CN118031904A - Expressway tunnel clearance measurement method and device based on vehicle-mounted laser point cloud - Google Patents
Expressway tunnel clearance measurement method and device based on vehicle-mounted laser point cloud Download PDFInfo
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Abstract
The invention belongs to the technical field of tunnel clearance measurement, and particularly relates to a highway tunnel clearance measurement method and device based on a vehicle-mounted laser point cloud. According to the method, a vehicle-mounted three-dimensional laser scanning system is used for acquiring three-dimensional laser point cloud data in an expressway tunnel, point cloud registration is carried out on the three-dimensional laser point cloud data based on actual measurement coordinates of a layout target, filtering is carried out on the three-dimensional laser point cloud data after registration, surface layer point cloud data are extracted, a ground irregular triangular network and a lining irregular triangular network are constructed by utilizing the surface layer point cloud data, and altitude values of the lining irregular triangular network and the ground irregular triangular network are differed to obtain clearance information of the tunnel. Compared with a manual measurement method, the method has higher measurement efficiency, higher integrity of the measured tunnel clearance information and convenience for repeated measurement.
Description
Technical Field
The invention belongs to the technical field of tunnel clearance measurement, and particularly relates to a highway tunnel clearance measurement method and device based on a vehicle-mounted laser point cloud.
Background
Tunnel clearance refers to the space enclosed by the contours within the tunnel, including the cross-sectional area required for highway tunnel building demarcation, ventilation, and other functions. The shape and size of the cross section should be designed according to the structural design to obtain the most economical value. Other sections included in the clearance include sections such as ventilators or ventilation pipelines, lighting fixtures and other equipment, monitoring equipment and operation management equipment, cable trenches or cable bridges, disaster prevention equipment and the like, and allowance and construction allowance errors and the like.
The expressway tunnel is taken as a particularly important part of traffic infrastructure, and has a very positive promotion effect on the promotion of national economy and the rapid development of society in China. While the expressway is continuously newly built, expressway tunnels which are built and completed in the past three decades are more and more, and long-term overload operation, insufficient maintenance and the like cause various defects such as insufficient lining structural strength, cracking, pavement cracking, staggering, uplift, insufficient filling thickness of pavement inverted arch and the like in the operation period of the tunnels. In the process of disease treatment, lining I-steel frame reinforcement, road surface re-paving and other treatment measures are generally adopted, and the treatment methods can encroach on tunnel clearance, reduce available clearance of traffic lanes and cause safety threat to vehicle running. Meanwhile, due to deformation of the tunnel, the position of the road marking may be adjusted in the process of disease treatment, and tunnel clearance of the new design marking position is also an important point. Therefore, the rapid, convenient and accurate extraction of the tunnel clearance of the operation expressway is an important link for the treatment of tunnel defects and the evaluation of the operation safety of the tunnel.
Along with the rapid development of the laser radar technology and the popularization and application of the laser radar technology in highway engineering, in the production process, the measurement of the tunnel clearance of the highway is mainly divided into two modes, one is that the measurement is carried out on the road in the field in a manual mode, and measuring equipment such as a range finder, a total station and the like is adopted, so that the method has low working efficiency, long road breaking time, has great influence on the traffic capacity of the highway network, only can measure specific positions, and the tunnel clearance information is incomplete; the other is that by three-dimensional laser scanning technology, at present, a station type laser radar scanning device is adopted in the method, the operation time is long, tunnel clearance convergence monitoring in the construction stage is mainly served, the operation process is complex, and the requirement of clearance measurement for repeated adjustment of tunnel marking lines in the operation stage cannot be met.
Disclosure of Invention
The invention aims to solve the problems of low measurement efficiency, low integrity of the measured tunnel clearance information and difficulty in repeated measurement of the existing expressway tunnel clearance measurement method and provides an expressway tunnel clearance measurement method and device based on a vehicle-mounted laser point cloud.
