Elevation measurement method and system for pavement slabs based on laser point cloud

Abstract

This invention relates to a method and system for measuring the elevation of pavement panels based on laser point clouds. The method includes: acquiring point cloud data of the pavement panels and setting joint control points for the horizontal elevation of the pavement panels; stitching together point cloud data from multiple stations; converting the stitched point cloud data to coordinate data in a specified coordinate system based on the coordinates of the joint control points in that system; and extracting the elevation values ​​of specified points on the pavement panels in the specified coordinate system from the valid point cloud data. This invention facilitates efficient and accurate measurement of airport pavement corner elevations, adapts to the requirements of non-stop airport operations, improves the operational efficiency and data accuracy of airport pavement measurement, and provides high-precision, highly adaptable technical support for airport pavement operation and maintenance.

Description

Technical Field

[0001] This invention relates to the field of airport pavement measurement technology, and more specifically, to a method and system for measuring the elevation of pavement panels based on laser point clouds. Background Technology

[0002] With the rapid development of civil aviation, the operation and maintenance of airport terminal pavement facilities has become a core aspect of ensuring flight safety and improving airport operational efficiency. As a key indicator for monitoring pavement smoothness and settlement deformation, accurate measurement of pavement slab elevation is crucial for routine pavement maintenance, early warning of pavement defects, and engineering upgrades. The industry has placed higher demands on the efficiency, data accuracy, and adaptability of pavement slab elevation measurement to airport operations. Among these, pavement corners—the intersections of pavement slab panels—are sensitive points for deformation and critical stress points in the pavement structure, making their elevation measurement paramount in pavement monitoring.

[0003] In the field of existing airport pavement elevation measurement, optical precision leveling obtains pavement elevation data by manually setting up a level instrument and measuring point by point, which can meet the high precision requirements of airport pavement measurement. In some scenarios, conventional three-dimensional laser scanning technology is used to collect pavement data, and elevation information is extracted by acquiring pavement point cloud data, in an attempt to overcome the efficiency limitations of traditional single-point measurement.

[0004] However, traditional optical precision leveling methods employ manual, single-point operation, resulting in long measurement cycles, high labor costs, and the need to occupy airport pavement areas, which can easily disrupt normal airport operations and is unsuitable for airport operations requiring uninterrupted service. On the other hand, conventional 3D laser scanning technology lacks professional data processing and optimization methods for complex airport operational scenarios in airport pavement measurement applications. It is difficult to effectively remove various environmental interference data such as aircraft and guidance vehicles, and the elevation extraction accuracy cannot meet the professional standards for airport pavement measurement. It is impossible to achieve a balance between measurement efficiency and high data accuracy, especially for core monitoring points such as pavement corners, where it is difficult to guarantee the accuracy and stability of elevation extraction. Summary of the Invention

[0005] Based on this, and to address the aforementioned problems, this invention provides a method and system for measuring the elevation of airport pavement panels based on laser point clouds. By acquiring laser point clouds, transforming coordinates, and iteratively extracting elevations, this method helps to achieve efficient and accurate measurement of the corner elevations of airport pavement panels, adapts to the requirements of airport non-stop operation, improves the operational efficiency and data accuracy of airport pavement measurement, and provides high-precision and highly adaptable technical support for airport pavement operation and maintenance.

[0006] In a first aspect, the present invention provides a method for measuring the elevation of a track panel based on laser point clouds. The method includes: acquiring point cloud data of the track panel and setting joint control points for the plane elevation of the track panel; stitching together point cloud data from multiple stations, and converting the stitched point cloud data to coordinate data in the specified coordinate system based on the coordinates of the joint control points in the specified coordinate system; and extracting the elevation values ​​of specified points of the track panel in the specified coordinate system from the valid point cloud data of the track panel.

[0007] Optionally, in this embodiment of the invention, the effective point cloud data is obtained through the following steps: The point cloud data after coordinate transformation is iteratively estimated using a stochastic parameter estimation method to obtain a pavement planar mathematical model; after the planar mathematical model converges iteratively, a distance threshold is set, and point clouds exceeding the distance threshold are identified as outliers and deleted to obtain coarse pavement point cloud data; the optimal DON feature value of the coarse pavement point cloud data is determined based on the difference in normal features at large and small scales, and a corresponding DON feature value threshold is set to exclude noise points exceeding the DON feature value threshold, thus obtaining effective pavement point cloud data. By iteratively fitting the pavement planar mathematical model using the stochastic parameter estimation method and combining it with a distance threshold to accurately remove outliers, and then using multi-scale normal feature analysis to determine the optimal DON feature value and corresponding threshold to effectively filter noise points, the pavement point cloud data is precisely purified layer by layer, resulting in high-purity effective pavement point cloud data. This lays a high-quality data foundation for the subsequent accurate extraction of pavement (especially pavement angle) elevation, further ensuring the accuracy and reliability of airport pavement elevation measurements.

[0008] In the above implementation process, the optimal DON feature value of the coarse-screened point cloud data of the road panel is determined based on the difference in normal features at large and small scales, and a corresponding DON feature value threshold is set. Noise points exceeding the DON feature value threshold are excluded to obtain effective point cloud data of the road panel. This includes: determining the unit normal vector of each point in the coarse-screened point cloud data of the road panel using different radius scales, and obtaining the DON feature unit vector of each point through vector operations; selecting multiple sets of different support radius parameters to compare and calculate the DON feature unit vector to determine the optimal DON feature value; setting the corresponding DON feature value threshold based on the optimal DON feature value, and excluding noise points in the coarse-screened point cloud data of the road panel that exceed the DON feature value threshold to obtain effective point cloud data of the road panel. By calculating the unit normal vector of the point cloud at multiple radius scales and deriving the DON feature unit vector, and combining multiple sets of support radius parameters for comparison and calculation, the optimal DON feature value for the suitable test area is determined. Based on this, a precise DON feature value threshold is set to remove noise points. This method can accurately capture the geometric feature differences between the pavement plane and noise points, and make the threshold setting more consistent with the actual point cloud data characteristics of the airport pavement. It effectively eliminates scattered and small noise points in the coarse screening point cloud of the pavement panel, and significantly improves the purity and effectiveness of the pavement panel point cloud data.

