CN115902919A - Tunnel reinforcing mesh ranging method and system, electronic device and storage medium - Google Patents
Tunnel reinforcing mesh ranging method and system, electronic device and storage medium Download PDFInfo
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Abstract
The invention provides a method, a system, electronic equipment and a storage medium for measuring the distance of a tunnel reinforcing mesh, wherein the method comprises the steps of preprocessing initial point cloud data of a tunnel to obtain down-sampling point cloud data, and filtering non-target point cloud data by a preset algorithm to obtain target point cloud data of a reinforcing mesh to be measured; combining with cross section design data, adopting an expanded coordinate reflection algorithm to calculate service characteristic information of each point in the target point cloud data between the cross section of each point and a corresponding design point, adopting a KMean cluster analysis method to perform hierarchical cluster processing on the target point cloud data and adopting an LOF algorithm to perform filtering processing to obtain target layered point cloud data of different layers of reinforcing steel bar nets, respectively adopting corresponding segmentation analysis algorithms to obtain paired anchor points for ranging according to the target layered point cloud data, and fitting the paired anchor points to output target ranging data. This application is through improving tunnel reinforcing bar net range finding whole efficiency and cutting apart the precision to promote tunnel reinforcing bar net data measurement's accuracy.
Description
Technical Field
The invention belongs to the technical field of point cloud data based distance measurement, and particularly relates to a tunnel reinforcing steel bar mesh distance measurement method, a system, electronic equipment and a storage medium.
Background
The appearance of the laser radar provides a convenient method for people to acquire space geometric structure data in a point cloud form. Compared with common two-dimensional image data, the three-dimensional point cloud data can accurately and densely depict the overall structure of an object (such as a tunnel original scanning sample shown in fig. 1), provide richer space geometry information, and be helpful for understanding the three-dimensional space characteristics of the environment and making decisions corresponding to the three-dimensional space characteristics.
Measurement of data relating to the tunnel mesh reinforcement (such as the layered spacing of the mesh reinforcement as shown in fig. 2, the transverse spacing between the reinforcement bars in the inner mesh reinforcement as shown in fig. 3, and the longitudinal spacing as shown in fig. 4) is an essential step in the monitoring of tunnel safety. At present, related data of the tunnel reinforcing bar net are generally scanned by a laser radar to obtain related point cloud data, and the obtained reinforcing bar net point cloud data is huge in amount and is a large scene formed by millions to even billions of points. However, the point cloud data of the reinforcing bar net has the characteristics of dispersibility, irregularity, disorder, non-uniformity in distribution and the like, and the measurement data such as the layered spacing of the reinforcing bar net, the transverse spacing and the longitudinal spacing between the reinforcing bars in the reinforcing bar net in the inner layer are calculated by processing the point cloud data of the reinforcing bar net by adopting the segmentation recognition calculation method in the prior art, so that the overall efficiency and the segmentation accuracy of the distance measurement of the tunnel reinforcing bar net are low due to the defects of insufficient real-time performance, difficult strict mathematical description of the reinforcing bar net, low signal to noise ratio and the like, and the accuracy of the data measurement of the tunnel reinforcing bar net is influenced.
Disclosure of Invention
In order to solve the technical problems, the invention provides a method, a system, an electronic device and a storage medium for measuring the distance of the tunnel reinforcing mesh, wherein a complex distance measuring scene is divided into a single distance measuring target combination by filtering and dividing point cloud data, only the local characteristics of a measuring object of each distance measuring target are concerned, other complex semantic information is abandoned, the overall efficiency and the dividing precision of the distance measuring of the tunnel reinforcing mesh are improved, and the accuracy of the data measuring of the tunnel reinforcing mesh is improved.
In a first aspect, the invention provides a distance measuring method for a tunnel reinforcing mesh, which comprises the following steps:
scanning and acquiring initial point cloud data of the tunnel through a laser radar;
preprocessing the initial point cloud data to obtain down-sampling point cloud data of the tunnel;
filtering non-target point cloud data from the down-sampling point cloud data through a preset algorithm to obtain target point cloud data of the reinforcing mesh to be measured, wherein the preset algorithm comprises a ground feature recognition algorithm and an octree algorithm;
calculating service characteristic information of each point in the target point cloud data between the cross section and a corresponding design point by adopting an expanded coordinate reflection algorithm in combination with cross section design data, wherein the service characteristic information comprises mileage information and deviation information;
based on the service characteristic information, performing hierarchical clustering processing on the target point cloud data by adopting a KMean clustering analysis method and performing filtering processing by adopting an LOF algorithm to obtain target layered point cloud data of reinforcing steel bar nets of different layers;
and respectively adopting corresponding segmentation analysis algorithms to obtain paired anchor points for ranging according to the target layered point cloud data, and fitting the paired anchor points to output target ranging data of the steel bar to be measured, wherein the target ranging data comprises the steel bar mesh layer distance, the single-layer steel bar mesh transverse and longitudinal distances.
Preferably, the step of preprocessing the initial point cloud data to obtain down-sampling point cloud data of the tunnel specifically includes:
selecting an adaptive target down-sampling method according to a down-sampling mode;
and carrying out down-sampling processing on the initial point cloud data based on the target down-sampling method, and converting the initial point cloud data into down-sampled point cloud data obtained by the down-sampling processing.
