CN111412842B - Method, device and system for measuring cross-sectional dimension of wall surface - Google Patents
Method, device and system for measuring cross-sectional dimension of wall surface Download PDFInfo
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- CN111412842B CN111412842B CN202010275617.XA CN202010275617A CN111412842B CN 111412842 B CN111412842 B CN 111412842B CN 202010275617 A CN202010275617 A CN 202010275617A CN 111412842 B CN111412842 B CN 111412842B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
- G01B11/06—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness for measuring thickness ; e.g. of sheet material
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10028—Range image; Depth image; 3D point clouds
Abstract
The application discloses a method, a device and a system for measuring the section size of a wall surface. Wherein, the method comprises the following steps: acquiring original point cloud data containing a wall surface to be detected, wherein the wall surface to be detected comprises a section to be detected; extracting point cloud data corresponding to the wall surface to be detected from the original point cloud data; extracting point cloud data corresponding to a section to be detected from the point cloud data corresponding to the wall surface to be detected; and determining the section size of the section to be measured at any preset height according to the point cloud data corresponding to the section to be measured. The technical problems that in the construction stage, the operation efficiency is low and the measurement precision is low due to the fact that the cross section size of the wall surface is measured manually are solved.
Description
Technical Field
The application relates to the field of building entity measurement, in particular to a method, a device and a system for measuring the section size of a wall surface.
Background
In the construction stage, the actual measurement actual quantity passes through the entity test of the construction site, and the quality state of the product is fed back in time, so that a project manager can improve the construction process in time. At present, the actual measurement is still carried out in a relatively old data acquisition mode, for example, the measurement of the wall section size (wall section thickness) is carried out by actual measurement personnel using a steel tape. The wall body of the construction site, even if same stifled wall, also difficult assurance is all unanimous at the cross-section thickness of different height position. Due to the limitation of the operation efficiency, the data acquisition can be carried out only on part of the measurement points manually, and the sampling rate is low. Furthermore, it is difficult for the operator to obtain a very accurate measurement, subject to the precision of the tool used.
Aiming at the problems of low operation efficiency and low measurement precision existing in the construction stage of the building and the problem of low measurement precision in the process of manually measuring the cross section of the wall body, an effective solution is not provided at present.
Disclosure of Invention
The embodiment of the application provides a method, a device and a system for measuring the sectional dimension of a wall surface, and aims to solve the technical problems of low operation efficiency and low measurement precision in the process of manually measuring the sectional dimension of the wall surface at least in the construction stage.
According to an aspect of an embodiment of the present application, there is provided a method for measuring a cross-sectional dimension of a wall surface, including: acquiring original point cloud data containing a wall surface to be detected, wherein the wall surface to be detected comprises a section to be detected; extracting point cloud data corresponding to the wall surface to be detected from the original point cloud data; extracting point cloud data corresponding to a section to be detected from the point cloud data corresponding to the wall surface to be detected; and determining the section size of the section to be measured at any preset height according to the point cloud data corresponding to the section to be measured.
Optionally, before extracting point cloud data corresponding to the wall surface to be detected from the original point cloud data, the method further includes: carrying out down-sampling processing on the original point cloud data; and carrying out filtering and denoising treatment on the original point cloud data.
Optionally, extracting point cloud data corresponding to the wall to be detected from the original point cloud data, including: screening point cloud data of the building surface from the original point cloud data; and extracting point cloud data of the wall surface to be detected from the point cloud data of the building surface.
Optionally, the step of screening out point cloud data of the building surface from the original point cloud data comprises: carrying out plane segmentation processing on the original point cloud data, and extracting point cloud data of different planes; and screening out point cloud data corresponding to three planes which are perpendicular to each other in pairs from the point cloud data of different planes as the point cloud data of the building surface.
Optionally, screening out point cloud data corresponding to three planes perpendicular to each other from point cloud data of different planes as point cloud data of a building surface, including: performing plane fitting processing on the point cloud data of different planes to obtain the direction of a plane normal vector corresponding to each plane, and classifying the planes based on the direction of the plane normal vector to obtain point cloud data corresponding to the planes in different directions; and screening out point cloud data corresponding to three planes which are perpendicular in pairs from the point cloud data corresponding to the planes in different directions to serve as the point cloud data of the building surface.
