A method and system for detecting the flatness of a wall surface based on laser scanning

Abstract

Embodiments of the present invention relate to the technical field of wall flatness detection, and specifically disclose a wall flatness detection method and system based on laser scanning. The present invention initializes monitoring and shooting, analyzes the monitoring and shooting data, and plans a flatness detection route; controls the movement and scanning of laser scanning, and receives laser scanning data in real time; fits a three-dimensional wall model, analyzes it, and determines whether there is a flatness abnormality; when there is a flatness abnormality, determines the flat abnormal area, obtains the area shape, and laser marks the flat abnormal area according to the area shape. It can plan a flatness detection route, control laser scanning, process and analyze data, determine whether there is a flatness abnormality, and when there is a flatness abnormality, laser mark the flat abnormal area, thereby effectively improving the accuracy of wall flatness detection and making an intuitive mark of the abnormality, which is convenient for actual popularization and use.

Description

Technical Field

[0001] The present invention belongs to the technical field of wall flatness detection, and particularly relates to a wall flatness detection method and system based on laser scanning. Background Art

[0002] Wall flatness refers to the state parameter that the surface of the wall should reach a certain flatness standard without unevenness, obvious bulges or depressions. Wall flatness detection is a series of professional inspections and measurements of the flatness of the wall surface, which involves flatness requirements, gap inspection, surface layer quality detection, and various detection methods.

[0003] In the prior art, wall flatness detection by laser scanning has a wide application prospect. However, the existing wall flatness detection based on laser scanning has the defects of low accuracy and inability to visually mark abnormal flatness areas, which is not convenient for actual popularization and use. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide a wall flatness detection method and system based on laser scanning, aiming to solve the problems proposed in the background art.

[0005] To achieve the above purpose, the embodiments of the present invention provide the following technical solutions:

[0006] A wall flatness detection method based on laser scanning, the method specifically includes the following steps:

[0007] Determine the wall detection space, perform initialization monitoring and shooting on the wall detection space, obtain monitoring and shooting data, and analyze the monitoring and shooting data to plan a flatness detection route;

[0008] Based on the flatness detection route, perform travel control and scanning control of laser scanning, and receive laser scanning data in real time;

[0009] Process the laser scanning data, fit a three-dimensional wall model, and analyze the three-dimensional wall model to determine whether there is flatness abnormality;

[0010] When there is flatness abnormality, determine the flat abnormal area, obtain the area shape, and perform laser marking on the flat abnormal area according to the area shape.

[0011] A wall flatness detection system based on laser scanning, the system includes a detection route planning unit, a travel and scanning control unit, a flatness abnormality judgment unit, and an abnormal area marking unit, wherein:

[0012] A detection route planning unit, configured to determine a wall detection space, perform initialization monitoring and shooting on the wall detection space, obtain monitoring shooting data, analyze the monitoring shooting data, and plan a flatness detection route;

[0013] A traveling and scanning control unit, configured to perform traveling control and scanning control of laser scanning based on the flatness detection route, and receive laser scanning data in real time;

[0014] A flatness anomaly judgment unit, configured to process the laser scanning data, fit a three-dimensional wall model, analyze the three-dimensional wall model, and judge whether there is a flatness anomaly;

[0015] An abnormal area marking unit, configured to determine a flatness abnormal area when there is a flatness anomaly, obtain the area shape, and perform laser marking on the flatness abnormal area according to the area shape.

[0016] Compared with the prior art, the beneficial effects of the present invention are:

[0017] In the embodiment of the present invention, by performing initialization monitoring and shooting, analyzing the monitoring shooting data, and planning a flatness detection route; performing traveling control and scanning control of laser scanning, and receiving laser scanning data in real time; fitting a three-dimensional wall model, and analyzing to judge whether there is a flatness anomaly; when there is a flatness anomaly, determining a flatness abnormal area, obtaining the area shape, and performing laser marking on the flatness abnormal area according to the area shape. It can plan a flatness detection route, perform laser scanning control, data processing and analysis, judge whether there is a flatness anomaly, and perform laser marking on the flatness abnormal area when there is a flatness anomaly, thereby effectively improving the detection accuracy of the wall flatness, and performing an intuitive marking of the anomaly, which is convenient for actual popularization and use. Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0019] Figure 1 Shows the flowchart of the method provided by the embodiment of the present invention.

[0020] Figure 2 Shows the flowchart of planning a flatness detection route in the method provided by the embodiment of the present invention.

[0021] Figure 3 Shows the flowchart of receiving laser scanning data in the method provided by the embodiment of the present invention.

[0022] Figure 4The flowchart for determining whether the flatness is abnormal in the method provided by the embodiments of the present invention is shown.

[0023] Figure 5 The flowchart for processing laser scanning data in the method provided by the embodiments of the present invention is shown.

[0024] Figure 6 The flowchart for laser marking of abnormal areas in the method provided by the embodiments of the present invention is shown.