Preferably, the target downsampling method is a uniform downsampling method or a voxel downsampling method.
Preferably, the target point cloud data of the reinforcing bar mesh to be measured is obtained by filtering non-target point cloud data from the down-sampling point cloud data through a preset algorithm, wherein the preset algorithm includes a ground feature identification algorithm and an octree algorithm, and the steps of the preset algorithm specifically include:
stripping point cloud data of the tunnel ground from the down-sampling point cloud data by adopting a ground object identification algorithm to obtain reserved point cloud data;
and calculating a point cloud neighborhood range of the reserved point cloud data through an octree algorithm, and filtering the reserved point cloud data based on a point spacing mode in the point cloud neighborhood range to obtain target point cloud data of the reinforcement mesh to be measured.
Preferably, the step of calculating, by using an extended coordinate reflection algorithm in combination with the section design data, service characteristic information between each point in the target point cloud data and its corresponding design point in the section thereof, where the service characteristic information includes mileage information and deviation information, specifically includes:
calculating the nearest line element of each point in the target point cloud data on a plane projection through XY coordinates according to preset line data;
calculating the projection distance from the projection point of the point corresponding to the line element to the end point of the line element according to the linear space;
calculating mileage information corresponding to each point in the target point cloud data according to the mileage of the line element end point and the projection distance;
calculating the horizontal deviation of each point distance in the target point cloud data corresponding to the line element according to the coordinates of the projection points;
calculating the vertical offset of each point in the target point cloud data relative to the corresponding alignment according to the mileage information and by combining the vertical curve information in the alignment data;
and determining the two-dimensional position of the axis on the section according to the section design model to obtain complete service characteristic information.
Preferably, the step of obtaining target layered point cloud data of different layers of reinforcing mesh by performing layered clustering processing on the target point cloud data by using a KMean clustering analysis method and performing filtering processing by using an LOF algorithm based on the service feature information specifically includes:
slicing the target point cloud data according to the mileage information to obtain point cloud slice data;
performing layered processing on the point cloud slice data through KMean cluster analysis aiming at the deviation information to obtain layered point cloud data of reinforcing steel bar nets of different layers;
and adopting an LOF algorithm to screen abnormal points for the layered point cloud data to obtain target layered point cloud data.
Preferably, the segmentation analysis algorithm comprises segmentation analysis which is based on the characteristics that the inner wall of the tunnel is smooth to cause deviation change smoothness and deviation gradient is uniform to facilitate the layer distance of the reinforcing mesh, and segmentation analysis which is based on the characteristic that the single-layer reinforcing mesh has obvious net shape to facilitate the transverse and longitudinal distances of the single-layer reinforcing mesh.
In a second aspect, the present invention provides a distance measuring system for a tunnel reinforcing mesh, comprising:
the acquisition module is used for scanning and acquiring initial point cloud data of the tunnel through a laser radar;
the preprocessing module is used for preprocessing the initial point cloud data to obtain down-sampling point cloud data of the tunnel;
the filtering module is used for filtering non-target point cloud data from the down-sampling point cloud data through a preset algorithm to obtain target point cloud data of the reinforcement mesh to be measured, wherein the preset algorithm comprises a ground feature recognition algorithm and an octree algorithm;
the operation module is used for combining with section design data and adopting an expanded coordinate reflection algorithm to calculate service characteristic information between each point in the target point cloud data and a corresponding design point in the section of the target point cloud data, wherein the service characteristic information comprises mileage information and deviation information;
the hierarchical clustering module is used for performing hierarchical clustering processing on the target point cloud data by adopting a KMean clustering analysis method and performing filtering processing by adopting an LOF algorithm to obtain target layered point cloud data of reinforcing steel bar nets in different layers;
and the segmentation analysis module is used for acquiring paired anchor points for ranging according to the target layered point cloud data by adopting corresponding segmentation analysis algorithms respectively, and fitting the paired anchor points to output target ranging data of the steel bar to be measured, wherein the target ranging data comprises the steel bar mesh layer distance, the single-layer steel bar mesh transverse and longitudinal distances.
Preferably, the preprocessing module comprises:
the selecting unit is used for selecting an adaptive target down-sampling method according to a down-sampling mode;
and the preprocessing unit is used for carrying out down-sampling processing on the initial point cloud data based on the target down-sampling method and converting the initial point cloud data into down-sampling point cloud data obtained by the down-sampling processing.
Preferably, the filtering module includes:
the stripping unit is used for stripping the point cloud data of the tunnel ground from the down-sampling point cloud data by adopting a ground object identification algorithm to obtain reserved point cloud data;
and the filtering module is used for calculating a point cloud neighborhood range of the reserved point cloud data through an octree algorithm, and filtering the reserved point cloud data based on a point spacing mode in the point cloud neighborhood range to obtain target point cloud data of the reinforcing mesh to be measured.