Optionally, extracting point cloud data of the wall surface to be detected from the point cloud data of the building surface, including: according to the data acquisition angle and the coordinate direction of the sensor, point cloud data parallel to the ground are screened out from the point cloud data of the building surface, and the point cloud data of the ground are extracted from the screened point cloud data parallel to the ground; and taking two groups of point cloud data which are perpendicular to each other except the point cloud data parallel to the ground in the point cloud data of the building surface as the point cloud data forming the wall surface to be detected.
Optionally, before two sets of point cloud data perpendicular to each other in the point cloud data of the building surface, except the point cloud data parallel to the ground, are used as the point cloud data of the wall surface to be measured, the method further includes: judging the concavity and convexity formed by two groups of point cloud data which are perpendicular to each other; if the concavity and the convexity formed by the two groups of mutually perpendicular point cloud data are convex, taking the two groups of mutually perpendicular point cloud data as the point cloud data of the wall surface to be detected; and if the concavity and the convexity formed by the two groups of mutually perpendicular point cloud data are concave, taking the two groups of mutually perpendicular point cloud data as interference point cloud data.
Optionally, extracting point cloud data of the ground from the screened point cloud data parallel to the ground includes: the Z direction of the data collected by the sensor faces upwards, and the point cloud data with the minimum Z value in the point cloud data of the building surface parallel to the ground, which is screened out, is the ground point cloud data.
Optionally, after extracting point cloud data corresponding to the wall surface to be detected from the original point cloud data, the method further includes: calculating the inclination angle of the point cloud data of the wall surface to be detected relative to the gravity surface; and carrying out rotation calibration on the point cloud data of the building surface according to the inclination angle, so that a plane normal vector corresponding to the point cloud data of the ground is vertically upward.
Optionally, extracting point cloud data corresponding to the section to be detected from the point cloud data corresponding to the wall surface to be detected, including: traversing point cloud data corresponding to the wall surface to be detected, comparing point cloud width data in the point cloud data of the wall surface to be detected with a preset cross section size range, taking the point cloud data of the wall surface to be detected corresponding to the point cloud width data falling into the preset cross section size range as point cloud data of a candidate cross section to be detected, wherein the point cloud width data is point cloud data of a short edge in the wall surface to be detected.
Optionally, after point cloud data of the to-be-detected wall surface corresponding to the point cloud width falling into the preset cross-section size range is used as point cloud data of the candidate to-be-detected cross section, the method further includes: selecting point cloud data with the largest cross section height from the point cloud data of the candidate cross sections to be measured as point cloud data of the target cross sections to be measured, searching point cloud data which are perpendicular to and adjacent to the point cloud data of the target cross sections to be measured from a set of the point cloud data of the wall surface to be measured, and using the point cloud data of the reference surface as the point cloud data of the reference surface, wherein the point cloud data of the reference surface is used for measuring the size of the cross sections to be measured.
Optionally, determining the section size of the section to be measured at any preset height according to the point cloud data corresponding to the section to be measured, including: and determining the section size of the section to be measured at any preset height according to the point cloud data of the section to be measured of the target and the point cloud data of the reference surface.
Optionally, determining the section size of the section to be measured at any preset height according to the point cloud data of the target section to be measured and the point cloud data of the reference surface, including: screening point cloud data, of which the distance from the intersection line of the target section to be measured and the reference surface is within a preset range, from the point cloud data of the reference surface to serve as first reference surface point cloud data; extracting edge contour data of point cloud data of a target section to be detected, extracting two edge contour data along the height direction of the target section to be detected from the edge contour data, and selecting the edge contour data far away from the first datum plane point cloud data from the two edge contour data as first edge contour data; intercepting second edge profile data from the first edge profile data according to a preset height range, and intercepting second datum plane point cloud data from the first datum plane point cloud data, wherein the preset height range is a measurement range determined by selecting heights of preset values upwards and downwards respectively on the basis of a preset height; and determining the section size of the section to be measured at the preset height according to the second datum plane point cloud data and the second edge profile data.
