Laser point cloud bridge pier top flatness detection method, device and equipment and storage medium

Abstract

The invention discloses a laser point cloud bridge pier top flatness detection method, device and equipment and a storage medium, and the method comprises the steps: obtaining laser point cloud data of the surface of the top of a bridge pier, carrying out the preprocessing of the laser point cloud data, and obtaining the preprocessed target point cloud data; performing data cutting on the target point cloud data according to a preset top area and fitting a first plane equation of the preset top area; combining the point cloud data after data cutting, taking a maximum fitting plane as a guiding rule plane, respectively calculating the average distance from the plane point cloud data of all preset top areas to the guiding rule plane as a height difference, and performing color assignment and nature determination on height difference results of different areas according to a first plane equation to obtain a color determination result; and the coloring qualitative result is used as the flatness detection result of the top surface of the pier, so that the precision of the flatness detection of the top of the pier and the level of intelligent construction of the bridge are effectively improved, and the speed and efficiency of laser point cloud pier top flatness detection are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge construction, and particularly to a method, device, equipment and storage medium for detecting the flatness of the top of a laser point cloud pier. Background Art

[0002] With the continuous development of bridge construction technology, the construction quality and safety of large-volume piers have become important concerns in the engineering field; the flatness of the top of the pier is directly related to the overall structural stability and durability of the bridge; traditional methods for detecting the flatness of the top of the pier mainly rely on manual measurement, such as using tools like straightedges and levels, and these methods have disadvantages such as low efficiency, limited measurement accuracy, and being greatly affected by environmental factors; currently, there is a method of projecting point clouds onto a plane and constructing a grid to calculate flatness, but it is only applicable to the flatness detection of a small range. Summary of the Invention

[0003] The main purpose of the present invention is to provide a method, device, equipment and storage medium for detecting the flatness of the top of a laser point cloud pier, aiming to solve the technical problems in the prior art that the detection of the flatness of the top of the pier relies on manual measurement, has low efficiency, is greatly affected by environmental factors, has low measurement accuracy, and the method of detecting the flatness of the top of the pier by point cloud projection is only applicable to the flatness detection of a small range.

[0004] In a first aspect, the present invention provides a method for detecting the flatness of the top of a laser point cloud pier, and the method for detecting the flatness of the top of a laser point cloud pier includes the following steps:

[0005] Obtain the laser point cloud data of the surface of the top of the pier, and perform preprocessing on the laser point cloud data to obtain the preprocessed target point cloud data;

[0006] According to a preset top area, crop the target point cloud data and fit the first plane equation of the preset top area;

[0007] Merge the point cloud data after data cropping, and use the fitted largest plane as the straightedge plane. Calculate the average distance from the plane point cloud data of all the preset top areas to the straightedge plane respectively, and use the average distance as the elevation difference between the top of the pier and the straightedge plane. According to the first plane equation, perform color-coding and qualitative analysis on the elevation difference results of different areas, and use the color-coding and qualitative analysis result as the flatness detection result of the surface of the top of the pier.

[0008] Optionally, the step of obtaining the laser point cloud data of the surface of the top of the pier, performing preprocessing on the laser point cloud data, and obtaining the preprocessed target point cloud data includes:

[0009] Perform laser scanning on the surface of the top of the pier through a three-dimensional laser scanner to obtain the laser point cloud data of the surface of the top of the pier;

[0010] Preprocess the laser point cloud data, remove the invalid data in the laser point cloud data, and obtain the preprocessed target point cloud data.

[0011] Optionally, the data clipping of the target point cloud data according to the preset top area and fitting the first plane equation of the preset top area includes:

[0012] Uniformly generate plane areas at preset intervals along the random coordinate direction of the top surface of the pier as the preset top area;

[0013] Perform data clipping on the target point cloud data according to the preset top area to obtain the sub-plane point cloud data of all sub-planes;

[0014] Merge the sub-plane point cloud data of each sub-plane, and generate the first plane equation of multi-plane fitting according to the merged point cloud data.

[0015] Optionally, merge the point cloud data after data clipping, use the fitted largest plane as the reference plane, calculate the average distance from the plane point cloud data of all the preset top areas to the reference plane respectively, use the average distance as the height difference between the top of the pier and the reference plane, perform color qualitative analysis on the height difference results of different areas according to the first plane equation, and use the color qualitative analysis result as the flatness detection result of the top surface of the pier, including:

[0016] Merge all the planes corresponding to the point cloud data after data clipping to obtain the data merged plane;

[0017] Randomly select a preset number of points from the data merged plane as seed points, and fit various sub-points on the same plane to generate the second plane equation;

[0018] Calculate the distance from each point in all the plane point cloud data in the data merged plane to the second plane equation, and record the number of point cloud points less than the preset distance threshold;

[0019] When the number of point cloud times reaches the preset number of loops, obtain the point cloud number set;

[0020] Select the plane with the largest number of point clouds from the point cloud number set as the reference plane;

[0021] Calculate the average distance from the plane point cloud data of all the preset top areas to the reference plane respectively, and use the average distance as the height difference between the top of the pier and the reference plane;

[0022] Color the height difference results of different regions according to the first plane equation and the preset coloring rule to obtain a colored qualitative result, and use the colored qualitative result as the flatness detection result of the top surface of the pier.

[0023] Optionally, the step of respectively calculating the average distance from the plane point cloud data of all the preset top regions to the straightedge plane and using the average distance as the height difference between the top of the pier and the straightedge plane includes:

[0024] Calculate the normal distance from the plane point cloud data of all the points on the planes of the preset top region to the straightedge plane, use the normal distance as the average distance, and use the average distance as the height difference between the top of the pier and the straightedge plane.

[0025] Optionally, the step of coloring and qualitatively determining the height difference results of different regions according to the first plane equation and the preset coloring rule to obtain a colored qualitative result and using the colored qualitative result as the flatness detection result of the top surface of the pier includes:

[0026] Discretize the average distance according to the first plane equation to obtain the height difference results of the average distances in different regions, and color and represent the height difference results according to the preset coloring rule to generate a height fluctuation display map of the top surface of the pier, and use the height fluctuation display map as the colored qualitative result;

[0027] Calculate a confusion matrix of the distances between all corresponding planes according to the colored qualitative result, and generate the flatness detection result of the top surface of the pier according to the confusion matrix.