Preferably, the operation module includes:
the first calculating unit is used for calculating a line element which is closest to each point in the target point cloud data on a plane projection through XY coordinates according to preset line data;
the second calculation unit is used for calculating the projection distance from the projection point of the point corresponding to the line element to the end point of the line element according to a linear space;
the third calculation unit is used for calculating mileage information corresponding to each point in the target point cloud data according to the mileage of the line element end point and the projection distance;
the fourth calculation unit is used for calculating the horizontal deviation of each point distance in the target point cloud data corresponding to the line element according to the coordinates of the projection points;
the fifth calculation unit is used for calculating the vertical offset of each point in the target point cloud data relative to the corresponding alignment according to the mileage information and by combining the vertical curve information in the alignment data;
and the determining unit is used for determining the two-dimensional position of the axis on the section according to the section design model to obtain complete service characteristic information.
Preferably, the hierarchical clustering module comprises:
the slicing unit is used for slicing the target point cloud data according to the mileage information to obtain point cloud slice data;
the layering unit is used for layering the point cloud slice data through KMean cluster analysis aiming at the deviation information to obtain layered point cloud data of reinforcing steel bar nets in different layers;
and the screening unit is used for screening abnormal points aiming at the layered point cloud data by adopting an LOF algorithm to obtain target layered point cloud data.
In a third aspect, the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the method for measuring distance of a tunnel reinforcing mesh according to the first aspect is implemented.
In a fourth aspect, the present embodiment provides a storage medium, on which a computer program is stored, wherein the program is executed by a processor to implement the method for measuring distance of a tunnel reinforcing mesh according to the first aspect.
Compared with the prior art, the tunnel reinforcing mesh ranging method, the tunnel reinforcing mesh ranging system, the electronic device and the storage medium provided by the invention have the advantages that the collected tunnel initial point cloud data is subjected to down-sampling pretreatment to obtain the down-sampling point cloud data which are uniformly distributed; filtering non-target point cloud data through a ground feature recognition algorithm and an octree algorithm to obtain target point cloud data of the reinforcement mesh to be measured; calculating the service characteristic information of each point in the target point cloud data between the cross section and the corresponding design point by adopting an expanded coordinate reflection algorithm in combination with the cross section design data; performing hierarchical clustering processing on the target point cloud data by adopting a KMean clustering analysis method and performing filtering processing by adopting an LOF algorithm to obtain target layered point cloud data of reinforcing steel bar nets of different layers; and acquiring paired anchor points for ranging by respectively adopting corresponding segmentation analysis algorithms according to the target layered point cloud data, and fitting the paired anchor points to output target ranging data of the steel bar to be measured. The complex ranging scene is split into the combination of single ranging targets through the filtering and the point cloud data segmentation in the steps, only the local characteristics of the measured object of each ranging target are concerned, other complex semantic information is abandoned, the overall efficiency and the segmentation precision of the ranging of the tunnel reinforcing steel bar net are improved, and the accuracy of the data measurement of the tunnel reinforcing steel bar net is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is an original scan sample after three-dimensional scanning of a tunnel;
fig. 2 is a schematic diagram of layer distance analysis of the reinforcing mesh of the tunnel;
fig. 3 is a schematic view illustrating analysis of the transverse spacing between the reinforcing bars of the inner layer of the reinforcing mesh of the tunnel;
fig. 4 is a schematic diagram illustrating the longitudinal spacing between the reinforcing bars in the inner layer of the reinforcing mesh of the tunnel;
fig. 5 is a flowchart of a distance measuring method for a reinforcing mesh of a tunnel according to embodiment 1 of the present invention;
fig. 6 is a steel bar layer distance measurement overall statistical chart of the tunnel steel bar mesh ranging method according to embodiment 1 of the present invention;
fig. 7 is a general statistical diagram of the transverse steel bar distance measurement of the distance measurement method for the tunnel reinforcing mesh according to embodiment 1 of the present invention;
fig. 8 is a general statistical chart of longitudinal reinforcement distance measurement in the method for measuring distance of a tunnel reinforcement mesh according to embodiment 1 of the present invention;
fig. 9 is a block diagram of a distance measuring system for a tunnel reinforcing mesh according to an embodiment 2 of the present invention, which corresponds to the method of embodiment 1;
fig. 10 is a schematic diagram of a hardware structure of an electronic device provided in embodiment 3 of the present invention.
Description of the reference numerals:
10-an acquisition module;
20-a pretreatment module, 21-a selection unit and 22-a pretreatment unit;
30-a filtering module, 31-a stripping unit and 32-a filtering module;
40-an operation module, 41-a first calculation unit, 42-a second calculation unit, 43-a third calculation unit, 44-a fourth calculation unit, 45-a fifth calculation unit and 46-a determination unit;
50-hierarchical clustering module, 51-slicing unit, 52-hierarchical unit and 53-screening unit;
60-a segmentation analysis module;
70-bus, 71-processor, 72-memory, 73-communication interface.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
Example 1
Specifically, fig. 5 is a schematic flow chart of the distance measuring method for the tunnel reinforcing mesh provided in this embodiment.
As shown in fig. 5, the method for measuring the distance of the tunnel reinforcing mesh of the present embodiment includes the following steps:
s101, scanning and collecting initial point cloud data of the tunnel through a laser radar.
