CN-121978319-A - Roadbed compactness rapid detection method and system based on three-dimensional point cloud technology

CN121978319ACN 121978319 ACN121978319 ACN 121978319ACN-121978319-A

Abstract

The invention discloses a roadbed compactness rapid detection method and system based on a three-dimensional point cloud technology, and relates to the technical field of three-dimensional scanning. The method comprises the steps of obtaining normalized point clouds of the inner wall of a roadbed compactness test hole, combining macroscopic priori knowledge of a test hole drilling tool, adopting a process of fusing two algorithms to fit matrix cylindrical model parameters and calculate internal point rate, calculating normal distance from the point clouds to the surface of the model as geometric residual errors, expanding cylindrical surfaces to two-dimensional planes, mapping the residual errors, interpolating to generate an initial residual error grid field, obtaining constraint conditions for deriving physical mechanical parameters of filler, optimizing the initial residual error grid field to obtain a physical compliance residual error grid field, mapping the physical compliance residual error grid field back to be overlapped to the matrix model in a three-dimensional mode, generating a triangular grid model, correcting the internal point rate through a numerical integration calculation volume, outputting a result, and combining the filler performance parameters to calculate compactness. The method effectively improves the accuracy of roadbed compactness detection.

Inventors

SUN HONGPENG
ZHU YOUWEI
Jiao Chenghu
WANG XINZHI
LIU YANCHENG
SUN MINGGANG

Assignees

龙建路桥股份有限公司

Dates

Publication Date: 20260505
Application Date: 20260409

Claims (10)