[0009] Optionally, in this embodiment of the invention, point cloud data from multiple stations are stitched together. Based on the coordinates of the joint plane elevation control points in a specified coordinate system, the stitched point cloud data is converted to coordinate data in the specified coordinate system. This includes: performing translation and rotation operations on the point cloud data of two adjacent stations based on preset corresponding points; stitching all station point cloud data using a pairwise stitching method to obtain stitched point cloud data, and importing the coordinate data of the joint plane elevation control points; and converting the stitched point cloud data to the specified coordinate system using a coordinate transformation algorithm. Precise anchoring of corresponding points effectively reduces the cumulative stitching error from multi-station scanning, ensuring the overall accuracy and coordinate uniformity of the point cloud stitching. Based on the joint plane elevation control points, precise matching of point cloud data with the specified coordinate system is achieved, giving the point cloud data standardized and engineering-grade application attributes. This lays a unified and high-precision coordinate foundation for subsequent point cloud filtering and purification of the track panel and accurate elevation extraction.

[0010] Optionally, in this embodiment of the invention, extracting the elevation values ​​of specified points of the pavement panel in a specified coordinate system from the effective point cloud data of the pavement panel includes: determining the coordinates of the specified points of the pavement panel based on the measured lines of the pavement panel to generate a location point cloud of the elevation to be extracted; storing the effective point cloud data of the pavement panel into a point set, and projecting the point set onto the plane containing the location point cloud of the elevation to be extracted, establishing an index relationship; determining the optimal parameter values ​​of the plane and setting a convergence criterion with the minimum distance between the elevation point to be extracted and the corresponding point in the point set as the objective; and deriving the elevation point to be extracted and the corresponding point in the point set after iterative convergence. The distance function is calculated; the distance function is minimized through neighborhood search to obtain the nearest point cloud data; the elevation value of the nearest point cloud data is captured and recorded to obtain the elevation value of the corresponding designated point on the pavement in the specified coordinate system; the elevation value of the designated point on the pavement can be extracted quickly and stably under a unified coordinate system, which not only ensures the automation and efficiency of elevation extraction, but also achieves millimeter-level extraction accuracy through iterative convergence and minimum distance solution, providing a stable and reliable extraction method for the whole area elevation measurement of airport pavement, and further improving the accuracy and practicality of the overall measurement scheme.

[0011] In the above implementation process, the distance function is minimized through neighborhood search to obtain the nearest point cloud data. This includes: setting the neighborhood search radius of the preset point set; establishing a KD-tree index for the point set using neighborhood search; quickly retrieving and solving the distance function based on the KD-tree index to obtain the minimum value of the distance function; and locating the point cloud data in the point set that is spatially closest to the elevation point to be extracted based on the minimum value. This achieves efficient and accurate location of the point cloud data that is spatially closest to the elevation point to be extracted from a massive amount of effective point cloud, significantly improving the computational efficiency and positioning accuracy of elevation extraction, avoiding redundant calculations and interference points, and further ensuring the stability and reliability of the elevation acquisition of specified points on the pavement. This provides efficient algorithmic support for high-precision and automated elevation measurement of airport pavements.

[0012] Optionally, in this embodiment of the invention, setting up joint control points for the plane elevation of the pavement includes: deploying joint control points for the plane elevation covering the survey area within the scanning range of a 3D laser scanner that collects point cloud data; determining the plane coordinates of all joint control points using RTK according to measurement specifications; and determining the elevation values ​​of all joint control points using leveling standards to form joint control points for the plane elevation. By constructing a high-precision, fully covered plane elevation benchmark system, the dual accuracy of the joint control point coordinates and elevation data is achieved. This provides a reliable coordinate benchmark for subsequent multi-site cloud stitching, effectively reducing accumulated stitching errors, and provides a precise reference for converting point cloud data to a specified coordinate system, ensuring the standardization and engineering practicality of point cloud coordinates. Simultaneously, it lays a high-precision benchmark foundation for subsequent effective point cloud filtering and specified point elevation extraction for the pavement, further improving the overall accuracy and standardization of airport pavement elevation measurement.

[0013] Optionally, in this embodiment of the invention, a three-dimensional laser scanner is used to collect point cloud data of the airport terminal area deck panels, and all field operations for collecting the point cloud data can be completed during breaks in airport operations without stopping flight operations.

[0014] Secondly, the present invention also provides a track panel elevation measurement system based on laser point clouds. The track panel elevation measurement system based on laser point clouds includes: a data acquisition module for acquiring point cloud data of the track panel and setting joint control points for the plane elevation of the track panel; a point cloud processing module for stitching together point cloud data from multiple stations and converting the stitched point cloud data to coordinate data in the specified coordinate system according to the coordinates of the joint control points for the plane elevation in the specified coordinate system; and an elevation extraction module for extracting the elevation values ​​of specified points of the track panel in the specified coordinate system from the effective point cloud data of the track panel.

[0015] Thirdly, embodiments of the present invention provide an electronic device, the electronic device including a memory and a processor, the memory storing program instructions, and the processor reading and running the program instructions, executing the steps in any of the above implementations.

[0016] According to the technical solution of the present invention, the point cloud data of the pavement is converted to coordinate data in a specified coordinate system based on the plane elevation joint control points, and then the elevation values ​​of specified points of the pavement are extracted from these coordinate data. Compared with the existing practice of directly collecting data using optical precision level instruments, the present invention's method of collecting laser point clouds, converting coordinates, and iteratively extracting elevations helps to achieve efficient and accurate measurement of airport pavement elevations, adapts to the requirements of airport non-stop operation, improves the operational efficiency and data accuracy of airport pavement measurement, and provides high-precision and highly adaptable technical support for airport pavement operation and maintenance. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the conventional technology, the drawings used in the description of the embodiments or the conventional technology will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a method for measuring the elevation of a road panel based on laser point clouds, provided in one embodiment of the present invention. Figure 2 A flowchart of an effective point cloud data acquisition method provided in another embodiment of the present invention; Figure 3A flowchart of a channel panel point cloud filtering method based on DON feature values ​​provided in another embodiment of the present invention; Figure 4 A flowchart of a point cloud data stitching method provided in another embodiment of the present invention; Figure 5 A flowchart of a method for extracting track panel elevation according to another embodiment of the present invention; Figure 6 A flowchart of a distance function minimization method based on neighborhood search provided in another embodiment of the present invention; Figure 7 A flowchart of a method for setting up a combined plane elevation control point according to another embodiment of the present invention; Figure 8 This is a schematic diagram of the structure of a track panel elevation measurement system based on laser point cloud provided in another embodiment of the present invention. Detailed Implementation

[0019] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings. For example, the flowcharts and block diagrams in the drawings illustrate the architecture, functions, and operations of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, program segment, or part of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions. In addition, the functional modules in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0021] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0022] As a crucial component of the comprehensive transportation system, civil aviation is a key support for regional economic interconnection and the efficient flow of personnel and goods. It plays an irreplaceable role, particularly in long-distance cross-regional transportation and international trade. The safety and efficiency of airport operations directly impact the safety of people's lives and property, the high-quality development of the civil aviation industry, and the overall efficiency of the social economy. With the continuous increase in passenger and cargo traffic, the operation and maintenance of airport terminal pavement facilities has become a core aspect of airport operation management. Pavement slab elevation, as a core indicator for monitoring pavement smoothness and settlement deformation, is essential for accurate measurement. Its precise measurement is a vital basis for routine pavement maintenance, early warning of defects, and engineering upgrades, significantly contributing to ensuring aircraft takeoff and landing safety and improving airport operational efficiency. Pavement slab corners, as the intersection points of pavement slab segments, are weak points and deformation-sensitive areas in the pavement structure. Their elevation measurement is of paramount importance in pavement monitoring, leading the industry to demand higher standards for the efficiency, data accuracy, and adaptability of pavement slab elevation measurements to airport operations.