[0025] Figure 7 The application architecture diagram of the system provided by the embodiments of the present invention is shown.

[0026] Figure 8 The structural block diagram of the detection route planning unit in the system provided by the embodiments of the present invention is shown.

[0027] Figure 9 The structural block diagram of the flatness abnormality determination unit in the system provided by the embodiments of the present invention is shown.

[0028] Figure 10 The structural block diagram of the abnormal area marking unit in the system provided by the embodiments of the present invention is shown. Detailed implementation manners

[0029] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. 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.

[0030] It can be understood that in the prior art, detecting the flatness of a wall surface by laser scanning has a wide range of application prospects. However, the existing wall surface flatness detection based on laser scanning has the defects of low accuracy and inability to visually mark abnormal flatness areas, which is not convenient for actual popularization and use.

[0031] To solve the above problems, in the embodiments of the present invention, a wall detection space is determined, initial monitoring and shooting are performed on the wall detection space to obtain monitoring and shooting data, the monitoring and shooting data is analyzed, and a flatness detection route is planned; based on the flatness detection route, the travel control and scanning control of laser scanning are performed, and laser scanning data is received in real time; the laser scanning data is processed, a three-dimensional wall model is fitted, and the three-dimensional wall model is analyzed to determine whether there is flatness abnormality; when there is flatness abnormality, the flatness abnormal area is determined, the area shape is obtained, and the flatness abnormal area is laser marked according to the area shape. It can plan a flatness detection route, perform laser scanning control, data processing and analysis, determine whether there is flatness abnormality, and when there is flatness abnormality, laser mark the flatness abnormal area, thereby effectively improving the detection accuracy of the wall flatness and making an intuitive mark of the abnormality, which is convenient for actual popularization and use.

[0032] Figure 1 The flowchart of the method provided by the embodiments of the present invention is shown.

[0033] Specifically, a method for detecting the flatness of a wall based on laser scanning, the method specifically includes the following steps:

[0034] Step S101, determine a wall detection space, perform initial monitoring and shooting on the wall detection space to obtain monitoring and shooting data, and analyze the monitoring and shooting data to plan a flatness detection route.

[0035] In the embodiments of the present invention, by receiving a wall flatness detection request, then identifying the wall flatness detection request, determining a wall detection space that needs to be detected for wall flatness, and then performing initial monitoring and shooting on the wall detection space to obtain monitoring and shooting data, and obtaining the three-dimensional model of the space of the wall detection space, and then obtaining the basic parameters of flatness detection, and then analyzing the monitoring and shooting data according to the three-dimensional model of the space and the basic parameters of flatness detection to plan a flatness detection route.

[0036] It can be understood that the basic parameters of flatness detection include detection height, detection angle, detection distance, etc.

[0037] Specifically, Figure 2 The flowchart of planning a flatness detection route in the method provided by the embodiments of the present invention is shown.

[0038] Among them, in the preferred embodiment provided by the present invention, the determining a wall detection space, performing initial monitoring and shooting on the wall detection space to obtain monitoring and shooting data, and analyzing the monitoring and shooting data to plan a flatness detection route specifically includes the following steps:

[0039] Step S1011: Receive a wall flatness detection request and determine the wall detection space.

[0040] Step S1012: Conduct an initial monitoring and shooting of the wall detection space to obtain monitoring shooting data.

[0041] Step S1013: Obtain the three-dimensional space model of the wall detection space.

[0042] Step S1014: Obtain the basic parameters for flatness detection.

[0043] Step S1015: Analyze the monitoring shooting data according to the three-dimensional space model and the basic parameters for flatness detection, and plan the flatness detection route.

[0044] As a preferred embodiment of the present invention, analyzing the monitoring shooting data according to the three-dimensional space model and the basic parameters for flatness detection, and planning the flatness detection route specifically includes the following steps:

[0045] Select each coordinate point and the adjacent eight coordinate points from the three-dimensional space model to form a 3×3 analysis window.

[0046] Perform local modular modeling on each analysis window using the least squares method, and then calculate the angle between the normal vector of each coordinate point and the normal vector of the adjacent window to obtain the curvature change amount of each point on the wall surface.

[0047] Normalize the curvature change amount of each point on the wall surface, then perform non-linear amplification on the normalized result, and then perform weighted calculation to obtain the curvature feature weight distribution map.

[0048] Set the detection accuracy threshold, and perform standardization processing on the detection accuracy threshold to obtain the standardized detection accuracy parameter value.

[0049] Take the reciprocal operation on the standardized detection accuracy parameter value, then perform linear weighting, and map the result to the coordinate system corresponding to the curvature feature weight distribution map to obtain the spatial distribution parameter map corresponding to the accuracy requirement.

[0050] Add the curvature feature weight distribution map and the spatial distribution parameter map point by point, and perform smoothing processing and numerical standard processing on the point-by-point addition result to generate a priority coefficient in the 0-1 interval, and output a heat map of the path priority distribution of the entire wall area according to the priority coefficient.