In order to achieve the above object, the present invention provides the following technical solutions:
The highway tunnel clearance measurement method based on the vehicle-mounted laser point cloud comprises the following steps:
s1: arranging a plurality of targets in a highway tunnel to be tested, measuring to obtain actual measurement coordinates of centers of the targets, and acquiring three-dimensional laser point cloud data of the highway tunnel to be tested in batches by using an on-board laser scanning system, wherein the three-dimensional laser point cloud data comprises point cloud data of the targets;
s2: after removing abnormal points and noise points in the three-dimensional laser point cloud data, carrying out point cloud registration on the three-dimensional laser point cloud data based on actual measurement coordinates of the centers of the targets;
S3: dividing and filtering the three-dimensional laser point cloud data subjected to point cloud registration, and calculating to generate a ground irregular triangular net and a lining irregular triangular net;
S4: converting continuous line bit data of marking line bits or continuous line bit data of design line bits of the expressway tunnel to be tested into point data, wherein the elevation value of the lining irregular triangular net at the point data position is different from the elevation value of the ground irregular triangular net, and the null value of the point data position is obtained.
Preferably, the point cloud registration includes performing point cloud rough registration on three-dimensional laser point cloud data of different batches, and performing point cloud fine registration on the three-dimensional laser point cloud data after the point cloud rough registration.
Preferably, the point cloud coarse registration includes:
S21: in the three-dimensional laser point cloud data of the current batch, manually marking a plurality of rectangular areas covering the point cloud data of a single target, constructing a three-dimensional statistical histogram corresponding to the rectangular areas, and carrying out averaging treatment on the three-dimensional statistical histogram to obtain a reference three-dimensional statistical histogram;
S22: marking three-dimensional laser point cloud data of an unmarked area into a plurality of rectangular areas to be matched, which have the same size as the rectangular areas in the step S21, according to a preset step distance, constructing a three-dimensional statistical histogram corresponding to the rectangular areas to be matched, and performing similarity matching with a reference three-dimensional statistical histogram to obtain rectangular areas covering point cloud data of a single target so as to obtain center point coordinates of all targets; the center point coordinates of the targets are geometric centroids of three-dimensional laser point cloud data in the rectangular area covering the point cloud data of the single target;
S23: correcting the center point coordinates of each target to be the same as the actual measurement coordinates of the center of the target, and obtaining three-dimensional laser point cloud data after rough registration of the batch of point clouds.
Preferably, the method for constructing the three-dimensional statistical histogram comprises the following steps: and projecting the three-dimensional laser point cloud data in each rectangular area onto a plane in the normal vector direction of the three-dimensional laser point cloud data in the rectangular area to form a point cloud intensity image, and generating a three-dimensional statistical histogram containing gray scale and gradient information.
Preferably, the point cloud fine registration includes: calculating the medium errors of the three-dimensional laser point cloud data after the rough registration of the point clouds of all batches, selecting the three-dimensional laser point cloud data of the corresponding batch with the minimum medium errors as reference point cloud data, and registering the three-dimensional laser point cloud data of the other batches to the reference point cloud data by adopting an ICP algorithm to obtain the three-dimensional laser point cloud data after the point cloud registration.
Preferably, the S3 includes: dividing the three-dimensional laser point cloud data subjected to point cloud registration into lower ground point cloud data and upper lining point cloud data; and respectively extracting surface layer point cloud data of the lower surface point cloud data and surface layer point cloud data of the upper lining point cloud data, and respectively calculating and generating a ground irregular triangular network and a lining irregular triangular network according to the surface layer point cloud data.
Preferably, the surface layer point cloud data of the lower ground point cloud data is extracted by using a progressive triangle mesh encryption filtering algorithm.
Preferably, the surface layer point cloud data extraction method of the upper lining point cloud data comprises the following steps: and extracting point cloud data lower than a preset elevation threshold value from the upper lining point cloud data as low point cloud clusters, extracting bottom surface layer point cloud data of the upper lining point cloud data by using a progressive triangular mesh encryption filtering algorithm, and merging the low point cloud clusters with the bottom surface layer point cloud data to obtain surface layer point cloud data of the upper lining point cloud data.
Preferably, the ground irregular triangular mesh and the lining irregular triangular mesh are generated by adopting a Delaunay triangular mesh growth algorithm.
The highway tunnel clearance measuring device based on the vehicle-mounted laser point cloud comprises at least one processor and a memory in communication connection with the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the above-described vehicle-mounted laser point cloud-based highway tunnel headroom measurement method.
