Laser point cloud plane detection and distance measurement method, device, equipment and medium

Abstract

The invention relates to the technical field of distance measurement, in particular to a laser point cloud plane detection and distance measurement method, device and equipment and a medium. And the automatic measurement of the underground tunnel wall distance is realized by using the laser radar. The measurement distance is the distance from the laser radar to the wall surface of the tunnel wall. The distance is equal to the plane distance from the viewpoint of the laser radar to the tunnel wall point set fitting, and is the shortest distance from the point to the plane. According to the strategy of the invention, the laser radar scans the tunnel wall area as much as possible, so that the area occupied by the tunnel wall area in the visual field of the laser radar is maximum. Then the vertical face with the largest area in the scene is obtained through the algorithm, and the distance between the vertical face and the laser radar is measured. And then, the least square method is adopted for plane fitting, so that the precision of plane extraction is improved, and the method is not influenced by outlier noise and is also not influenced by the unevenness and unevenness properties of the tunnel wall surface.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of distance measurement, in particular to a laser point cloud plane detection and distance measurement method, device, equipment and medium. BACKGROUND

[0002] The coal wall in the coal mine and the tunnel wall in the tunnel generated by tunneling all belong to the tunnel wall, and the tunnel wall changes constantly with the progress of the tunneling operation. For example, mining operations can cause new coal seams to collapse constantly, resulting in a concave-convex structure on the coal wall surface, and the degree of concave-convex changes constantly with the progress of the tunneling operation. The prior art measures the distance of the tunnel wall (which can be the distance from the tunnel wall to the tunnel entrance or the distance from the tunnel wall to the measuring tool) manually, and the distance measured by manual measurement is the local distance of the tunnel wall, which is prone to cause the measurement point to fall on the concave-convex position on the tunnel wall, so the measured distance cannot represent the true distance of the whole tunnel wall.

[0003] In summary, the distance of the tunnel wall measured by the prior art deviates from the true distance.

[0004] Therefore, the prior art needs to be improved and improved. SUMMARY

[0005] To solve the above technical problems, the present application provides a laser point cloud plane detection and distance measurement method, device, equipment and medium, which solves the problem that the distance of the tunnel wall measured by the prior art deviates from the true distance.

[0006] To achieve the above purpose, the present application adopts the following technical solutions: In a first aspect, the present application provides a laser point cloud plane detection and distance measurement method, which comprises: Obtaining original point cloud data of a tunnel wall detected by a laser radar, and establishing a vertical plane point cloud representing the tunnel wall based on the original point cloud data; Determining a measurement distance of the laser radar to the vertical plane point cloud; Based on the measurement distance, determining a real-time distance of the laser radar to the tunnel wall.

[0007] In an implementation mode, the vertical plane point cloud representing the tunnel wall is established based on the original point cloud data, which comprises: Performing voxelization processing on the original point cloud data to obtain a voxelized point cloud; Based on the voxelized point cloud, a vertical plane point cloud representing the tunnel wall is established.

[0008] In an implementation mode, the voxelized point cloud is obtained by performing voxelization processing on the original point cloud data, which comprises: grid dividing the original point cloud data to obtain a plurality of original points falling into a grid; replacing all the original points in the grid with a point located at the center position of the grid to obtain the voxelized point cloud.

[0009] In an implementation manner, based on the voxelized point cloud, a facade point cloud representing the tunnel wall is established, comprising: determining a point set of coplanar points in the voxelized point cloud; selecting a point set with the largest number of points from the point set, and establishing a facade point cloud representing the tunnel wall based on the point set with the largest number of points.

[0010] In an implementation manner, determining a point set of coplanar points in the voxelized point cloud comprises: selecting three points from the voxelized point cloud, and constructing a plane where the three points are located, denoted as a coplanar plane; determining a point-to-plane distance of a remaining point in the voxelized point cloud to the coplanar plane, the remaining point being a point other than the three points in the voxelized point cloud; selecting a point belonging to the coplanar plane from the remaining points according to the point-to-plane distance corresponding to the remaining point, and constructing the point set according to the three points and the selected point belonging to the coplanar plane.