[0023] In existing airport pavement elevation measurement technologies, the mainstream approach is based on optical precision leveling. Specifically, using national or city elevation benchmarks as a reference, technicians manually set up a leveling instrument and leveling rod. They read the leveling rod's scale through the horizontal line of sight of the leveling instrument and measure point by point, sequentially acquiring elevation data for monitoring points such as pavement corners, center, and edges. The final elevation measurement result is then obtained through data processing and adjustment calculations. This technology, through precise manual operation and multiple re-measurements, can meet the millimeter-level elevation measurement accuracy requirements of airport pavements and is currently a common method for airport pavement elevation measurement. In some scenarios, conventional 3D laser scanning technology is also used to collect pavement point cloud data, and elevation information is obtained through simple point cloud extraction. However, a professional data processing system specifically for airport scenarios has not yet been established, and the core measurement still relies primarily on optical precision leveling.

[0024] The inventors discovered that existing pavement elevation measurement technologies based on optical precision leveling have fundamental flaws, making them unsuitable for the operational requirements of airports during non-stop service and the need for efficient and accurate measurements. Specifically, these drawbacks are: First, the operation mode is manual, single-point operation, resulting in long measurement cycles and high labor costs. Facing the need for large-scale pavement monitoring in airport terminal areas, significant manpower and resources are required, and completing the full measurement area takes a considerable amount of time, making it impossible to quickly obtain pavement elevation data across the entire area. Second, field measurement operations require direct occupation of airport pavement areas, necessitating the closure of parts of the apron and taxiways during the measurement process, which can easily interfere with normal aircraft takeoffs and landings and taxiing operations. First, it conflicts with the principle of uninterrupted operation during airport operation, significantly reducing airport operational efficiency. Second, manual operation is easily affected by environmental factors and the skill level of personnel, and random errors in the measurement process are difficult to completely avoid. Moreover, single-point measurement cannot achieve synchronous monitoring of the entire pavement elevation, and cannot accurately reflect the overall settlement and deformation pattern of the pavement. Third, the application of conventional three-dimensional laser scanning technology lacks targeted optimization. It has not designed point cloud stitching and filtering purification methods adapted to the airport scenario, and it is difficult to effectively remove interference data such as aircraft and guidance vehicles in the airport operating environment. The elevation extraction accuracy cannot meet the professional standards of airport pavement measurement, and it is impossible to achieve a balance between measurement efficiency and accuracy.

[0025] Taking the actual application scenario of measuring the elevation of the apron pavement corner in an airport terminal area as an example, an airport needs to conduct elevation settlement monitoring on the corner of the apron pavement covering nearly 10,000 square meters in the terminal area. When using existing optical precision leveling technology for measurement, the specific operation is as follows: Organize multiple professional surveyors, divide them into multiple measurement groups, delineate the measurement operation area on the apron and implement closed management, each group manually sets up the level instrument and lays out the leveling rod, and performs single-point elevation measurement on the corner of the apron pavement one by one. After completing the measurement of each point, disassemble the instrument and move it to the next point, repeating the process of setting up, measuring and disassembling. After the measurement is completed, the measurement data of all points are manually sorted and adjusted. Several problems arose during the measurement process: First, the entire measurement area included over a thousand pavement corner monitoring points. Each team adopted a single-point measurement mode, with a total of 10 surveyors involved, and it took 5 working days to complete the measurement of the entire area, resulting in a long operation cycle and high labor costs. Second, the measurement operation closed off one-third of the apron area, leading to a reduction in available parking spaces at the airport, severely disrupting aircraft parking and taxiing operations, causing delays to multiple flights, and significantly reducing airport operational efficiency. Third, the measurement data at some points were affected by factors such as wind force at the airport ground and the flatness of the instrument setup, resulting in occasional errors. Corrections were only made after remeasurement, adding extra operational costs. Fourth, due to airport operational needs, some pavement areas could not be closed off for extended periods, resulting in the failure to complete the measurement of pavement corners in these areas, creating monitoring blind spots and preventing the realization of full-area monitoring of pavement elevation. Analysis reveals that the core reasons for the aforementioned problems are as follows: the manual single-point operation mode of existing optical precision leveling technology inherently results in low operational efficiency and high labor costs; the pavement occupancy characteristics of field operations fundamentally conflict with the airport's requirement to maintain uninterrupted operation; errors in manual operation are unavoidable, and single-point measurement cannot achieve synchronous monitoring across the entire area; at the same time, conventional 3D laser scanning technology lacks specific optimization for airport scenarios and is difficult to serve as an alternative to achieve efficient and accurate measurement; existing technologies can no longer meet the actual needs of airport pavement elevation measurement.

[0026] Based on this, the present application provides a method and system for measuring the elevation of pavement panels based on laser point clouds. By collecting laser point clouds, transforming coordinates, and iteratively extracting elevations, this method helps to achieve efficient and accurate measurement of the corner elevations of airport pavement panels, adapts to the requirements of airport non-stop operation, improves the operational efficiency and data accuracy of airport pavement measurement, and provides high-precision and highly adaptable technical support for airport pavement operation and maintenance.

[0027] Please refer to Figure 1 , Figure 1 A flowchart illustrating a method for measuring the elevation of a track panel based on laser point clouds, provided as an embodiment of the present invention; the method includes: Step S100: Collect point cloud data of the road panel and set the joint control points for the plane elevation of the road panel.

[0028] In step S100 above, within the airport terminal area pavement measurement area, scanning stations can be planned according to scanning accuracy and coverage. At the same time, special targets are set up at a density of about one every 50 meters (which can be adjusted appropriately according to the actual measurement scenario and terrain conditions). The special target is a 45-degree fan-shaped black and white mark design with a circular main body diameter of about 0.1 meters. The substrate surface is covered with a high reflectivity metal coating, which can achieve strong reflection of laser signals. The adjustable tripod base supports 360° free rotation in the horizontal and vertical directions, and can meet the scanning and recognition needs of different measurement stations and scanners in different directions without moving the base.

