Wall surface flatness detection method and system based on laser scanning

Abstract

The embodiment of the invention relates to the technical field of wall flatness detection, and particularly discloses a wall flatness detection method and system based on laser scanning. The method comprises the following steps: initializing monitoring shooting, analyzing monitoring shooting data, and planning a flatness detection route; performing advancing control and scanning control of laser scanning, and receiving laser scanning data in real time; fitting the three-dimensional model of the wall surface, analyzing and judging whether the flatness is abnormal or not; and when the flatness is abnormal, a flatness abnormal area is determined, the area shape is acquired, and laser marking is performed on the flatness abnormal area according to the area shape. According to the method, a flatness detection route can be planned, laser scanning control and data processing and analysis can be carried out, whether flatness abnormity exists or not is judged, and when the flatness abnormity exists, laser marking is carried out on a flatness abnormity area, so that the wall flatness detection precision is effectively improved, visual marking of the abnormity is carried out, and actual application and popularization are facilitated.

Description

Technical Field

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

[0002] Wall flatness refers to the parameters that the surface of the wall should meet certain flatness standards and should not be uneven, with obvious bulges or sinks. Wall flatness detection is a series of professional inspections and measurements of the flatness of the wall surface, which involves flatness requirements, gap inspections, surface quality inspections, and a variety of detection methods.

[0003] In the prior art, wall flatness detection by laser scanning has broad application prospects. However, the existing wall flatness detection based on laser scanning has the defects of low accuracy and inability to intuitively mark abnormal flatness areas, which is not convenient for practical promotion 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 raised in the background technology.

[0005] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions: A wall flatness detection method based on laser scanning, the method specifically comprising 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, the flatness anomaly region is determined, and the region shape is obtained, and the flatness anomaly region is laser marked according to the region shape.

[0006] A wall flatness detection system based on laser scanning, the system comprises 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.

[0007] Compared with the prior art, the present invention has the following beneficial effects: The embodiment of the present invention performs initialization monitoring shooting, analyzes the monitoring shooting data, and plans the flatness detection route; performs laser scanning travel control and scanning control, and receives laser scanning data in real time; fits the three-dimensional model of the wall surface, and performs analysis to determine whether there is a flatness anomaly; when there is a flatness anomaly, determines the flatness anomaly area, obtains the area shape, and laser marks the flatness anomaly area according to the area shape. It is possible to plan the flatness detection route, perform laser scanning control, data processing and analysis, determine whether there is a flatness anomaly, and laser mark the flatness anomaly area when there is a flatness anomaly, thereby effectively improving the wall flatness detection accuracy, and performing intuitive marking of the anomaly, which is convenient for practical promotion and use. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0009] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0010] Figure 2 A flow chart of planning a flatness detection route in the method provided in an embodiment of the present invention is shown.

[0011] Figure 3 A flow chart of receiving laser scanning data in the method provided by an embodiment of the present invention is shown.

[0012] Figure 4 A flow chart of determining whether the flatness is abnormal in the method provided by an embodiment of the present invention is shown.

[0013] Figure 5 A flow chart of laser scanning data processing in the method provided in an embodiment of the present invention is shown.

[0014] Figure 6A flow chart of laser marking of abnormal areas in the method provided by an embodiment of the present invention is shown.

[0015] Figure 7 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0016] Figure 8 A structural block diagram of a detection route planning unit in a system provided by an embodiment of the present invention is shown.

[0017] Fig. 9 The structure block diagram of the flatness abnormality judgment unit in the system provided by the embodiment of the present invention is shown.

[0018] Fig.10 A structural block diagram of an abnormal area marking unit in a system provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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 intended to limit the present invention.

[0020] It is understandable that in the prior art, wall flatness detection by laser scanning has broad application prospects. However, the existing wall flatness detection based on laser scanning has the defects of low accuracy and inability to intuitively mark abnormal flatness areas, which is not convenient for practical promotion and use.

[0021] To solve the above problems, the embodiment of the present invention determines the wall detection space, initializes monitoring and shooting of the wall detection space, obtains monitoring shooting data, analyzes the monitoring shooting data, and plans the flatness detection route; based on the flatness detection route, performs laser scanning travel control and scanning control, and receives laser scanning data in real time; performs data processing on the laser scanning data, fits the three-dimensional model of the wall, and analyzes the three-dimensional model of the wall to determine whether there is a flatness anomaly; when there is a flatness anomaly, determines the flatness anomaly area, obtains the area shape, and laser marks the flatness anomaly area according to the area shape. It is possible to plan the flatness detection route, perform laser scanning control, data processing and analysis, determine whether there is a flatness anomaly, and when there is a flatness anomaly, laser mark the flatness anomaly area, thereby effectively improving the wall flatness detection accuracy, and performing intuitive marking of the anomaly, which is convenient for practical promotion and use.

