Research On Classification And Extraction Of Railway Cross-Section Contour And Catenary Based On Vehicle LiDAR Laser Point Cloud Data

During the last few decades,the railway has become the primary choice for the interconnection of transportation infrastructure due to the advantages of safety,convenience,large traffic volume,and all-weather capability.The detection of existing railway infrastructure is a crucial part of railway operation.Disease detection and maintenance of the operational railway need to accurately master the basic data along the line through the repetition measurement of the railway.These basic data include the spatial information data of the railway,which is an important guarantee for railway shift arrangement,passenger comfort,and railway safety.The traditional repetition measurement of railway spatial information data requires a lot of manual field detection,which is potentially dangerous and accuracy of which highly depends on the working experience of the surveyors.Therefore,research on new methods of repetition measurement of railway spatial information data which have the characteristics of high-precision,high-efficiency and low-risk has become a major topic of current research.One of ways to accomplish this is through using vehicle LiDAR(Light Detection and Ranging,LiDAR),which has incomparable advantages in the collection of three-dimensional spatial information in the railway scene due to its fast scanning speed,non-contact,and high measurement accuracy.This thesis takes the post-processing of vehicle LiDAR point cloud data as the research goal and carries out the following research on the point cloud data processing of the crosssectional contour of the railway,the vertex of the rail and the contact network:(1)This thesis gives the overview of the current state of research on the vehicle LiDAR system,and compares it with airborne LiDAR technology to find its applicability in existing railway measurements.Previous research findings and problems to be solved in the application of LiDAR technology in the existing railway resurvey are also summarized.On this basis,the research content of this thesis is put forward.(2)Research on the principle of the commonly used point cloud segmentation algorithm is studied in this thesis,including advantages and disadvantages of each algorithm and the applicability of the scene.The segmentation algorithm of Euclidean distance clustering based on distance attributes is adopted to achieve the segmentation of railway ground point cloud and non-ground point cloud.(3)Aiming at the railway ground point cloud,a method of cutting railway cross-section based on the POS(Position and Orientation System,POS)line is proposed and the Alpha Shape algorithm is utilized to extract the contour of the railway cross-section point cloud data..According to the characteristics of the railway cross-section and the spatial characteristics of the POS line,an algorithm for extracting the point cloud of cross-section rail vertex based on POS line projection is proposed and the extraction of the rail vertex is realized in this thesis.The average value of the overall accuracy of the extraction of the rail vertex of the final straight line area and the curve area are 98.66% and 96.50% respectively.(4)In view of the spatial characteristics of the contact network,a coarse-to-fine method of classification and extraction is adopted in this thesis.The classification strategy based on the combination of multi-scale adaptive feature classification algorithm and DBSCAN(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)algorithm considering contact network features to achieve the fine classification of contact network point cloud.Firstly,the adaptive multi-scale space algorithm is used to roughly classify the contact network dataset,and the contact network data with linear characteristics are retained.Secondly,DBSCAN algorithm considering contact network features is used to further finely classify the rough classified contact network data,so that the contact network can be accurately classified into contact cable,catenary cables,and return current cable.The average value of the overall accuracy of the final classification results can reach 99.69%.In summary,this thesis studies a new method of cross-section point cloud cutting and a new algorithm for extracting the point cloud of cross-section rail vertex.A coarse-to-fine contact network classification extraction method is also adopted in vehicle LiDAR point cloud data processing.It plays a guiding role in the actual production and operation of the railway and enriched the efficient,accurate,and automatic processing methods of railway spatial information data.