A method for monitoring the displacement of a target based on laser point cloud

Abstract

The application discloses a kind of target space displacement monitoring methods based on laser point cloud, it is related to civil engineering operation maintenance technical field, comprising: S1, setting monitoring scheme, arranging strong reflection circular target on structure;S2, utilize laser scanning equipment to emit point cloud to structure, based on reflection intensity characteristic identification and extraction target point cloud;S3, the point cloud data of target is modeled to error, based on inscribed circle fitting, calculate the uniformity of point cloud on spatial distribution;S4, based on error propagation theory, calculate effective acquisition period;All effective measurement datum point coordinates are weighted average, obtain final monitoring coordinate value;S5, compare monitoring coordinate value with historical monitoring data, obtain the spatial displacement variation of structure in different time periods, form displacement time sequence.The application is based on the targeted monitoring idea of laser point cloud reflection intensity characteristic, combined with uniformity test and error propagation theory, realize the high-precision, long-term stable measurement of structure datum point.

Description

A method for monitoring targeted spatial displacement based on laser point clouds Technical Field

[0001] This invention relates to the field of civil engineering operation and maintenance technology, specifically to a targeted spatial displacement monitoring method based on laser point clouds. Background Technology

[0002] With the continuous expansion of the scale and extension of the design service life of civil engineering structures in my country, the safe operation of bridges, tunnels, subways, underground spaces, slopes, and large buildings has become increasingly prominent. During long-term service, structures inevitably experience deformation, cracking, settlement, and displacement due to the combined effects of geological conditions, environmental loads, temperature and humidity changes, and extreme natural disasters. If these defects are not monitored and warned of in a timely manner, they may lead to serious safety accidents and huge economic losses. Therefore, how to conduct high-precision, long-term, and stable displacement monitoring of civil engineering structures during the operation and maintenance phase is a crucial problem that urgently needs to be solved in the field of civil engineering.

[0003] Currently, displacement monitoring of engineering structures mainly relies on the following types of technologies: Traditional measurement methods such as total stations and levels can achieve spatial measurement of benchmark points, but they depend on manual observation, which is inefficient, time-consuming, and difficult to adapt to the needs of long-term continuous monitoring; GNSS measurement methods have high accuracy in outdoor bridges, dams, and other structures, but are difficult to apply in tunnels, underground spaces, or densely populated urban environments due to limited satellite signals; Sensor monitoring methods such as fiber optic gratings, strain gauges, and displacement gauges can provide high-precision data, but they are complex to deploy, subject to significant construction interference, have high long-term maintenance costs, and are easily damaged by environmental factors; Image and video recognition methods are gradually being applied to structural displacement measurement with the development of computer vision, but they are greatly affected by lighting, obstruction, and environmental noise, making it difficult to guarantee high accuracy and long-term stability.

[0004] In recent years, laser point cloud technology has been increasingly applied to civil engineering monitoring due to its advantages of non-contact operation, high precision, rapid modeling, and wide coverage. However, most existing point cloud monitoring methods focus on overall geometric shape or defect identification, lacking targeted high-precision displacement monitoring of fixed reference points. Single point cloud measurements are often affected by equipment vibration, environmental noise, humidity, and dust interference, resulting in insufficient measurement accuracy and stability, making it difficult to meet long-term monitoring needs. When applied to structural monitoring, point cloud technology faces technical challenges such as insufficient utilization of light intensity characteristics, unreliability of single measurements, and difficulty in leveraging the advantages of multiple measurements. Furthermore, most point cloud methods only focus on geometric information, failing to fully utilize the relatively stable feature of reflection intensity to assist in positioning and error control, lacking multiple sampling and statistical modeling, leading to significant fluctuations in results. Moreover, existing methods mostly rely on direct comparisons, failing to incorporate error propagation theory to assess the confidence level of the results. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a targeted spatial displacement monitoring method based on laser point clouds.