1. The method for rapidly detecting the compactness of the roadbed based on the three-dimensional point cloud technology is characterized by comprising the following steps of: based on macroscopic prior knowledge formed by a roadbed compactness pilot tunnel drilling tool, adopting a fitting flow of a fusion random sampling consistency algorithm and a minimum median flattening method to the normalized point cloud, solving a matrix cylindrical model parameter representing the macroscopic shape of the roadbed compactness pilot tunnel, and calculating an internal point rate; The method comprises the steps of calculating the normal distance from each point in the normalized point cloud to the surface of a matrix cylindrical model, defining the normal distance as a geometric residual error, expanding the cylindrical surface of the matrix cylindrical model to a two-dimensional parameter plane, mapping each point and the geometric residual error corresponding to each point to the two-dimensional parameter plane to form sparse residual error distribution, and interpolating the sparse residual error distribution to generate a continuous initial residual error grid field; obtaining physical mechanical parameters of roadbed filling, and deducing maximum surface gradient constraint and minimum surface fluctuation wavelength constraint based on the physical mechanical parameters to serve as boundary conditions, wherein the physical mechanical parameters at least comprise an internal friction angle and maximum particle size; Based on the initial residual grid field, taking the boundary condition as constraint, establishing an optimization function taking the smoothness of the residual field and the fitting degree of the measurement data as targets, and solving the optimization function to obtain an optimized physical compliance residual grid field; Mapping the physical compliance residual error grid field back to a three-dimensional space, overlapping the three-dimensional space on the surface of the matrix cylindrical model to generate a final three-dimensional composite curved surface triangular grid model, and obtaining the calculated volume of the roadbed compactness test hole by adopting a numerical integration method; And (3) obtaining performance parameters of the roadbed filling, constructing a compactness calculation formula based on the performance parameters, substituting the compactness calculation formula into the final volume value, and calculating to obtain the roadbed compactness, wherein the performance parameters comprise humidity, density and water content.
2. The method for rapidly detecting the compactness of the roadbed based on the three-dimensional point cloud technology according to claim 1, wherein the method for solving the matrix cylindrical model parameters representing the macroscopic shape of the compactness test hole of the roadbed and calculating the interior point rate by adopting a fitting flow of a fusion random sampling consistency algorithm and a minimum median flattening method for the normalized point cloud based on macroscopic priori knowledge formed by the compactness test hole drilling tool of the roadbed comprises the following steps: acquiring the macroscopic priori knowledge based on parameters of the subgrade compactness pilot hole drilling tool, wherein the macroscopic priori knowledge at least comprises a pilot hole diameter range, an approximate cylinder shape and a pilot hole longitudinal axis direction; setting a reasonable sampling range of the cylinder radius based on the macroscopic prior knowledge, and randomly selecting a minimum point set from the normalized point cloud to generate a plurality of initial cylinder hypotheses; For each initial cylindrical hypothesis, calculating the distances from all points to the surface of the cylindrical hypothesis to obtain a distance set, and selecting the median of the distance set as a goodness-of-fit index of the initial cylindrical hypothesis; Selecting an initial cylindrical assumption with the minimum fitting goodness index as an optimal initial solution of the matrix cylindrical model; Setting a distance threshold based on the optimal initial solution, judging points with the distance from the surface of the matrix cylindrical model in the normalized point cloud smaller than the distance threshold as inner points and judging points with the distance greater than or equal to the distance threshold as outer points; based on a point set formed by all the inner points, carrying out iterative optimization by adopting a least square method, and solving the matrix cylindrical model parameters; And calculating an interior point rate, wherein the interior point rate is the ratio of the number of the interior points to the total number of the normalized point cloud.
3. The method for rapidly detecting the compactness of the roadbed based on the three-dimensional point cloud technology according to claim 1, wherein the normal distance from each point in the normalized point cloud to the surface of the matrix cylindrical model is calculated and defined as a geometric residual, and the method comprises the following steps: Calculating the shortest distance vector from each normalized point to the surface of the matrix cylindrical model by an analytic geometry method based on the matrix cylindrical model parameters; Verifying whether the shortest distance vector is consistent with the normal vector direction of the matrix cylindrical model surface at the projection position of the corresponding point, if so, determining that the shortest distance vector is a real normal distance, and taking the absolute value of the real normal distance as the geometric residual error; and recording symbols of the normal distance, wherein the symbols comprise positive signs and negative signs.
4. The method for rapidly detecting the compactness of the roadbed based on the three-dimensional point cloud technology according to claim 1, wherein the steps of expanding the cylindrical surface of the matrix cylindrical model to a two-dimensional parameter plane, mapping each point and the corresponding geometric residual to the two-dimensional parameter plane to form a sparse residual distribution, interpolating the sparse residual distribution, and generating a continuous initial residual grid field include: Establishing a two-dimensional parameter plane, wherein a U-axis of the two-dimensional parameter plane represents the circumferential angle of the cylindrical surface, and a V-axis represents the axial height of the cylindrical surface; for each point in the normalized point cloud, calculating the axial height and the circumferential angle of a projection point of the point on the surface of the matrix cylindrical model to determine the coordinate position of the corresponding point on the two-dimensional parameter plane; Sequentially mapping all points in the normalized point cloud to the two-dimensional parameter plane to form a discrete point set with attribute values of geometric residuals to form sparse residual distribution; Generating a two-dimensional parameter plane rule grid based on a preset rule; And estimating residual values at each grid node of the two-dimensional parameter plane regular grid by adopting a preset interpolation algorithm for the sparse residual distribution, so as to generate the continuous initial residual grid field covering the whole parameter domain.
5. The method for rapidly detecting the compactness of the roadbed based on the three-dimensional point cloud technology according to claim 4, wherein the generating of the two-dimensional parameter plane rule grid based on the preset rule comprises the following steps: according to the height and perimeter of the matrix cylindrical model, a rectangular calculation domain covering the whole cylindrical surface is predefined on the two-dimensional parameter plane; and dividing the rectangular calculation domain along the U axis and the V axis at preset intervals in the rectangular calculation domain, and generating the two-dimensional parameter plane regular grid.