[0029] All field operations were carried out during breaks in airport operations. Tripods were erected and leveled at the planned sites, and the stand-alone 3D laser scanner was fixed to the tripods. After the instrument completed its self-test, a new project was created, and the scanning density and range were set. The pavement area was scanned to collect point cloud data for the entire area. After scanning a single site, the data was saved, and the instrument was moved to the next site to repeat the operation until the pavement point cloud data for all sites was collected. At the same time, 3-4 points were selected within the effective scanning range of the scanner to establish a plane elevation joint control point covering the survey area. The plane coordinates of all joint control points were accurately determined according to the survey specifications using RTK (Real-Time Kinematics) technology, and their elevation values ​​were determined according to the second-order leveling standard. This formed a plane elevation joint control point with both accurate plane coordinates and elevation values, providing a unified benchmark for subsequent point cloud processing.

[0030] Step S200: Stitch together the point cloud data from multiple stations, and convert the stitched point cloud data to coordinate data in the specified coordinate system based on the coordinates of the joint plane elevation control points in the specified coordinate system.

[0031] In step S200 above, the specially designed target set up during field scanning can be used as a common target point (i.e., a common target point that can be observed simultaneously by two adjacent scanning stations and whose spatial position is fixed, providing a unified spatial reference for the point cloud data of different survey stations). The point cloud data of the track panel collected by the two adjacent survey stations are translated and rotated to reduce the coordinate deviation between the survey stations. Then, the pairwise splicing method is used to complete the splicing of all the point cloud data of the survey stations, forming the spliced ​​point cloud data of the entire survey area. Subsequently, the plane coordinates and elevation data of the plane and elevation joint control points set up and measured in the early stage are imported. The coordinate transformation algorithm is used to transform the spliced ​​point cloud data of the entire area to the specified coordinate system, completing the coordinate system unification of the point cloud data and forming standardized track panel point cloud data.

[0032] Step S300: Extract the elevation values ​​of specified points of the road panel in the specified coordinate system from the effective point cloud data of the road panel.

[0033] In step S300 above, the coordinates of a specified point on the road panel can be calculated based on the measured lines of the road panel in a specified coordinate system, and a point cloud of the location to be extracted elevation can be generated. The effective point cloud data of the road panel is stored in a point set and then projected onto the plane where the point cloud is located. An index relationship between the two is established. Then, with the minimum distance between the point to be extracted elevation and the corresponding point in the point set as the objective, the optimal value of the plane parameters is determined and a convergence criterion is set. After iterative convergence, the distance function between the two is derived. A KD-tree index is established through neighborhood search to minimize the distance function. The point cloud data with the closest spatial distance to the point to be extracted elevation in the point set is accurately located. Finally, the elevation value of the point cloud data is captured and recorded, which is the elevation value of the specified point on the road panel in the specified coordinate system.

[0034] Therefore, the laser point cloud-based pavement elevation measurement method provided by the embodiments of the present invention helps to achieve efficient and accurate measurement of airport pavement corner elevation by collecting laser point clouds, transforming coordinates, and iteratively extracting elevations. It is suitable for airport non-stop operation requirements, improves the operational efficiency and data accuracy of airport pavement measurement, and provides high-precision and highly adaptable technical support for airport pavement operation and maintenance.

[0035] Please refer to Figure 2 , Figure 2 A flowchart of an effective point cloud data acquisition method provided for an embodiment of the present invention; the effective point cloud data acquisition method includes: Step S20: Use the random parameter estimation method to iteratively estimate the mathematical model of the road panel plane for the point cloud data after coordinate transformation.

[0036] In step S20 above, for the standardized point cloud data of the entire road panel that has been transformed to a specified coordinate system, a random parameter estimation method that can eliminate erroneous samples can be used. With the road panel plane as the fitting target, the plane model parameters are accurately estimated through multiple iterative calculations, and the model fitting accuracy is continuously optimized until the model converges to a stable state. Finally, a road panel plane mathematical model that closely matches the actual terrain of the road panel in the survey area is obtained, providing an accurate plane judgment benchmark for subsequent outlier removal.

[0037] Step S21: After the planar mathematical model converges iteratively, a distance threshold is set. Points exceeding the distance threshold are identified as external points and deleted to obtain the coarse point cloud data of the track panel.

[0038] In step S21 above, after the pavement planar mathematical model converges to a stable state through iterative calculations, a reasonable distance threshold is set based on the actual thickness characteristics of the airport pavement. Points in the coordinate-transformed point cloud data whose spatial distance from the planar mathematical model exceeds the threshold are identified as non-pavement external points and deleted. Only point cloud data that fits the pavement plane is retained, completing the first round of coarse filtering of the point cloud data and obtaining coarsely screened pavement point cloud data. For example, if the actual construction thickness of the pavement in the survey area is 25cm, considering the flatness deviation of the pavement and the small systematic error of laser scanning, a distance threshold of 28cm is set. Points in the coordinate-transformed global point cloud data whose vertical spatial distance from each point to the iteratively converged pavement planar mathematical model exceeds 28cm are uniformly identified as non-pavement external points such as aircraft, guide vehicles, and temporary pavement stacks. These external point clouds are then deleted in batches, retaining only point cloud data that are within the threshold range and fit the pavement plane.

[0039] Step S22: Determine the optimal DON feature value of the coarse point cloud data of the track panel based on the difference in normal features at large and small scales, and set the corresponding DON feature value threshold to exclude noise points that exceed the DON feature value threshold, so as to obtain the effective point cloud data of the track panel.

[0040] In step S22 above, multi-scale normal calculations can be performed on the coarse point cloud data of the runway panel. Support radii of large scale (e.g., 5cm radius) and small scale (e.g., 1cm radius) are selected respectively to solve the unit normal vector of each point in the coarse point cloud at different scales. The DON feature unit vector of each point is obtained by vector difference operation. Then, multiple sets of different support radius parameters (e.g., 1cm, 2cm, 3cm, 4cm, 5cm) are selected to compare and calculate the DON feature unit vector. Combined with the surface features of the airport runway panel being flat and without obvious texture, the optimal DON feature value (e.g., 0.15) that can maximize the distinction between runway panel planar points and noise points is determined. Based on this optimal value, 0.15 is set as the DON feature value threshold. Points in the coarse point cloud with DON feature values ​​exceeding 0.15 are judged as runway sand grains, scanning noise points, and other noise points and deleted. Finally, effective point cloud data that retains only the main features of the runway panel is obtained.