[0006] The objective of this invention is achieved through the following technical solution:

[0007] This application discloses a method for monitoring targeted spatial displacement based on laser point clouds, comprising the following steps:

[0008] S1. Set up a monitoring plan and place highly reflective circular targets on the structure;

[0009] S2. Use a laser scanning device to emit point clouds onto the structure, identify and extract target point clouds based on reflection intensity characteristics, and ensure that the extracted point clouds are within the target by enhancing the light intensity of the reflection points during the extraction process.

[0010] S3. Perform error modeling on the point cloud data of the target, and calculate the uniformity of the point cloud in spatial distribution based on inscribed circle fitting.

[0011] S4. Based on the error propagation theory, calculate the effective acquisition period required to meet the measurement accuracy requirements; obtain the average center coordinates of the point cloud samples that meet the conditions, and use them as the reference point coordinates for this measurement; repeatedly measure the reference point multiple times within the same monitoring period; and take a weighted average of all the effective measured reference point coordinates to obtain the final monitoring coordinate value.

[0012] S5. Compare the monitoring coordinate values ​​obtained in step S4 with historical monitoring data to obtain the spatial displacement changes of the structure at different time periods, and form a displacement time program sequence.

[0013] Furthermore, step S2 specifically includes the following steps:

[0014] S21. Use a laser scanning device to project a point cloud onto the structure and obtain the three-dimensional coordinates of the returned signal. and its corresponding reflection intensity ;

[0015] S22. By judging the reflection intensity of the point cloud, the effective point cloud of the target area is extracted.

[0016] Preferably, step S22 specifically includes: setting an intensity threshold. , ,in Indicates sampling point The average value, Indicates sampling point variance Represents the empirical coefficient; if If so, then the point belongs to the valid point cloud set. .

[0017] Preferably, step S3 specifically includes the following steps:

[0018] S31. Based on the target area fitting plane, project all sampling points onto the fitting plane, and use the inscribed circle fitting method to fit the effective point cloud. Let the equation of the fitting circle be... ,in To fit the coordinates of the center of the circle, This represents the radius of the fitted circle; the fitted circle is optimized using the least squares method to adjust its radius. With known target radius Perform a comparison; if the conditions are met... If the fitted circle is found to be valid, then the process is considered valid; otherwise, return to step S2 to re-identify and extract the circle.

[0019] S32. Perform spatial uniformity checks on the points within the target, including radial distribution consistency checks and quadrant uniformity checks. If the results of both radial distribution consistency and quadrant uniformity checks meet the requirements, the spatial uniformity of the points within the target is considered to meet the requirements. If not, return to step S2 to re-identify and extract.

[0020] Preferably, the radial distribution consistency check in step S32 includes the following steps:

[0021] S321. Assuming points are uniformly distributed within a circle, their theoretical radial distance distribution is as follows: ,in Represented as the theoretical distribution function, Let be the radial distance from a point to the center of the fitted circle. To check the radius, Indicates the radius of the circle;

[0022] S322. Use the Kolmogorov–Smirnov test to examine the distribution: ,in This represents the Kolmogorov–Smirnov statistic. Denotes the supremum function. The total number of sample points. For indicator functions, Let Kolmogorov–Smirnov statistic represent the actual observed radius from the nth sample point to the center of the circle. satisfy If the radial distribution is not significantly different from the uniform distribution within the circle, then the radial distribution consistency is considered to meet the requirements. Is the Kolmogorov–Smirnov test in a given and The maximum allowable tolerance.

[0023] Preferably, the quadrant uniformity test in step S32 includes: dividing the fitted circle into subdomains based on the four quadrants, wherein the four subdomains have equal areas, and counting the number of points in different subdomains. Calculate the average number of points in different subdomains. If satisfied If the result of the quadrant homogeneity test is satisfactory, then the quadrant homogeneity test result is considered to meet the requirements. express The number of points within the subfield.

[0024] Preferably, step S4 specifically includes the following steps:

[0025] S41. Based on the accuracy of the monitored target and the accuracy of the laser point cloud, the number of acquisition cycles is calculated using error propagation theory. : ,in The standard deviation of instrument measurements for laser point clouds. The standard deviation of the monitoring target;

[0026] S42, Set of Statistically Efficient Points The average coordinates, as Target coordinates during one effective acquisition cycle ;

[0027] S43, Calculation Target coordinates during one effective acquisition cycle average value They are used as target coordinates under the required accuracy of the monitoring target.