[0022] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0023] Specifically, a wall flatness detection method based on laser scanning comprises the following steps: Step S101, determining a 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 a flatness detection route.

[0024] In an embodiment of the present invention, a wall flatness detection request is received and then identified to determine a wall detection space where wall flatness detection needs to be performed, and then the wall detection space is initialized for monitoring and shooting, and monitoring and shooting data are obtained, and a spatial three-dimensional model of the wall detection space is obtained, and then basic parameters for flatness detection are obtained, and then the monitoring and shooting data are analyzed according to the spatial three-dimensional model and the basic parameters for flatness detection, and a flatness detection route is planned.

[0025] It can be understood that the basic parameters of flatness detection include detection height, detection angle and detection distance.

[0026] Specifically, Figure 2 A flow chart of planning a flatness detection route in the method provided in an embodiment of the present invention is shown.

[0027] Among them, in the preferred embodiment provided by the present invention, the determining of the wall detection space, initializing monitoring and shooting of the wall detection space, obtaining monitoring and shooting data, and analyzing the monitoring and shooting data, and planning the flatness detection route specifically include the following steps: Step S1011, receiving a wall flatness detection request and determining a wall detection space; Step S1012, initializing monitoring and shooting of the wall detection space to obtain monitoring and shooting data; Step S1013, obtaining a three-dimensional spatial model of the wall detection space; Step S1014, obtaining basic parameters for flatness detection; Step S1015: analyzing the monitoring and shooting data according to the spatial three-dimensional model and the flatness detection basic parameters, and planning a flatness detection route.

[0028] As a preferred embodiment of the present invention, the monitoring shooting data is analyzed according to the spatial three-dimensional model and the flatness detection basic parameters, 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.

[0029] In the above scheme, the present invention realizes the multi-dimensional coordination of physical characteristics and detection standards by nonlinearly superimposing the geometric features of the wall (curvature change) and the engineering parameters (detection accuracy). For example, when a fine crack (high curvature) appears on the wall, the system automatically increases the scanning density of the area to 0.2mm interval, and uses 1mm interval scanning in the flat area (low curvature), which can effectively improve the detection efficiency compared with the traditional fixed spacing detection method. In addition, the curvature is nonlinearly amplified, so that the 1mm concave and convex deviation is amplified to 4 times the weight value during calculation, and the 2mm deviation is amplified to 16 times. This design increases the system's recognition sensitivity for hidden dangers such as initial hollowing and cracks by 2.8 times, effectively reducing the missed detection rate. And the detection accuracy parameters are integrated into the calculation model in the form of inverse numbers to realize the mathematical association between physical detection capabilities and engineering standards. When the detection standard is increased from 1mm to 0.5mm, the system will automatically encrypt the scanning line spacing from 20cm to 10cm to ensure that there is no need to manually re-plan the path when the standard changes.

[0030] The curvature part and the precision part are weighted accordingly. When inspecting gypsum board walls, the geometric features are emphasized (α=0.7), while when inspecting tile walls, the precision weight is automatically increased (β=0.5), so that the inspection strategy can dynamically adapt to the differences in the characteristics of walls of different materials.

[0031] Furthermore, the wall flatness detection method based on laser scanning also includes the following steps: Step S102, based on the flatness detection route, performing laser scanning travel control and scanning control, and receiving laser scanning data in real time.

[0032] In an embodiment of the present invention, by acquiring the flatness detection motion parameters, a travel control signal is generated in real time according to the flatness detection route and the flatness detection motion parameters, and then according to the travel control signal, the laser scanning of the wall in the wall detection space is controlled, and a scanning control signal is generated in real time, and scanning control is performed according to the scanning control signal, and laser scanning data is received in real time.

[0033] It can be understood that the flatness detection motion parameters include moving direction, moving speed, etc.

[0034] Specifically, Figure 3 A flow chart of receiving laser scanning data in the method provided by an embodiment of the present invention is shown.

[0035] Among them, in the preferred embodiment provided by the present invention, 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: Step S1021, obtaining flatness detection motion parameters; Step S1022, generating a travel control signal in real time according to the flatness detection route and the flatness detection motion parameter; Step S1023, performing laser scanning travel control according to the travel control signal; Step S1024, generating a scanning control signal in real time; Step S1025, performing scanning control according to the scanning control signal, and receiving laser scanning data in real time.

[0036] As a preferred embodiment of the present invention, according to the flatness detection route and the flatness detection motion parameter, generating a travel control signal in real time 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.

[0037] In the above scheme, the present invention achieves a nonlinear synergistic effect among the parameters through the nested processing of curvature ratio calculation and safety distance correction, and simultaneously integrates three types of key information, namely, equipment performance parameters, path geometry characteristics, and real-time environmental data. It can be shown by actual measurements that the path tracking error of complex wall detection can be reduced.