[0028] Optionally, the step of calculating a confusion matrix of the distances between all corresponding planes according to the colored qualitative result and generating the flatness detection result of the top surface of the pier according to the confusion matrix includes:

[0029] Calculate a confusion matrix of the distances between all corresponding planes according to the colored qualitative result, determine the height difference of each plane at different distances according to the confusion matrix, and use the height difference as the flatness detection result.

[0030] In a second aspect, to achieve the above object, the present invention also proposes a laser point cloud pier top flatness detection device, and the laser point cloud pier top flatness detection device includes:

[0031] A preprocessing module, configured to obtain the laser point cloud data of the top surface of the pier, and preprocess the laser point cloud data to obtain the preprocessed target point cloud data;

[0032] A clipping and fitting module, configured to clip the target point cloud data according to a preset top region and fit the first plane equation of the preset top region;

[0033] The flatness detection module is used to merge the point cloud data after data clipping, take the fitted largest plane as the leveling staff plane, calculate the average distance from the plane point cloud data of all the preset top regions to the leveling staff plane respectively, take the average distance as the elevation difference between the top of the pier and the leveling staff plane, qualitatively color the elevation difference results of different regions according to the first plane equation, and take the qualitatively colored result as the flatness detection result of the surface of the top of the pier.

[0034] In a third aspect, to achieve the above object, the present invention also provides a laser point cloud flatness detection device for the top of a pier. The laser point cloud flatness detection device for the top of a pier includes: a memory, a processor, and a laser point cloud flatness detection program stored on the memory and executable on the processor. The laser point cloud flatness detection program is configured to implement the steps of the laser point cloud flatness detection method as described above.

[0035] In a fourth aspect, to achieve the above object, the present invention also provides a storage medium. A laser point cloud flatness detection program is stored on the storage medium. When the laser point cloud flatness detection program is executed by a processor, it implements the steps of the laser point cloud flatness detection method as described above.

[0036] The laser point cloud flatness detection method proposed by the present invention obtains the laser point cloud data of the surface of the top of the pier, preprocesses the laser point cloud data to obtain the preprocessed target point cloud data; performs data clipping on the target point cloud data according to the preset top region and fits the first plane equation of the preset top region; merges the point cloud data after data clipping, takes the fitted largest plane as the leveling staff plane, calculates the average distance from the plane point cloud data of all the preset top regions to the leveling staff plane respectively, takes the average distance as the elevation difference between the top of the pier and the leveling staff plane, qualitatively colors the elevation difference results of different regions according to the first plane equation, and takes the qualitatively colored result as the flatness detection result of the surface of the top of the pier. By using high-precision point cloud data, it avoids a large amount of manual operations and subjective reading deviations based on the original leveling staff, can finely obtain the flatness of the large-volume top of the pier, effectively improves the accuracy of the flatness detection of the top of the pier and the level of intelligent bridge construction, and improves the speed and efficiency of the laser point cloud flatness detection of the top of the pier. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic diagram of the device structure of the hardware operating environment related to the solution of the embodiment of the present invention;

[0038] Figure 2 It is a schematic flowchart of the first embodiment of the laser point cloud flatness detection method of the present invention;

[0039] Figure 3 It is a schematic flowchart of the second embodiment of the method for detecting the flatness of the top of a pier using laser point cloud in the present invention;

[0040] Figure 4 It is a schematic flowchart of the third embodiment of the method for detecting the flatness of the top of a pier using laser point cloud in the present invention;

[0041] Figure 5 It is a schematic flowchart of the fourth embodiment of the method for detecting the flatness of the top of a pier using laser point cloud in the present invention;

[0042] Figure 6 It is a visualization schematic diagram of the original point cloud and the intercepted plane in the method for detecting the flatness of the top of a pier using laser point cloud in the present invention;

[0043] Figure 7 It is a schematic diagram of the quantitative visualization flatness result in the method for detecting the flatness of the top of a pier using laser point cloud in the present invention;

[0044] Figure 8 It is a functional module diagram of the first embodiment of the device for detecting the flatness of the top of a pier using laser point cloud in the present invention.

[0045] The realization, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiment

[0046] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0047] The solution of the embodiment of the present invention is mainly as follows: By acquiring the laser point cloud data of the top surface of the pier, preprocessing the laser point cloud data to obtain the target point cloud data after preprocessing; performing data clipping on the target point cloud data according to the preset top area and fitting the first plane equation of the preset top area; merging the point cloud data after data clipping, and taking the fitted largest plane as the straightedge plane, calculating the average distance from the plane point cloud data of all the preset top areas to the straightedge plane respectively, taking the average distance as the height difference between the top of the pier and the straightedge plane, performing color assignment and qualitative analysis on the height difference results of different areas according to the first plane equation, taking the color assignment and qualitative analysis result as the flatness detection result of the top surface of the pier. By using high-precision point cloud data, a large number of manual operations and subjective reading deviations based on the original straightedge are avoided, the flatness of the large-volume pier top can be obtained in a refined manner, the accuracy of the flatness detection of the pier top and the level of intelligent bridge construction are effectively improved, the speed and efficiency of the flatness detection of the laser point cloud pier top are improved, and the technical problems in the prior art that the flatness detection of the pier top depends on manual measurement, has low efficiency, is greatly affected by environmental factors, has low measurement accuracy, and the flatness detection of the pier top by point cloud projection is only applicable to small-range flatness detection are solved.

[0048] Refer to Figure 1 , Figure 1 which is a schematic diagram of the device structure of the hardware operating environment involved in the solution of the embodiment of the present invention.

[0049] As Figure 1 shown, the device may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display) and an input unit such as a keyboard (Keyboard). Optionally, the user interface 1003 may further include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a Wi-Fi interface). The memory 1005 may be a high-speed RAM memory or a stable memory (Non-Volatile Memory), such as a disk memory. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0050] Those skilled in the art can understand that Figure 1 the device structure shown in

[0051] As Figure 1As shown in the figure, the memory 1005 as a storage medium may include an operating device, a network communication module, a user interface module, and a laser point cloud pier top flatness detection program.

