Urban rail tunnel global full-station scanning of clearance section measurement and automatic evaluation method

Abstract

The application is suitable for the field of urban rail transit tunnel engineering, and provides a clearance section measurement and automatic evaluation method for urban rail tunnel global total station scanning, which comprises the following steps: survey area reconnaissance and measurement scheme formulation, precise traverse measurement and control network construction, instrument erection and scanning parameter optimization setting, tunnel global total station laser scanning data acquisition, point cloud data fine pretreatment, tunnel three-dimensional digital model construction and precision verification, import of line design parameters and clearance standards, clearance section automatic batch extraction and deviation calculation, clearance automatic evaluation and result output. The application adopts a total station integrated scanning mode, does not need multi-station splicing, combines a precise traverse control network to guarantee positioning accuracy, realizes automatic extraction of sections, automatic calculation of deviations and automatic determination of limit invasion through self-developed analysis software, solves the problems of low measurement efficiency, insufficient point cloud splicing accuracy and poor automatic processing capacity of the prior art, and is suitable for urban rail tunnel construction acceptance and operation and maintenance.

Description

Technical Field

[0001] The present invention belongs to the field of urban rail transit tunnel engineering, and particularly relates to a method for measuring and automatically evaluating the clearance section of full-region and full-station scanning of urban rail tunnels. Background Art

[0002] As an efficient, green and high-capacity public transportation mode, the construction scale and coverage of urban rail transit continue to expand. As a key part of urban rail transit, the construction quality and operation safety of tunnel engineering are directly related to the safety of people's lives and property and the healthy development of the rail transit industry. The measurement and evaluation of the tunnel clearance section are the core and key links in the whole process of tunnel engineering construction acceptance and operation maintenance. The purpose is to accurately collect the actual cross-section contour data of the tunnel, compare and analyze it with the design clearance standard, and determine whether there are potential hazards such as intrusion. At present, the construction of urban rail tunnels presents characteristics such as denser lines, more diverse cross-section forms, and more complex construction environments. At the same time, with the continuous improvement of the intelligent demand for operation maintenance, the industry has an urgent need for efficient, high-precision, all-dimensional and automated measurement and evaluation technologies.

[0003] At present, the measurement of the tunnel clearance section is mainly divided into two categories: traditional measurement methods and new measurement technologies. Traditional methods generally adopt the mode of manual operation and single-point sampling. The mainstream means include measuring point by point with a total station and detecting with a special section instrument. When operating, sections are arranged at intervals of about 5 meters along the longitudinal direction of the tunnel. The tunnel contour is reconstructed by manually collecting multiple discrete coordinate points of a single section, and then manually compared and analyzed with the designed standard section. This traditional operation mode has obvious shortcomings: low measurement efficiency and high manual labor intensity; limited by discrete point layout, it is impossible to completely cover the whole contour of the tunnel wall, and it is easy to miss local deformation and intrusion hazards, making it difficult to accurately restore the overall spatial structure and actual service state of the tunnel; moreover, the manual comparison and analysis is highly subjective and has large errors, and it is impossible to realize the automation and standardization of the evaluation process.

[0004] New measurement technologies mainly include station-based three-dimensional laser scanning and SLAM mobile scanning detection: Station-based scanning relies on single-site collection and multi-site data stitching and fusion to obtain the overall point cloud data of the tunnel; SLAM mobile scanning technology does not rely on GNSS positioning, has the characteristics of measuring while walking and continuous data collection, and can quickly collect the three-dimensional point cloud of the inner wall of the tunnel. However, in the environment of urban rail tunnels with small cross-sections, smooth inner walls and few feature marks, both have problems such as difficult to guarantee the accuracy of point cloud registration and stitching, large cumulative drift in long tunnels, and insufficient automation processing ability due to the dependence on manual step-by-step operations for cross-section extraction and intrusion evaluation. Overall, it is impossible to achieve the coordinated goal of full-region coverage, efficient measurement and automatic evaluation. Summary of the Invention

[0005] The purpose of this invention is to provide a method for measuring and automatically evaluating the clearance section of urban rail tunnels using full-area, full-station scanning, in order to solve the problems existing in the background art.