[0011] In an implementation manner, determining a measurement distance of the laser radar to the facade point cloud comprises: constructing a covariance matrix of the facade point cloud; determining an eigenvalue of the covariance matrix, and determining an eigenvector of the covariance matrix corresponding to the smallest eigenvalue; determining a centroid of the facade point cloud; constructing a fitting plane according to the centroid and the eigenvector corresponding to the smallest eigenvalue; determining a fitting distance of the laser radar to the fitting plane, and determining a measurement distance of the laser radar to the facade point cloud according to the fitting distance.

[0012] In an implementation manner, the original point cloud data comprises a historical frame point cloud and a current frame point cloud varying with the tunnel wall changing in real time, and the measurement distance comprises a historical distance corresponding to the historical frame point cloud and a current distance corresponding to the current frame point cloud; based on the measurement distance, determining a real-time distance of the laser radar to the tunnel wall comprises: applying a moving median filter to the historical distance and the current distance to obtain a filtering result; determine a real-time distance of the laser radar to the tunnel wall based on the filtering result and the current distance.

[0013] In a second aspect, the embodiments of the present application further provide a laser point cloud plane detection and distance measurement device, wherein the device comprises the following components: a facade point cloud construction module, configured to acquire original point cloud data of a tunnel wall detected by a laser radar, and to construct a facade point cloud representing the tunnel wall based on the original point cloud data; a measurement distance calculation module, configured to determine a measurement distance of the laser radar to the facade point cloud; a real-time distance calculation module, configured to determine a real-time distance of the laser radar to the tunnel wall based on the measurement distance.

[0014] In a third aspect, the embodiments of the present application further provide a terminal device, wherein the terminal device comprises a memory, a processor, and a laser point cloud plane detection and distance measurement program stored in the memory and executable on the processor, and the processor implements the steps of the laser point cloud plane detection and distance measurement method when executing the laser point cloud plane detection and distance measurement program.

[0015] In a fourth aspect, the embodiments of the present application further provide a computer readable storage medium, wherein the computer readable storage medium stores a laser point cloud plane detection and distance measurement program, and the steps of the laser point cloud plane detection and distance measurement method are implemented when the laser point cloud plane detection and distance measurement program is executed by a processor.

[0016] Advantages: The original point cloud data of the tunnel wall is detected by the laser radar, and then the facade point cloud representing the tunnel wall is constructed based on the original point cloud data, that is, the facade point cloud represents the plane where the tunnel wall is located, the measurement distance of the laser radar to the facade point cloud is calculated, and finally the real-time distance of the tunnel wall is calculated based on the measurement distance. Since the facade point cloud represents the plane where the tunnel wall is located, the plane is the operation plane that affects the next step of construction, and therefore the facade point cloud can compensate for the interference of the uneven surface of the tunnel wall on the real operation plane. Therefore, the real-time distance of the tunnel wall measured by the facade point cloud is closer to the real distance of the laser radar to the operation plane of the tunnel wall, and the accuracy of the real-time distance of the present application is finally improved. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 is a flowchart of the present application; Figure 2 is an effect diagram of the facade point cloud extraction in the embodiments of the present application; Figure 3 is a structure diagram of the laser point cloud plane detection and distance measurement device provided by the present application; Figure 4 The internal structure principle block diagram of the terminal device provided by the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0018] The technical solutions in the present application are described clearly and completely below in combination with the embodiments and the accompanying drawings. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

[0019] It is found through research that the coal wall in the coal mine underground and the tunnel wall in the tunnel generated by tunnel excavation both belong to the tunnel wall, and the tunnel wall changes constantly with the progress of the excavation operation. For example, the mining operation will cause the new coal seam to collapse constantly, thereby causing the surface of the coal wall to present a concave-convex structure, and the degree of concave-convex will change constantly with the progress of the excavation operation. The prior art measures the distance of the tunnel wall (which can be the distance from the tunnel wall to the tunnel mouth or the distance from the tunnel wall to the measuring tool) manually, and the distance measured by manual measurement is the local distance of the tunnel wall, which is extremely easy to cause the measurement point to fall on the concave-convex position on the tunnel wall, so that the measured distance cannot represent the real distance of the whole tunnel wall.

[0020] To solve the above technical problems, the present application provides a laser point cloud plane detection and distance measurement method, device, equipment and medium, which solves the problem that the distance of the tunnel wall measured by the prior art deviates from the real distance.