[0038] Furthermore, the wall flatness detection method based on laser scanning also includes the following steps: Step S103, 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.

[0039] In an embodiment of the present invention, laser scanning data is processed to identify noise data in the laser scanning data, and then the noise data in the laser scanning data is removed, and filtering and smoothing are performed to generate point cloud data. The point cloud data is used to fit a three-dimensional model of the wall, and then the three-dimensional model of the wall is analyzed to calculate a real-time deviation value. By comparing the real-time deviation value with a preset standard deviation value, it is determined 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.

[0040] Specifically, Figure 4 A flow chart of determining whether the flatness is abnormal in the method provided by an embodiment of the present invention is shown.

[0041] Among them, in the preferred embodiment provided by the present invention, the data processing of the laser scanning data, fitting the three-dimensional model of the wall, and analyzing the three-dimensional model of the wall to determine whether there is a flatness abnormality specifically includes the following steps: Step S1031, processing the laser scanning data to generate point cloud data.

[0042] Specifically, Figure 5 A flow chart of laser scanning data processing in the method provided in an embodiment of the present invention is shown.

[0043] 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: Step S10311, identifying noise data in the laser scanning data; Step S10312, removing noise data from the laser scanning data; Step S10313, perform filtering and smoothing to generate point cloud data.

[0044] Furthermore, the step of processing the laser scanning data, fitting a three-dimensional wall model, and analyzing the three-dimensional wall model to determine whether there is a flatness anomaly further includes the following steps: Step S1032: fitting a three-dimensional wall model using the point cloud data.

[0045] Step S1033, analyzing the three-dimensional model of the wall and calculating a real-time deviation value.

[0046] Step S1034, comparing the real-time deviation value with a preset standard deviation value to determine whether there is a flatness anomaly.

[0047] 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: 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.

[0048] In the above scheme, the present invention breaks through the traditional single height difference detection mode through the coupled calculation of gradient vector, curvature difference and safety attenuation coefficient. The detection dimension is expanded from single point height to surface gradient, path geometric characteristics, and environmental safety triple parameters, which can reduce the misjudgment rate of structural features such as positive corners / negative corners. In addition, the weight of high curvature areas is automatically reduced through the exponential decay mechanism of the curvature difference factor. The "false unevenness" signal of the inherent structure of the building is automatically filtered in areas such as corners, door and window openings to eliminate the problem of missed detection caused by the mismatch between the path planning curvature and the detection algorithm in the traditional scheme. In addition, the safety attenuation coefficient is linked to the obstacle distance in real time. When adjacent to obstacles such as pipelines and cables, the detection data of the area will be automatically shielded, and the detection function will be maintained in the dangerous area instead of simply shutting down. In addition, a dynamic window division mechanism is designed, which is combined with the gradient vector fast solution to meet the high-speed scanning requirements.

[0049] Furthermore, the wall flatness detection method based on laser scanning also includes the following steps: Step S104, 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.

[0050] In an embodiment of the present invention, in the case of flatness anomalies, the flatness anomaly area is determined, and then the regional shape of the flatness anomaly area is identified, the regional position of the flatness anomaly area is located, and a laser mark is created according to the regional shape. Then, at the regional position, the flatness anomaly area is marked and projected according to the laser mark, so that the staff can intuitively view the specific position and shape of the flatness anomaly area.

[0051] Specifically, Figure 6 A flow chart of laser marking of abnormal areas in the method provided by an embodiment of the present invention is shown.

[0052] Among them, in the preferred embodiment provided by the present invention, when there is a flatness anomaly, determining the flatness anomaly area, obtaining the area shape, and laser marking the flatness anomaly area according to the area shape specifically includes the following steps: Step S1041, when there is a flatness anomaly, determining a flatness anomaly area; Step S1042, identifying and determining the regional shape and regional position of the flattened abnormal region; Step S1043, creating a laser mark according to the shape of the area; Step S1044, marking and projecting the flattened abnormal area at the area position according to the laser marking.

[0053] As a preferred embodiment of the present invention, according to the area shape, creating a 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.

[0054] In the above scheme, the present invention uses the curvature data in path planning to dynamically adjust the laser power, thereby optimizing the effect when marking abnormal areas. And the laser equipment hardware does not need to be changed, and performance can be improved only by upgrading the software algorithm, saving costs. The calculation time is low and suitable for real-time processing, which is particularly important for high-speed detection equipment, ensuring the synchronization of detection and marking. The visibility and accuracy of the marking are improved, especially in high curvature areas such as corners, avoiding the fuzzy marking caused by the inertia of the equipment in traditional methods.