[0041] Therefore, the effective point cloud data acquisition method provided by the embodiments of the present invention achieves accurate removal of outliers by iteratively fitting the mathematical model of the pavement plane using the random parameter estimation method and combining it with a distance threshold. Then, the optimal DON feature value and corresponding threshold are determined through multi-scale normal feature analysis to effectively filter noise points. This step-by-step process achieves accurate purification of pavement point cloud data, resulting in high-purity effective pavement point cloud data. This lays a high-quality data foundation for the subsequent accurate extraction of pavement (especially pavement angle) elevation, further ensuring the accuracy and reliability of airport pavement elevation measurement.

[0042] Please refer to Figure 3 , Figure 3 A flowchart of a track panel point cloud filtering method based on DON feature values ​​provided for an embodiment of the present invention; the track panel point cloud filtering method based on DON feature values ​​includes: Step S30: Determine the unit normal vector of each point in the coarse screening point cloud data of the track panel using different radius scales, and obtain the DON feature unit vector of each point through vector operation.

[0043] In step S30 above, two sets of differentiated radius scales can be selected as calculation benchmarks for the coarse screening point cloud data of the track panel. For example, a support radius of 1cm is selected for the small scale and a support radius of 5cm is selected for the large scale. Neighborhood search and normal fitting are performed on each point in the coarse screening point cloud based on the two radius scales respectively. The two unit normal vectors corresponding to each point at the large and small scales are obtained. Then, the difference vector operation is performed on the two unit normal vectors. After the operation result is normalized, the DON feature unit vector corresponding to each point in the coarse screening point cloud of the track panel is obtained, thus completing the accurate extraction of the geometric features of the point cloud.

[0044] Step S31: Select multiple sets of different support radius parameters to compare and calculate the DON feature unit vector in order to determine the optimal DON feature value.

[0045] In step S31 above, based on the DON feature unit vectors of each point in the coarse screening point cloud of the runway panel, multiple sets of gradient-graded large and small-scale matching support radius parameters (such as 1cm small scale paired with 3cm large scale, 1cm small scale paired with 4cm large scale, 2cm small scale paired with 5cm large scale, 2cm small scale paired with 6cm large scale, etc.) can be selected. For each set of parameters, the DON feature unit vectors of the coarse screening point cloud can be calculated in batches to obtain the DON feature value set corresponding to each set of parameters. Combining the surface characteristics of the airport runway panel being flat without obvious protrusions or depressions, the DON feature value sets of each set of parameters are compared and analyzed with the criteria of having the smallest concentrated DON feature values ​​of the runway panel planar points and the discrete and significantly different DON feature values ​​of the noise points. The DON feature values ​​corresponding to the parameter sets that can maximize the distinction between the runway panel body points and noise points are selected and determined as the optimal DON feature values ​​for the runway panel point cloud in this survey area.

[0046] Step S32: Based on the optimal DON feature value, set the corresponding DON feature value threshold to exclude noise points in the coarse point cloud data of the track panel that exceed the DON feature value threshold, so as to obtain the effective point cloud data of the track panel.

[0047] In step S32 above, the optimal DON feature value of the selected pavement point cloud can be used as a benchmark. Based on the flat surface geometry of the airport pavement and the noise distribution pattern of the point cloud in the survey area, a matching DON feature value threshold can be set (if the optimal DON feature value is 0.15, then 0.15 is set as the judgment threshold). The DON feature value of each point in the coarsely screened pavement point cloud data is compared with the threshold one by one. Point clouds with feature values ​​exceeding the threshold are judged as noise points that are not part of the pavement body and are deleted in batches. Only point cloud data with DON feature values ​​within the threshold range and conforming to the planar features of the pavement are retained, and finally, high-purity pavement effective point cloud data is obtained.

[0048] Therefore, the pavement point cloud filtering method based on DON feature values ​​provided by the embodiments of the present invention calculates the unit normal vector of the point cloud at multiple radius scales and derives the DON feature unit vector. It determines the optimal DON feature value for the suitable test area by comparing and calculating multiple sets of support radius parameters. Then, it sets a precise DON feature value threshold to remove noise points. This method can accurately capture the geometric feature differences between the pavement plane and noise points, and the threshold setting is more in line with the actual point cloud data characteristics of the airport pavement. It effectively eliminates scattered and small noise points in the coarse screening point cloud of the pavement, and greatly improves the purity and effectiveness of the pavement point cloud data.

[0049] Please refer to Figure 4 , Figure 4 A flowchart of a point cloud data stitching method provided in an embodiment of the present invention; the point cloud data stitching method includes: Step S40: Perform translation and rotation calculations on the point cloud data of two adjacent stations based on preset points of the same name.

[0050] In step S40 above, a specially designed target set up in the survey area before field scanning can be used as a preset corresponding point. The point cloud feature points corresponding to the specially designed target in the point cloud data of the track panel collected by two adjacent survey stations are extracted. Based on the spatial coordinates of the corresponding point, a spatial translation operation is performed on the point cloud data of one of the survey stations to eliminate the positional offset of the point clouds of the two survey stations in the plane and elevation directions. Then, a rotation operation is performed to keep the spatial attitude of the point clouds of the two survey stations consistent, thus completing the spatial registration and preliminary alignment of the point cloud data of the two adjacent survey stations, so that the two form a continuous point cloud data segment.

[0051] Step S41: Use the pairwise splicing method to splice all the station cloud data to obtain spliced ​​point cloud data, and import the coordinate data of the plane elevation joint control points.

[0052] In step S41 above, based on the spatially registered point cloud data of adjacent stations, the point cloud data of the track panels of all stations in the survey area can be spliced ​​segment by segment using a pairwise splicing method. The registered point cloud segments are then merged and aligned with the point cloud data of the next station after translation and rotation calculations are completed. This process is repeated until the splicing of all station point cloud data is completed, forming spliced ​​point cloud data covering the entire survey area. Subsequently, the plane coordinates and elevation values ​​of the plane elevation joint control points determined by RTK (real-time dynamic positioning technology) and second-order leveling are completely imported into the spliced ​​point cloud data of the entire area, completing the binding of the point cloud data with the high-precision measurement benchmark, and providing an accurate benchmark reference for subsequent coordinate transformation.

[0053] Step S42: Transform the stitched point cloud data to the specified coordinate system using a coordinate transformation algorithm.

[0054] In step S42 above, the precise plane coordinates and elevation values ​​of the imported plane-elevation joint control points in the specified coordinate system are used as the transformation control points. A seven-parameter coordinate transformation algorithm (including 3 translation parameters, 3 rotation parameters and 1 scale parameter) can be selected to match the original coordinate system of the global stitched point cloud data with the specified coordinate system (such as the city independent coordinate system). By solving the transformation parameters, a mapping relationship between the two coordinate systems is established. Then, the mapping relationship is used to perform transformation operations on each point cloud coordinate in the stitched point cloud data one by one. Finally, the global stitched point cloud data is completely transformed into the specified coordinate system to obtain the road panel point cloud data with standardized coordinate attributes.