[0021] The laser point cloud plane detection and distance measurement method of the present embodiment can be applied to a terminal device, which can be a terminal product with point cloud data processing function, such as a computer, etc. In the present embodiment, as shown in Figure 1 The laser point cloud plane detection and distance measurement method specifically includes the following steps: S100, obtaining the original point cloud data of the tunnel wall detected by the laser radar, and establishing a vertical plane point cloud representing the tunnel wall based on the original point cloud data; S200, determining the measurement distance of the laser radar to the vertical plane point cloud; S300, determining the real-time distance of the laser radar to the tunnel wall based on the measurement distance.

[0022] When the tunnel wall in steps S100 and S300 is a coal wall, the distance measurement method based on steps S100, S200 and S300 can be used to measure the distance between the coal wall and the laser radar, and the specific application process is as follows: The laser radar is arranged at the entrance of the coal mine or at a position close to the entrance of the coal mine, a transmitter of the laser radar emits a laser pulse to the inside of the coal mine, the laser pulse is reflected when encountering the coal wall, the reflected laser is received by a receiver of the laser radar, the distance between each point on the coal wall and the laser radar is calculated according to the time difference from emission to reception of the laser and the speed of light, and the distance between each point on the coal wall and the laser radar is represented by original point cloud data. A facade point cloud representing the coal wall is established based on the original point cloud data, the plane formed by the points on the facade point cloud, that is, the working face of the coal wall, can remove the points located at the uneven or rough places of the coal wall, which are not the points to be considered for the operation work. Then the measurement distance of the laser radar to the facade point cloud is calculated, and finally the real-time distance between the laser radar and the coal wall is calculated.

[0023] When the tunnel wall is a tunnel wall, the distance measurement method based on steps S100, S200 and S300 can be used to measure the distance between the tunnel wall and the laser radar, and the specific application process is as follows: The laser radar is arranged in a safety area set in the tunnel, a transmitter of the laser radar emits a laser pulse to the construction direction of the tunnel, the laser pulse is reflected when encountering the tunnel wall, the reflected laser is received by a receiver of the laser radar, the distance between each point on the tunnel wall and the laser radar is calculated according to the time difference from emission to reception of the laser and the speed of light, and the distance between each point on the tunnel wall and the laser radar is represented by original point cloud data. The same processing method as the above coal wall is adopted on the original point cloud data to obtain the real-time distance between the laser radar and the tunnel wall.

[0024] The original point cloud data in step S100 represents the coordinates of each point on the tunnel wall in the coordinate system of the laser radar, the origin of the coordinate system is the laser radar, the horizontal coordinate is in the length direction of the tunnel, the vertical coordinate is in the width direction of the tunnel, and the vertical coordinate is in the depth direction of the tunnel. The facade point cloud representing the tunnel wall based on the original point cloud data in step S100 includes the following specific steps S101, S102, S103, S104, S105 and S106: S101, the original point cloud data is divided into a plurality of original points.

[0025] The original point cloud data is a plurality of single-frame point cloud data detected by the laser radar, each single-frame point cloud data is voxelized, that is, the single-frame point cloud data is three-dimensionally divided into a grid, so that each original point of the single-frame point cloud data is divided into a corresponding grid. The voxel size is 0.2 meters, that is, the distance between two original points in the same three-dimensional grid.

[0026] S102, replace all the original points in the grid with a point located at the center position of the grid to obtain the voxelized point cloud.