[0052] The device of the present invention calls the laser point cloud pier top flatness detection program stored in the memory 1005 through the processor 1001, and performs the following operations:

[0053] Obtain the laser point cloud data of the pier top surface, preprocess the laser point cloud data, and obtain the preprocessed target point cloud data;

[0054] According to the preset top area, perform data clipping on the target point cloud data and fit the first plane equation of the preset top area;

[0055] Merge the point cloud data after data clipping, and use the fitted largest plane as the straightedge plane. Calculate the average distance from the plane point cloud data of all the preset top areas to the straightedge plane respectively, and use the average distance as the elevation difference between the pier top and the straightedge plane. According to the first plane equation, perform color-coding qualitative analysis on the elevation difference results of different areas, and use the color-coding qualitative analysis result as the flatness detection result of the pier top surface.

[0056] The device of the present invention calls the laser point cloud pier top flatness detection program stored in the memory 1005 through the processor 1001, and also performs the following operations:

[0057] Perform laser scanning on the pier top surface through a three-dimensional laser scanner to obtain the laser point cloud data of the pier top surface;

[0058] Preprocess the laser point cloud data to remove the invalid data in the laser point cloud data, and obtain the preprocessed target point cloud data.

[0059] The device of the present invention calls the laser point cloud pier top flatness detection program stored in the memory 1005 through the processor 1001, and also performs the following operations:

[0060] Uniformly generate plane areas with a preset interval along the random coordinate direction of the pier top surface as the preset top areas;

[0061] According to the preset top area, perform data clipping on the target point cloud data to obtain the sub-plane point cloud data of all sub-planes;

[0062] Merge the sub-plane point cloud data of each sub-plane, and generate the first plane equation of multi-plane fitting according to the merged point cloud data.

[0063] The device of the present invention calls the laser point cloud pier top flatness detection program stored in the memory 1005 through the processor 1001, and also performs the following operations:

[0064] Merge all the planes corresponding to the point cloud data after data clipping to obtain a data merged plane;

[0065] Randomly select a preset number of points from the data merged plane as seed points, and fit various sub-points in the same plane to generate a second plane equation;

[0066] Calculate the distance from each point in all the plane point cloud data in the data merged plane to the second plane equation, and record the number of point cloud points less than the preset distance threshold;

[0067] When the number of point cloud count loops reaches the preset number of loops, obtain a set of point cloud counts;

[0068] Select the plane with the largest number of point clouds from the set of point cloud counts as the straightedge plane;

[0069] Calculate the average distance from the plane point cloud data of all the preset top regions to the straightedge plane respectively, and use the average distance as the height difference between the pier top and the straightedge plane;

[0070] Qualitatively color the height difference results of different regions according to the first plane equation and the preset coloring rule to obtain a colored qualitative result, and use the colored qualitative result as the flatness detection result of the pier top surface.

[0071] The device of the present invention calls the laser point cloud pier top flatness detection program stored in the memory 1005 through the processor 1001, and also performs the following operations:

[0072] Calculate the normal distance from the plane point cloud data of all the points in the preset top region to the straightedge plane, use the normal distance as the average distance, and use the average distance as the height difference between the pier top and the straightedge plane.

[0073] The device of the present invention calls the laser point cloud pier top flatness detection program stored in the memory 1005 through the processor 1001, and also performs the following operations:

[0074] Discretize the average distance according to the first plane equation to obtain the height difference results of the average distances in different regions, and color and represent the height difference results according to the preset coloring rule to generate a height fluctuation display map of the pier top surface, and use the height fluctuation display map as the colored qualitative result;

[0075] Calculate the confusion matrix for the distances between all corresponding planes according to the coloring qualitative result, and generate the flatness detection result of the top surface of the pier according to the confusion matrix. Calculate the confusion matrix for the distances between all corresponding planes according to the coloring qualitative result.

[0076] The device of the present invention calls the laser point cloud pier top flatness detection program stored in the memory 1005 through the processor 1001, and also performs the following operations:

[0077] Calculate the confusion matrix for the distances between all corresponding planes according to the coloring qualitative result, determine the height difference between each plane at different distances according to the confusion matrix, and use the height difference as the flatness detection result.

[0078] In this embodiment, through the above solution, by obtaining the laser point cloud data of the top surface of the pier, preprocessing the laser point cloud data to obtain the preprocessed target point cloud data; performing data clipping on the target point cloud data according to the preset top area and fitting the first plane equation of the preset top area; merging the point cloud data after data clipping, and using the fitted largest plane as the straightedge plane, calculating the average distance from the plane point cloud data of all the preset top areas to the straightedge plane respectively, using the average distance as the height difference between the top of the pier and the straightedge plane, performing coloring qualitative analysis on the height difference results of different areas according to the first plane equation, and using the coloring qualitative result as the flatness detection result of the top surface of the pier, it can avoid a large amount of manual operations and subjective reading deviations based on the original straightedge, can obtain the flatness of the large-volume pier top in a refined manner, effectively improve the accuracy of the pier top flatness detection and the level of intelligent bridge construction, and improve the speed and efficiency of the laser point cloud pier top flatness detection.

[0079] Based on the above hardware structure, an embodiment of the laser point cloud pier top flatness detection method of the present invention is proposed.

[0080] Refer to Figure 2 , Figure 2 which is a schematic flowchart of the first embodiment of the laser point cloud pier top flatness detection method of the present invention.

[0081] In the first embodiment, the laser point cloud pier top flatness detection method includes the following steps:

[0082] Step S10: Obtain the laser point cloud data of the top surface of the pier, and preprocess the laser point cloud data to obtain the preprocessed target point cloud data.

[0083] It should be noted that the laser point cloud data is the point cloud data generated after laser scanning the top surface of the pier, and the laser point cloud data is preprocessed to obtain the preprocessed target point cloud data.

[0084] Step S20: Crop the target point cloud data according to a preset top region and fit the first plane equation of the preset top region.