[0051] Set the priority threshold, and divide the path priority distribution heat map into priority areas according to the priority threshold.

[0052] Automatically generate a dense detection dot matrix for high-priority areas, and adopt a sparse sampling strategy in low-priority areas to obtain a flatness detection route including the coordinates of scanning points and the moving order.

[0053] In the above solution, the present invention realizes the multi-dimensional coordination of physical characteristics and detection standards by non-linearly superimposing the wall geometric features (curvature change) and engineering parameters (detection accuracy). For example, when there are fine cracks (high curvature) on the wall, the system automatically increases the scanning density of this area to a 0.2 mm interval, while in a flat area (low curvature), it scans at a 1 mm interval. Compared with the traditional fixed-spacing detection method, it can effectively improve the detection efficiency. And a non-linear amplification process is performed on the curvature, so that a 1 mm uneven deviation is amplified to a 4-fold weight value during calculation, while a 2 mm deviation is amplified to 16 times. This design improves the recognition sensitivity of the system to potential hazards such as initial hollowing and cracks by 2.8 times, effectively reducing the missed detection rate. And the detection accuracy parameter is incorporated into the calculation model in the form of a reciprocal to realize the mathematical correlation between the physical detection ability and the engineering standard. When the detection standard is increased from 1 mm to 0.5 mm, the system will automatically encrypt the scanning line spacing from 20 cm to 10 cm to ensure that there is no need to manually re-plan the path when the standard changes.

[0054] And corresponding weights are set for the curvature part and the accuracy part. When detecting a gypsum board wall, it focuses on geometric features (α = 0.7), while when detecting a ceramic tile wall, it automatically increases the accuracy weight (β = 0.5), so that the detection strategy can dynamically adapt to the characteristic differences of different material walls.

[0055] Furthermore, the laser scanning-based wall flatness detection method further includes the following steps:

[0056] Step S102, based on the flatness detection route, perform the travel control and scanning control of laser scanning, and receive the laser scanning data in real time.

[0057] In the embodiment of the present invention, by obtaining the flatness detection motion parameters, and then according to the flatness detection route and the flatness detection motion parameters, a travel control signal is generated in real time, and then according to the travel control signal, the travel control of laser scanning is performed on the wall in the wall detection space, and a scanning control signal is generated in real time, and according to the scanning control signal, the scanning control is performed, and the laser scanning data is received in real time.

[0058] It can be understood that the flatness detection motion parameters include the moving direction, moving speed, etc.

[0059] Specifically, Figure 3 shows the flowchart of receiving laser scanning data in the method provided by the embodiment of the present invention.

[0060] Among them, in the preferred embodiment provided by the present invention, the travel control and scanning control of laser scanning based on the flatness detection route, and the real-time reception of laser scanning data specifically include the following steps:

[0061] Step S1021, obtain the flatness detection motion parameters;

[0062] Step S1022, generate a travel control signal in real time according to the flatness detection route and the flatness detection motion parameters;

[0063] Step S1023, perform the travel control of laser scanning according to the travel control signal;

[0064] Step S1024, generate a scanning control signal in real time;

[0065] Step S1025, perform scanning control according to the scanning control signal, and receive laser scanning data in real time.

[0066] As a preferred embodiment of the present invention, generating a travel control signal in real time according to the flatness detection route and the flatness detection motion parameters specifically includes the following steps:

[0067] Obtain the maximum safe travel speed value and the safety protection distance threshold of the device from the flatness detection motion parameters, use the maximum safe travel speed value as the speed upper limit constraint, and use the safety protection distance threshold as the danger determination benchmark;

[0068] Extract the geometric features from the flatness detection route, and use the differential geometry method to calculate the quantization value of the bending degree of the current detection point on the flatness detection route to obtain the path curvature characteristic value of the current detection point;

[0069] Obtain the original instantaneous distance data between the laser ranging module and the obstacle through the laser ranging module, perform noise filtering and moving average processing on the original instantaneous distance data to obtain the accurate distance value of the current obstacle;

[0070] Set a critical curvature threshold, and calculate the curvature ratio using the path curvature characteristic value of the current detection point and the critical curvature threshold;

[0071] And calculate the basic speed coefficient according to the curvature ratio, and perform non-linear response strengthening on the basic speed coefficient to obtain the curvature influence factor;

[0072] Calculate the ratio of the accurate distance value of the current obstacle to the safety protection distance threshold, and take the reciprocal of the ratio to establish an inverse proportional relationship to obtain the safety correction coefficient;

[0073] Use the safety correction coefficient and the curvature influence factor to adjust the maximum safe travel speed value to obtain the real-time control speed command value;

[0074] Map the real-time control speed command value according to a preset motor speed - PWM duty ratio look-up table to generate a motor drive pulse signal, and obtain a travel control signal.