[0027] The three-dimensional grid center point (the center point is a point located at the center position) is used to replace all the original points in the three-dimensional grid to re-express the single-frame point cloud data, that is, one point represents one grid. When the grid falls into the single-frame point cloud data, the network is a non-empty voxel. For scene (the scene is the scene inside the coal mine) re-expression, all non-empty voxels are replaced with the voxel center point in the original point in the voxel, thereby generating new single-frame point cloud data (the new single-frame point cloud data is voxelized point cloud). The purpose of expressing the scene with voxelized point cloud is to eliminate the scanning property of the laser radar, because the nearby coal wall or the wrinkle structure on the coal wall may scan a large number of point clouds, and the distant coal wall has fewer point clouds. The scanning property of the laser radar for data acquisition will cause the obtained point cloud scene to have the disadvantage of "more near and less far", that is, the point cloud obtained by scanning the nearby coal wall is relatively dense, and the point cloud obtained by scanning the distant coal wall is relatively sparse. Through the voxelization of the point cloud, the voxel center point is replaced for the point cloud of the scene regardless of the distance or special structure, and all positions of data are normalized to a lower limit of density (the density is a voxel distance), which overcomes the disadvantage of "more near and less far". That is, the original single-frame point cloud data is expressed by voxelized point cloud, which can make the single-frame point cloud data become homogeneous point cloud, that is, the point cloud on the tunnel wall is expressed in an averaging manner.

[0028] The above-mentioned voxelized point cloud expresses the original single-frame point cloud data, that is, the original single-frame point cloud data is expressed by fewer points. For example, the original single-frame point cloud data contains 1000 points, which are divided into 100 grids after voxelization processing, and a point located at the center position of the grid represents the grid. Therefore, 1000 points become 100 points, that is, the single-frame point cloud data containing 1000 points becomes voxelized point cloud containing 100 points after voxelization processing.

[0029] The voxelized point cloud is applied to the RANSAC algorithm (RANSAC is Random Sample Consensus, RANSAC algorithm is random sample consensus algorithm) to extract the maximum vertical plane point cloud from the voxelized point cloud. The detailed steps of the RANSAC algorithm include steps S103, S104, S105 and S106.

[0030] S103, three points are selected from the voxelized point cloud, and a plane where the three points are located is constructed, which is denoted as a coplanar plane.

[0031] Three points are randomly sampled from the several points contained in the voxelized point cloud, and a plane where the three points are located is constructed, which is denoted as a coplanar plane. Represents the coordinates of the first sampling point, using Represents the coordinates of the second sampling point, using Represents the coordinates of the third sampling point. According to the principle of three points being coplanar, a plane can be obtained from the above three points (the plane is the coplanar plane), and the normal vector of the coplanar plane is Calculated by the following formula: ; in Represents the cross product, the normal vector Perform normalization to obtain the unit normal vector , using the unit normal vector Represent the coplanar plane: ; Where, and Known, so the plane equation parameters can be calculated by the above formula ,in, For Random, Stands for random.

[0032] S104 , determining point-to-plane distances from remaining points in the voxelized point cloud to the coplanar plane, where the remaining points are points other than the three points in the voxelized point cloud.

[0033] Calculate Then, using the unit normal vector and Calculate the distance from the remaining points in the voxelized point cloud to the coplanar plane, which is the point-to-plane distance.

[0034] ; Where, Representative The coordinates of the remaining points, Representative The distance from the remaining points to the coplanar plane (this distance is the point-to-plane distance).

[0035] S105 , based on the point-to-plane distances corresponding to the remaining points, screen out points belonging to the coplanar plane from the remaining points, and construct the point set based on the three points and the screened out points belonging to the coplanar plane.

[0036] Filter out points belonging to the coplanar plane from the remaining points, and the filtering conditions are: ,in represents the threshold, The value of can be 0.4 meters. Less than When The remaining points belong to the coplanar plane, so the The remaining points are included in the point set.

[0037] By randomly sampling three points multiple times, each random sampling will generate a corresponding point set. In this embodiment, the number of random sampling times is 500, so 500 point sets can be generated (that is, the number of point sets that can be generated is 500).

[0038] S106 , selecting a point set with the largest number of points from the point sets, and establishing a vertical point cloud representing the tunnel wall based on the point set with the largest number of points.

[0039] The point sets generated by multiple random samplings contain different numbers of points. The point set with the largest number of points is screened out from these point sets, and the point cloud composed of the points in the point set with the largest number of points is used as the facade point cloud.

[0040] Continuing with the above example, among the 500 samplings, the sampling result with the largest number of plane points is retained as the large-scale extracted facade point cloud.

[0041] This embodiment extracts the facade point cloud on a large scale, which can bypass the geometric fluctuations caused by small-scale unevenness on the tunnel wall. In other words, points on the unevenness are not allowed to appear in the facade point cloud, thereby eliminating the influence of the unevenness on the distance measurement.

