-scale laser scanning data: 1) The calculation of local geometric features is sensitive to point density changes, noise, etc., resulting in low accuracy of feature calculation; 2) Railway track The correct rate and accuracy of extraction are relatively low, and it is difficult to meet the needs of automatic extraction; 3) It is only suitable for railway track extraction in a single type of data and a single scene, lacking wide applicability, and the degree of automation in actual production is low And other issues

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