[0006] This invention is implemented as follows: a method for measuring and automatically evaluating the clearance section of urban rail tunnels using full-area, full-station scanning, comprising the following steps: Step 1: Survey Area Reconnaissance and Measurement Plan Development. Upon entering the tunnel construction site, conduct a site survey of the tunnel's alignment, cross-sectional shape, construction environment, visibility conditions, and interference factors to clarify the scope, starting and ending mileage of the measurement and evaluation. Based on the tunnel length, cross-sectional dimensions, and site environment, plan the location, scanning sequence, and station relocation intervals of the total-station 3D laser scanner to ensure complete tunnel coverage without blind spots, while avoiding obstructions from construction equipment and temporary supports to guarantee the integrity of the point cloud acquisition. Simultaneously, integrate the precise traverse points established after tunnel completion to plan the line-of-sight relationship between the station locations and the traverse points, ensuring that each station can achieve accurate positioning based on the precise traverse points, providing a unified benchmark for subsequent measurement and evaluation.

[0007] Step 2: Precision Traverse Survey and Control Network Construction. Using the primary control points of the urban rail transit line as a benchmark, precision traverse surveys are conducted within the tunnel to densify the control points. Precision traverse points are laid out at approximately 120-meter intervals, avoiding obstructions such as pipelines and lighting facilities within the tunnel. Locations with stable tunnel sidewalls, good visibility, and minimal interference from construction and operation are selected. Traverse point markers are securely installed to ensure long-term stability. Precision traverse surveys are strictly performed in accordance with the "Urban Rail Transit Engineering Surveying Specifications," with strict control over angle and distance measurement accuracy. Traverse closure adjustments are completed to construct a high-precision control network within the tunnel, serving as the core benchmark for subsequent total-station scanning, point cloud calibration, and results verification.

[0008] Step 3: Instrument Setup and Scanning Parameter Optimization. A total-station 3D laser scanner is set up at the preset measurement station locations. Using adjacent precision traverse points as positioning references, the instrument is leveled, centered, and undergoes a power-on self-test. The instrument's spatial positioning is calibrated using a resection method with prisms backsighted to at least three precision traverse points, ensuring the instrument's positioning accuracy meets the tunnel clearance measurement requirements. Based on the tunnel environment (including dust concentration and lighting conditions), cross-sectional dimensions, and accuracy requirements, key parameters such as scanning resolution, scanning angle range, and point cloud density are optimized to achieve a balance between measurement accuracy and data acquisition efficiency. Simultaneously, refined registration is performed using repeated point cloud data from adjacent stations to further improve positioning accuracy and stability.

[0009] Step 4: Full-area, full-station laser scanning data acquisition for the tunnel. The scanning program is initiated to perform a comprehensive, all-around 3D laser scan of the tunnel interior walls, ancillary structures, and surrounding environment. The instrument simultaneously acquires the 3D coordinates and reflection intensity information of the measuring points, completing the single-station full-area point cloud acquisition in one go. This eliminates the need for multi-station stitching, directly generating continuous, complete, and high-precision single-station tunnel point cloud data. For long-distance tunnels, measuring stations are deployed according to a preset station spacing. Each station is positioned and verified using surrounding precision traverse points to ensure smooth data transition between adjacent stations, guaranteeing the overall consistency and accuracy of the full-area point cloud data for the entire tunnel, achieving complete and thorough scanning coverage of the entire tunnel.

[0010] Step 5: Refined Preprocessing of Point Cloud Data. The raw point cloud data collected on-site is imported into a self-developed point cloud data processing platform for refined preprocessing operations, including refined point cloud registration, noise reduction, filtering, segmentation, and effective data selection. The focus is on eliminating noise points and redundant points caused by interference factors such as construction dust, temporary debris, and personnel movement, while accurately retaining effective point cloud data for tunnel lining, structural edges, and ancillary facilities. Considering the spatial characteristics of the tunnel, effective point cloud data within a 50-meter radius before and after each monitoring station is selected, while point clouds outside this range and with lower accuracy are discarded, further improving the accuracy of subsequent model construction and cross-section extraction.