[0042] The coal wall elevation point cloud extracted by the RANSAC algorithm in step S100 is as follows: Figure 2 As shown, Figure 2 The gray points in the figure are single-frame point clouds, and the blue points are coal wall elevation point clouds estimated by the RANSAC algorithm. Figure 2 The green frame in the figure shows the extraction result of the same coal wall point cloud facade from a bird’s-eye view.

[0043] Step S200 determines the measurement distance between the laser radar and the facade point cloud based on the least squares fitting method, including the following specific steps S201, S202, S203, S204, and S205: S201, constructing the covariance matrix of the facade point cloud : ; ; ; ; ; ; ; wherein, represents a covariance, represents a horizontal coordinate of a point on the facade point cloud, represents a vertical coordinate of a point on the facade point cloud, represents a vertical coordinate of a point on the facade point cloud, represents a vertical coordinate of a point on the facade point cloud, represents a vertical coordinate of a point on the facade point cloud, represents a horizontal coordinate of the geometric center of the facade point cloud, represents a vertical coordinate of the geometric center of the facade point cloud, represents a vertical coordinate of the geometric center of the facade point cloud, represents a vertical coordinate of the geometric center of the facade point cloud, represents a total number of points contained in the facade point cloud.

[0044] S202, determining eigenvalues of the covariance matrix, and determining an eigenvector of the covariance matrix corresponding to the smallest eigenvalue.

[0045] Eigenvalues of the covariance matrix are calculated in the prior art, and represents all eigenvalues of the covariance matrix , and represents a matrix composed of all eigenvectors of the covariance matrix , , , satisfy the following relationship: ; wherein, and are known, so can be calculated, and since the covariance matrix is a 3x3 matrix, the covariance matrix has three eigenvectors, so can be decomposed into three eigenvectors, and the three eigenvectors are arranged in order of the eigenvalues corresponding to the three eigenvectors, that is, wherein is an eigenvector corresponding to the largest eigenvalue, is an eigenvector corresponding to an eigenvalue between the largest and smallest eigenvalues, and is an eigenvector corresponding to the smallest eigenvalue.

[0046] S203, determining the centroid of the facade point cloud: The centroid of the facade point cloud is a point located at the geometric center of the facade point cloud, and the centroid The prior art.

[0047] S204, constructing a fitting plane according to the centroid and the eigenvector corresponding to the smallest eigenvalue.

[0048] The equation of the constructed fitting plane is as follows: ; wherein, represents the parameters of the fitting plane to be solved, i.e. Fitting, represents the meaning of fitting, and is known, so the value of can be calculated. According to the decomposition property of the three-dimensional covariance matrix, the eigenvector corresponding to the smallest eigenvalue constitutes the fitting plane as a normal vector. When the fitting plane passes through the centroid of the facade point cloud and the residual square sum of each point in the facade point cloud to the fitting plane is the smallest (the residual refers to the shortest distance of each point to the fitting plane), the fitting plane can optimally represent the orientation of the facade point cloud.

[0049] S205, determining the fitting distance of the laser radar to the fitting plane, and determining the measurement distance of the laser radar to the facade point cloud according to the fitting distance.

[0050] Let represent the position of the laser radar, since the laser radar is located at the origin of the coordinate system, , represents the transpose of the matrix. Let represent the fitting distance of the laser radar to the fitting plane, then , and is taken as the measurement distance of the laser radar to the facade point cloud.

[0051] The step S100 of the embodiment adopts the RANSAC algorithm for large-scale extraction, and the step S200 adopts the least square method for small-scale fitting, which fully guarantees the correctness of the facade point cloud extraction and overcomes the influence of irregular tunnel wall surface and local irregularity on the measurement distance.

[0052] The original point cloud data in step S100 includes a plurality of single-frame point cloud data collected in sequence over time. Since the distance between the tunnel wall and the laser radar changes over time as the excavation operation proceeds, the plurality of single-frame point cloud data are also different from each other. Step S100 is to construct a facade point cloud based on each single-frame point cloud data. Step S200 is to construct a fitting plane based on the single-frame point cloud data and measure the distance from the laser radar to the fitting plane. Therefore, the real-time distance between the tunnel wall and the laser radar can be calculated according to the respective measured distances of the plurality of single-frame point cloud data, so as to determine the position of the working face where the tunnel wall is located through the real-time distance. Step S300 is to calculate the real-time distance through the respective measured distances of the plurality of single-frame point cloud data. Step S300 includes the following specific steps S301 and S302: S301, applying a moving median filter to the historical distance and the current distance to obtain a filtering result.