[0055] Therefore, the point cloud data stitching method provided by the embodiments of the present invention effectively reduces the cumulative stitching error of multi-station scanning through precise anchoring of corresponding points, ensuring the overall accuracy and coordinate uniformity of point cloud stitching. Based on the joint control points of plane elevation, it realizes the precise matching of point cloud data with the specified coordinate system, giving point cloud data standardized and engineering application attributes, and laying a unified and high-precision coordinate foundation for subsequent point cloud filtering and purification of track panel and precise elevation extraction.

[0056] Please refer to Figure 5 , Figure 5 A flowchart illustrating a method for extracting track panel elevation according to an embodiment of the present invention; the method for extracting track panel elevation includes: Step S50: Determine the coordinates of designated points on the road panel based on the measured lines of the road panel to generate a point cloud of the elevation to be extracted.

[0057] In step S50 above, the measured engineering line data (including the precise coordinates of the pavement edge lines and joint lines) of the airport terminal area pavement can be retrieved in the specified coordinate system after coordinate transformation. The selection rules for the specified points are clarified according to the technical requirements of pavement elevation measurement. The three-dimensional coordinates of all specified points of the pavement in the survey area are obtained through coordinate calculation. The coordinate information of these n specified points is integrated to generate the location point cloud of the elevation to be extracted. This location point cloud serves as the target point set for subsequent distance vector calculation and square sum optimization, defining a clear calculation object for batch elevation extraction.

[0058] Step S51: Store the effective point cloud data of the track panel into a point set, and project the point set onto the plane where the position point cloud of the elevation to be extracted is located, and establish an index relationship.

[0059] In step S51 above, the effective point cloud data of the road surface obtained by the above filtering can be stored in batches into a three-dimensional point set Ω. Taking the plane where the position point cloud of the elevation to be extracted is located as the projection reference plane, all effective point clouds in the point set Ω are vertically projected onto the reference plane using the orthogonal projection method to obtain the two-dimensional projection coordinates of each effective point cloud on the reference plane. Subsequently, based on the projected two-dimensional coordinates, a spatial index relationship is established between each specified point in the position point cloud of the elevation to be extracted and the projection points of the effective point cloud in the point set Ω. Through this index, the neighborhood range of the effective point cloud corresponding to each group of points to be extracted can be quickly locked, which is helpful for subsequent iterative solution of the distance vector d. i Provides efficient data retrieval support.

[0060] Step S52: Taking the minimum distance between the elevation point to be extracted and the corresponding point in the point set as the objective, determine the optimal values ​​of the plane parameters and set the convergence criteria.

[0061] In step S52 above, using n specified points in the point cloud of elevation locations to be extracted as a reference, the distance vector d between each group of points to be extracted and the corresponding neighboring points in the point set Ω is calculated. i norm Minimization is used as the iterative optimization objective, and iterative parameters are introduced. (Corresponding to the optimization variables of the plane parameters), construct a vector function S(u,v) to represent the distance vector in the iteration process; by continuously adjusting the iteration parameters Solve In addition, considering the millimeter-level accuracy requirements of airport runway elevation measurement, convergence criteria are set (such as the parameter difference between two adjacent iterations being less than 0.1 mm and the change in the sum of squares of the distance vector norm being less than 0.001 mm²). When the iterative calculation meets the convergence criteria, parameter adjustment is stopped, the plane parameters at this time are determined to be the optimal values, and the iterative optimization process is completed.

[0062] Step S53: After the iteration converges, derive the distance function between the elevation point to be extracted and the corresponding point in the point set.

[0063] In step S53 above, after the iterative calculation of the plane parameters satisfies the preset convergence criterion, the optimal distance vector d between each set of elevation points to be extracted and the corresponding points in the point set Ω is extracted based on the determined optimal values ​​of the plane parameters. i The distance vector satisfies The iterative optimization requirements; based on this, for all distance vectors d of the n elevation points to be extracted within the survey area. i By summing the squares of their norms and expanding the squares of the norms into a vector inner product, the distance function is finally derived: .

[0064] Step S54: Minimize the distance function through neighborhood search to obtain the nearest point cloud data.

[0065] In step S54 above, based on the derived distance function and the established spatial index relationship, for each elevation point to be extracted, firstly, taking its projected coordinates as the center, the effective point cloud neighborhood within a preset radius (e.g., 3cm) around the center in the point set Ω is quickly retrieved based on the spatial index, forming a candidate point cloud subset for the point to be extracted to narrow the solution range of the distance function; then, for each point in the candidate point cloud subset, the distance vector d between the point and the elevation point to be extracted is calculated based on the iteratively optimized plane parameters (u and v take the optimal values). i And solve its norm. Subsequently As a criterion, the point cloud data corresponding to the distance vector with the smallest norm is selected from the subset of candidate point clouds. This point cloud is the one with the closest spatial location to the elevation point to be extracted. Finally, the above operation is repeated for all n elevation points to be extracted to complete the solution of minimizing the distance function and finally obtain the nearest point cloud data corresponding to each elevation point to be extracted.

[0066] Step S55: Capture and record the elevation values ​​of the most recent point cloud data to obtain the elevation values ​​of the specified points on the corresponding track panel in the specified coordinate system.

[0067] In step S55 above, based on the nearest point cloud data corresponding to each elevation point to be extracted, the elevation component (Z value) in the three-dimensional coordinates of the point cloud in the specified coordinate system can be directly read. The elevation value is captured in real time and structured and recorded. The recorded content includes the plane coordinates (X, Y) of the specified point on the track panel, the unique identifier of the corresponding nearest point cloud, and the extracted elevation value (Z). At the same time, the coordinate system consistency of all extracted elevation values ​​is checked to ensure that they are completely matched with the specified coordinate system after the previous coordinate transformation. Finally, a complete result table containing the plane coordinates and corresponding elevation values ​​of all specified points on the track panel in the survey area is formed, and the accurate elevation value of each specified point on the track panel in the specified coordinate system is obtained.

[0068] Therefore, the effective point cloud data acquisition method provided by the embodiments of the present invention can quickly and stably extract the elevation values ​​of specified points on the pavement under a unified coordinate system. It not only ensures the automation and efficiency of elevation extraction, but also achieves millimeter-level extraction accuracy through iterative convergence and minimum distance solution. This provides a stable and reliable extraction method for the whole-area elevation measurement of airport pavement, and further improves the accuracy and practicality of the overall measurement scheme.