[0085] It should be understood that after cropping the target point cloud data according to a preset plane region, the first plane equation of the preset top region can be fitted. The plane equation refers to the equation corresponding to all points in the same plane in space, and its general form is Ax + By + Cz + D = 0.

[0086] Step S30: Merge the point cloud data after data cropping, use the fitted largest plane as the leveling rod plane, calculate the average distance from the plane point cloud data of all the preset top regions to the leveling rod plane respectively, take the average distance as the elevation difference between the top of the pier and the leveling rod plane, perform color-coding qualitative analysis on the elevation difference results of different regions according to the first plane equation, and take the color-coding qualitative analysis result as the flatness detection result of the top surface of the pier.

[0087] It can be understood that by merging the point cloud data after data cropping, the largest fitted plane in the fitted planes after merging the point cloud data can be selected as the leveling rod plane, calculate the average distance from the plane point cloud data of all the preset top regions to the leveling rod plane respectively, take the average distance as the elevation difference between the top of the pier and the leveling rod plane, perform color-coding qualitative analysis on the elevation difference results of different regions according to the first plane equation, and take the color-coding qualitative analysis result as the flatness detection result of the top surface of the pier.

[0088] In this embodiment, through the above solution, by acquiring the laser point cloud data of the top surface of the pier, preprocessing the laser point cloud data to obtain the preprocessed target point cloud data; cropping the target point cloud data according to a preset top region and fitting the first plane equation of the preset top region; merging the point cloud data after data cropping, using the fitted largest plane as the leveling rod plane, calculating the average distance from the plane point cloud data of all the preset top regions to the leveling rod plane respectively, taking the average distance as the elevation difference between the top of the pier and the leveling rod plane, performing color-coding qualitative analysis on the elevation difference results of different regions according to the first plane equation, and taking the color-coding qualitative analysis result as the flatness detection result of the top surface of the pier, it is possible to avoid a large amount of manual operations and subjective reading deviations based on the original leveling rod, accurately obtain the flatness of the large-volume pier top, effectively improve the accuracy of the flatness detection of the pier top and the level of intelligent bridge construction, and improve the speed and efficiency of the flatness detection of the laser point cloud pier top.

[0089] Furthermore, Figure 3 is a flowchart of the second embodiment of the method for detecting the flatness of the top of a pier using laser point cloud in the present invention. As Figure 3As shown, based on the first embodiment, the second embodiment of the laser point cloud bridge pier top flatness detection method of the present invention is proposed. In this embodiment, step S10 specifically includes the following steps:

[0090] Step S11: Laser scan the surface of the bridge pier top through a three-dimensional laser scanner to obtain the laser point cloud data of the bridge pier top surface.

[0091] It should be noted that the surface of the bridge pier top is laser scanned through a three-dimensional laser scanner to obtain the laser point cloud data of the bridge pier top surface.

[0092] In specific implementation, the laser point cloud data of the bridge pier top surface can be obtained through various ways that can acquire laser point clouds, such as base station scanners, handheld three-dimensional laser scanners, photogrammetry, etc.

[0093] Step S12: Preprocess the laser point cloud data, remove the invalid data in the laser point cloud data, and obtain the preprocessed target point cloud data.

[0094] It can be understood that after preprocessing the laser point cloud data, the invalid data in the laser point cloud data can be removed, thereby obtaining the preprocessed target point cloud data.

[0095] In specific implementation, the preprocessing methods include but are not limited to removing outliers, data clipping, multi-station stitching, etc., and this embodiment does not limit this; high-precision point cloud data of the bridge pier top can be obtained and preprocessed. Based on a base station three-dimensional scanner, it is erected in the middle of the pier body, about 1.2 m higher than the pier body, and then high-resolution scanning is carried out; the redundant part in the scanning result is deleted, the surface of the pier body is extracted, and Gaussian filtering is used to remove outliers to make the point cloud more regular.

[0096] Through the above solution in this embodiment, the surface of the bridge pier top is laser scanned through a three-dimensional laser scanner to obtain the laser point cloud data of the bridge pier top surface; the laser point cloud data is preprocessed to remove the invalid data in the laser point cloud data, and the preprocessed target point cloud data is obtained, which can avoid a large amount of manual operations and subjective reading deviations based on a straightedge in the original method, and can finely obtain the flatness of the large-volume bridge pier top.

[0097] Furthermore, Figure 4 is a schematic flow chart of the third embodiment of the laser point cloud bridge pier top flatness detection method of the present invention. As Figure 4 shown, based on the first embodiment, the third embodiment of the laser point cloud bridge pier top flatness detection method of the present invention is proposed. In this embodiment, step S20 specifically includes the following steps:

[0098] Step S21: Uniformly generate plane regions at preset intervals along the random coordinate directions on the top surface of the pier as the preset top regions.

[0099] It should be noted that plane regions with preset intervals can be uniformly generated along the random coordinate directions on the top surface of the pier as the preset top regions.

[0100] In specific implementation, a certain coordinate direction on the top of the pier can be the transverse bridge direction or the longitudinal bridge direction. The uniformly generated regions should be carried out according to the requirements of the flatness specification. Generally, the distance between regions is required to be at least 1 m at intervals.

[0101] Step S22: Perform data clipping on the target point cloud data according to the preset top regions to obtain the sub-plane point cloud data of all sub-planes.

[0102] It can be understood that according to the preset plane regions, the target point cloud data can be clipped to obtain the sub-plane point cloud data of all sub-planes.

[0103] It should be understood that based on the original point cloud, the extracted polygon regions can be clipped according to the contour coordinates.

[0104] Step S23: Merge the sub-plane point cloud data of each sub-plane, and generate the first plane equation of multi-plane fitting according to the merged point cloud data.

[0105] It can be understood that based on the preset top regions, data can be clipped from the original point cloud and the first plane equation of the preset top regions can be fitted. The random sample consensus algorithm can be used to extract the planes fitted by these point clouds from the clipped point cloud data, and the first plane equation of multi-plane fitting is generated according to the merged point cloud data.