[0075] In the above solution, the present invention enables the parameters to produce a non-linear synergistic effect through the nested processing of curvature ratio calculation and safety distance correction, and at the same time integrates three types of key information: equipment performance parameters, path geometric features, and real-time environmental data, and can actually measure and display that the path tracking error of complex wall detection can be reduced.

[0076] Furthermore, the wall surface flatness detection method based on laser scanning further includes the following steps:

[0077] Step S103: Process the laser scanning data, fit a three-dimensional wall model, and analyze the three-dimensional wall model to determine whether there is an abnormal flatness.

[0078] In the embodiment of the present invention, by processing the laser scanning data, identifying the noise data in the laser scanning data, then removing the noise data in the laser scanning data, and performing filtering and smoothing processing to generate point cloud data, using the point cloud data to fit a three-dimensional wall model, and then analyzing the three-dimensional wall model to calculate the real-time deviation value, by comparing the real-time deviation value with a preset standard deviation value, it is determined whether there is an abnormal flatness. Specifically, when the real-time deviation value is greater than the standard deviation value, it is determined that there is an abnormal flatness.

[0079] Specifically, Figure 4 Shows the flowchart for determining whether there is an abnormal flatness in the method provided by the embodiment of the present invention.

[0080] Among them, in the preferred embodiment provided by the present invention, the processing of the laser scanning data, fitting a three-dimensional wall model, and analyzing the three-dimensional wall model to determine whether there is an abnormal flatness specifically includes the following steps:

[0081] Step S1031: Process the laser scanning data to generate point cloud data.

[0082] Specifically, Figure 5 Shows the flowchart for processing laser scanning data in the method provided by the embodiment of the present invention.

[0083] Among them, in the preferred embodiment provided by the present invention, the processing of the laser scanning data to generate point cloud data specifically includes the following steps:

[0084] Step S10311: Identify the noise data in the laser scanning data;

[0085] Step S10312, remove the noise data from the laser scanning data;

[0086] Step S10313, perform filtering and smoothing processing to generate point cloud data.

[0087] Furthermore, the data processing of the laser scanning data, fitting the three-dimensional wall model, and analyzing the three-dimensional wall model to determine whether there is abnormal flatness further includes the following steps:

[0088] Step S1032, use the point cloud data to fit the three-dimensional wall model.

[0089] Step S1033, analyze the three-dimensional wall model and calculate the real-time deviation value.

[0090] Step S1034, compare the real-time deviation value with the preset standard deviation value to determine whether there is abnormal flatness.

[0091] As a preferred embodiment of the present invention, analyzing the three-dimensional wall model and calculating the real-time deviation value specifically includes the following steps:

[0092] Dynamically adjust the detection window size according to the magnitude of the travel control speed of the laser scanning. Different detection window sizes contain different numbers of sampling points;

[0093] For each sampling point in the detection window, select 9 sampling points within the 3×3 neighborhood around the current sampling point and use the least squares method to fit the local plane equation of the corresponding area;

[0094] Then extract the gradient component from the local plane equation to obtain the surface gradient vector of each sampling point;

[0095] Calculate the included angle between the gradient vectors of the current sampling point and the adjacent front and rear sampling points to obtain the forward included angle and the backward included angle, and construct the actual curvature value according to the forward included angle and the backward included angle;

[0096] According to the critical curvature threshold, calculate the absolute difference between the actual curvature and the planned curvature using the forward included angle and the backward included angle;

[0097] Take the average value of the included angles between the normal vectors of each coordinate point and the normal vectors of the adjacent windows as the curvature reference value, take the standard deviation of the included angles between the normal vectors of each coordinate point and the normal vectors of the adjacent windows as the threshold band, and construct the planned curvature threshold using the curvature reference value and the threshold band;

[0098] Calculate the absolute difference between the actual curvature value and the planned curvature threshold to obtain the curvature difference factor of each sampling point;

[0099] Normalize the accurate distance value of the current obstacle, compare the normalization result with the safety protection distance threshold, and obtain the safety attenuation coefficient of each sampling point according to the comparison result size;

[0100] Calculate the dynamic response weight of each sampling point according to the standardized detection accuracy parameter value, curvature difference factor, and safety attenuation coefficient;

[0101] Weight the surface gradient vector of each sampling point with the corresponding dynamic response weight to obtain the weighted gradient amplitude, and take the average value of the weighted gradient amplitudes of all sampling points within the detection window to obtain the real-time deviation value of the current detection window.

[0102] In the above solution, the present invention breaks through the traditional single height difference detection mode through the coupled calculation of gradient vectors, curvature differences, and safety attenuation coefficients. The detection dimension is extended from single-point height to three parameters of surface gradient, path geometric features, and environmental safety, which can reduce the misjudgment rate of structural features such as external corners / internal corners. And, the weight of the high-curvature area is automatically reduced through the exponential decay mechanism of the curvature difference factor. In areas such as wall corners and door / window openings, the "false unevenness" signals of the building's inherent structure are automatically filtered to eliminate the missed detection problem caused by the mismatch between the path planning curvature and the detection algorithm in the traditional solution. And, through the real-time linkage of the safety attenuation coefficient and the obstacle distance. When approaching obstacles such as pipelines and cables, the detection data in this area will be automatically shielded, and the detection function will still be maintained in dangerous areas, rather than simply shutting down. And a dynamic window division mechanism is designed, and through the dynamic window division mechanism combined with the rapid calculation of gradient vectors, the high-speed scanning requirements are met.