Compared with the prior art, the invention has the beneficial effects that:
According to the invention, a three-dimensional laser scanning system is used for acquiring three-dimensional laser point cloud data in an expressway tunnel, point cloud registration is carried out on the three-dimensional laser point cloud data based on actual measurement coordinates of a layout target, filtering is carried out on the three-dimensional laser point cloud data after registration, surface layer point cloud data are extracted, a ground irregular triangular net and a lining irregular triangular net are constructed by utilizing the surface layer point cloud data, and elevation values of the lining irregular triangular net and the ground irregular triangular net are subjected to difference to obtain clearance information of the tunnel. Compared with a manual measurement method, the method can acquire the clear height information of each position of the full tunnel area through one-time measurement work, and the integrity of the measured tunnel clear height information is higher; under the condition of line position adjustment and change, the clearance value of the changed position can be obtained only by re-dispersing line positions and re-assigning the difference according to the elevation value of the irregular triangular network, the measurement efficiency is higher, and the repeated measurement is convenient.
Drawings
Fig. 1 is a flowchart of a highway tunnel clearance measurement method based on a vehicle-mounted laser point cloud according to embodiment 1 of the present invention;
FIG. 2 is a schematic diagram of a three-dimensional statistical histogram constructed according to example 1 of the present invention;
Fig. 3 is three-dimensional laser point cloud data of the fully enclosed highway unidirectional tunnel of the embodiment 1 after point cloud rough registration in the transverse cross section direction;
Fig. 4 is three-dimensional laser point cloud data of the fully closed expressway unidirectional tunnel of the embodiment 1 after point cloud fine registration in the transverse cross section direction;
FIG. 5 is a ground irregular triangular network constructed for a fully enclosed highway unidirectional tunnel according to embodiment 1 of the present invention;
Fig. 6 is a lined irregular triangular mesh constructed by a fully enclosed highway unidirectional tunnel according to embodiment 1 of the present invention.
Detailed Description
The present invention will be described in further detail with reference to test examples and specific embodiments. It should not be construed that the scope of the above subject matter of the present invention is limited to the following embodiments, and all techniques realized based on the present invention are within the scope of the present invention.
Example 1
The highway tunnel clearance measurement method based on the vehicle-mounted laser point cloud is shown in fig. 1, and comprises the following steps:
S1: arranging a plurality of targets in the expressway tunnel to be tested, measuring to obtain actual measurement coordinates of centers of the targets, and acquiring three-dimensional laser point cloud data of the expressway tunnel to be tested in batches by using an on-board laser scanning system, wherein the three-dimensional laser point cloud data comprises point cloud data for arranging the targets.
In a highway tunnel, an on-board three-dimensional laser scanning system is difficult to acquire GNSS (Global Navigation SATELLITE SYSTEM ) data, so that the acquired three-dimensional laser point cloud data often depend on targets for point cloud registration; the density of target placement is typically proportional to the length of the tunnel and the linear complexity. According to the method, a proper collection vehicle speed and target layout scheme is designed according to the length, the width and the line type of a highway tunnel to be detected, and a target is generally laid every 10-20 m according to actual measurement experience; whereas to acquire denser point cloud data, the acquisition speed is generally inversely proportional to the tunnel width. After the targets are distributed, the total station is used for measuring the space three-dimensional coordinates of the centers of the distributed targets AS actual measurement coordinates, and a vehicle-mounted three-dimensional laser scanning system is used for scanning a tunnel to be tested to acquire three-dimensional laser point cloud data containing point cloud data of the distributed targets, wherein the model of the vehicle-mounted three-dimensional laser scanning system adopted in the embodiment is AS900-HL.
For the vehicle-mounted three-dimensional laser point cloud data acquisition work of the expressway tunnel, a differential acquisition strategy is required to be formulated according to different tunnel environments and operation states: if the tunnel to be tested is a fully-closed expressway unidirectional tunnel, three-dimensional laser point cloud data are collected back and forth in a fast lane and a slow lane; if the tunnel is a totally-enclosed expressway bidirectional tunnel, three-dimensional laser point cloud data are collected back and forth in a bidirectional expressway; if the tunnel is a semi-closed expressway unidirectional tunnel, three-dimensional laser point cloud data are collected back and forth in a closed lane; and if the tunnel is a semi-closed expressway bidirectional tunnel, acquiring three-dimensional laser point cloud data in all directions of a closed lane.
The whole course of the expressway tunnel to be detected is scanned by using a vehicle-mounted laser scanning system in a single way, so that single-batch three-dimensional laser point cloud data can be obtained; in order to ensure the coverage integrity of the three-dimensional laser point cloud data, at least two single-pass continuous scans are required to be carried out on the expressway tunnel to be detected so as to acquire multiple batches of three-dimensional laser point cloud data.