Optionally, determining the section size of the section to be measured at the preset height according to the second datum plane point cloud data and the second edge profile data, including: fitting the second datum plane point cloud data to obtain a plane equation of the second datum plane point cloud data in a preset coordinate system, wherein the preset coordinate system is a coordinate system determined by taking the position of a ground wall angle as an origin and the direction of a wall surface of the datum plane as the direction of a coordinate axis; and determining the distance between the point cloud data corresponding to the second edge profile data and the plane equation, and taking the distance as the section size of the section to be measured at the preset height.
According to another aspect of the embodiments of the present application, there is also provided a device for measuring a cross-sectional dimension of a wall surface, including: the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring original point cloud data containing a wall surface to be detected, and the wall surface to be detected comprises a section to be detected; the first extraction module is used for extracting point cloud data corresponding to the wall surface to be detected from the original point cloud data; the second extraction module is used for extracting point cloud data corresponding to the section to be detected from the point cloud data corresponding to the wall surface to be detected; and the determining module is used for determining the section size of the section to be measured at any preset height according to the point cloud data corresponding to the section to be measured.
According to another aspect of the embodiments of the present application, there is also provided a system for measuring a cross-sectional dimension of a wall surface, including: the system comprises a vision sensor and a processor, wherein the vision sensor is used for acquiring original point cloud data of a wall surface to be detected and sending the original point cloud data to the processor, and the wall surface to be detected comprises a section to be detected; the processor is in communication connection with the visual sensor and used for extracting point cloud data corresponding to the wall surface to be detected from the original point cloud data; extracting point cloud data corresponding to a section to be detected from the point cloud data corresponding to the wall surface to be detected; and determining the section size of the section to be measured at any preset height according to the point cloud data corresponding to the section to be measured.
According to still another aspect of the embodiments of the present application, there is provided a non-volatile storage medium, where the non-volatile storage medium includes a stored program, and the apparatus in which the non-volatile storage medium is located is controlled to execute the above method for measuring the cross-sectional dimension of the wall surface when the program is running.
According to still another aspect of the embodiments of the present application, there is also provided a processor for executing a program stored in a memory, wherein the program executes the above method for measuring the sectional dimension of the wall surface.
In the embodiment of the application, the method comprises the steps of obtaining original point cloud data containing a wall surface to be detected; extracting point cloud data corresponding to a wall surface to be detected from the original point cloud data, wherein the wall surface to be detected comprises a section to be detected; extracting point cloud data corresponding to a section to be detected from the point cloud data corresponding to the wall surface to be detected; the method comprises the steps of determining the sectional dimension of the wall surface to be measured according to point cloud data corresponding to the section to be measured, automatically measuring the sectional dimension of the section at any preset height by using the point cloud data of a building, thereby realizing the technical effects of improving the measuring efficiency and the measuring precision of the sectional dimension of the wall surface of the building, and further solving the technical problems of low operating efficiency and low measuring precision in the construction stage of manually measuring the sectional dimension of the wall surface.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a flow chart of a method for measuring a cross-sectional dimension of a wall surface according to an embodiment of the present application;
FIG. 2 is a block diagram of a wall section dimension measuring device according to an embodiment of the present application;
fig. 3 is a block diagram of a measurement system for measuring a cross-sectional dimension of a wall surface according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In accordance with an embodiment of the present application, there is provided an embodiment of a method for measuring wall cross-sectional dimensions, where the steps illustrated in the flowchart of the drawings may be performed in a computer system, such as a set of computer-executable instructions, and where a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than that illustrated herein.
Fig. 1 is a flowchart of a method for measuring a cross-sectional dimension of a wall surface according to an embodiment of the present application, as shown in fig. 1, the method including the steps of:
step S102, obtaining original point cloud data containing a to-be-detected wall surface, wherein the to-be-detected wall surface comprises to-be-detected sections.