[0103] Further, the wall surface flatness detection method based on laser scanning further includes the following steps:

[0104] Step S104, when there is flatness abnormality, determine the flatness abnormal area, obtain the area shape, and perform laser marking on the flatness abnormal area according to the area shape.

[0105] In the embodiment of the present invention, in the case of flatness abnormality, determine the flatness abnormal area, then identify the area shape of the flatness abnormal area, locate the area position of the flatness abnormal area, create a laser marking according to the area shape, and then project the marking on the flatness abnormal area at the area position according to the laser marking, so that the staff can intuitively view the specific position and shape of the flatness abnormal area.

[0106] Specifically, Figure 6 The flowchart of laser marking of the abnormal area in the method provided by the embodiment of the present invention is shown.

[0107] Among them, in the preferred embodiment provided by the present invention, when there is an abnormality in flatness, determining the flatness abnormal area, obtaining the area shape, and laser marking the flatness abnormal area according to the area shape specifically include the following steps:

[0108] Step S1041, when there is an abnormality in flatness, determining the flatness abnormal area;

[0109] Step S1042, identifying the area shape and area position of the determined flatness abnormal area;

[0110] Step S1043, creating a laser mark according to the area shape;

[0111] Step S1044, projecting a mark on the flatness abnormal area at the area position according to the laser mark.

[0112] As a preferred embodiment of the present invention, creating a laser mark according to the area shape specifically includes the following steps:

[0113] Obtaining a set of path point curvature data in the flatness detection route, the flatness detection motion parameters further include obtaining the minimum bending curvature value preset by the device, and using the minimum bending curvature value as the curvature activation threshold;

[0114] Taking the shortest distance from each path point in the flatness detection route to the boundary of the flatness abnormal area as the abnormal boundary distance;

[0115] Obtaining the real-time physical coordinates fed back during the laser scanning process, performing spatio-temporal matching and calibration on the path point curvature data set and the real-time physical coordinates to obtain a calibrated path point curvature sequence;

[0116] Calculating the difference between the curvature of each path point in the calibrated path point curvature sequence and the curvature activation threshold, and normalizing the result to obtain the normalized curvature difference of each path point;

[0117] Non-linearly mapping the normalized curvature difference of the path point, and performing offset adjustment on the mapping result to obtain the curvature response factor weight of each path point;

[0118] Fusing the curvature response factor weight of the path point with the abnormal boundary distance of the corresponding path point to obtain the weight-distance scalar of each path point;

[0119] The flatness detection motion parameters further include a motion window for dynamically adjusting the spatial calculation interval according to the device movement state, taking the arithmetic mean of the weight-distance scalars of all path points within the current motion window to obtain the window comprehensive weighted value;

[0120] Dynamically adjust the basic power of the laser device based on the window comprehensive weighted value, generate real-time power instructions corresponding to each path point in the flatness detection route of the laser device, and create laser marks according to the real-time power instructions.

[0121] In the above solution, the present invention utilizes the curvature data in path planning to dynamically adjust the laser power, thereby optimizing the effect when marking abnormal areas. Moreover, no modification is required for the hardware of the laser device, and the performance can be improved only by upgrading the software algorithm, saving costs. The calculation time-consuming is low and suitable for real-time processing, which is particularly important for high-speed detection devices, ensuring the synchronization of detection and marking. It improves the visibility and accuracy of the marks, especially in high-curvature areas such as corners, avoiding the mark blurring caused by the inertia of the device in the traditional method.

[0122] Furthermore, Figure 7 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0123] Among them, in another preferred embodiment provided by the present invention, a wall flatness detection system based on laser scanning includes:

[0124] A detection route planning unit 101, configured to determine the wall detection space, perform initial monitoring shooting on the wall detection space, obtain monitoring shooting data, and analyze the monitoring shooting data to plan a flatness detection route.

[0125] In the embodiment of the present invention, the detection route planning unit 101 receives a wall flatness detection request, then identifies the wall flatness detection request, determines the wall detection space that needs to perform wall flatness detection, and then performs initial monitoring shooting on the wall detection space to obtain monitoring shooting data, and obtains the three-dimensional model of the space of the wall detection space, and then obtains the basic parameters of flatness detection, and then analyzes the monitoring shooting data according to the three-dimensional model of the space and the basic parameters of flatness detection to plan a flatness detection route.

[0126] Specifically, Figure 8 The structural block diagram of the detection route planning unit 101 in the system provided by the embodiment of the present invention is shown.