S2: and (3) after removing the abnormal points and noise points in the three-dimensional laser point cloud data acquired in the step (S1), carrying out point cloud registration on the three-dimensional laser point cloud data based on the actual measurement coordinates of the centers of the targets.
In order to improve the quality and accuracy of the point cloud data, the embodiment adopts a neighboring point searching method, namely, in all the point cloud data in the searching radius, points with the elevation difference larger than a certain threshold value are regarded as abnormal points and noise points, the abnormal points and the noise points are removed, and the three-dimensional laser point cloud data after the noise points and the abnormal points are removed are subjected to point cloud registration.
The point cloud registration work of the embodiment is divided into point cloud coarse registration and point cloud fine registration, wherein the point cloud coarse registration is firstly carried out on three-dimensional laser point cloud data of all batches according to the actually measured target center under a tunnel coordinate system measured by using a total station, and reference point cloud data is provided for the point cloud fine registration while registering the point cloud; and performing point cloud fine registration on the three-dimensional laser point cloud data after the rough registration of the rest batches by using an ICP algorithm according to the datum point cloud data, further reducing registration errors among the three-dimensional laser point cloud data of different batches, and improving the quality of the point cloud data.
The point cloud coarse registration is implemented according to the following steps:
s21: and in the three-dimensional laser point cloud data of the current batch, manually marking a plurality of rectangular areas covering the point cloud data of the single target, constructing a three-dimensional statistical histogram corresponding to the rectangular areas, and carrying out averaging treatment on the three-dimensional statistical histogram to obtain a reference three-dimensional statistical histogram.
In the embodiment, three-dimensional laser point cloud data in each rectangular area covering the point cloud data of a single target are projected onto a plane in the normal vector direction of the three-dimensional laser point cloud data in the rectangular area to form a point cloud intensity image, then image gray scale and gradient information in the point cloud intensity image are counted to generate a three-dimensional statistical histogram, and the three-dimensional statistical histograms generated in the rectangular areas are subjected to averaging treatment to eliminate accidental differences to obtain a reference three-dimensional statistical histogram; the three-dimensional statistical histogram containing gray scale and gradient information can reflect the characteristics of point cloud data in the rectangular area more, so that accuracy of similarity matching is improved, a schematic diagram of the three-dimensional statistical histogram constructed in the embodiment is shown in fig. 2, and the gray scale of the X-axis represents statistics of gray scale values of image pixels of the rectangular area marked in the point cloud intensity image, wherein the gray scale values are divided into 8 numerical intervals of [0,32 ], [32,64 ], [64,96 ], [96,128 ], [128,160 ], [160,192 ], [192,224 ] and [224-255 ]. The Y-axis gradient represents statistics of gradient directions of image pixels of marked circumscribed rectangles in the point cloud intensity image, wherein the gradient directions are clockwise differences between a gradient direction of a certain pixel and a central point direction of the image, and the gradient direction differences are divided into 8 numerical intervals of [0,45 ], [45,90 ], [90,135 ], [135,180 ], [180,225 ], [225,270 ], [270,315) and [315,360). The number of Z axes represents the number of pixels of a certain gray scale interval and gradient interval corresponding to the point cloud intensity image.
S22: marking three-dimensional laser point cloud data of an unmarked area into a plurality of rectangular areas to be matched, which have the same size as the rectangular areas in the step S21, constructing a three-dimensional statistical histogram of the three-dimensional laser point cloud data in the rectangular areas to be matched, and carrying out similarity matching with the reference three-dimensional statistical histogram constructed in the step S21 one by one according to a preset similarity threshold, wherein the set step distance is 0.05 m, the similarity threshold is 0.9, the rectangular areas above the similarity threshold are rectangular areas covering point cloud data of a single target, and geometric centroids of the three-dimensional laser point cloud data in the rectangular areas are the central point coordinates of the target in the embodiment.
S23: correcting the center point coordinate of the target obtained in the step S22 to be the same as the actual measurement coordinate of the center of the target, and obtaining three-dimensional laser point cloud data after rough registration of the batch of point clouds. In this embodiment, for a fully-closed expressway unidirectional tunnel, three-dimensional laser point cloud data after rough point cloud registration is shown in fig. 3 in the transverse cross-section direction.