[0069] Please refer to Figure 6 , Figure 6 A flowchart illustrating a distance function minimization method based on neighborhood search provided in an embodiment of the present invention; the distance function minimization method based on neighborhood search includes: Step S60: Preset the neighborhood search radius of the point set.

[0070] In step S60 above, the actual scanning density of the effective point cloud of the runway panel is first obtained. Combining the convergence accuracy of the iterative optimization of the planar parameters and the millimeter-level accuracy requirement of airport runway panel elevation measurement, the baseline value of the neighborhood search radius is determined to be 3cm. Then, it is dynamically fine-tuned according to the actual point cloud density of the survey area: if the point cloud density of the survey area is higher than 500 points / m² (the point cloud distribution is denser), the radius is reduced to 2cm to avoid including too many redundant point clouds; if the point cloud density of the survey area is lower than 200 points / m² (the point cloud distribution is sparser), the radius is increased to 4cm to ensure coverage of all possible matching point clouds around the elevation point to be extracted; finally, the adjusted radius value is used as the neighborhood search radius of the point set Ω, defining a clear and suitable spatial range for subsequent neighborhood retrieval.

[0071] Step S61: Use neighborhood search to build a KD-tree index for the point set.

[0072] In step S61 above, all three-dimensional coordinates (X, Y, Z) of the effective point cloud set Ω of the track panel can be used as input data to construct a KD-tree index with "equal spatial dimension distribution" as the core rule: First, the median of the point cloud set on the X-axis is selected as the root node, and the point cloud is divided into two subsets, left and right; then, the median of the Y-axis and Z-axis is selected as the child node for each subset, and the subdivision is recursively carried out layer by layer until each leaf node contains only a small amount (such as 5-10) of point cloud data; after the index is constructed, combined with the preset neighborhood search radius (2-4cm), the "radius retrieval" rule is configured for the index to ensure that all neighborhood point clouds centered on the point to be extracted and within the preset radius can be quickly located during retrieval.

[0073] Step S62: Quickly retrieve and solve the distance function based on the KD-tree index to obtain the minimum value of the distance function.

[0074] In step S62 above, using the projected coordinates of each elevation point to be extracted as the retrieval center, the KD-tree index of the already constructed point set Ω is called, and a preset neighborhood search radius (2-4cm) is loaded as the retrieval threshold to trigger the radius neighborhood retrieval of the KD-tree. The index first locates the leaf node where the retrieval center is located, and then expands the retrieval range layer by layer outward to filter out all valid point clouds falling within the preset radius, forming a candidate point cloud subset for the point to be extracted. For each point cloud data in the subset, the distance vector d between it and the point to be extracted is calculated in combination with the iteratively optimized plane parameters (optimal values ​​of u and v). i And solve for the vector norm. ; Traverse all candidates within the subset Values, filter out those that meet the criteria The distance vector corresponding to the smallest norm is the minimum solution of the distance function in the neighborhood, and the corresponding point cloud is the nearest matching point cloud of the point to be extracted.

[0075] Step S63: Locate the point cloud data that is spatially closest to the elevation point to be extracted from the minimum value point set.

[0076] In step S63 above, the distance vector d corresponding to the minimum norm value can be matched. i The valid point cloud data within the corresponding point set Ω is traced back to the point cloud that is spatially closest to the elevation point to be extracted. During the positioning process, it is necessary to ensure that each nearest point cloud falls within the preset neighborhood radius of 2-4cm to avoid matching errors caused by exceeding the search range.

[0077] Therefore, the effective point cloud data acquisition method provided by the embodiments of the present invention can efficiently and accurately locate the point cloud data that is spatially closest to the elevation point to be extracted in a massive effective point cloud, significantly improve the computational efficiency and positioning accuracy of elevation extraction, avoid redundant calculations and interference points, further ensure the stability and reliability of elevation acquisition of designated points on the pavement, and provide efficient algorithm support for high-precision and automated elevation measurement of airport pavement.

[0078] Please refer to Figure 7 , Figure 7 A flowchart illustrating a method for setting up a combined plane elevation control point according to an embodiment of the present invention; the method for setting up the combined plane elevation control point includes: Step S70: Set up joint control points for plane elevation covering the survey area within the scanning range of the 3D laser scanner that collects point cloud data.

[0079] In step S70 above, the effective scanning range of the stand-up 3D laser scanner used to collect the point cloud data of the track panel is used as the deployment boundary. Plane elevation joint control points that can simultaneously provide planar coordinates and elevation information are evenly deployed in the interior and edge areas of the survey area. This ensures that all control points cover the entire survey area, guaranteeing that the scanner can clearly observe the identification of each control point during the scanning operation. Furthermore, the control points are evenly distributed without obvious blind spots, providing a stable and reliable spatial control benchmark for subsequent coordinate correction, plane fitting optimization, and elevation accuracy verification of the point cloud data.

[0080] Step S71: Use RTK to determine the plane coordinates of all joint control points according to the measurement specifications.

[0081] In step S71 above, the RTK (Real-Time Kinematic) measurement equipment can be debugged first, and the parameters of the base station and rover station can be configured and initialized to ensure that the equipment signal is stable and the positioning accuracy meets the specifications. Then, in accordance with the current "Engineering Surveying Specifications" (such as GB50026-2020) and the special requirements for airport pavement surveying, each of the joint plane elevation control points is measured. The rover station is aligned with the center of the control point marker. After the RTK positioning data is stable (the positional error is ≤2mm), the plane coordinates (X, Y) data of the control point are recorded. After all control points are measured, the measured plane coordinates are checked and verified, and abnormal data is eliminated to ensure that the plane coordinates of all joint control points are accurate and consistent. Finally, a complete control point plane coordinate result table is formed as the plane control basis for subsequent point cloud data processing and elevation extraction.

[0082] Step S72: Determine the elevation values ​​of all joint control points using leveling standards to form a joint horizontal elevation control point.

[0083] In step S72 above, leveling operations can be carried out on all joint control points whose plane coordinates have been determined in accordance with relevant national standards for leveling and the accuracy requirements for airport runway panel measurement. The elevation value of each control point in the specified engineering coordinate system is determined point by point, and the measurement data is checked and adjusted to ensure that the elevation measurement accuracy meets the millimeter-level control requirements. The measured elevation values ​​are matched and integrated with the plane coordinates of the corresponding control points to form a plane elevation joint control point result that simultaneously contains plane coordinates (X, Y) and elevation values ​​(Z), which serves as the control basis for subsequent point cloud data coordinate transformation, plane parameter optimization, and elevation accuracy verification.