[0106] Through the above solution in this embodiment, by uniformly generating plane regions at preset intervals along the random coordinate directions on the top surface of the pier as the preset top regions; performing data clipping on the target point cloud data according to the preset top regions to obtain the sub-plane point cloud data of all sub-planes; merging the sub-plane point cloud data of each sub-plane, and generating the first plane equation of multi-plane fitting according to the merged point cloud data, the plane equation can be quickly generated, avoiding a large amount of manual operations and subjective reading deviations based on the original straightedge, enabling the refined acquisition of the flatness of the large-volume pier top, improving the accuracy of pier top flatness detection and the level of intelligent bridge construction, and enhancing the speed and efficiency of laser point cloud pier top flatness detection.

[0107] Further, Figure 5 This is the flowchart of the fourth embodiment of the method for detecting the flatness of the top of a pier using laser point cloud in the present invention. As Figure 5As shown, based on the first embodiment, the fourth embodiment of the method for detecting the flatness of the top of a laser point cloud pier of the present invention is proposed. In this embodiment, step S30 specifically includes the following steps:

[0108] Step S31: Merge all the planes corresponding to the point cloud data after data clipping to obtain a merged data plane.

[0109] It should be noted that all small-plane point cloud data can be merged, that is, all the planes corresponding to the point cloud data after data clipping can be merged to obtain a merged data plane.

[0110] Step S32: Randomly select a preset number of points from the merged data plane as seed points, and fit various sub-points on the same plane to generate a second plane equation.

[0111] It can be understood that a preset number of points can be randomly selected from the merged data plane as seed points, and then various sub-points on the same plane are used for point cloud data point fitting to generate a second plane equation.

[0112] In a specific implementation, all small-plane point cloud data is merged, and a preset number of points, such as 3 points, are randomly selected from all the merged small-plane point clouds as seed points. Based on the seed points, the second plane equation of the preset top region is fitted. Of course, it can also be other preset numbers, such as: 2, 4, 5, 8, 12, etc. This embodiment does not limit this.

[0113] Step S33: Calculate the distance from each point in all the plane point cloud data in the merged data plane to the second plane equation, and record the number of point clouds with a distance less than a preset distance threshold.

[0114] It should be noted that the distance from other points to the plane equation is calculated, and the number of point clouds with a distance less than a certain threshold is recorded, that is, the distance from each point in all the plane point cloud data in the merged data plane to the second plane equation is calculated, and the number of point clouds with a distance less than a preset distance threshold is recorded.

[0115] Step S34: When the loop count of the number of point clouds reaches a preset loop count, obtain a set of point cloud numbers.

[0116] It can be understood that by repeatedly merging all small-plane point cloud data, randomly selecting corresponding seed points from all the merged small-plane point clouds, and fitting the first plane equation of the preset top region based on the seed points, when the loop test of the number of point clouds reaches a preset loop count, a set of point cloud numbers can be obtained. For example, if the preset loop count is 1000 times, 1000 sets of point cloud numbers can be obtained.

[0117] Step S35: Select the plane with the largest number of point clouds from the set of point cloud numbers as the spirit level plane.

[0118] It should be understood that selecting the plane with the largest number of point clouds from the set as the spirit level plane means selecting the plane with the largest number of point clouds as the final spirit level plane.

[0119] In a specific implementation, the point clouds on all small planes can be merged into a complete pcd file, and then the plane with the smallest distance passing through all the point clouds in this pcd file is calculated as the "spirit level plane".

[0120] Step S36: Calculate the average distance from the plane point cloud data of all the preset top regions to the spirit level plane respectively, and use the average distance as the elevation difference between the top of the pier and the spirit level plane.

[0121] It can be understood that after calculating the plane with the smallest distance from all small planes as the spirit level plane, the distances between all small planes and this spirit level plane can be calculated, that is, the average distance from the plane point cloud data of all the preset top regions to the spirit level plane is calculated respectively, and then the average distance is used as the elevation difference between the top of the pier and the spirit level plane.

[0122] Furthermore, step S36 specifically includes the following steps:

[0123] Calculate the normal distance from the plane point cloud data of all the points on all the planes in the preset top region to the spirit level plane, use the normal distance as the average distance, and use the average distance as the elevation difference between the top of the pier and the spirit level plane.

[0124] It should be understood that when calculating the distance from the small plane to the spirit level plane, the normal distance from the point to the plane should be the normal distance from the point to the plane, and then the normal distance is used as the average distance, and the average distance can be used as the elevation difference between the top of the pier and the spirit level plane.

[0125] In a specific implementation, as Figure 6 shown, Figure 6 is a visualization schematic diagram of the original point cloud and the intercepted plane in the method for detecting the flatness of the top of a pier by laser point cloud of the present invention. Refer to Figure 6 , several regions can be uniformly generated along a certain coordinate direction at the top of the pier. Along the longitudinal direction of the pier, a polygon region is drawn at a certain distance interval. The way to establish the region is to use K-means clustering to cluster the regions with a distance less than 1 mm and number them.

[0126] Step S37: Color and qualitatively analyze the elevation difference results of different regions according to the first plane equation and the preset coloring rule to obtain the color qualitative result, and use the color qualitative result as the flatness detection result of the surface of the top of the pier.

[0127] It should be understood that according to the first plane equation and the preset coloring rule, the height difference results of different regions can be colored and characterized, and then the colored and characterized result can be obtained. The colored and characterized result can be used as the flatness detection result of the top surface of the pier.

[0128] Further, step S37 specifically includes the following steps:

[0129] Discretize the average distance according to the first plane equation to obtain the height difference results of the average distances of different regions, and color and represent the height difference results according to the preset coloring rule to generate a height fluctuation display diagram of the top surface of the pier. Use the height fluctuation display diagram as the colored and characterized result;

[0130] Calculate the confusion matrix of the distances between all corresponding planes according to the colored and characterized result, and generate the flatness detection result of the top surface of the pier according to the confusion matrix. Calculate the confusion matrix of the distances between all corresponding planes according to the colored and characterized result.