6. The method for rapidly detecting the compactness of the roadbed based on the three-dimensional point cloud technology according to claim 1, wherein the steps of obtaining the physical and mechanical parameters of the roadbed filling material, deriving the maximum surface gradient constraint and the minimum surface relief wavelength constraint based on the physical and mechanical parameters, and taking the maximum surface gradient constraint and the minimum surface relief wavelength constraint as boundary conditions comprise: Obtaining an internal friction angle and the maximum particle size of roadbed filling as physical and mechanical parameters; Setting the tangent value of the internal friction angle as the allowable maximum surface gradient ratio, calculating the allowable maximum variation of geometric residual errors between any adjacent grid nodes in a two-dimensional parameter plane expanded by a cylindrical surface based on the radius of the matrix cylindrical model and the maximum surface gradient ratio, and constructing the maximum surface gradient constraint based on the maximum variation of the geometric residual errors; And setting a minimum surface characteristic wavelength according to the maximum particle size, establishing a spatial low-pass filter corresponding to the minimum surface characteristic wavelength in a two-dimensional parameter plane, and constructing the minimum surface fluctuation wavelength constraint by restraining the change amplitude of the initial residual grid field after passing through the spatial low-pass filter.
7. The method for rapidly detecting the compactness of the roadbed based on the three-dimensional point cloud technology according to claim 6, wherein based on the initial residual grid field, taking the boundary condition as a constraint, establishing an optimization function targeting the smoothness of the residual field and the fitness to measured data, solving the optimization function, and obtaining an optimized physical compliance residual grid field, comprises: The optimization function comprises two weighted sums, wherein the first term is a smooth term and consists of a second-order differential modular length of grid node values, and the second term is a data fitting term and consists of an interpolation residual at the grid nodes and a mean square error of a geometric residual of the corresponding grid nodes; converting the maximum surface gradient constraint and the minimum surface fluctuation wavelength constraint into an upper limit constraint on the grid node gradient modular length and an upper limit constraint on the grid node second-order differential modular length respectively; And converting the constraint optimization problem formed by the optimization function and the boundary condition into a Lagrange dual problem to solve, and finally obtaining an optimized physical compliance residual grid field and an overall smoothness evaluation result of the physical compliance residual grid field.
8. The method for rapidly detecting the compactness of the roadbed based on the three-dimensional point cloud technology according to claim 1, wherein mapping the physical compliance residual grid field back to a three-dimensional space, overlapping the physical compliance residual grid field on the surface of the matrix cylindrical model to generate a final three-dimensional compound curved surface triangular grid model, and obtaining the calculated volume of the compactness test hole of the roadbed by adopting a numerical integration method comprises the following steps: Reversely calculating each grid node of the physical compliance residual error grid field on the two-dimensional parameter plane to the corresponding three-dimensional space coordinate of the surface of the matrix cylindrical model according to the coordinate of the grid node on the two-dimensional parameter plane; Translating residual values corresponding to the grid nodes along the normal vector direction of each grid node at the corresponding point on the surface of the matrix cylindrical model to obtain a three-dimensional point set of the composite curved surface; Performing Delaunay triangulation on the three-dimensional point set of the composite curved surface to generate a three-dimensional composite curved surface triangular mesh model representing the inner wall surface of the pilot tunnel; carrying out equidistant slicing on the three-dimensional composite curved surface triangular mesh model at preset intervals along the axial direction of the matrix cylindrical model; calculating the area of a polygonal section formed by intersecting each slice plane with the triangular mesh model; and carrying out numerical integration on the areas of all polygonal sections along the axial direction by adopting a Simpson integration method to obtain the calculated volume of the subgrade compactness test hole.
9. The method for rapidly detecting the compactness of the roadbed based on the three-dimensional point cloud technology according to claim 7, wherein the confidence correction is performed on the calculated volume by using the interior point rate, and a final volume value and a corresponding uncertainty range are output, comprising: Inquiring a preset confidence coefficient correction table based on the internal point rate to obtain a volume correction coefficient corresponding to the current internal point rate, wherein the confidence coefficient correction coefficient table defines a monotonically increasing mapping relation between the internal point rate and the correction coefficient which is more than 0 and less than or equal to 1; multiplying the calculated volume by the volume correction coefficient to obtain a preliminary correction volume value; calculating a comprehensive uncertainty factor based on the current interior point rate and the overall smoothness evaluation result of the physical compliance residual grid field, wherein the comprehensive uncertainty factor is inversely related to the interior point rate and is inversely related to the overall smoothness of the physical compliance residual grid field; and calculating and outputting an uncertainty range corresponding to the final volume value according to the comprehensive uncertainty factor and the preliminary correction volume value, wherein the interval width of the uncertainty range is positively correlated with the comprehensive uncertainty factor.
10. The rapid roadbed compactness detection system based on the three-dimensional point cloud technology is characterized in that the system is used for realizing the rapid roadbed compactness detection method based on the three-dimensional point cloud technology as claimed in any one of claims 1 to 9, and the system comprises: based on macroscopic priori knowledge formed by the roadbed compactness pilot tunnel drilling tool, adopting a fitting flow of fusion random sampling consistency algorithm and a minimum median flattening method to the normalized point cloud, solving matrix cylindrical model parameters representing the macroscopic shape of the roadbed compactness pilot tunnel, and calculating the internal point rate; The initial residual error grid acquisition module is used for calculating the normal distance from each point in the normalized point cloud to the surface of the matrix cylindrical model, and defining the normal distance as a geometric residual error; expanding the cylindrical surface of the matrix cylindrical model to a two-dimensional parameter plane, mapping each point and the geometric residual error corresponding to each point to the two-dimensional parameter plane to form sparse residual error distribution, and interpolating the sparse residual error distribution to generate a continuous initial residual error grid field; The optimizing boundary condition acquisition module is used for acquiring physical and mechanical parameters of roadbed filling, deriving maximum surface gradient constraint and minimum surface fluctuation wavelength constraint based on the physical and mechanical parameters, and taking the maximum surface gradient constraint and the minimum surface fluctuation wavelength constraint as boundary conditions, wherein the physical and mechanical parameters at least comprise an internal friction angle and a maximum particle size; The residual grid optimization module is used for establishing an optimization function taking the smoothness of the residual field and the fitting degree of the measurement data as targets based on the initial residual grid field and taking the boundary condition as a constraint, and solving the optimization function to obtain an optimized physical compliance residual grid field; The final volume acquisition module is used for mapping the physical compliance residual error grid field back to a three-dimensional space, superposing the physical compliance residual error grid field on the surface of the matrix cylindrical model to generate a final three-dimensional compound curved surface triangular grid model, and obtaining the calculated volume of the roadbed compactness test hole by adopting a numerical integration method; the roadbed compactness measuring and calculating module is used for obtaining performance parameters of roadbed fillers, constructing a compactness calculation formula based on the performance parameters, substituting the final volume value, and calculating to obtain the roadbed compactness, wherein the performance parameters comprise humidity, density and water content.