[0028] The beneficial effects of this invention are:

[0029] 1) Compared with previous structural displacement monitoring methods that relied on manual measurement, single-point sensors or simple statistical fitting, this application introduces for the first time a targeted monitoring approach based on the reflection intensity characteristics of laser point clouds, and combines uniformity testing and error propagation theory to achieve high-precision and long-term stable measurement of structural reference points.

[0030] 2) By arranging highly reflective circular targets on the surface of the structure, this application can extract an effective set of point clouds that better conforms to the actual distribution pattern under the constraint of multiple point cloud acquisitions, and use inscribed circle fitting and spatial uniformity analysis to eliminate abnormal data and ensure the reliability of the measurement results.

[0031] 3) This application proposes a periodic acquisition and weighted averaging strategy based on error propagation, which reduces the number of redundant measurements while ensuring monitoring accuracy, making the monitoring process both statistically sound and economically feasible for engineering applications.

[0032] 4) Compared with existing technologies that rely solely on point cloud geometry or single measurements, this application not only comprehensively considers key factors such as reflection intensity discrimination, point cloud distribution uniformity, and measurement cycle accuracy constraints, but also constructs a time series of structural displacement while ensuring monitoring accuracy, supporting long-term health monitoring and safety early warning. Attached Figure Description

[0033] Figure 1 is a schematic diagram of the steps of a targeted spatial displacement monitoring method based on laser point cloud according to an embodiment of the present invention;

[0034] Figure 2 is a schematic diagram of the target and four-quadrant division in an embodiment of the present invention. Detailed Implementation

[0035] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] This application discloses a targeted spatial displacement monitoring method based on laser point clouds. Combining reflection intensity characteristics, multiple point cloud sampling, and error propagation theory, it enables long-term and stable displacement monitoring of bridges, tunnels, subways, underground spaces, and other civil engineering structures, providing reliable technical support for the safe operation and intelligent maintenance of civil engineering structures. A schematic diagram of the method's steps is shown in Figure 1. The method specifically includes the following steps:

[0037] S1. Set up a monitoring scheme and place a highly reflective circular target on the structure. In this embodiment, the target can be made of a high-reflection film or a metal coating material, which can significantly enhance the laser reflection intensity so as to maintain signal stability in complex environments. The target diameter can be selected between 1cm and 20cm according to the monitoring accuracy requirements. The placement location should prioritize the key stress parts or easily deformable areas of the structure.

[0038] S2. A point cloud is emitted to the structure using a laser scanning device. The target point cloud is identified and extracted based on the reflection intensity characteristics. During the extraction of the target point cloud, the light intensity of the reflection point is enhanced to ensure that the extracted point cloud is within the target. In this embodiment, the laser scanning device can be a fixed three-dimensional laser scanner. Through multiple consecutive scans, high-density point cloud data of the target area can be collected in a short time to form a reusable monitoring benchmark.

[0039] S3. Perform error modeling on the point cloud data of the target, and calculate the uniformity of the point cloud in spatial distribution based on inscribed circle fitting; if the uniformity meets the preset threshold, the measurement result is deemed valid; otherwise, return to step S2 to re-identify and extract.

[0040] S4. Based on the error propagation theory, calculate the effective acquisition period required to meet the measurement accuracy requirements; obtain the average center coordinates of the point cloud samples that meet the conditions, and use them as the reference point coordinates for this measurement; repeatedly measure the reference point multiple times within the same monitoring period; and take a weighted average of all the effective measured reference point coordinates to obtain the final monitoring coordinate value.

[0041] S5. Compare the monitoring coordinate values ​​obtained in step S4 with historical monitoring data to obtain the spatial displacement changes of the structure at different time periods, and form a displacement time sequence to provide a basis for structural health monitoring and safety early warning.