[0011] Step 6: Construction and Accuracy Verification of the Tunnel's 3D Digital Model. Based on the preprocessed high-quality point cloud data, a comprehensive 3D digital model of the tunnel is constructed, fully reproducing its true spatial morphology. This model includes all information such as the tunnel's cross-sectional outline, longitudinal alignment, structural undulations, and ancillary facilities, providing a visual representation of the tunnel's actual construction status and offering a 3D visualization foundation for subsequent automatic clearance assessment. Simultaneously, the accuracy of the 3D digital model is verified using precise traverse point coordinates to ensure consistency between the model and the actual tunnel's spatial location, guaranteeing that the model's accuracy meets the clearance assessment requirements.

[0012] Step 7: Import line design parameters and clearance standards. Simultaneously load data such as the horizontal and vertical profile design parameters of the urban rail transit line, the design location of the track centerline, and the design clearance outline into the self-developed automatic clearance evaluation and analysis software. Based on relevant specifications and design documents, set control standards and encroachment judgment thresholds for various clearances. At the same time, import the coordinates of precise traverse points and the data of the line's primary control points to establish a correlation benchmark between design parameters and actual measurement data, ensuring the accuracy and standardization of the comparative analysis.

[0013] Step 8: Automatic Batch Extraction and Deviation Calculation of Clearance Sections. Input core parameters such as the design clearance control point height and section mileage interval into the self-developed automatic clearance assessment and analysis software. The system automatically and accurately extracts cross-sections along the tunnel longitudinal direction at the set mileage. A dedicated algorithm automatically identifies the key feature points of each section profile, calculates the plane offset and elevation deviation of each feature point relative to the track centerline, and generates standardized clearance section measurement data in batches. This achieves automated and batch extraction of section data, significantly reducing manual intervention.

[0014] Step 9: Automatic Clearance Assessment and Result Output. The self-developed automatic clearance assessment and analysis software automatically compares the batch-extracted cross-sectional measured data with the design clearance standards. Based on preset clearance encroachment judgment thresholds, it automatically determines whether each cross-section has encroachment or excessive deviation issues, clearly identifying the location, degree, and deviation value of the encroachment, and generating a standardized automatic clearance assessment report. This report includes cross-sectional measured data, deviation analysis results, clearance encroachment judgment conclusions, and 3D visualization, providing accurate decision-making data for design alignment and slope adjustment.

[0015] The method for measuring and automatically evaluating the clearance section of urban rail tunnels using full-area, full-station scanning provided by this invention has the following beneficial effects: (1) Achieve full coverage and efficient measurement. The whole-station integrated scanning mode is adopted, which does not require targets or multi-station splicing. A single station can complete the large-scale full-area point cloud collection. Combined with the precision traverse points laid at intervals of about 120 meters, it can achieve rapid station positioning. Compared with the traditional point-by-point measurement method, the efficiency is improved by several times and the operation process is simpler. It is suitable for rapid full-area detection of long-distance and long-mileage urban rail tunnels.

[0016] (2) High point cloud accuracy, good integrity, and strong detection reliability. Based on the control network encrypted by precision traverse surveying, the system integrates the advantages of high-precision positioning by total station and high-density acquisition by laser scanning. The point cloud data is continuous and complete, and the positioning accuracy is controllable. This avoids the contour distortion and missed detection problems caused by traditional discrete point measurement, and also solves the defects of insufficient point cloud stitching accuracy in existing new measurement technologies. At the same time, relying on the same control benchmark, it is consistent with the subsequent track laying control benchmark, further improving the reliability of measurement and evaluation results.

[0017] (3) Strong adaptability and outstanding anti-interference ability. It can achieve independent spatial positioning without external targets, which is particularly suitable for tunnel environments with small cross sections, smooth inner walls and scarce feature points, effectively avoiding the overall accuracy reduction caused by splicing errors. Optimized scanning parameter settings and data preprocessing process improve the equipment's ability to resist dust and dim environment interference, and adapt to the measurement needs in complex construction environments.