Specifically, the laser radar is classified into an MEMS type laser radar, a Flash type laser radar, a phased array laser radar, a ground laser radar, and a mechanical rotation type laser radar according to a scanning manner. In this embodiment, ground laser radar is selected for use to the laser radar, ground laser radar's characteristics: the system has a one-key vegetation filtering function, can analyze multiple echo data, and has the types not less than four; the scanner operation can be controlled, the data is processed, the data (LAS format point cloud data and image data) are exported, contour lines are generated, and the filling and digging amount is analyzed; three-dimensional point cloud data of different point densities can be collected; three-dimensional color point cloud data can be obtained; mass point cloud data browsing can be realized; the method can realize one-key generation of the triangulation network model and can edit the model; a section line can be generated; the data echoes can be classified and displayed, and at least four classes are provided. In specific practice, the present embodiment may obtain an original tunnel scanning sample as shown in fig. 1 by scanning a road tunnel to be measured by a ground laser radar.
S102, preprocessing the initial point cloud data to obtain down-sampling point cloud data of the tunnel.
Specifically, the ground laser radar is used for acquiring data, due to the fact that scanning precision is high, a single scanning range is large, the acquired point cloud data has the characteristics of being uneven, massive, non-sequential and the like, if direct processing is conducted, high calculation cost is needed, and errors can also occur in analysis due to the fact that data are not uniform.
Further, S102 in this embodiment specifically includes:
s1021, selecting an adaptive target down-sampling method according to a down-sampling mode;
specifically, the target down-sampling method includes a uniform down-sampling method, a voxel down-sampling method, and the like. The uniform down-sampling has a plurality of different sampling modes, wherein the sampling of the farthest point is a simpler one, firstly, a seed point is selected, an interior point set is set, and a point which is farthest from the interior point is found out from a set which does not belong to the interior point in the point cloud each time; the characteristics of the farthest point sampling include: the sampling points are uniformly distributed, the algorithm time complexity is higher, and the sampling points are generally distributed near the boundary first. Voxel down-sampling refers to voxelization of a three-dimensional space, then a point is sampled in each voxel, and usually a central point or a point closest to the central point can be used as a sampling point; the specific method comprises the following steps: 1. creating a voxel: bounding boxes for the point cloud are computed and then discretized into small voxels. The length, width and height of the voxel can be set by a user, and can also be obtained by setting the number of lattice points in three directions of the bounding box; 2. each small element comprises a plurality of points, and the central point or the point closest to the central point is taken as a sampling point. Characteristics of voxel sampling include: the efficiency is very high, and the distribution of sampling point is more even, but the homogeneity is not even sampling height, can pass through the size control point interval of voxel, can not accurate control sampling point number. In the embodiment, in order to take efficiency and data information into consideration, a mode combining uniform down-sampling and voxel down-sampling is selected according to a down-sampling mode, the data volume is controlled by using uniform down-sampling at a field client for data acquisition, then the point cloud is stored in a database, and in the subsequent analysis pretreatment, the uniformity of the point cloud is controlled as much as possible by using voxel down-sampling secondary treatment.
And S1022, performing downsampling processing on the initial point cloud data based on the target downsampling method, and converting the initial point cloud data into downsampled point cloud data obtained through the downsampling processing.
Specifically, a common preprocessing measure used before analysis in this embodiment is to perform downsampling on the point cloud, convert the operation on all the point clouds to points obtained by downsampling, make the distribution of the sample overall as uniform as possible, and reduce the total amount of calculation.
S103, filtering non-target point cloud data from the down-sampling point cloud data through a preset algorithm to obtain target point cloud data of the reinforcement mesh to be measured, wherein the preset algorithm comprises a ground feature recognition algorithm and an octree algorithm.
In particular, because the data volume of the steel bar mesh point cloud collected from the real world is huge, and the data volume is a large scene consisting of millions to even billions of points, the required calculation cost is high. And be different from the simpler structure of two-dimensional traditional image, there are a large amount of noise points in three-dimensional space including reinforcing bar net three-dimensional point cloud data, and point cloud data itself has: dispersibility-points in the point cloud are not connected with each other and exist dispersedly; irregularity-the scene where the point cloud needs to be identified has no uniform shape and there is a lot of noise; distribution nonuniformity-point cloud density is not uniformly distributed in space, and the trend is that the point cloud density from a scanning point to a far point exponentially decreases.
Further, S103 in this embodiment specifically includes:
and S1031, stripping the point cloud data of the tunnel ground from the down-sampling point cloud data by adopting a ground object identification algorithm to obtain reserved point cloud data.
Specifically, the surface feature identification algorithm adopts a hyperspectral imaging technology, and analysis of a spectral curve is generally difference analysis, for example, model algorithms such as minimum distance, spectral angle matching, spectral similarity and the like are adopted. In practical application, it may happen that the difference between different kinds of spectra of the same type of ground features is too small, resulting in failure of the curve similarity measurement method. At this time, spectral curve feature analysis can be carried out from feature points such as peaks, troughs and inflection points of a spectral curve, a large amount of information is contained in the feature points, the shape of the curve is determined, hyperspectrum is used for ground object identification, and point cloud data of the tunnel ground can be stripped from the point cloud data of down sampling.