Description

Roadbed compactness rapid detection method and system based on three-dimensional point cloud technology Technical Field The invention relates to the technical field of three-dimensional scanning, in particular to a roadbed compactness rapid detection method and system based on a three-dimensional point cloud technology. Background Along with the rapid development of highway construction and the continuous acceleration of construction rhythm, the real-time performance and the efficiency requirement of roadbed compactness detection are increasingly improved, and the traditional detection means which rely on the sand filling method and the like and are lossy and time-consuming are difficult to adapt to the requirements of modern engineering. Under the background, the volume measurement method based on the three-dimensional point cloud technology provides a new technical path for the rapid detection of the compactness due to the advantages of non-contact, high speed and the like. However, the prior art does not form an adaptive scheme for specific scenes of highway subgrade compactness detection, and many difficulties are faced when a general three-dimensional reconstruction algorithm is directly applied to compactness test holes formed by loose subgrade fillers. The problems of sparsity, large noise and data hollowness of the point cloud on the inner wall of the subgrade compactness test hole exist, geometric distortion of a curved surface reconstructed by a general algorithm is easy to occur, and forms of overhang, sharp bulge and the like against the mechanical rule of a soil body can be generated, so that the calculation error is overlarge and the accuracy is insufficient. Disclosure of Invention The invention provides a roadbed compactness rapid detection method and system based on a three-dimensional point cloud technology, and aims to solve the technical problem of insufficient roadbed compactness detection precision in the prior art. In view of the above problems, the invention provides a method and a system for rapidly detecting the compactness of a roadbed based on a three-dimensional point cloud technology. In a first aspect, the invention provides a method for rapidly detecting the compactness of a roadbed based on a three-dimensional point cloud technology, which comprises the following steps: based on macroscopic prior knowledge formed by a roadbed compactness pilot tunnel drilling tool, adopting a fitting flow of a fusion random sampling consistency algorithm and a minimum median flattening method to the normalized point cloud, solving a matrix cylindrical model parameter representing the macroscopic shape of the roadbed compactness pilot tunnel, and calculating an internal point rate; The method comprises the steps of calculating the normal distance from each point in the normalized point cloud to the surface of a matrix cylindrical model, defining the normal distance as a geometric residual error, expanding the cylindrical surface of the matrix cylindrical model to a two-dimensional parameter plane, mapping each point and the geometric residual error corresponding to each point to the two-dimensional parameter plane to form sparse residual error distribution, and interpolating the sparse residual error distribution to generate a continuous initial residual error grid field; obtaining physical mechanical parameters of roadbed filling, and deducing maximum surface gradient constraint and minimum surface fluctuation wavelength constraint based on the physical mechanical parameters to serve as boundary conditions, wherein the physical mechanical parameters at least comprise an internal friction angle and maximum particle size; Based on the initial residual grid field, taking the boundary condition as constraint, establishing an optimization function taking the smoothness of the residual field and the fitting degree of the measurement data as targets, and solving the optimization function to obtain an optimized physical compliance residual grid field; Mapping the physical compliance residual error grid field back to a three-dimensional space, overlapping the three-dimensional space on the surface of the matrix cylindrical model to generate a final three-dimensional composite curved surface triangular grid model, and obtaining the calculated volume of the roadbed compactness test hole by adopting a numerical integration method; And (3) obtaining performance parameters of the roadbed filling, constructing a compactness calculation formula based on the performance parameters, substituting the compactness calculation formula into the final volume value, and calculating to obtain the roadbed compactness, wherein the performance parameters comprise humidity, density and water content. In a second aspect, the present invention provides a rapid roadbed compactness detection system based on a three-dimensional point cloud technology, comprising: based on macroscopic priori knowledge formed by the roadbed com