[0018] (4) Achieve full-process automation, reduce labor costs and improve the consistency of results. Through self-developed analysis software, complete the automatic extraction of cross sections, automatic calculation of deviations, automatic determination of intrusion limits and automatic generation of assessment reports, which greatly reduces manual intervention, reduces the workload of internal work and human error, improves the standardization level and consistency of results of limit assessment, and meets the intelligent detection needs of rail transit.

[0019] (5) High degree of 3D visualization, adaptable to intelligent operation and maintenance needs. The constructed 3D digital model of the tunnel can intuitively restore the real state of the tunnel, integrate information such as precision traverse points, clearance measurement data and evaluation results, realize the 3D visualization expression of clearance measurement and evaluation results, and facilitate integration with BIM model and operation and maintenance platform. At the same time, complete control network data and measurement evaluation results provide convenience for subsequent data traceability and routine tunnel maintenance. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the structure of the urban rail tunnel full-area full-station scanning boundary section measurement and automatic evaluation method provided by the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0022] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0023] Example 1 This example uses a tunnel section of a city rail transit system as the engineering object. The tunnel is a shield tunneling tunnel, approximately 3km long, with a cross-sectional diameter of 7.7m. The construction environment presents challenges such as high dust concentration, dim lighting in some areas, and smooth inner walls with no obvious feature points.

[0024] like Figure 1 As shown, in this embodiment of the invention, the method described in this invention is used for boundary section measurement and automatic evaluation. The specific implementation process is as follows.

[0025] Step 1: Survey Area Reconnaissance and Measurement Plan Development The tunnel construction site was surveyed to determine the tunnel's alignment, cross-sectional dimensions, equipment layout, visibility conditions, and interference factors such as dust and light. The measurement and evaluation range was defined as K84+349.235 to K87+454.577. Based on the tunnel's length and cross-sectional dimensions, the locations of the total-station 3D laser scanners were planned, with a station relocation interval of 100m. The scanning sequence proceeded longitudinally along the tunnel, from the initial lower mileage to the higher mileage. Station locations were carefully chosen to avoid obstructions from construction equipment and temporary supports, ensuring complete coverage of the entire tunnel without blind spots. Simultaneously, the visibility relationship between each station and at least three precision traverse points was planned, in conjunction with the precise traverse points established after tunnel completion, to provide a benchmark for subsequent station positioning.

[0026] Step 2: Precision traverse measurement and control network construction Using the primary control points of the urban rail transit line as a benchmark, precision traverse surveying was conducted within the tunnel to densify the control points. Precision traverse points were laid out at intervals of approximately 120 meters on straight sections and approximately 80 meters on curved sections, totaling 28 traverse points. During the layout process, obstructions such as pipelines and lighting facilities within the tunnel were avoided, and locations with stable tunnel sidewalls, good visibility, and minimal construction interference were selected to ensure long-term stability of the points. The precision traverse surveying was strictly performed in accordance with the "Urban Rail Transit Engineering Surveying Specifications," controlling the angular measurement error to be no greater than ±2.5″ and the distance measurement error to be no greater than ±(1mm + 2ppm × D). Traverse closure adjustments were completed, constructing a high-precision control network within the tunnel, which serves as the benchmark for subsequent scanning, station positioning, and result verification.

[0027] Step 3: Instrument setup and scanning parameter optimization A Trimble SX10 total-station 3D laser scanner was used. The instrument was set up at the pre-defined measurement station location, using three adjacent precision traverse points as positioning references. Instrument leveling, centering, and self-testing were performed. Resection was used to calibrate the instrument's spatial positioning, ensuring positioning accuracy met the clearance inspection requirements. Based on tunnel dust concentration, lighting conditions, and cross-sectional dimensions, scanning parameters were optimized: coarse scanning mode, 0.5 mm resolution, 360° horizontal and 180° vertical scanning angle, and a point cloud density of 100 points / cm². Experimental results showed that these parameter settings ensured both the point cloud density and clearance measurement accuracy requirements while also balancing data acquisition efficiency, with a single-station scanning time of approximately 13 minutes and 41 seconds.