S1032, calculating a point cloud neighborhood range of the reserved point cloud data through an octree algorithm, and filtering the reserved point cloud data based on a point spacing mode in the point cloud neighborhood range to obtain target point cloud data of the reinforcing mesh to be measured.
Specifically, the octree algorithm is an important means for processing three-dimensional point cloud data, and neighborhood search of octree realizes the function of searching neighborhoods by short and short sentences of codes. The number of the sub-nodes of the octree is either 8 or 0, namely, the existence of the empty node is caused by the absence of the data point inside the empty node, and thus, the traversing efficiency is improved. The method can eliminate the influence of outliers in the point cloud data through the octree algorithm, and further improve the accuracy of the data.
And S104, calculating the service characteristic information of each point in the target point cloud data between the cross section and the corresponding design point by adopting an expanded coordinate reflection algorithm in combination with the cross section design data, wherein the service characteristic information comprises mileage information and deviation information.
Specifically, the traditional inverse calculation of the design coordinates calculates the mileage, horizontal and vertical offset corresponding to each point in the point cloud by resolving the information of the horizontal and vertical curves. In the embodiment, by combining section design data, the deviation of each point in the section where the point is located from the corresponding design point and the position information of the point in the section design contour line are calculated simultaneously.
Further, S104 in this embodiment specifically includes:
s1041, calculating a line element which is closest to each point in the target point cloud data on a plane projection through XY coordinates according to preset line data;
s1042, calculating the projection distance from the projection point of the point corresponding to the line element to the end point of the line element according to the linear space;
s1043, calculating mileage information corresponding to each point in the target point cloud data according to the mileage of the line element end point and the projection distance;
s1044, calculating the horizontal deviation of each point distance in the target point cloud data corresponding to the line element according to the coordinates of the projection points;
s1045, calculating the vertical deviation of each point in the target point cloud data relative to the corresponding alignment according to the mileage information and the vertical curve information in the alignment data;
and S1046, determining the two-dimensional position of the axis on the section according to the section design model to obtain complete service characteristic information.
And S105, based on the service characteristic information, performing hierarchical clustering processing on the target point cloud data by adopting a KMean clustering analysis method and performing filtering processing by adopting an LOF algorithm to obtain target layered point cloud data of different layers of reinforcing steel bar nets.
Specifically, because the reinforcing mesh has a clear layering characteristic, the point cloud can be divided into a plurality of layers by directly performing KMean cluster analysis on design deviation data, and only the data of the outermost layers (determined according to the number of layers of the reinforcing mesh on site) is reserved; and the outermost layer of the reinforcing mesh is tightly attached to the tunnel wall, so that the outermost layer of the reinforcing mesh is regarded as the same layer in clustering.
Further, S105 of this embodiment specifically includes:
s1051, slicing the target point cloud data according to the mileage information to obtain point cloud slice data.
Specifically, based on the design mileage information, the complete tunnel is sliced, and each slice is analyzed, so that the single calculation amount is reduced, and the characteristics of the service data in the subsequent analysis are more beneficial to the analysis.
And S1052, performing layering processing on the point cloud slice data through KMean cluster analysis aiming at the deviation information to obtain layered point cloud data of reinforcing steel meshes in different layers.
Specifically, the KMean cluster analysis divides the data into k designated clusters, and calculates the center point of each cluster from the mean value of each cluster sample, and the process is to continuously calculate the distance between each sample point and the center of each cluster until convergence.
S1053, adopting LOF algorithm to screen abnormal points for the layered point cloud data to obtain target layered point cloud data.
Specifically, the reinforcing bar net data after preliminary layering can carry out secondary filtering, because the cloud of the points scope dwindles once more this moment, its statistical characteristic takes place obvious change, can further screen out the abnormal point with the help of LOF algorithm's filtering processing.
And S106, acquiring paired anchor points for ranging according to the target layered point cloud data by adopting corresponding segmentation analysis algorithms respectively, and fitting the paired anchor points to output target ranging data of the steel bar to be measured, wherein the target ranging data comprises the steel bar mesh layer distance, the single-layer steel bar mesh transverse and longitudinal distances.
Specifically, when facing different scenes, there are different semantic recognition emphasis:
1. the layer distance report is more concerned about the distance measurement between different layers, and the reinforcing mesh needs to be separated from the tunnel wall more accurately; analytical indices of layer spacing report: a. the standard distance between the steel bars is 0.54-0.66 m, the distance between the steel bars b and exceeds 0.66 m is considered as the distance overrun, and the distance between the steel bars c and is lower than 0.54 m and is considered as the distance excessively low; a specific statistical report is shown in fig. 6. In this embodiment, after the second lining construction, the whole inner wall of the tunnel is relatively smooth, the deviation change between each point of the corresponding inner wall of the tunnel and the design is relatively smooth, the gradient of the deviation of each point is relatively uniform, and the reinforcing mesh presents more sudden changes due to the mesh structure. The layer distance measurement statistical table of this example is shown in table 1.