[0131] It should be noted that the colored and characterized representation is a colored representation according to the magnitude of the deviation. At the same time, the coloring interval should be reasonably selected according to the design requirements. Generally, the maximum and minimum height differences are normalized to 20 color bands for display. Both the horizontal axis and the vertical axis in the confusion matrix should be the numbers of the small planes. The height fluctuations of different regions of the entire pier top are discretely displayed according to the different distances and colored and characterized for display.

[0132] In specific implementation, the distances between the small planes and the straightedge plane can be calculated respectively and the flatness can be colored and characterized. Iteratively calculate the distances between the point clouds on all small planes and the "straightedge plane" in turn, and take the average distance from all the point clouds in each small point cloud to the "straightedge plane" as the distance from the small plane to the "straightedge plane"; generally, the maximum and minimum values of all distances can be normalized between 20 color bands, from green to red, to qualitatively represent the change in height.

[0133] Further, the step of calculating the confusion matrix of the distances between all corresponding planes according to the colored and characterized result, and generating the flatness detection result of the top surface of the pier according to the confusion matrix. Calculate the confusion matrix of the distances between all corresponding planes according to the colored and characterized result specifically includes the following steps:

[0134] Calculate the confusion matrix of the distances between all corresponding planes according to the colored and characterized result, determine the height differences of each plane at different distances according to the confusion matrix, and use the height differences as the flatness detection result.

[0135] It can be understood that, according to the coloring qualitative result, a confusion matrix for calculating the distances between all corresponding planes can be calculated, a confusion matrix for calculating the distances between all small planes with numbers can be calculated, and the height difference between planes at different distances can be quantitatively displayed as the result of the flatness detection.

[0136] In a specific implementation, a confusion matrix for calculating the distances between all small planes is calculated as the result of the flatness detection. The distances between all small planes are calculated respectively. The distance calculation algorithm is the same as the method in Article 5. The calculated results are combined with the numbers of the planes and output as a confusion matrix to display the flatness as a whole and draw it as a heat map, as Figure 7 shown Figure 7 is a schematic diagram of quantitatively visualizing the flatness result in the method for detecting the flatness of the top of a laser point cloud bridge pier of the present invention. Refer to Figure 7 , through the average height difference heat map, the distances between different planes can be more intuitively displayed. According to the number and the height difference, the elevation change per meter can be calculated. By comparing with the design value, the flatness can be obtained. From the result, it can be seen that the planes with larger height differences are also far apart and the flatness meets the requirements.

[0137] In this embodiment, through the above solution, all planes corresponding to the point cloud data after data clipping are merged to obtain a data merged plane; a preset number of points are randomly selected from the data merged plane as seed points, and various sub-points in the same plane are fitted to generate a second plane equation; the distances from each point in all plane point cloud target point cloud data in the data merged plane to the second plane equation are calculated, and the number of point clouds less than a preset distance threshold is recorded; when the number of point cloud cycles reaches a preset number of cycles, a set of point cloud numbers is obtained; the plane with the largest number of point clouds is selected from the set of point cloud numbers as the leveling staff plane; the average distances from the plane point cloud data of all the preset top regions to the leveling staff plane are calculated respectively, and the average distance is used as the height difference between the top of the bridge pier and the leveling staff plane; according to the first plane equation and a preset coloring rule, the height difference results in different regions are colored qualitatively to obtain a coloring qualitative result, and the coloring qualitative result is used as the result of the flatness detection of the surface of the top of the bridge pier; it can avoid a large amount of manual operations and subjective reading deviations based on the original leveling staff, can finely obtain the flatness of the large-volume top of the bridge pier, effectively improve the accuracy of the flatness detection of the top of the bridge pier and the level of intelligent bridge construction, and improve the speed and efficiency of the flatness detection of the top of the laser point cloud bridge pier.

[0138] Correspondingly, the present invention further provides a device for detecting the flatness of the top of a laser point cloud bridge pier.

[0139] Referring to Figure 8 , Figure 8 is a functional module diagram of the first embodiment of the device for detecting the flatness of the top of a laser point cloud bridge pier of the present invention.

[0140] In the first embodiment of the laser point cloud bridge pier top flatness detection device of the present invention, the laser point cloud bridge pier top flatness detection device includes:

[0141] A preprocessing module 10, configured to obtain laser point cloud data of the surface of the bridge pier top, preprocess the laser point cloud data, and obtain preprocessed target point cloud data.

[0142] A clipping and fitting module 20, configured to clip the target point cloud data according to a preset top area and fit the first plane equation of the preset top area.

[0143] A flatness detection module 30, configured to merge the point cloud data after data clipping, use the fitted maximum plane as the straightedge plane, calculate the average distance from the plane point cloud data of all the preset top areas to the straightedge plane respectively, use the average distance as the elevation difference between the bridge pier top and the straightedge plane, perform color-coding and qualitative analysis on the elevation difference results of different areas according to the first plane equation, and use the color-coding and qualitative analysis result as the flatness detection result of the surface of the bridge pier top.

[0144] The preprocessing module 10 is further configured to perform laser scanning on the surface of the bridge pier top through a three-dimensional laser scanner to obtain laser point cloud data of the surface of the bridge pier top; preprocess the laser point cloud data to remove invalid data in the laser point cloud data, and obtain preprocessed target point cloud data.

[0145] The clipping and fitting module 20 is further configured to uniformly generate plane areas at preset intervals along the random coordinate direction of the surface of the bridge pier top as the preset top area; clip the target point cloud data according to the preset top area to obtain sub-plane point cloud data of all sub-planes; merge the sub-plane point cloud data of each sub-plane, and generate the first plane equation of multi-plane fitting according to the merged point cloud data.

[0146] The flatness detection module 30 is further configured to merge all the planes corresponding to the point cloud data after data clipping to obtain a data-merged plane; randomly select a preset number of points from the data-merged plane as seed points, and fit various sub-points in the same plane to generate a second plane equation; calculate the distance from each point in all the plane point cloud target point cloud data in the data-merged plane to the second plane equation, and record the number of point clouds with a distance less than a preset distance threshold; when the number of point cloud cycles reaches a preset number of cycles, obtain a set of point cloud numbers; select the plane with the largest number of point clouds from the set of point cloud numbers as the leveling ruler plane; calculate the average distance from the plane point cloud data of all the preset top regions to the leveling ruler plane respectively, and use the average distance as the height difference between the top of the pier and the leveling ruler plane; perform color-coding and qualitative analysis on the height difference results of different regions according to the first plane equation and a preset color-coding rule to obtain a color-coding qualitative result, and use the color-coding qualitative result as the flatness detection result of the surface of the top of the pier.