[0053] The original point cloud data includes a historical frame point cloud and a current frame point cloud which change with the real-time tunnel wall. The historical distance is the distance between the laser radar and the facade point cloud calculated based on the historical frame point cloud. The current distance is the distance between the laser radar and the facade point cloud calculated based on the current frame point cloud.

[0054] The moving median filter is: , represents the moving median filter, represents the current distance between the laser radar and the facade point cloud calculated based on the current frame point cloud, represents the current time; represents the historical distance between the laser radar and the facade point cloud calculated based on the first historical frame point cloud, The value of represents the threshold value of the moving median filter, may be 10% of .

[0055] For , as long as is replaced by , is replaced by the feature vector corresponding to the minimum feature value calculated based on the current frame point cloud, and is replaced by the parameters of the fitting plane calculated based on the current frame point cloud, the value of can be calculated. The value of can be calculated by the same method.

[0056] ​S302, determining a real-time distance of the laser radar to the tunnel wall based on the filtering result and the current distance.

[0057] current distance satisfies , then is taken as the real-time distance of the laser radar to the tunnel wall.

[0058] If the current distance does not satisfy , then is taken as the real-time distance of the laser radar to the tunnel wall, wherein is a historical distance between the laser radar and the facade point cloud calculated based on the first historical frame point cloud.

[0059] Although the current distance does not satisfy , but can still be used to establish the following inequality: , wherein is a distance between the laser radar and a next-time facade point cloud, wherein the next-time facade point cloud is a facade point cloud calculated based on a single frame point cloud data collected at the next time. The reason why is still used is to prepare for the change of the tunnel wall distance, when the tunnel wall distance actually changes, a correct measurement value can be obtained after a window period (i.e. ), so as to prevent the median lock situation, and thus the measurement result of the tunnel wall distance can be output in real time, which is continuously output every frame, and the output frequency is consistent with the frequency of the laser radar.

[0060] The moving median filtering is used for processing, and the output that does not meet the condition is limited to be reserved, so as to ensure the stability of the algorithm, and the actual change of the tunnel wall distance can be adapted.

[0061] The embodiment also provides a laser point cloud plane detection and distance measurement device, as shown in Figure 3 , the device comprises the following components: a facade point cloud construction module 01, configured to acquire original point cloud data of a tunnel wall detected by a laser radar, and establish a facade point cloud representing the tunnel wall based on the original point cloud data; a measurement distance calculation module 02, configured to determine a measurement distance of the laser radar to the facade point cloud; a real-time distance calculation module 03, configured to determine a real-time distance of the laser radar to the tunnel wall based on the measurement distance.

[0062] Based on the above embodiments, the application further provides a terminal device, a principle block diagram of which can be shown in Figure 4 The terminal device includes a processor, a memory, a network interface, and a display screen connected through a system bus. The processor of the terminal device is configured to provide computing and control capabilities. The memory of the terminal device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The network interface of the terminal device is configured to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement the laser point cloud plane detection and distance measurement method. The display screen of the terminal device can be a liquid crystal display screen or an electronic ink display screen.

[0063] Those skilled in the art can understand that Figure 4 The principle block diagram shown in the above embodiments is only a block diagram of part of the structure related to the application scheme, and does not constitute a limitation on the terminal device to which the application scheme is applied. The specific terminal device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0064] In one embodiment, a terminal device is provided, which includes a memory, a processor, and a laser point cloud plane detection and distance measurement program stored in the memory and executable on the processor. When the processor executes the laser point cloud plane detection and distance measurement program, the following operation instructions are implemented: Obtain original point cloud data of a tunnel wall detected by a laser radar, and establish a facade point cloud representing the tunnel wall based on the original point cloud data; Determine a measurement distance of the laser radar to the facade point cloud; Based on the measurement distance, determine a real-time distance of the laser radar to the tunnel wall.