Table 1: layer distance measurement statistical table
2. The horizontal report and the vertical report pay more attention to the semantic division in the same layer of reinforcing mesh; the single-layer steel bar mesh has an obvious mesh characteristic, according to the characteristic, service data (mileage, horizontal deviation and vertical deviation) are used as a new local coordinate system, octree is combined to perform slicing analysis on point cloud, the point cloud is sliced through an empirical value of the gap size of the steel bar mesh, connectivity analysis is performed through the octree, and the point cloud is subjected to grouping fitting in the direction of a contour line to obtain paired anchor points for ranging.
2.1, transverse report analysis index: a. the standard distance between the steel bars is 0.15-0.18 m, the distance between the steel bars b and the steel bars b exceeds 0.18 m, and the distance is considered to be too low when the distance between the steel bars c and the steel bars is less than 0.15 m. The transverse steel bar distance measurement statistical table of the embodiment is shown in table 2; a specific statistical report is shown in fig. 7.
Table 2: transverse steel bar distance measuring statistical table
2.2, transverse report analysis indexes: a. the standard distance between the steel bars is 0.20-0.26 m, the distance exceeding 0.26 m is considered as the distance overrun when the distance between the steel bars b is larger than 0.26 m, and the distance lower than 0.20 m is considered as the distance overlow when the distance between the steel bars c is smaller than 0.20 m. The transverse steel bar distance measurement statistical table of the embodiment is shown in table 3; a specific statistical report is shown in fig. 8.
Table 3: longitudinal steel bar distance measuring statistical table
In summary, through the steps, the acquired tunnel point cloud data is filtered and segmented, so that a complex ranging scene is split into a single ranging target combination, only local characteristics of a measuring object of each ranging target are concerned, other complex semantic information is abandoned, the overall efficiency and segmentation precision of tunnel reinforcing steel bar net ranging are improved, and the accuracy of tunnel reinforcing steel bar net data measurement is improved.
Example 2
This embodiment provides a block diagram of a system corresponding to the method described in embodiment 1. Fig. 9 is a block diagram showing the structure of the distance measuring system for the reinforcing mesh of the tunnel according to the embodiment, as shown in fig. 9, the system includes:
the acquisition module 10 is used for scanning and acquiring initial point cloud data of the tunnel through a laser radar;
the preprocessing module 20 is configured to preprocess the initial point cloud data to obtain down-sampling point cloud data of the tunnel;
the filtering module 30 is configured to filter non-target point cloud data from the down-sampling point cloud data through a preset algorithm to obtain target point cloud data of the reinforcing mesh to be measured, where the preset algorithm includes a ground feature identification algorithm and an octree algorithm;
the operation module 40 is used for calculating service characteristic information of each point in the target point cloud data between the cross section and a corresponding design point by adopting an expanded coordinate reflection algorithm in combination with cross section design data, wherein the service characteristic information comprises mileage information and deviation information;
the hierarchical clustering module 50 is configured to perform hierarchical clustering processing on the target point cloud data by using a KMean clustering analysis method and perform filtering processing by using an LOF algorithm to obtain target layered point cloud data of different layers of reinforcing steel meshes;
and a segmentation analysis module 60, configured to obtain paired anchor points for ranging according to the target layered point cloud data by using corresponding segmentation analysis algorithms, and fit the paired anchor points to output target ranging data of the steel bar to be measured, where the target ranging data includes a steel bar mesh layer distance, a single-layer steel bar mesh horizontal and longitudinal distances. In this embodiment, the segmentation analysis algorithm includes a segmentation analysis that is beneficial to the layer distance of the reinforcing mesh based on the characteristics of smooth deviation change and uniform deviation gradient caused by the smooth inner wall of the tunnel, and a segmentation analysis that is beneficial to the transverse and longitudinal distances of the single-layer reinforcing mesh based on the obvious mesh characteristics of the single-layer reinforcing mesh.
Further, the preprocessing module 20 includes:
a selecting unit 21, configured to select an adaptive target downsampling method according to a downsampling method;
a preprocessing unit 22, configured to perform downsampling processing on the initial point cloud data based on the target downsampling method, and convert the initial point cloud data into downsampled point cloud data obtained through the downsampling processing.
Further, the filtering module 30 includes:
the stripping unit 31 is configured to strip point cloud data of the tunnel ground from the down-sampling point cloud data by using a ground object identification algorithm to obtain reserved point cloud data;
and the filtering module 32 is configured to calculate a point cloud neighborhood range of the retained point cloud data through an octree algorithm, and filter the retained point cloud data based on a point spacing manner in the point cloud neighborhood range to obtain target point cloud data of the reinforcing mesh to be measured.
Further, the operation module 40 includes:
a first calculating unit 41, configured to calculate, according to preset alignment data, a line element that is closest to each point in the target point cloud data on a planar projection through XY coordinates;
a second calculating unit 42, configured to calculate a projection distance from a projection point of the point corresponding to the line element to the end point of the line element according to a linear space;
a third calculating unit 43, configured to calculate mileage information corresponding to each point in the target point cloud data according to the mileage of the line element end point and the projection distance;
a fourth calculating unit 44, configured to calculate, according to the coordinates of the projection points, a horizontal deviation of each point distance in the target point cloud data corresponding to the line element;
a fifth calculating unit 45, configured to calculate, according to the mileage information and by combining the vertical curve information in the alignment data, a vertical offset of each point in the target point cloud data with respect to a corresponding alignment;
and the determining unit 46 is used for determining the two-dimensional position of the axis on the section according to the section design model to obtain complete service characteristic information.