After coarse registration of point clouds, a certain error exists between the center point coordinate of a target in the three-dimensional laser point cloud data and the actually measured coordinate of the target in a tunnel space coordinate system, and the errors of different targets are different.
The middle error calculation formula is as follows:
；
wherein RMSE is the middle error of the point cloud data, n is the number of targets, x i is the difference between the center point coordinates of the targets in the three-dimensional laser point cloud data and the actually measured center point coordinates of the targets, The average value of the differences between the center point coordinates of all targets in the batch of three-dimensional laser point cloud data and the measured center point coordinates is obtained.
The ICP algorithm (ITERATIVE CLOSEST POINT, the nearest point iterative algorithm) has the core idea that the medium error between two point clouds is continuously optimized in an iterative mode, so that the optimal point cloud registration result is found.
Specifically, in this embodiment, an initial point set P i is first selected from point cloud data to be aligned, and a point closest to P i is searched in reference point cloud data to form a point set Q i, so that the point set Q i-Pi is minimum, where the euclidean distance between two point sets is represented by the sum of the values; calculating a rotation matrix R and a translation matrix t required by transforming the point set P i to the point set Q i, and performing rotation translation transformation on point cloud data to be corrected by using the rotation matrix R and the translation matrix t to obtain corrected point cloud P i'; calculating the middle error of P i 'and Q i again, if the middle error is less than or equal to the set threshold, finishing the fine registration, otherwise, taking P i' as an initial point set, and carrying out the iterative calculation again until the middle error is less than or equal to the set threshold or exceeds the maximum iterative times; in this embodiment, the threshold is set to be 1.5cm, and three-dimensional laser point cloud data after point cloud fine registration is shown in fig. 4 in the transverse cross section direction for a fully-closed expressway unidirectional tunnel.
S3: and (3) dividing and filtering the three-dimensional laser point cloud data subjected to point cloud registration, and generating a ground irregular triangular network and a lining irregular triangular network.
The three-dimensional laser point cloud data after point cloud registration is generally divided into lower ground point cloud data and upper lining point cloud data at 1m to 1.5 m; in order to improve accuracy of headroom information measurement, it is necessary to filter the lower ground point cloud data and the upper lining point cloud data to extract surface layer point cloud data of the lower ground point cloud data and surface layer point cloud data of the upper lining point cloud data.
Specifically, the embodiment extracts surface layer point cloud data of lower ground point cloud data and surface layer point cloud data of upper lining point cloud data based on a progressive triangle mesh encryption filtering algorithm. The basic idea of the algorithm is to gradually compose discrete point cloud data into an irregular triangle network from an initial ground point. According to the embodiment, the whole point cloud data are firstly segmented, the point with the lowest elevation value in each block is used as an initial ground seed point, and an initial irregular triangular network is constructed on the basis of the initial ground seed point. And then, based on the initial irregular triangular network, carrying out iterative encryption on all the rest point cloud data. Firstly, finding a triangular surface corresponding to a projection coordinate of a point to be judged on an xOy plane, calculating the gradient of the triangular surface, and judging the category of the point by using a mirror image point of the point relative to the highest point in the triangle if the gradient is larger than a preset terrain inclination angle threshold; if the gradient of the triangular surface is smaller than a preset terrain inclination angle threshold value, calculating the distance from the point to be judged to the triangular surface and the included angle between the connecting line of the point to be judged and the vertex of the triangular surface closest to the point to be judged and the triangular surface, and if the distance and the included angle are smaller than the preset distance threshold value and the preset angle threshold value, the point is regarded as surface layer point cloud data, otherwise, the point is non-surface layer point cloud data. The above process is repeated after a new irregular triangle network is constructed until no new points to be judged are added or the maximum number of iterations is reached. If a certain point is judged to be surface layer point cloud data, adding the surface layer point cloud data into an irregular triangular network; if the non-surface point cloud data is judged, the non-surface point cloud data is filtered.
For the lower ground point cloud data, as non-surface point cloud data such as sign labels, operation vehicles, sundries and the like possibly exist on the tunnel ground, a progressive triangular mesh encryption filtering algorithm is required to be used for filtering classification; firstly, partitioning lower ground point cloud data, selecting the point with the lowest elevation value in each block as a seed point to establish an initial sparse triangular network, setting a distance threshold value and an angle threshold value, gradually encrypting the triangular network, filtering non-surface point cloud data, and extracting surface point cloud data of the lower ground point cloud data.