[0028] Step 4: Full-area, full-station laser scanning data acquisition in the tunnel The scanning program was initiated to perform a comprehensive, all-around scan of the tunnel's inner walls, lining structure, and auxiliary pipelines. The total-station 3D laser scanner simultaneously acquired the 3D coordinates and reflection intensity information of the measuring points, completing the single-station full-area point cloud acquisition in one go. This eliminated the need for multi-station stitching, directly generating continuous and complete single-station tunnel point cloud data. Scanning operations were completed at 32 stations with approximately 100m intervals. Each station was positioned and verified using surrounding precision traverse points to ensure smooth data transition between adjacent stations, achieving complete and thorough scanning coverage of the entire tunnel section from K84+349.235 to K87+454.577, and acquiring high-precision point cloud data for the entire tunnel.

[0029] Step 5: Refined Preprocessing of Point Cloud Data The raw point cloud data collected from 32 monitoring stations on-site were imported into a self-developed point cloud data processing platform for refined preprocessing. Specific operations included: using an iterative nearest-point algorithm for refined point cloud registration; using a Gaussian filtering algorithm to remove noise and redundant points generated by construction dust, temporary debris, and personnel movement; and using a region growing algorithm to segment the effective point cloud from interfering point clouds, accurately preserving effective point cloud data for tunnel lining, structural edges, and ancillary facilities. Furthermore, considering the confined space and complex lighting conditions of the tunnel, to further ensure point cloud accuracy, only high-precision point clouds within a 50-meter radius before and after each station were selected from the raw point cloud data collected, discarding point clouds outside this range with lower accuracy. This preprocessing significantly improved the accuracy of subsequent model construction and cross-section extraction.

[0030] Step 6: Construction and Accuracy Verification of the 3D Digital Model of the Tunnel Based on the preprocessed high-quality point cloud data, a comprehensive 3D digital model of the tunnel section was constructed using 3D modeling software. This model fully reproduces the tunnel's true spatial morphology, clearly presenting the tunnel's cross-sectional outline, longitudinal alignment, lining structure undulations, and ancillary structures, providing a direct visual representation of the tunnel's actual construction status. The accuracy of the 3D digital model was verified using precise traverse point coordinates to ensure consistency between the model and the actual tunnel's spatial location, and that the model's accuracy meets the requirements for clearance assessment.

[0031] Step 7: Importing Line Design Parameters and Clearance Standards The design parameters of the urban rail transit line's horizontal and vertical profiles, the design coordinates of the track centerline, and the design clearance outline of the tunnel section were loaded into the self-developed automatic clearance assessment and analysis software. Based on the "Urban Rail Transit Engineering Surveying Specifications" and design requirements, control standards for vehicle clearance, equipment clearance, and building clearance were established, and the threshold for encroachment judgment was clarified. Furthermore, the design parameters were correlated with actual measurement data to establish a unified comparison benchmark.

[0032] Step 8: Automatic batch extraction and deviation calculation of boundary sections Input the design clearance control point height (set to 0m, 2.8m, and 4.7m according to the design documents) and cross-sectional mileage interval (1m) into the self-developed automatic clearance assessment and analysis software. The system automatically and accurately extracts cross-sections along the tunnel longitudinal direction at 1m mileage intervals. During extraction, the mileage coordinates of the tunnel centerline are used as a reference to perform mileage constraint cutting on the preprocessed point cloud data. The cutting range is controlled within 50mm before and after the target mileage point. This parameter setting meets the accuracy requirement of no more than ±50mm in the cross-sectional mileage measurement of the "Urban Rail Transit Engineering Surveying Specification", while ensuring the density and integrity of the point cloud data at the extracted cross-section.