[0127] Among them, in the preferred embodiment provided by the present invention, the detection route planning unit 101 specifically includes:

[0128] A space determination module 1011, configured to receive a wall flatness detection request and determine the wall detection space;

[0129] An initial monitoring shooting module 1012, configured to perform initial monitoring shooting on the wall detection space to obtain monitoring shooting data;

[0130] A three-dimensional space model acquisition module 1013 is configured to acquire a three-dimensional space model of the wall detection space;

[0131] A basic parameter acquisition module 1014 is configured to acquire basic parameters for flatness detection;

[0132] A route planning module 1015 is configured to analyze the monitoring and shooting data according to the three-dimensional space model and the basic parameters for flatness detection, and plan a flatness detection route.

[0133] Further, the wall flatness detection system based on laser scanning further includes:

[0134] A traveling and scanning control unit 102 is configured to perform traveling control and scanning control of laser scanning based on the flatness detection route, and receive laser scanning data in real time.

[0135] In an embodiment of the present invention, the traveling and scanning control unit 102 acquires flatness detection motion parameters, and then generates a traveling control signal in real time according to the flatness detection route and the flatness detection motion parameters. Further, according to the traveling control signal, it performs traveling control of laser scanning on the wall in the wall detection space, and generates a scanning control signal in real time. According to the scanning control signal, it performs scanning control and receives laser scanning data in real time.

[0136] A flatness abnormality determination unit 103 is configured to perform data processing on the laser scanning data, fit a three-dimensional wall model, and analyze the three-dimensional wall model to determine whether there is a flatness abnormality.

[0137] In an embodiment of the present invention, the flatness abnormality determination unit 103 processes the laser scanning data to identify noise data in the laser scanning data, then removes the noise data in the laser scanning data, performs filtering and smoothing processing to generate point cloud data, uses the point cloud data to fit a three-dimensional wall model, and then analyzes the three-dimensional wall model to calculate a real-time deviation value. By comparing the real-time deviation value with a preset standard deviation value, it determines whether there is a flatness abnormality. Specifically, when the real-time deviation value is greater than the standard deviation value, it is determined that there is a flatness abnormality.

[0138] Specifically, Figure 9 shows a structural block diagram of the flatness abnormality determination unit 103 in the system provided by the embodiment of the present invention.

[0139] Among them, in a preferred embodiment provided by the present invention, the flatness abnormality determination unit 103 specifically includes:

[0140] A point cloud data generation module 1031 is configured to process the laser scanning data to generate point cloud data;

[0141] The wall three-dimensional model fitting module 1032 is used to fit a wall three-dimensional model by using the point cloud data;

[0142] The deviation value calculation module 1033 is used to analyze the wall three-dimensional model and calculate a real-time deviation value;

[0143] The abnormality determination module 1034 is used to compare the real-time deviation value with a preset standard deviation value to determine whether there is a flatness abnormality.

[0144] Furthermore, the wall flatness detection system based on laser scanning further includes:

[0145] The abnormal area marking unit 104 is used to determine an abnormal flatness area and obtain the area shape when there is a flatness abnormality, and perform laser marking on the abnormal flatness area according to the area shape.

[0146] In an embodiment of the present invention, when there is a flatness abnormality, the abnormal area marking unit 104 determines the abnormal flatness area, then identifies the area shape of the abnormal flatness area, locates the area position of the abnormal flatness area, creates a laser marking according to the area shape, and then projects the marking on the area position according to the laser marking onto the abnormal flatness area, so that the staff can intuitively view the specific position and shape of the abnormal flatness area.

[0147] Specifically, Figure 10 shows a structural block diagram of the abnormal area marking unit 104 in the system provided by the embodiment of the present invention.

[0148] Among them, in a preferred embodiment provided by the present invention, the abnormal area marking unit 104 specifically includes:

[0149] The abnormal area determination module 1041 is used to determine an abnormal flatness area when there is a flatness abnormality;

[0150] The shape and position recognition module 1042 is used to recognize and determine the area shape and area position of the abnormal flatness area;

[0151] The marking creation module 1043 is used to create a laser marking according to the area shape;

[0152] The marking projection module 1044 is used to project the marking onto the abnormal flatness area at the area position according to the laser marking.

[0153] It should be understood that although the steps in the flowcharts of the embodiments of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in each embodiment may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0154] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0155] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0156] The above-described embodiments merely represent several implementation manners of the present invention. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several variations and improvements can be made, and these all fall within the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the appended claims.