The point cloud data refers to the scanning data recorded in the form of points, each point includes three-dimensional coordinates, and some points may include color information or reflection intensity information. The color information is usually obtained by a camera to obtain a color image, and then the color information of the pixel at the corresponding position is assigned to the corresponding point in the point cloud. The intensity information is obtained by the echo intensity collected by the receiving device of the laser scanner, and the intensity information is related to the surface material, roughness and incident angle direction of the target, the emission energy of the instrument and the laser wavelength.
In an alternative embodiment of the present application, the point cloud data may be acquired by a visual sensor.
The vision sensor is a direct source of information for the whole machine vision system, mainly consisting of one or two pattern sensors, sometimes accompanied by a light projector and other auxiliary equipment. The primary function of the vision sensor is to acquire enough of the most primitive image to be processed by the machine vision system. The image sensor may use a laser scanner, a linear and area CCD camera or a TV camera, or may be a digital camera that is newly developed.
And step S104, extracting point cloud data corresponding to the wall surface to be detected from the original point cloud data.
Since the original point cloud data includes non-buildings in addition to buildings, the point cloud data of the buildings needs to be distinguished from the point cloud data in the collected target area.
And S106, extracting point cloud data corresponding to the section to be detected from the point cloud data corresponding to the wall surface to be detected.
And S108, determining the section size of the section to be measured at any preset height according to the point cloud data corresponding to the section to be measured.
Through the steps, the point cloud data of the building are collected, and the sectional dimension of the wall surface at any height is automatically measured by utilizing the point cloud data, so that the technical effects of improving the measuring efficiency and the measuring precision of the sectional dimension of the wall surface of the building are achieved.
In an optional embodiment of the present application, before performing step S104, the raw point cloud data needs to be down-sampled; and carrying out filtering and denoising treatment on the original point cloud data.
After three-dimensional point cloud data including a complete wall surface to be detected is acquired, the acquired point cloud data is subjected to down-sampling processing, and the down-sampling processing is a process for reducing a signal sampling rate and is generally used for reducing a data transmission rate or data size. By down-sampling the data, the computational complexity of the computer can be reduced. And then, carrying out filtering and denoising processing on the point cloud data, eliminating outliers in the data, and removing environmental noise and invalid points.
In an alternative embodiment of the present application, the step S104 can be implemented by the following method: screening point cloud data of the building surface from the original point cloud data; and extracting point cloud data of the wall surface to be detected from the point cloud data of the building surface.
According to an alternative embodiment of the application, the point cloud data of the building surface is screened out from the original point cloud data by: carrying out plane segmentation processing on the original point cloud data, and extracting point cloud data of different planes; and screening out point cloud data corresponding to three planes which are perpendicular to each other in pairs from the point cloud data of different planes as the point cloud data of the building surface.
According to an optional embodiment of the present application, screening out point cloud data corresponding to three planes perpendicular to each other from point cloud data of different planes as point cloud data of a building surface, includes: performing plane fitting processing on the point cloud data of different planes to obtain the direction of a plane normal vector corresponding to each plane, and classifying the planes based on the direction of the plane normal vector to obtain point cloud data corresponding to the planes in different directions; and screening out point cloud data corresponding to three planes which are perpendicular in pairs from the point cloud data corresponding to the planes in different directions to serve as the point cloud data of the building surface.
The building information extraction mainly comprises the following steps:
and performing plane segmentation on the acquired original three-dimensional point cloud data, extracting all plane point clouds, performing plane fitting to acquire direction information, and calculating the main direction of the point clouds and the size of a minimum bounding box, wherein the minimum bounding box is a simple geometric space.
And classifying all the plane point clouds according to the plane direction to obtain a plurality of groups of point cloud data in different directions.
Screening is carried out in the point cloud data in different directions, three groups of planes which are perpendicular to each other in pairs are extracted to serve as data of the building, and interference data in other directions (such as randomly placed workpieces with surfaces not parallel to the wall surface) are roughly filtered.
By the method, the buildings and the non-buildings in the point cloud data can be distinguished according to the design characteristic information of the buildings.