For the upper lining point cloud data, because ventilation and illumination facilities are arranged at the top of the tunnel, if the tunnel lining is subjected to disease treatment, devices such as I-steel frames and the like can be arranged, a reasonable elevation threshold value is required to be set according to the condition of the top facilities of the tunnel to be tested, point cloud data lower than a preset elevation threshold value is extracted from the upper lining point cloud data to serve as a low point cloud cluster, and then the bottom surface layer point cloud data of the upper lining point cloud data is extracted by using a progressive triangular mesh encryption filtering algorithm: firstly, the upper lining point cloud data are segmented, the point with the lowest elevation value in each block is selected as a seed point to establish an initial sparse triangular network, a distance threshold value and an angle threshold value are set, the triangular network is gradually encrypted, non-surface point cloud data are filtered, and bottom surface point cloud data of the upper lining point cloud data are extracted. And finally merging the extracted low-point cloud clusters with the bottom surface layer point cloud data to obtain surface layer point cloud data of the upper lining point cloud data.
And respectively constructing a ground irregular triangular net and a lining irregular triangular net according to the surface layer point cloud data of the lower ground point cloud data and the surface layer point cloud data of the upper lining point cloud data.
In the embodiment, a Delaunay triangular net growth algorithm is used for respectively constructing a ground irregular triangular net and a lining irregular triangular net, any point in the extracted surface layer point cloud data is firstly taken as a starting point, and a point closest to the point is connected to be taken as an initial base line; searching a third point with the shortest distance from the base line on the right side of the initial base line, wherein the point is required to meet the condition that no other point is contained in a circumscribed circle of a triangle formed by the point and two end points of the base line; generating a Delaunay triangle by taking two endpoints of the initial baseline and the found third point as vertexes; and continuing to generate triangles by taking two sides of the newly generated triangle net as new base lines until all points in the surface layer point cloud data are processed, and forming an irregular triangle net by the generated triangles.
S4: converting continuous line bit data of marking line bits or continuous line bit data of design line bits of the expressway tunnel to be tested into point data, wherein the elevation value of the lining irregular triangular net at the point data position is different from the elevation value of the ground irregular triangular net, and the null value of the point data position is obtained.
The vertices of the lining irregular triangular net and the ground irregular triangular net generated through the surface layer point cloud data are self-contained elevation values, so that when the clearance value of a certain point on a marked line position or a design line position is measured, only the triangle containing the point is found in the irregular triangular net, the elevation value of the point in the lining irregular triangular net and the ground irregular triangular net is calculated by using a linear interpolation method based on the elevation value of the vertex of the triangle corresponding to the point, the elevation value of the lining irregular triangular net corresponding to the point is subtracted from the elevation value of the ground irregular triangular net corresponding to the point, namely the clearance value of the position of the point, continuous line position data of the marked line position or continuous line position data of the design line position of a highway tunnel to be measured are converted into point data, and then the clearance value of discrete point data on all the marked line positions or the design line position is measured by the method, so that the complete clearance information of the highway tunnel to be measured can be obtained.
Example 2
The embodiment provides an expressway tunnel clearance measurement device based on a vehicle-mounted laser point cloud, which comprises at least one processor and a memory in communication connection with the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the on-board laser point cloud based highway tunnel headroom measurement method of embodiment 1.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (10)
1. The expressway tunnel clearance measurement method based on the vehicle-mounted laser point cloud is characterized by comprising the following steps of:
s1: arranging a plurality of targets in a highway tunnel to be tested, measuring to obtain actual measurement coordinates of centers of the targets, and acquiring three-dimensional laser point cloud data of the highway tunnel to be tested in batches by using an on-board laser scanning system, wherein the three-dimensional laser point cloud data comprises point cloud data of the targets;
s2: after removing abnormal points and noise points in the three-dimensional laser point cloud data, carrying out point cloud registration on the three-dimensional laser point cloud data based on actual measurement coordinates of the centers of the targets;
s3: dividing and filtering the three-dimensional laser point cloud data subjected to point cloud registration, and generating a ground irregular triangular net and a lining irregular triangular net;
S4: converting continuous line bit data of marking line bits or continuous line bit data of design line bits of the expressway tunnel to be tested into point data, wherein the elevation value of the lining irregular triangular net at the point data position is different from the elevation value of the ground irregular triangular net, and the null value of the point data position is obtained.