[0042] Specifically, step S2 includes the following steps:

[0043] S21. Use a laser scanning device to project a point cloud onto the structure and obtain the three-dimensional coordinates of the returned signal. and its corresponding reflection intensity ;

[0044] S22. By judging the reflection intensity of the point cloud, the effective point cloud of the target area is extracted.

[0045] Specifically, step S22 includes: setting an intensity threshold. , ,in Indicates sampling point The average value, Indicates sampling point variance This represents an empirical coefficient, typically ranging from 2 to 3; in this embodiment, the empirical coefficient is set to 3. If so, then the point belongs to the valid point cloud set. The intensity threshold setting method can automatically adjust the screening criteria according to different environments, avoiding misjudgments caused by background lighting or reflection interference.

[0046] Specifically, step S3 includes the following steps:

[0047] S31. Based on the target area fitting plane, project all sampling points onto the fitting plane, and use the inscribed circle fitting method to fit the effective point cloud. Let the equation of the fitting circle be... ,in To fit the coordinates of the center of the circle, This represents the radius of the fitted circle; the fitted circle is optimized using the least squares method to adjust its radius. With known target radius Perform a comparison; if the conditions are met... If the fitted circle is found to be valid, then the process is considered valid; otherwise, return to step S2 to re-identify and extract the circle.

[0048] S32. Perform spatial uniformity checks on the points within the target, including radial distribution consistency checks and quadrant uniformity checks. If the results of both radial distribution consistency and quadrant uniformity checks meet the requirements, the spatial uniformity of the points within the target is considered to meet the requirements. If not, return to step S2 to re-identify and extract.

[0049] Specifically, the radial distribution consistency check described in step S32 includes the following steps:

[0050] S321. Assuming points are uniformly distributed within a circle, their theoretical radial distance distribution is as follows: ,in Represented as the theoretical distribution function, Let be the radial distance from a point to the center of the fitted circle. To check the radius, This represents the radius of the circle. The circle and the fitted circle in this step have a two-stage verification relationship. First, the first verification checks whether the outline range of the circle is valid (fitted circle). Then, the second verification gives the center of the fitted circle and calculates the uniformity of each point relative to the center.

[0051] S322. Use the Kolmogorov–Smirnov test to examine the distribution: ,in This represents the Kolmogorov–Smirnov statistic. Denotes the supremum function. The total number of sample points. This is an indicator function; if the condition is true, the indicator function... The value is 1, otherwise it indicates the function. =0, Let Kolmogorov–Smirnov statistic represent the actual observed radius from the nth sample point to the center of the circle. satisfy If the radial distribution is not significantly different from the uniform distribution within the circle, then the radial distribution consistency is considered to meet the requirements. Is the Kolmogorov–Smirnov test in a given and The maximum allowable tolerance.

[0052] Specifically, the quadrant uniformity test in step S32 includes: dividing the fitted circle into subdomains based on the four quadrants. The target and the schematic diagram of the four quadrant division are shown in Figure 2, where the four subdomains have equal areas, and counting the number of points in different subdomains. Calculate the average number of points in different subdomains. If satisfied If the result of the quadrant homogeneity test is satisfactory, then the quadrant homogeneity test result is considered to meet the requirements. express The number of points within the subfield.

[0053] Specifically, step S4 includes the following steps:

[0054] S41. Based on the accuracy of the monitored target and the accuracy of the laser point cloud, the number of acquisition cycles is calculated using error propagation theory. : ,in The standard deviation of instrument measurements for laser point clouds. The standard deviation of the monitoring target;

[0055] S42, Set of Statistically Efficient Points The average coordinates, as Target coordinates during one effective acquisition cycle ;

[0056] S43, Calculation Target coordinates during one effective acquisition cycle average value They are used as target coordinates under the required accuracy of the monitoring target.