According to an optional embodiment of the application, the point cloud data of the wall surface to be detected is extracted from the point cloud data of the building surface, and the method is realized in the following manner: screening point cloud data parallel to the ground from the point cloud data of the building surface according to the data acquisition angle and the coordinate direction of the sensor; extracting point cloud data of the ground from the screened point cloud data parallel to the ground; and taking two groups of point cloud data which are perpendicular to each other except the point cloud data parallel to the ground in the point cloud data of the building surface as the point cloud data forming the wall surface to be detected.
In an optional embodiment of the present application, before two sets of point cloud data perpendicular to each other, except for the point cloud data parallel to the ground, in the point cloud data of the building surface are used as the point cloud data of the wall surface to be detected, the concavity and the convexity formed by the two sets of point cloud data perpendicular to each other need to be determined; if the concavity and the convexity formed by the two groups of mutually perpendicular point cloud data are convex, taking the two groups of mutually perpendicular point cloud data as the point cloud data of the wall surface to be detected; and if the concavity and the convexity formed by the two groups of mutually perpendicular point cloud data are concave, taking the two groups of mutually perpendicular point cloud data as interference point cloud data.
And judging whether the concavity and the convexity of the point cloud formed by two groups of point cloud data which are perpendicular to each other meet the actual condition (the section size is convex from the observation angle of the viewpoint) from the azimuth angle of the original point cloud data coordinate origin, if so, taking the two groups of point cloud data which are perpendicular to each other as the point cloud data of the final wall surface to be detected, and otherwise, taking the two groups of point cloud data which are perpendicular to each other as interference data.
According to an optional embodiment of the present application, extracting point cloud data of the ground from the screened point cloud data parallel to the ground includes: the Z direction of the data collected by the sensor faces upwards, and the point cloud data with the minimum Z value in the point cloud data of the building surface parallel to the ground, which is screened out, is the ground point cloud data.
According to the data acquisition angle of the visual sensor for acquiring the point cloud data, a plane data Set1 parallel to the ground (including the ground) is screened out, and according to the coordinate direction of the visual sensor, ground data F1 is extracted (for example, if the Z direction of the data acquired by the sensor is upward, the point cloud data with the minimum Z value in Set1 is the ground data).
By the method, ground point cloud data and wall point cloud data can be distinguished from point cloud data of a building.
In some optional embodiments of the present application, after extracting point cloud data corresponding to a wall surface to be detected from the original point cloud data, an inclination angle of the point cloud data of the wall surface to be detected with respect to the gravity surface needs to be calculated; and carrying out rotation calibration on the point cloud data of the building surface according to the inclination angle, so that a plane normal vector corresponding to the point cloud data of the ground is vertically upward.
Considering that the ground flatness is poor, the direction orientation of the building wall surface is utilized to calculate the inclination angle of the actual wall surface point cloud relative to the gravity surface, and all the building point cloud data are rotationally calibrated to the direction vertical to the world coordinate system. And translating the point cloud data of all the building information according to the Z value of the ground plane near the wall surface, so that the average Z value of the ground near the wall corner is 0.
After the point cloud data of the building are calibrated by the method, a new coordinate system can be established by taking the position of the wall corner on the ground as the origin of coordinates and the direction of the wall surface as the direction of coordinate axes, and the new coordinate system is the basis for calculating the section size of the wall body according to the point cloud data of the wall body subsequently.
According to an alternative embodiment of the present application, step S106 may be implemented by: traversing point cloud data corresponding to the wall surface to be detected, comparing point cloud width data in the point cloud data of the wall surface to be detected with a preset cross section size range, taking the point cloud data of the wall surface to be detected corresponding to the point cloud width data falling into the preset cross section size range as point cloud data of a candidate cross section to be detected, wherein the point cloud width data is point cloud data of a short edge in the wall surface to be detected.
The method for extracting the point cloud data corresponding to the section to be detected from the point cloud data corresponding to the wall surface to be detected mainly comprises the following steps:
traversing two groups of vertical wall surface point cloud data Set2 and Set3, screening the data therein, and initially selecting a candidate measuring object Set4 with the point cloud width meeting the design value of section size (the wall thickness has a certain design range).