[0065] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, storage, database or other medium used in the embodiments of the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0066] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A laser point cloud plane detection and distance measurement method, characterized in that: include: Acquire original point cloud data of the tunnel wall detected by the laser radar, and establish a vertical point cloud representing the tunnel wall based on the original point cloud data; Determining a measurement distance from the laser radar to the facade point cloud; Based on the measured distance, the real-time distance between the laser radar and the tunnel wall is determined.

2. The laser point cloud plane detection and distance measurement method according to claim 1, characterized in that: Creating a vertical point cloud representing the tunnel wall based on the original point cloud data includes: Performing voxelization processing on the original point cloud data to obtain a voxelized point cloud; Based on the voxelized point cloud, a facade point cloud representing the tunnel wall is established.

3. The laser point cloud plane detection and distance measurement method according to claim 2, characterized in that: Performing voxelization processing on the original point cloud data to obtain a voxelized point cloud includes: Performing grid division on the original point cloud data to obtain a plurality of original points falling into the grid; All the original points in the grid are replaced by a point located at the center of the grid to obtain the voxelized point cloud.

4. The laser point cloud plane detection and distance measurement method according to claim 2, characterized in that: Based on the voxelized point cloud, a vertical point cloud representing the tunnel wall is established, including: Determining a set of coplanar points in the voxelized point cloud; A point set with the largest number of points is selected from the point sets, and a vertical point cloud representing the tunnel wall is established based on the point set with the largest number of points.

5. The laser point cloud plane detection and distance measurement method according to claim 4, characterized in that: Determining a set of coplanar points in the voxelized point cloud includes: Selecting three points from the voxelized point cloud and constructing a plane where the three points lie, which is recorded as a coplanar plane; Determining point-to-plane distances from remaining points in the voxelized point cloud to the coplanar plane, the remaining points being points other than the three points in the voxelized point cloud; Points belonging to the coplanar plane are screened out from the remaining points according to the point-plane distances corresponding to the remaining points, and the point set is constructed based on the three points and the screened points belonging to the coplanar plane.

6. The laser point cloud plane detection and distance measurement method according to claim 1, characterized in that: Determining a measurement distance from the laser radar to the facade point cloud includes: Constructing a covariance matrix of the facade point cloud; Determining the eigenvalues ​​of the covariance matrix, and determining the eigenvector of the covariance matrix corresponding to the smallest eigenvalue; determining a centroid of the point cloud located on the facade; constructing a fitting plane according to the centroid and the eigenvector corresponding to the minimum eigenvalue; A fitting distance between the laser radar and the fitting plane is determined, and a measurement distance between the laser radar and the vertical point cloud is determined based on the fitting distance.

7. The laser point cloud plane detection and distance measurement method according to claim 1, characterized in that: The original point cloud data includes a historical frame point cloud and a current frame point cloud that change with the tunnel wall that changes in real time, and the measured distance includes a historical distance corresponding to the historical frame point cloud and a current distance corresponding to the current frame point cloud; Determining a real-time distance between the laser radar and the tunnel wall based on the measured distance includes: Applying a moving median filter to the historical distance and the current distance to obtain a filtering result; Based on the filtering result and the current distance, a real-time distance between the laser radar and the tunnel wall is determined.

8. A laser point cloud plane detection and distance measurement device, characterized in that: The device comprises the following components: A facade point cloud construction module is used to obtain original point cloud data of the tunnel wall detected by the laser radar, and to establish a facade point cloud representing the tunnel wall based on the original point cloud data; A measurement distance calculation module, used to determine the measurement distance from the laser radar to the facade point cloud; A real-time distance calculation module is used to determine the real-time distance between the laser radar and the tunnel wall based on the measured distance.

9. A terminal device, characterized in that: The terminal device includes a memory, a processor, and a laser point cloud plane detection and distance measurement program stored in the memory and runnable on the processor. When the processor executes the laser point cloud plane detection and distance measurement program, it implements the steps of the laser point cloud plane detection and distance measurement method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a laser point cloud plane detection and distance measurement program. When the laser point cloud plane detection and distance measurement program is executed by the processor, the steps of the laser point cloud plane detection and distance measurement method according to any one of claims 1 to 7 are implemented.

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