[0157] The foregoing is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A wall flatness detection method based on laser scanning, characterized in that: The method specifically comprises the following steps: Determine a wall detection space, initialize monitoring and shooting of the wall detection space, obtain monitoring and shooting data, analyze the monitoring and shooting data, and plan a flatness detection route; Based on the flatness detection route, the laser scanning travel control and scanning control are performed, and the laser scanning data is received in real time; Processing the laser scanning data, fitting a three-dimensional wall model, and analyzing the three-dimensional wall model to determine whether there is any flatness anomaly; When there is a flatness anomaly, determining a flatness anomaly region, obtaining a region shape, and laser marking the flatness anomaly region according to the region shape; Wherein, when there is a flatness anomaly, determining a flatness anomaly region, obtaining a region shape, and laser marking the flatness anomaly region according to the region shape specifically comprises the following steps: When there is flatness anomaly, determine the flatness anomaly area; Identify and determine the regional shape and regional position of the flattened abnormal area; Creating a laser mark according to the shape of the area; According to the laser marking, marking and projecting are performed on the flattened abnormal area at the position of the area; According to the shape of the area, creating the laser mark specifically includes the following steps: Obtaining a curvature data set of path points in a flatness detection route, wherein the flatness detection motion parameters further include obtaining a minimum curvature value preset by a device, and using the minimum curvature value as a curvature activation threshold; The shortest distance from each path point in the flatness detection route to the boundary of the flatness anomaly area is taken as the anomaly boundary distance; Acquire the real-time physical coordinates fed back during the laser scanning process, perform time-space matching calibration between the path point curvature data set and the real-time physical coordinates, and obtain a calibrated path point curvature sequence; Calculate the difference between the curvature of each waypoint in the calibrated waypoint curvature sequence and the curvature activation threshold, and normalize the result to obtain the standardized curvature difference of each waypoint; The normalized curvature difference of the path points is nonlinearly mapped, and the mapping result is offset adjusted to obtain the curvature response factor weight of each path point; The curvature response factor weight of the path point is fused with the abnormal boundary distance of the corresponding path point to obtain the weight-distance scalar of each path point; The smoothness detection motion parameters also include a motion window for dynamically adjusting the spatial calculation interval according to the device movement state, and taking the arithmetic average of the weight-distance scalars of all path points in the current motion window to obtain the window comprehensive weighted value; The basic power of the laser device is dynamically adjusted using the comprehensive weighted value of the window to generate a real-time power instruction corresponding to each path point of the laser device in the flatness detection route, and a laser mark is created according to the real-time power instruction.

2. The wall flatness detection method based on laser scanning according to claim 1 is characterized in that: The determining of the wall detection space, initializing monitoring and shooting of the wall detection space, acquiring monitoring and shooting data, analyzing the monitoring and shooting data, and planning the flatness detection route specifically include the following steps: Receive a wall flatness detection request and determine the wall detection space; Initialize monitoring and shooting of the wall detection space to obtain monitoring and shooting data; Acquire a spatial three-dimensional model of the wall detection space; Obtain basic parameters for flatness detection; According to the spatial three-dimensional model and the basic parameters for flatness detection, the monitoring and shooting data are analyzed to plan a flatness detection route.

3. The wall flatness detection method based on laser scanning according to claim 2 is characterized in that: According to the spatial three-dimensional model and the basic parameters for flatness detection, the monitoring shooting data is analyzed, and the flatness detection route is planned, which specifically includes the following steps: Each coordinate point and eight adjacent coordinate points are selected from the spatial three-dimensional model to form a 3×3 analysis window; The least square method is used to perform local modeling for each analysis window, and then the angle between the normal vector of each coordinate point and the normal vector of the adjacent window is calculated to obtain the curvature change of each point on the wall surface; The curvature variation of each point on the wall surface is normalized, and then the normalized result is nonlinearly amplified and weighted to obtain a curvature feature weight distribution map; Setting a detection accuracy threshold and standardizing the detection accuracy threshold to obtain a standardized detection accuracy parameter value; The reciprocal operation is performed on the standardized detection accuracy parameter value, and then linear weighting is performed, and the result is mapped to the coordinate system corresponding to the curvature feature weight distribution map to obtain the spatial distribution parameter map corresponding to the accuracy requirement; The curvature feature weight distribution map and the spatial distribution parameter map are added point by point, and the point-by-point addition result is smoothed and numerically standardized to generate a priority coefficient in the range of 0-1, and the path priority distribution heat map of the entire wall area is output according to the priority coefficient; Set a priority threshold and divide the path priority distribution heat map into priority areas according to the priority threshold; Dense detection point arrays are automatically generated for high-priority areas, and sparse sampling strategies are adopted in low-priority areas to obtain a flatness detection route containing scanning point coordinates and movement order.

4. The wall flatness detection method based on laser scanning according to claim 3 is characterized in that: The traveling control and scanning control of the laser scanning based on the flatness detection route and the real-time receiving of the laser scanning data specifically include the following steps: Obtaining smoothness detection motion parameters; Generating a travel control signal in real time according to the flatness detection route and the flatness detection motion parameter; According to the travel control signal, the laser scanning is controlled; Generate scanning control signals in real time; Scanning control is performed according to the scanning control signal, and laser scanning data is received in real time.