[0033] The system automatically identifies key feature points of each cross-section contour using a dedicated feature point recognition algorithm, including vertices, base points, upper left, upper right, middle left, middle right, lower left, lower right, and evacuation platforms. The specific recognition algorithm is as follows: Based on preset design clearance control point elevation parameters, a 100mm radius around the design clearance control point is defined as the effective fitting region. The least squares method is used to perform plane fitting of the point cloud, ensuring consistency between the fitted plane and the actual tunnel lining surface. Then, the intersection point of the normal vector at the centerline mileage point of the urban rail transit line and the fitted plane is calculated, along with the plane offset and elevation deviation of this intersection point relative to the track centerline. Since the positional error between this intersection point and the corresponding clearance control point is in the millimeter range and negligible, this intersection point is considered the measured clearance control point, and its plane offset and elevation deviation are used as the plane offset and elevation deviation data of the corresponding clearance control point. The system generates standardized clearance cross-section measurement data in batches, requiring no manual intervention throughout the process, significantly improving data processing efficiency.

[0034] Step 9: Automatic boundary assessment and result output The self-developed automatic clearance assessment and analysis software automatically compares the measured data of the cross-section with the design clearance standards. Based on the preset clearance encroachment judgment threshold, it automatically determines whether there are any encroachment or deviation exceeding the standard issues in each cross-section, clarifies the location of the encroachment, and provides specific encroachment values. Finally, it automatically generates a standardized automatic clearance assessment report, which includes the measured cross-section data, deviation analysis charts, encroachment judgment conclusions, and 3D visualization, providing accurate and reliable decision-making basis for the construction and rectification of the tunnel section.

[0035] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for measuring and automatically evaluating the clearance section of a metro tunnel by full-area and full-station scanning, characterized in that, The method for measuring and automatically evaluating the clearance section of the urban rail tunnel using a full-area, full-station scanning approach includes: Step 1: Based on the measurement range of the tunnel site reconnaissance, plan the station location, scanning sequence and station relocation spacing of the total station 3D laser scanner, and plan the line-of-sight relationship in conjunction with the precision traverse points; Step 2: Using the primary control points of the urban rail transit line as a benchmark, conduct precise traverse surveys within the tunnel to construct a control network within the tunnel; Step 3: According to the plan, set up a total station 3D laser scanner at the survey station location, use the precision traverse points as the positioning reference for positioning calibration, and optimize the scanning parameters. Step 4: Perform a full-range 3D laser scan using a scanning program to collect point cloud data for a single station and generate single-station tunnel point cloud data. Step 5: Import the single-station tunnel point cloud data into the processing platform for registration, noise reduction, filtering, segmentation, and effective data screening to obtain high-quality point cloud data; Step 6: Construct an overall three-dimensional digital model of the tunnel based on the high-quality point cloud data obtained after preprocessing, and verify the accuracy by using precise traverse point coordinates; Step 7: Load the urban rail transit line data into the automatic clearance assessment and analysis software, and set the design clearance standards and encroachment judgment thresholds; Step 8: Input the design clearance control point height and cross-section mileage interval into the clearance automatic evaluation and analysis software, automatically extract the cross-section and identify key feature points, calculate the plane offset and elevation deviation of each feature point relative to the track centerline, and generate standardized clearance cross-section measurement data in batches. Step 9: Compare the clearance section measurement data with the design clearance standard, and automatically determine whether each section has encroachment or deviation exceeding the standard based on the encroachment judgment threshold, and generate an automatic clearance assessment report. Specifically, step 8, which involves automatically capturing the cross-section and identifying key feature points, includes: Based on the mileage coordinates of the tunnel centerline, the preprocessed high-quality point cloud data is cut with mileage constraints, and the cutting range is controlled within 50mm before and after the target mileage point. Based on the preset design limit control point elevation parameters, the point cloud within a 100mm radius of the design limit control point is defined as the effective fitting region. The least squares method is used to complete the point cloud plane fitting and obtain the fitting plane. Find the intersection point of the normal vector at the centerline mileage point of the urban rail transit line and the fitted plane, and calculate the plane offset and elevation deviation of the intersection point relative to the track centerline.