[0084] Therefore, the effective point cloud data acquisition method provided by the embodiments of the present invention achieves dual accuracy of joint control point coordinates and elevation data by constructing a high-precision, fully covered plane elevation benchmark system. This provides a reliable coordinate benchmark for subsequent multi-measurement point cloud stitching, effectively reducing the cumulative stitching error, and provides a precise reference for converting point cloud data to a specified coordinate system, ensuring the standardization and engineering practicality of point cloud coordinates. At the same time, it lays a high-precision benchmark foundation for subsequent effective point cloud filtering of pavement panels and extraction of specified point elevations, further improving the overall accuracy and standardization of airport pavement panel elevation measurement.

[0085] Please refer to Figure 8 , Figure 8A schematic diagram of the structure of a track panel elevation measurement system based on laser point cloud provided for an embodiment of the present invention includes: a data acquisition module 10, used to acquire point cloud data of the track panel and set joint control points for the plane elevation of the track panel; a point cloud processing module 20, used to stitch together point cloud data from multiple stations and convert the stitched point cloud data to coordinate data in a specified coordinate system according to the coordinates of the joint control points for the plane elevation in a specified coordinate system; and an elevation extraction module 30, used to extract the elevation values ​​of specified points of the track panel in a specified coordinate system from the effective point cloud data of the track panel.

[0086] Based on the same inventive concept, the present invention also provides an electronic device, which includes a memory and a processor. The memory stores program instructions, and when the processor reads and runs the program instructions, it executes the steps in any of the above implementation methods.

[0087] In the embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. The system implementations described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some communication interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0088] Furthermore, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0089] Furthermore, in the various embodiments of the present invention, the functional modules can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0090] In this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, without necessarily requiring or implying any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0091] The above description is merely an embodiment of the present invention and is not intended to limit the scope of protection of the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for measuring the elevation of a track panel based on laser point clouds, characterized in that, include: Collect point cloud data of the track panel and set the joint control points for the plane elevation of the track panel; The point cloud data from multiple stations are stitched together, and the stitched point cloud data is converted to coordinate data in the specified coordinate system based on the coordinates of the plane elevation joint control points in the specified coordinate system. Extract the elevation values ​​of specified points on the road panel in the specified coordinate system from the effective point cloud data of the road panel.

2. The method according to claim 1, characterized in that, The valid point cloud data is obtained according to the following steps: A stochastic parameter estimation method is used to iteratively estimate the mathematical model of the road panel plane in the point cloud data after coordinate transformation; After the planar mathematical model converges iteratively, a distance threshold is set, and point clouds exceeding the distance threshold are identified as external points and deleted to obtain the coarse screening point cloud data of the track panel. Based on the difference in normal features at large and small scales, the optimal DON feature value (optimal normal vector feature value) of the coarse point cloud data of the track panel is determined, and the corresponding DON feature value threshold is set to exclude noise points that exceed the DON feature value threshold in order to obtain effective point cloud data of the track panel.

3. The method according to claim 2, characterized in that, Based on the difference in normal features at large and small scales, the optimal DON feature value of the coarse-screened point cloud data of the track panel is determined, and a corresponding DON feature value threshold is set. Noise points exceeding the DON feature value threshold are excluded to obtain effective point cloud data of the track panel, including: The unit normal vector of each point in the coarse screening point cloud data of the track panel is determined by using different radius scales, and the DON feature unit vector of each point is obtained by vector operation. Multiple sets of different support radius parameters are selected to compare and calculate the DON feature unit vector in order to determine the optimal DON feature value; Based on the optimal DON feature value, a corresponding DON feature value threshold is set to exclude noise points in the coarse point cloud data of the track panel that exceed the DON feature value threshold, so as to obtain the effective point cloud data of the track panel.

4. The method according to claim 1, characterized in that, The process of stitching together point cloud data from multiple stations involves converting the stitched point cloud data to coordinate data in the specified coordinate system based on the coordinates of the joint plane elevation control points. This includes: The point cloud data of two adjacent stations are translated and rotated based on the preset points of the same name. All station cloud data are spliced ​​together using a pairwise splicing method to obtain spliced ​​point cloud data, and the coordinate data of the plane elevation joint control points are imported. The spliced ​​point cloud data is transformed to a specified coordinate system using a coordinate transformation algorithm based on the coordinate data of the plane elevation joint control points.

5. The method according to claim 1, characterized in that, Extracting the elevation value of a specified point on the road panel in the specified coordinate system from the effective point cloud data of the road panel includes: The coordinates of a specified point on the track panel are determined based on the measured lines of the track panel to generate a point cloud of the location where the elevation to be extracted is to be generated. The effective point cloud data of the track panel is stored in a point set, and the point set is projected onto the plane where the position point cloud of the elevation to be extracted is located, and an index relationship is established. Using the minimum distance between the elevation point to be extracted and the corresponding point in the point set as the objective, determine the optimal values ​​of the plane parameters and set the convergence criteria; After iterative convergence, the distance function between the elevation point to be extracted and the corresponding point in the point set is derived. The distance function is minimized by neighborhood search to obtain the nearest point cloud data; Capture and record the elevation values ​​of the most recent point cloud data to obtain the elevation values ​​of the corresponding specified points on the road panel in the specified coordinate system.

6. The method according to claim 5, characterized in that, The step of minimizing the distance function through neighborhood search to obtain the nearest point cloud data includes: The neighborhood search radius of the point set is preset; A KD-tree index for the point set is established using neighborhood search; The distance function is quickly retrieved and solved based on the KD-tree index to obtain the minimum value of the distance function; The minimum value is used to locate the point cloud data that is spatially closest to the elevation point to be extracted from the set of points.

7. The method according to claim 1, characterized in that, The method of setting the plane elevation joint control points for the track panel includes: Within the scanning range of the 3D laser scanner used to collect point cloud data, planar elevation joint control points covering the survey area are deployed; The plane coordinates of all joint control points were determined using RTK according to measurement specifications. The elevation values ​​of all joint control points were determined using leveling standards to form a joint horizontal elevation control point.

8. The method according to claim 1, characterized in that, in, Point cloud data of the airport terminal area pavement panels were collected using a 3D laser scanner, and all field operations for collecting the point cloud data were completed during breaks in airport operations without interrupting flight operations.

9. A system for measuring the elevation of a track panel based on laser point clouds, characterized in that, include: The data acquisition module is used to acquire point cloud data of the road panel and set the plane elevation joint control points of the road panel. The point cloud processing module is used to stitch together point cloud data from multiple stations and, based on the coordinates of the plane elevation joint control points in the specified coordinate system, convert the stitched point cloud data into coordinate data in the specified coordinate system. The elevation extraction module is used to extract the elevation values ​​of specified points of the road panel in the specified coordinate system from the effective point cloud data of the road panel.

10. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing program instructions, and the processor executing the steps of the method according to any one of claims 1-8 when running the program instructions.

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