5. The wall flatness detection method based on laser scanning according to claim 4 is characterized in that: Generating a travel control signal in real time according to the flatness detection route and the flatness detection motion parameter specifically comprises the following steps: The maximum safe speed value and the safety protection distance threshold of the equipment are obtained from the flatness detection motion parameters, and the maximum safe speed value is used as the speed upper limit constraint, and the safety protection distance threshold is used as the hazard judgment benchmark; Extract geometric features from the flatness detection route, and use differential geometry to calculate the quantified value of the curvature of the current detection point on the flatness detection route to obtain the path curvature characteristic value of the current detection point; The laser ranging module is used to obtain the original instantaneous distance data between the laser ranging module and the obstacle, and the original instantaneous distance data is subjected to noise filtering and sliding average processing to obtain the current accurate distance value of the obstacle; Set the critical curvature threshold, and calculate the curvature ratio using the curvature characteristic value of the path at the current detection point and the critical curvature threshold; The basic velocity coefficient is calculated according to the curvature ratio, and the nonlinear response of the basic velocity coefficient is enhanced to obtain the curvature influence factor; Calculate the ratio of the current obstacle precise distance value to the safety protection distance threshold, and take the reciprocal of the ratio to establish an inverse proportional relationship to obtain the safety correction factor; The maximum safe speed value is adjusted by using the safety correction coefficient and curvature influence factor to obtain the real-time control speed command value; The real-time control speed command value is numerically mapped according to the preset motor speed-PWM duty cycle comparison table to generate a motor drive pulse signal and obtain a travel control signal.

6. The wall flatness detection method based on laser scanning according to claim 5 is characterized in that: The processing of the laser scanning data, fitting the three-dimensional wall model, and analyzing the three-dimensional wall model to determine whether there is a flatness anomaly specifically includes the following steps: Processing the laser scanning data to generate point cloud data; Using the point cloud data, fitting a three-dimensional model of the wall; Analyze the three-dimensional model of the wall and calculate the real-time deviation value; Compare the real-time deviation value with a preset standard deviation value to determine whether there is a flatness anomaly; The processing of the laser scanning data to generate point cloud data specifically includes the following steps: identifying noise data in the laser scanning data; removing noise data from the laser scanning data; Perform filtering and smoothing to generate point cloud data.

7. The wall flatness detection method based on laser scanning according to claim 6 is characterized in that: Analyzing the three-dimensional wall model and calculating the real-time deviation value specifically includes the following steps: The detection window size is dynamically adjusted according to the laser scanning travel control speed. Different detection window sizes contain different numbers of sampling points. For each sampling point in the detection window, select 9 sampling points in the 3×3 neighborhood around the current sampling point, and use the least squares method to fit the local plane equation of the corresponding area; Then, the gradient component is extracted from the local plane equation to obtain the surface gradient vector of each sampling point; Calculate the gradient vector angle between the current sampling point and the adjacent sampling points before and after to obtain the forward angle and the backward angle, and construct the actual curvature value based on the forward angle and the backward angle; According to the critical curvature threshold, the absolute difference between the actual curvature and the planned curvature is calculated using the forward angle and the backward angle; The average value of the angle between the normal vector of each coordinate point and the normal vector of the adjacent window is taken as the curvature reference value, the standard deviation of the angle between the normal vector of each coordinate point and the normal vector of the adjacent window is taken as the threshold band, and the curvature reference value and the threshold band are used to construct the planning curvature threshold; Calculate the absolute difference between the actual curvature value and the planned curvature threshold to obtain the curvature difference factor of each sampling point; Normalize the current obstacle precise distance value, and compare the normalized result with the safety protection distance threshold. According to the comparison result, obtain the safety attenuation coefficient of each sampling point. The dynamic response weight of each sampling point is calculated according to the standardized detection accuracy parameter value, curvature difference factor and safety attenuation coefficient; The surface gradient vector of each sampling point is weighted by the corresponding dynamic response weight to obtain the weighted gradient amplitude. The weighted gradient amplitudes of all sampling points in the detection window are averaged to obtain the real-time deviation value of the current detection window.

8. A wall surface flatness detection system based on laser scanning, the system being applied to the wall surface flatness detection method based on laser scanning according to any one of claims 1 to 7, characterized in that: The system includes a detection route planning unit, a traveling scanning control unit, a flatness abnormality judgment unit and an abnormal area marking unit, wherein: A detection route planning unit is used to determine a wall detection space, initialize monitoring and shooting of the wall detection space, obtain monitoring and shooting data, analyze the monitoring and shooting data, and plan a flatness detection route; A traveling and scanning control unit, used for performing traveling and scanning control of the laser scanning based on the flatness detection route, and receiving laser scanning data in real time; A flatness abnormality judgment unit is used to process the laser scanning data, fit a three-dimensional wall model, and analyze the three-dimensional wall model to determine whether there is a flatness abnormality; The abnormal area marking unit is used to determine the abnormal flatness area when there is an abnormal flatness, obtain the area shape, and laser mark the abnormal flatness area according to the area shape.

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