Further, the hierarchical clustering module 50 includes:
a slicing unit 51, configured to slice the target point cloud data according to the mileage information to obtain point cloud slice data;
the layering unit 52 is configured to perform layering processing on the point cloud slice data through KMean cluster analysis on the deviation information to obtain layered point cloud data of different layers of reinforcing steel meshes;
and the screening unit 53 is configured to perform screening processing on outliers for the layered point cloud data by using an LOF algorithm to obtain target layered point cloud data.
The above modules may be functional modules or program modules, and may be implemented by software or hardware. For a module implemented by hardware, the modules may be located in the same processor; or the modules may be located in different processors in any combination.
Example 3
The method of measuring the distance of the tunnel reinforcing mesh described in conjunction with fig. 5 can be implemented by electronic equipment. Fig. 10 is a schematic diagram of a hardware structure of the electronic device according to the present embodiment.
The electronic device may comprise a processor 71 and a memory 72 in which computer program instructions are stored.
Specifically, the processor 71 may include a Central Processing Unit (CPU), or A Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the present embodiment.
The memory 72 may be used to store or cache various data files that need to be processed and/or used for communication, as well as possible computer program instructions executed by the processor 71.
The processor 71 reads and executes the computer program instructions stored in the memory 72 to implement the tunnel reinforcing bar ranging method of embodiment 1.
In some of these embodiments, the electronic device may also include a communication interface 73 and a bus 70. As shown in fig. 10, the processor 71, the memory 72, and the communication interface 73 are connected via a bus 70 to complete mutual communication.
The communication interface 73 is used to implement communication between the respective modules, devices, units and/or apparatuses in the present embodiment. The communication interface 73 may also enable communication with other components such as: the data communication is carried out among external equipment, image/data acquisition equipment, a database, external storage, an image/data processing workstation and the like.
The bus 70 includes hardware, software, or both to couple the components of the device to one another. Bus 70 includes, but is not limited to, at least one of the following: data Bus (Data Bus), address Bus (Address Bus), control Bus (Control Bus), expansion Bus (Expansion Bus), and Local Bus (Local Bus). By way of example and not limitation, bus 70 may include an Accelerated Graphics Port (AGP) or other Graphics Bus, an Enhanced Industry Standard Architecture (EISA) Bus, a Front-Side Bus (FSB), a Hyper Transport (HT) Interconnect, an ISA (ISA) Bus, an InfiniBand (InfiniBand) Interconnect, a Low Pin Count (LPC) Bus, a memory Bus, a microchannel Architecture (MCA) Bus, a PCI (Peripheral Component Interconnect) Bus, a PCI-Express (PCI-X) Bus, a Serial Advanced Technology Attachment (SATA) Bus, a vlslave Bus, a Video Bus, or a combination of two or more of these suitable electronic buses. Bus 70 may include one or more buses, where appropriate. Although this embodiment describes and illustrates a particular bus, this application contemplates any suitable bus or interconnect.
The device may execute the tunnel reinforcing mesh ranging method of this embodiment 1 based on the obtained tunnel reinforcing mesh ranging system.
In addition, in combination with the distance measuring method for the tunnel reinforcing mesh in embodiment 1, this embodiment can provide a storage medium to implement. The storage medium having stored thereon computer program instructions; the computer program instructions, when executed by the processor, implement the method for measuring distance of a tunnel reinforcing mesh of embodiment 1.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. A distance measurement method for a tunnel reinforcing mesh is characterized by comprising the following steps:
scanning and acquiring initial point cloud data of the tunnel through a laser radar;
preprocessing the initial point cloud data to obtain down-sampling point cloud data of the tunnel;
filtering non-target point cloud data from the down-sampling point cloud data through a preset algorithm to obtain target point cloud data of the reinforcing mesh to be measured, wherein the preset algorithm comprises a ground feature recognition algorithm and an octree algorithm;
calculating service characteristic information of each point in the target point cloud data between the cross section and a corresponding design point by adopting an expanded coordinate reflection algorithm in combination with cross section design data, wherein the service characteristic information comprises mileage information and deviation information;
based on the service characteristic information, performing hierarchical clustering processing on the target point cloud data by adopting a KMean clustering analysis method and performing filtering processing by adopting an LOF algorithm to obtain target layered point cloud data of reinforcing steel bar nets of different layers;
and respectively adopting corresponding segmentation analysis algorithms to obtain paired anchor points for ranging according to the target layered point cloud data, and fitting the paired anchor points to output target ranging data of the steel bar to be measured, wherein the target ranging data comprises the steel bar mesh layer distance, the single-layer steel bar mesh transverse and longitudinal distances.