Traversing all candidate measuring objects Set4, acquiring each plane point cloud element O, and searching point cloud B adjacent to O in a point cloud data Set in another wall direction perpendicular to O to serve as adjacent wall/cylindrical surface data.
In another optional embodiment of the present application, after point cloud data of a to-be-measured wall surface corresponding to a point cloud width falling into a preset cross-sectional dimension range is used as point cloud data of a candidate to-be-measured cross section, point cloud data with a largest cross-sectional height is selected from the point cloud data of the candidate to-be-measured cross section to serve as point cloud data of a target to-be-measured cross section, and then point cloud data which is perpendicular to and adjacent to the point cloud data of the target to-be-measured cross section is searched in a set of the point cloud data of the to-be-measured wall surface and serves as point cloud data of a reference surface, wherein the point cloud data of the reference surface is used for dimension measurement of the to-be-measured cross section.
If a plurality of groups of point cloud data which accord with the screening result exist, one point with the largest cross section height (the cross section data is relatively more complete, and the measurable area is wider) is selected as a final measuring object.
By the method, on the basis of identifying the building information data, the cross section contained in the data can be identified according to the prior characteristics of the cross section size, and the cross section data to be measured is identified in a plurality of possible cross sections by combining the pertinence of data acquisition.
According to an alternative embodiment of the present application, step S108 may be implemented by: and determining the section size of the section to be measured at any preset height according to the point cloud data of the section to be measured of the target and the point cloud data of the reference surface.
In an optional embodiment of the present application, determining the section size of the target section to be measured at any preset height according to the point cloud data of the target section to be measured and the point cloud data of the reference surface may be implemented by the following method: screening point cloud data, of which the distance from the intersection line of the target section to be measured and the reference surface is within a preset range, from the point cloud data of the reference surface to serve as first reference surface point cloud data; extracting edge contour data of point cloud data of a target section to be detected, extracting two edge contour data along the height direction of the target section to be detected from the edge contour data, and selecting the edge contour data far away from the first datum plane point cloud data from the two edge contour data as first edge contour data; intercepting second edge profile data from the first edge profile data according to a preset height range, and intercepting second datum plane point cloud data from the first datum plane point cloud data according to a preset cross-section measurement height, wherein the preset height range is a measurement range determined by selecting heights of preset values upwards and downwards respectively on the basis of the preset height; and determining the section size of the section to be measured at the preset height according to the second datum plane point cloud data and the second edge profile data.
Optionally, determining the section size of the section to be measured at the preset height according to the second datum plane point cloud data and the second edge profile data, including: fitting the second datum plane point cloud data to obtain a plane equation of the second datum plane point cloud data in a preset coordinate system, wherein the preset coordinate system is a coordinate system determined by taking the position of a ground wall angle as an origin and the direction of a wall surface of the datum plane as the direction of a coordinate axis; and determining the distance between the point cloud data corresponding to the second edge profile data and the plane equation, and taking the distance as the section size of the section to be measured at the preset height.
According to the above-mentioned data information of the wall section O to be measured, only data in which the distance from the wall section O to be measured in the reference plane B is within a specific range is retained. The preset range can be set according to requirements.
Extracting the edge contour of the section to be measured to obtain closed point cloud contour data C; and extracting two edge profiles along the height direction of the wall surface from the C, and screening out one side edge profile data L far away from the corresponding reference surface.
Intercepting corresponding edge profile data D in the L within a certain range above and below the measurement height specified by the section; and simultaneously intercepting corresponding wall surface data T at the corresponding height in the corresponding reference surface B.
And fitting a plane equation by taking the T as reference data, and calculating the distance from the point cloud data corresponding to the D to the plane equation, namely the measurement result of the section size of the specified measurement height.
By the method, the ground height is taken as the original point, only the specified height of the wall surface can be measured according to the specific requirement of actual measurement, and different heights of the whole wall can also be measured; and the device can be adapted to various possible wall conditions and automatically avoid abnormal positions.