2. The method for measuring and automatically evaluating the clearance section of urban rail tunnels using full-area, full-station scanning as described in claim 1, is characterized in that... Step 1 specifically includes: Clearly define the scope, starting mileage, and ending mileage of the measurement and assessment; Based on the tunnel length, cross-sectional dimensions, and site environment, plan the location, scanning sequence, and station relocation interval of the full-station 3D laser scanner to ensure that the scanning covers the entire tunnel area without blind spots, while avoiding obstructed areas. Simultaneously, the precise traverse points laid out after the tunnel is completed are used to plan the line-of-sight relationship between the station locations and the traverse points.

3. The method for measuring and automatically evaluating the clearance section of urban rail tunnels using full-area, full-station scanning as described in claim 2, is characterized in that... Step 2 specifically includes: Precision guide points are laid out at intervals of approximately 120 meters; Straight sections are laid out at intervals of approximately 120 meters, while curved sections are laid out at intervals of approximately 80 meters.

4. The method for measuring and automatically evaluating the clearance section of urban rail tunnels using full-area, full-station scanning as described in claim 1, is characterized in that... Step 3 specifically includes: Using adjacent precision guide points as positioning references, the instrument spatial positioning calibration is completed by using a prism that back-views at least three precision guide points and employing the resection method. Based on the tunnel environment, cross-sectional dimensions, and accuracy requirements, optimize the settings for scanning resolution, scanning angle range, and point cloud density. Fine-grained registration is performed by combining duplicate point cloud data between adjacent sites.

5. The method for measuring and automatically evaluating the clearance section of urban rail tunnels using full-area, full-station scanning as described in claim 4, is characterized in that... The optimized scanning parameters specifically include: A full-station 3D laser scanner was used, with the scanning mode set to coarse scanning, a scanning resolution of 0.5mm, a scanning angle range of 360° horizontally and 180° vertically, a point cloud density of 100 points / cm², and a single-station scanning time controlled at 13 minutes and 41 seconds.

6. The method for measuring and automatically evaluating the clearance section of urban rail tunnels using full-area, full-station scanning as described in claim 1, is characterized in that... Step 4 specifically includes: The full-station 3D laser scanner simultaneously acquires the 3D coordinates and reflection intensity information of the measurement points, performs single-station full-domain point cloud acquisition, and generates single-station tunnel point cloud data; For long tunnels, stations are set up according to the preset station spacing. Each station is located and verified by relying on the surrounding precision traverse points to ensure smooth data connection between adjacent stations.

7. The method for measuring and automatically evaluating the clearance section of urban rail tunnels using full-area, full-station scanning as described in claim 1, is characterized in that... Step 5 specifically includes: The iterative nearest point algorithm was used to complete the fine registration of the point cloud. The Gaussian filtering algorithm was used to remove noise points and redundant points caused by construction dust, temporary debris and personnel movement. The effective point cloud and the interference point cloud were segmented by the region growing algorithm. Select the effective point cloud within 50 meters before and after each station, and discard the point cloud that exceeds this range and has an accuracy lower than the preset threshold.

8. The method for measuring and automatically evaluating the clearance section of urban rail tunnels using full-area, full-station scanning as described in claim 1, is characterized in that... Step 7, which involves loading urban rail transit line data into the automatic clearance assessment and analysis software, specifically includes: The horizontal and vertical profile design parameters of urban rail transit lines, the design location of the track centerline, and the design clearance profile data are loaded into the clearance automatic evaluation and analysis software.

9. The method for measuring and automatically evaluating the clearance section of urban rail tunnels using full-area, full-station scanning as described in claim 1, is characterized in that... Step 8 specifically includes: The height of the input design clearance control points is set to 0m, 2.8m and 4.7m, and the section mileage interval is 1m; Automatically and precisely cut cross-sections along the longitudinal direction of the tunnel at 1m intervals; Using a dedicated feature point recognition algorithm, the key feature points of each cross-section contour are automatically identified. These key feature points include the vertex, bottom point, upper left, upper right, middle left, middle right, lower left, lower right, and evacuation platform.

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