2. The method as claimed in claim 1, wherein the step of preprocessing the initial point cloud data to obtain down-sampled point cloud data of the tunnel comprises:
selecting an adaptive target down-sampling method according to a down-sampling mode;
and carrying out down-sampling processing on the initial point cloud data based on the target down-sampling method, and converting the initial point cloud data into down-sampled point cloud data obtained by the down-sampling processing.
3. A tunnel reinforcing mesh ranging method according to claim 2, wherein the target down-sampling method is a uniform down-sampling method or a voxel down-sampling method.
4. The method for measuring the distance between the tunnel reinforcing mesh according to claim 1, wherein the step of filtering the non-target point cloud data from the down-sampled point cloud data by a predetermined algorithm to obtain the target point cloud data of the reinforcing mesh to be measured, wherein the predetermined algorithm includes a feature recognition algorithm and an octree algorithm specifically comprises:
stripping point cloud data of the tunnel ground from the down-sampling point cloud data by adopting a ground object identification algorithm to obtain reserved point cloud data;
and calculating a point cloud neighborhood range of the reserved point cloud data through an octree algorithm, and filtering the reserved point cloud data based on a point spacing mode in the point cloud neighborhood range to obtain target point cloud data of the reinforcing mesh to be measured.
5. The method for measuring the distance between the tunnel reinforcing mesh according to claim 1, wherein the step of calculating the service characteristic information of each point in the target point cloud data between the cross section of each point and the corresponding design point by using an extended coordinate reflection algorithm in combination with the cross section design data specifically comprises the steps of:
calculating the nearest line element of each point in the target point cloud data on a plane projection through XY coordinates according to preset line data;
calculating the projection distance from the projection point of the point corresponding to the line element to the end point of the line element according to the linear space;
calculating mileage information corresponding to each point in the target point cloud data according to the mileage of the line element end point and the projection distance;
calculating the horizontal deviation of each point distance in the target point cloud data corresponding to the line element according to the coordinates of the projection points;
calculating the vertical offset of each point in the target point cloud data relative to the corresponding alignment according to the mileage information and by combining the vertical curve information in the alignment data;
and determining the two-dimensional position of the axis on the section according to the section design model to obtain complete service characteristic information.
6. The method for measuring the distance between the tunnel reinforcing mesh according to claim 1, wherein the step of performing hierarchical clustering processing on the target point cloud data by using a KMean clustering analysis method and performing filtering processing by using an LOF algorithm based on the service characteristic information to obtain the target layered point cloud data of the reinforcing mesh at different layers specifically comprises:
slicing the target point cloud data according to the mileage information to obtain point cloud slice data;
performing layered processing on the point cloud slice data through KMean cluster analysis aiming at the deviation information to obtain layered point cloud data of reinforcing steel bar nets of different layers;
and adopting an LOF algorithm to screen abnormal points for the layered point cloud data to obtain target layered point cloud data.
7. The method as claimed in claim 1, wherein the segmentation analysis algorithm includes segmentation analysis for the layer pitch of the reinforcing bar mesh based on the characteristics of smooth inner wall of the tunnel, smooth variation of deviation, and uniform gradient of deviation, and segmentation analysis for the transverse and longitudinal pitches of the single-layer reinforcing bar mesh based on the obvious mesh characteristics of the single-layer reinforcing bar mesh.
8. The utility model provides a tunnel reinforcing bar net ranging system which characterized in that includes:
the acquisition module is used for scanning and acquiring initial point cloud data of the tunnel through a laser radar;
the preprocessing module is used for preprocessing the initial point cloud data to obtain down-sampling point cloud data of the tunnel;
the filtering module is used for filtering non-target point cloud data from the down-sampling point cloud data through a preset algorithm to obtain target point cloud data of the reinforcing mesh to be measured, wherein the preset algorithm comprises a ground feature identification algorithm and an octree algorithm;
the operation module is used for combining with section design data and adopting an expanded coordinate reflection algorithm to calculate service characteristic information between each point in the target point cloud data and a corresponding design point in the section of the target point cloud data, wherein the service characteristic information comprises mileage information and deviation information;
the hierarchical clustering module is used for carrying out hierarchical clustering processing on the target point cloud data by adopting a KMean clustering analysis method and filtering the target point cloud data by adopting an LOF algorithm to obtain target layered point cloud data of different layers of reinforcing steel bar nets;
and the segmentation analysis module is used for acquiring paired anchor points for ranging according to the target layered point cloud data by adopting corresponding segmentation analysis algorithms respectively, and fitting the paired anchor points to output target ranging data of the steel bar to be measured, wherein the target ranging data comprises the steel bar mesh layer distance, the single-layer steel bar mesh transverse and longitudinal distances.
9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program implements the method of tunneling mesh reinforcement ranging according to any one of claims 1 to 7.
10. A storage medium having stored thereon a computer program which, when executed by a processor, implements a method of ranging a tunnel reinforcing mesh as defined in any one of claims 1 to 7.
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| CN116258969A (en) * | 2023-05-12 | 2023-06-13 | 宁波市天一测绘设计研究有限公司 | Structural member measuring method and device based on point cloud data |
| CN116258969B (en) * | 2023-05-12 | 2023-08-25 | 宁波市天一测绘设计研究有限公司 | Structural member measuring method and device based on point cloud data |
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