[0147] The flatness detection module 30 is further configured to calculate the normal distance from the plane point cloud data of all the points in all the planes in the preset top region to the leveling ruler plane, use the normal distance as the average distance, and use the average distance as the height difference between the top of the pier and the leveling ruler plane.

[0148] The flatness detection module 30 is further configured to discretize the average distance according to the first plane equation to obtain the height difference results of the average distances in different regions, and perform color-coding representation on the height difference results according to a preset color-coding rule to generate a height fluctuation display map of the surface of the top of the pier, and use the height fluctuation display map as the color-coding qualitative result; calculate the confusion matrix of the distances between all corresponding planes according to the color-coding qualitative result, and generate the flatness detection result of the surface of the top of the pier according to the confusion matrix.

[0149] The flatness detection module 30 is further configured to calculate the confusion matrix of the distances between all corresponding planes according to the color-coding qualitative result, determine the height difference of each plane at different distances according to the confusion matrix, and use the height difference as the flatness detection result.

[0150] Wherein, the steps implemented by each functional module of the laser point cloud pier top flatness detection device can refer to each embodiment of the laser point cloud pier top flatness detection method of the present invention, and will not be elaborated here.

[0151] In addition, an embodiment of the present invention further provides a storage medium, on which a laser point cloud pier top flatness detection program is stored. When the laser point cloud pier top flatness detection program is executed by a processor, the following operations are implemented:

[0152] Obtain the laser point cloud data of the top surface of the pier, preprocess the laser point cloud data, and obtain the target point cloud data after preprocessing;

[0153] According to the preset top area, crop the target point cloud data and fit the first plane equation of the preset top area;

[0154] Merge the point cloud data after data cropping, and use the fitted largest plane as the straightedge plane. Calculate the average distance from the plane point cloud data of all the preset top areas to the straightedge plane respectively, and take the average distance as the height difference between the top of the pier and the straightedge plane. According to the first plane equation, color and qualitatively analyze the height difference results of different areas, and take the color and qualitative analysis result as the flatness detection result of the top surface of the pier.

[0155] Further, when the laser point cloud pier top flatness detection program is executed by the processor, the following operations are also implemented:

[0156] Perform laser scanning on the top surface of the pier through a three-dimensional laser scanner to obtain the laser point cloud data of the top surface of the pier;

[0157] Preprocess the laser point cloud data, remove the invalid data in the laser point cloud data, and obtain the target point cloud data after preprocessing.

[0158] Further, when the laser point cloud pier top flatness detection program is executed by the processor, the following operations are also implemented:

[0159] Uniformly generate plane areas at preset intervals along the random coordinate direction of the top surface of the pier as the preset top areas;

[0160] According to the preset top area, crop the target point cloud data to obtain the sub-plane point cloud data of all sub-planes;

[0161] Merge the sub-plane point cloud data of each sub-plane, and generate the first plane equation of multi-plane fitting according to the merged point cloud data.

[0162] Further, when the laser point cloud pier top flatness detection program is executed by the processor, the following operations are also implemented:

[0163] Merge the data of all the planes corresponding to the point cloud data after data cropping to obtain the data merged plane;

[0164] Randomly select a preset number of points from the data merged plane as seed points, and fit the various sub-points on the same plane to generate the second plane equation;

[0165] Calculate the distances from each point in all the plane point cloud target point cloud data in the data merging plane to the second plane equation, and record the number of point clouds with distances less than the preset distance threshold;

[0166] When the number of point cloud cycles reaches the preset number of cycles, obtain the set of point cloud numbers;

[0167] Select the plane with the largest number of point clouds from the set of point cloud numbers as the leveling staff plane;

[0168] Calculate the average distance from the plane point cloud data of all the preset top regions to the leveling staff plane respectively, and use the average distance as the elevation difference between the top of the pier and the leveling staff plane;

[0169] According to the first plane equation and the preset coloring rule, qualitatively color the elevation difference results of different regions to obtain the colored qualitative result, and use the colored qualitative result as the flatness detection result of the top surface of the pier.

[0170] Furthermore, when the laser point cloud pier top flatness detection program is executed by the processor, the following operations are also implemented:

[0171] Calculate the normal distance from the plane point cloud data of all the points on all the planes in the preset top region to the leveling staff plane, use the normal distance as the average distance, and use the average distance as the elevation difference between the top of the pier and the leveling staff plane.

[0172] Furthermore, when the laser point cloud pier top flatness detection program is executed by the processor, the following operations are also implemented:

[0173] Discretize the average distance according to the first plane equation to obtain the elevation difference results of the average distances in different regions, and color and represent the elevation difference results according to the preset coloring rule to generate the height fluctuation display map of the top surface of the pier, and use the height fluctuation display map as the colored qualitative result;

[0174] Calculate the confusion matrix of the distances between all corresponding planes according to the colored qualitative result, and generate the flatness detection result of the top surface of the pier according to the confusion matrix.

[0175] Furthermore, when the laser point cloud pier top flatness detection program is executed by the processor, the following operations are also implemented:

[0176] Calculate the confusion matrix of the distances between all corresponding planes according to the colored qualitative result, determine the elevation differences of each plane at different distances according to the confusion matrix, and use the elevation differences as the flatness detection result.

[0177] Those skilled in the art can understand that all or part of the steps in the above implementation methods can be completed by instructing relevant hardware through a program. The program is stored in a storage medium, including several instructions to enable a device (which can be a single-chip microcomputer, a chip, etc.) or a processor to execute all or part of the steps of the methods described in various embodiments of the present application; and the aforementioned storage medium is a computer-readable storage medium, including but not limited to: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs and other various media that can store program codes.

[0178] It should be noted that in this article, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including that element.