[0057] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A method for monitoring targeted spatial displacement based on laser point clouds, characterized in that, Includes the following steps: S1. Set up a monitoring plan and place highly reflective circular targets on the structure; S2. Use a laser scanning device to project a point cloud onto the structure. Identify and extract the target point cloud based on the reflection intensity characteristics. During the extraction process, enhance the light intensity of the reflection points to ensure that the extracted point cloud is within the target. S3. Perform error modeling on the target point cloud data. Calculate the uniformity of the point cloud's spatial distribution based on inscribed circle fitting. S4. Calculate the effective acquisition period required to meet the measurement accuracy requirements based on error propagation theory. Obtain the average center coordinates of the point cloud samples that meet the conditions, using them as the reference point coordinates for this measurement. Repeat the measurement of the reference point multiple times within the same monitoring period. Calculate the weighted average of all effective measured reference point coordinates to obtain the final monitoring coordinate values. S5. Compare the monitoring coordinate values ​​obtained in step S4 with historical monitoring data to obtain the spatial displacement changes of the structure at different times, forming a displacement time sequence. Step S3 specifically includes the following steps: S31. Based on the target area fitting plane, project all sampling points onto the fitted plane. Use the inscribed circle fitting method to fit the effective point cloud. Let the equation of the fitted circle be... ,in To fit the coordinates of the center of the circle, This represents the radius of the fitted circle; the fitted circle is optimized using the least squares method to adjust its radius. With known target radius Perform a comparison; if the conditions are met... If the fitted circle is valid, the process is considered valid. If not, the process returns to step S2 for re-identification and extraction. S32: Perform spatial uniformity checks on the points within the target, including radial distribution consistency checks and quadrant uniformity checks. If both radial distribution consistency and quadrant uniformity check results meet the requirements, the spatial uniformity of the points within the target is considered to meet the requirements. If not, the process returns to step S2 for re-identification and extraction.

2. The targeted spatial displacement monitoring method based on laser point clouds according to claim 1, characterized in that, Step S2 specifically includes the following steps: S21, using a laser scanning device to emit point cloud data onto the structure and obtain the three-dimensional coordinates of the returned signal. and its corresponding reflection intensity S22. By judging the reflection intensity of the point cloud, the effective point cloud of the target area is extracted.

3. The targeted spatial displacement monitoring method based on laser point clouds according to claim 2, characterized in that, Step S22 specifically includes: setting an intensity threshold. ， ,in Indicates sampling point The average value, Indicates sampling point variance Represents the empirical coefficient; if If so, then the point belongs to the valid point cloud set. 。 4. The targeted spatial displacement monitoring method based on laser point clouds according to claim 3, characterized in that, The radial distribution consistency check described in step S32 includes the following steps: S321, assuming the points are uniformly distributed within the circle, the theoretical radial distance distribution is as follows: ,in Represented as the theoretical distribution function, Let be the radial distance from a point to the center of the fitted circle. To check the radius, S322. The distribution is tested using the Kolmogorov–Smirnov test. ,in This represents the Kolmogorov–Smirnov statistic. Denotes the supremum function. The total number of sample points. For indicator functions, Let Kolmogorov–Smirnov statistic represent the actual observed radius from the i-th sample point to the center of the circle. satisfy If the radial distribution is not significantly different from the uniform distribution within the circle, then the radial distribution consistency is considered to meet the requirements; where Is the Kolmogorov–Smirnov test in a given and The maximum allowable tolerance.

5. A method for monitoring targeted spatial displacement based on laser point clouds according to claim 4, characterized in that, The quadrant uniformity test described in step S32 includes: dividing the fitted circle into subdomains based on the four quadrants, wherein the four subdomains have equal areas, and counting the number of points in different subdomains. Calculate the average number of points in different subdomains. If satisfied If the result of the quadrant homogeneity test is satisfactory, then the quadrant homogeneity test result is considered to meet the requirements. express The number of points within the subfield.

6. The targeted spatial displacement monitoring method based on laser point clouds according to claim 5, characterized in that, Step S4 specifically includes the following steps: S41, based on the accuracy of the monitored target and the accuracy of the laser point cloud, calculate the number of acquisition cycles using error propagation theory. ： ,in The standard deviation of instrument measurements for laser point clouds. S42 represents the standard deviation of the monitoring target; set of statistically valid points. The average coordinates, as Target coordinates during one effective acquisition cycle S43, Calculation Target coordinates during one effective acquisition cycle average value They are used as target coordinates under the required accuracy of the monitoring target.

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