[0055] Furthermore, Figure 7 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0056] Among them, in another preferred embodiment provided by the present invention, a wall flatness detection system based on laser scanning includes: The detection route planning unit 101 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.

[0057] In an embodiment of the present invention, the detection route planning unit 101 receives a wall flatness detection request, identifies the wall flatness detection request, determines the wall detection space that needs to be tested for wall flatness, and then initializes monitoring and shooting of the wall detection space to obtain monitoring and shooting data, and obtains a spatial three-dimensional model of the wall detection space, and then obtains basic parameters for flatness detection, and then analyzes the monitoring and shooting data according to the spatial three-dimensional model and the basic parameters for flatness detection to plan a flatness detection route.

[0058] Specifically, Figure 8 It shows a structural block diagram of the detection route planning unit 101 in the system provided by an embodiment of the present invention.

[0059] Among them, in the preferred embodiment provided by the present invention, the detection route planning unit 101 specifically includes: The space determination module 1011 is used to receive a wall flatness detection request and determine a wall detection space; Initialization monitoring and shooting module 1012, used for initialization monitoring and shooting of the wall detection space to obtain monitoring and shooting data; A spatial three-dimensional model acquisition module 1013 is used to acquire a spatial three-dimensional model of the wall detection space; A basic parameter acquisition module 1014 is used to acquire basic parameters for flatness detection; The route planning module 1015 is used to analyze the monitoring shooting data according to the spatial three-dimensional model and the flatness detection basic parameters, and plan a flatness detection route.

[0060] Furthermore, the wall flatness detection system based on laser scanning also includes: The traveling and scanning control unit 102 is used to perform traveling control and scanning control of the laser scanning based on the flatness detection route, and receive the laser scanning data in real time.

[0061] In an embodiment of the present invention, the traveling scanning control unit 102 obtains the 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, and then performs traveling control of laser scanning on the wall in the wall detection space according to the traveling control signal, and generates a scanning control signal in real time, performs scanning control according to the scanning control signal, and receives laser scanning data in real time.

[0062] The flatness abnormality judgment unit 103 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.

[0063] In an embodiment of the present invention, the flatness abnormality judgment unit 103 processes the laser scanning data, identifies the noise data in the laser scanning data, removes the noise data in the laser scanning data, and performs filtering and smoothing to generate point cloud data. The point cloud data is used to fit a three-dimensional model of the wall, and then the three-dimensional model of the wall is analyzed to calculate a real-time deviation value. By comparing the real-time deviation value with a preset standard deviation value, it is determined 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.

[0064] Specifically, Fig. 9 The structure block diagram of the flatness abnormality judgment unit 103 in the system provided by the embodiment of the present invention is shown.

[0065] Among them, in the preferred embodiment provided by the present invention, the flatness abnormality judgment unit 103 specifically includes: A point cloud data generating module 1031 is used to process the laser scanning data to generate point cloud data; A wall 3D model fitting module 1032 is used to fit a wall 3D model using the point cloud data; The deviation value calculation module 1033 is used to analyze the three-dimensional wall model and calculate the real-time deviation value; The abnormality judgment module 1034 is used to compare the real-time deviation value with a preset standard deviation value to judge whether there is a flatness abnormality.

[0066] Furthermore, the wall flatness detection system based on laser scanning also includes: The abnormal area marking unit 104 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.

[0067] In an embodiment of the present invention, in the case of a flatness anomaly, the abnormal area marking unit 104 determines the flatness anomaly area, then identifies the regional shape of the flatness anomaly area, locates the regional position of the flatness anomaly area, creates a laser mark according to the regional shape, and then marks and projects the flatness anomaly area at the regional position according to the laser mark, so that the staff can intuitively view the specific position and shape of the flatness anomaly area.

[0068] Specifically, Fig.10 It shows a structural block diagram of the abnormal area marking unit 104 in the system provided by an embodiment of the present invention.

[0069] In a preferred embodiment of the present invention, the abnormal region marking unit 104 specifically includes: The abnormal area determination module 1041 is used to determine the flatness abnormal area when there is a flatness abnormality; A shape and position identification module 1042 is used to identify and determine the region shape and region position of the flattened abnormal region; A mark creation module 1043, used to create a laser mark according to the shape of the area; The marking projection module 1044 is used to project a mark on the flattened abnormal area at the area position according to the laser mark.

[0070] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0071] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and 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-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0072] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0073] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0074] The above description 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 principles of the present invention should 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, the flatness anomaly region is determined, and the region shape is obtained, and the flatness anomaly region is laser marked according to the region shape.

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. The wall flatness detection method based on laser scanning according to claim 7 is characterized in that: 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, a marking projection is performed on the flat abnormal area at the position of the area.

9. The wall flatness detection method based on laser scanning according to claim 8, characterized in that: 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 flatness 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.

10. 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 9, 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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