[0179] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0180] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A method for detecting the flatness of the top of a laser point cloud pier, characterized in that, The laser point cloud bridge pier top flatness detection method includes: Obtain the laser point cloud data of the bridge pier top surface, preprocess the laser point cloud data, and obtain the target point cloud data after preprocessing; According to the preset top area, perform data clipping on the target point cloud data and fit the first plane equation of the preset top area; Merge the point cloud data after data clipping, take the fitted largest plane as the straightedge plane, calculate the average distance from the plane point cloud data of all the preset top areas to the straightedge plane respectively, take the average distance as the height difference between the bridge pier top and the straightedge plane, perform color-coding qualitative analysis on the height difference results of different areas according to the first plane equation, and take the color-coding qualitative analysis result as the flatness detection result of the bridge pier top surface.

2. The laser point cloud pier top flatness detection method according to claim 1, characterized in that The obtaining the laser point cloud data of the bridge pier top surface, preprocessing the laser point cloud data, and obtaining the target point cloud data after preprocessing includes: Perform laser scanning on the bridge pier top surface through a three-dimensional laser scanner to obtain the laser point cloud data of the bridge pier top surface; Preprocess the laser point cloud data to remove the invalid data in the laser point cloud data and obtain the target point cloud data after preprocessing.

3. The laser point cloud pier top flatness detection method according to claim 1, characterized in that, The performing data clipping on the target point cloud data according to the preset top area and fitting the first plane equation of the preset top area includes: Uniformly generate plane areas at preset intervals along the random coordinate direction of the bridge pier top surface as the preset top area; Perform data clipping on the target point cloud data according to the preset top area to obtain the sub-plane point cloud data of all sub-planes; Merge the sub-plane point cloud data of each sub-plane, and generate the first plane equation of multi-plane fitting according to the merged point cloud data.

4. The laser point cloud pier top flatness detection method according to claim 1, wherein The merging the point cloud data after data clipping, taking the fitted largest plane as the straightedge plane, calculating the average distance from the plane point cloud data of all the preset top areas to the straightedge plane respectively, taking the average distance as the height difference between the bridge pier top and the straightedge plane, performing color-coding qualitative analysis on the height difference results of different areas according to the first plane equation, and taking the color-coding qualitative analysis result as the flatness detection result of the bridge pier top surface includes: Merge all the planes corresponding to the point cloud data after data clipping to obtain the data merged plane; Randomly select a preset number of points from the data merged plane as seed points, and fit various sub-points on the same plane to generate the second plane equation; Calculate the distance from each point in all the plane point cloud data in the data merged plane to the second plane equation, and record the number of point cloud points less than the preset distance threshold; When the number of point cloud times reaches the preset number of cycles, obtain the point cloud number set; Select the plane with the largest number of point clouds from the point cloud number set as the straightedge plane; Calculate the average distance from the plane point cloud data of all the preset top areas to the straightedge plane respectively, and take the average distance as the height difference between the bridge pier top and the straightedge plane; Color - code and qualitatively analyze the height difference results of different regions according to the first plane equation and the preset color - coding rule to obtain the color - coding qualitative result, and use the color - coding qualitative result as the flatness detection result of the top surface of the pier.

5. The laser point cloud pier top flatness detection method according to claim 4, characterized in that, The step of calculating the average distance from the plane point cloud data of all the preset top regions to the straightedge plane respectively, and using the average distance as the height difference between the top of the pier and the straightedge plane includes: Calculate the normal distance from the plane point cloud data of all the points on the planes in the preset top region to the straightedge plane, use the normal distance as the average distance, and use the average distance as the height difference between the top of the pier and the straightedge plane.

6. The laser point cloud pier top flatness detection method according to claim 4, characterized in that The step of color - code and qualitatively analyze the height difference results of different regions according to the first plane equation and the preset color - coding rule to obtain the color - coding qualitative result, and use the color - coding qualitative result as the flatness detection result of the top surface of the pier includes: Discretize the average distance according to the first plane equation to obtain the height difference results of the average distances in different regions, and color - code the height difference results according to the preset color - coding rule to generate a height fluctuation display map of the top surface of the pier, and use the height fluctuation display map as the color - coding qualitative result; Calculate the confusion matrix corresponding to the distances between all the planes according to the color - coding qualitative result, and generate the flatness detection result of the top surface of the pier according to the confusion matrix.

7. The laser point cloud pier top flatness detection method according to claim 6, characterized in that, The step of calculating the confusion matrix corresponding to the distances between all the planes according to the color - coding qualitative result, and generating the flatness detection result of the top surface of the pier according to the confusion matrix includes: Calculate the confusion matrix corresponding to the distances between all the planes according to the color - coding qualitative result, determine the height difference of each plane at different distances according to the confusion matrix, and use the height difference as the flatness detection result.

8. A laser point cloud bridge pier top flatness detection device, characterized in that, The laser point cloud flatness detection device for the top of the pier includes: A pre - processing module, configured to obtain the laser point cloud data of the top surface of the pier, and pre - process the laser point cloud data to obtain the pre - processed target point cloud data; A clipping and fitting module, configured to clip the target point cloud data according to the preset top region and fit the first plane equation of the preset top region; A flatness detection module, configured to merge the point cloud data after data clipping, use the fitted largest plane as the straightedge plane, calculate the average distance from the plane point cloud data of all the preset top regions to the straightedge plane respectively, use the average distance as the height difference between the top of the pier and the straightedge plane, color - code and qualitatively analyze the height difference results of different regions according to the first plane equation, and use the color - coding qualitative result as the flatness detection result of the top surface of the pier.

9. A laser point cloud pier top flatness detection device, characterized in that, The laser point cloud flatness detection equipment for the top of the pier includes: a memory, a processor, and a laser point cloud flatness detection program stored on the memory and executable on the processor. The laser point cloud flatness detection program is configured to implement the steps of the laser point cloud flatness detection method according to any one of claims 1 to 7.

10. A storage medium, characterized in that, A laser point cloud pier top flatness detection program is stored on the storage medium. When the laser point cloud pier top flatness detection program is executed by a processor, the steps of the laser point cloud pier top flatness detection method according to any one of claims 1 to 7 are implemented.

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