2. The expressway tunnel clearance measurement method based on the vehicle-mounted laser point cloud according to claim 1, wherein the point cloud registration comprises performing point cloud rough registration on three-dimensional laser point cloud data of different batches and performing point cloud fine registration on the three-dimensional laser point cloud data after the point cloud rough registration.
3. The highway tunnel headroom measurement method based on the vehicle-mounted laser point cloud of claim 2, wherein the point cloud coarse registration comprises:
S21: in the three-dimensional laser point cloud data of the current batch, manually marking a plurality of rectangular areas covering the point cloud data of a single target, constructing a three-dimensional statistical histogram corresponding to the rectangular areas, and carrying out averaging treatment on the three-dimensional statistical histogram to obtain a reference three-dimensional statistical histogram;
S22: marking three-dimensional laser point cloud data of an unmarked area into a plurality of rectangular areas to be matched, which have the same size as the rectangular areas in the step S21, according to a preset step distance, constructing a three-dimensional statistical histogram corresponding to the rectangular areas to be matched, and performing similarity matching with a reference three-dimensional statistical histogram to obtain rectangular areas covering point cloud data of a single target so as to obtain center point coordinates of all targets; the center point coordinates of the targets are geometric centroids of three-dimensional laser point cloud data in the rectangular area covering the point cloud data of the single target;
S23: correcting the center point coordinates of each target to be the same as the actual measurement coordinates of the center of the target, and obtaining three-dimensional laser point cloud data after rough registration of the batch of point clouds.
4. The expressway tunnel headroom measurement method based on the vehicle-mounted laser point cloud of claim 3, wherein the three-dimensional statistical histogram construction method comprises the following steps: and projecting the three-dimensional laser point cloud data in each rectangular area onto a plane in the normal vector direction of the three-dimensional laser point cloud data in the rectangular area to form a point cloud intensity image, and generating a three-dimensional statistical histogram containing gray scale and gradient information.
5. The expressway tunnel headroom measurement method based on the vehicle-mounted laser point cloud of claim 2, wherein the point cloud fine registration comprises: calculating the medium errors of the three-dimensional laser point cloud data after the rough registration of the point clouds of all batches, selecting the three-dimensional laser point cloud data of the corresponding batch with the minimum medium errors as reference point cloud data, and registering the three-dimensional laser point cloud data of the other batches to the reference point cloud data by adopting an ICP algorithm to obtain the three-dimensional laser point cloud data after the point cloud registration.
6. The expressway tunnel clearance measurement method based on the vehicle-mounted laser spot cloud according to any one of claims 1 to 5, wherein said S3 comprises: dividing the three-dimensional laser point cloud data subjected to point cloud registration into lower ground point cloud data and upper lining point cloud data; and respectively extracting surface layer point cloud data of the lower surface point cloud data and surface layer point cloud data of the upper lining point cloud data, and respectively calculating and generating a ground irregular triangular network and a lining irregular triangular network according to the surface layer point cloud data.
7. The expressway tunnel headroom measurement method based on the vehicle-mounted laser point cloud of claim 6, wherein the surface layer point cloud data of the lower surface point cloud data is extracted using a progressive triangulation encryption filtering algorithm.
8. The expressway tunnel clearance measurement method based on the vehicle-mounted laser point cloud of claim 6, wherein the surface layer point cloud data extraction method of the upper lining point cloud data comprises the following steps: and extracting point cloud data lower than a preset elevation threshold value from the upper lining point cloud data as low point cloud clusters, extracting bottom surface layer point cloud data of the upper lining point cloud data by using a progressive triangular mesh encryption filtering algorithm, and merging the low point cloud clusters with the bottom surface layer point cloud data to obtain surface layer point cloud data of the upper lining point cloud data.
9. The expressway tunnel clearance measurement method based on the vehicle-mounted laser spot cloud according to claim 6, wherein the ground irregular triangular mesh and the lining irregular triangular mesh are generated by adopting a Delaunay triangular mesh growth algorithm.
10. The highway tunnel clearance measuring device based on the vehicle-mounted laser point cloud is characterized by comprising at least one processor and a memory in communication connection with the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the vehicle-mounted laser point cloud-based highway tunnel clearance measurement method of any one of claims 1 to 9.
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