The measuring method for the section size of the wall surface provided by the embodiment of the application can automatically complete the measurement of the designated position (or the height of the whole wall) of the section size. In the inclined three-dimensional point cloud data acquired at any angle acquired by a computer, the interference of non-building information can be correctly eliminated by the measuring method, and the point cloud information of a section to be measured is extracted and distinguished from the point cloud data of the building information; after a specific section to be measured is obtained, a new coordinate system is established by taking the position of a wall corner on the ground as the origin of coordinates and the direction of the wall surface as the direction of coordinate axes. On the basis of a new coordinate system, according to specific measurement requirements, the section size measurement is carried out on the specified height of the section and even the whole height. When only the specified height position is measured, the measured height can be slightly adjusted according to the actual condition of the wall. The measuring method is completely consistent with the measuring means of actual measurement workers, the measuring result depends on the precision of equipment, the algorithm error is small, the method is not influenced by any subjective factor, the limitation of manual measurement is avoided, and the confidence coefficient is high.
It should be noted that the above method for measuring the wall cross-sectional dimension is also applicable to the measurement of the door opening depth.
Fig. 2 is a structural view of a measuring apparatus for a sectional dimension of a wall surface according to an embodiment of the present application, as shown in fig. 2, the apparatus including:
the acquiring module 20 is configured to acquire original point cloud data including a wall surface to be detected, where the wall surface to be detected includes a cross section to be detected.
The first extraction module 22 is configured to extract point cloud data corresponding to the wall surface to be detected from the original point cloud data.
And the second extraction module 24 is configured to extract point cloud data corresponding to the section to be detected from the point cloud data corresponding to the wall surface to be detected.
And the determining module 26 is configured to determine the section size of the section to be measured at any preset height according to the point cloud data corresponding to the section to be measured.
It should be noted that, reference may be made to the description related to the embodiment shown in fig. 1 for a preferred implementation of the embodiment shown in fig. 2, and details are not described here again.
Fig. 3 is a block diagram of a measurement system for measuring a sectional dimension of a wall surface according to an embodiment of the present application, as shown in fig. 3, the system including: a vision sensor 30 and a processor 32, wherein,
the vision sensor 30 is configured to obtain original point cloud data including a wall surface to be detected, and send the original point cloud data to the processor 32, where the wall surface to be detected includes a cross section to be detected.
The vision sensor is a direct source of information for the whole machine vision system, mainly consisting of one or two pattern sensors, sometimes accompanied by a light projector and other auxiliary equipment. The primary function of the vision sensor is to acquire enough of the most primitive image to be processed by the machine vision system. The image sensor may use a laser scanner, a linear and area CCD camera or a TV camera, or may be a digital camera that is newly developed.
The processor 32 is in communication connection with the visual sensor 30 and is used for extracting point cloud data corresponding to the wall surface to be detected from the original point cloud data; extracting point cloud data corresponding to a section to be detected from the point cloud data corresponding to the wall surface to be detected; and determining the section size of the section to be measured at any preset height according to the point cloud data corresponding to the section to be measured.
The point cloud data refers to the scanning data recorded in the form of points, each point includes three-dimensional coordinates, and some points may include color information or reflection intensity information. The target area is an area where a building is located.
Through the system, the point cloud data of the building are collected, and the sectional dimension of the wall is automatically measured by using the point cloud data, so that the technical effects of improving the measuring efficiency and the measuring precision of the sectional dimension of the wall of the building are achieved.
It should be noted that, reference may be made to the description related to the embodiment shown in fig. 1 for a preferred implementation of the embodiment shown in fig. 3, and details are not described here again.
The embodiment of the application provides a nonvolatile storage medium, the nonvolatile storage medium comprises a stored program, and when the program runs, equipment where the nonvolatile storage medium is located is controlled to execute the measuring method of the section size of the wall surface.
The nonvolatile storage medium is used for storing a program for executing the following functions: acquiring original point cloud data containing a wall surface to be detected, wherein the wall surface to be detected comprises a section to be detected; extracting point cloud data corresponding to the wall surface to be detected from the original point cloud data; extracting point cloud data corresponding to a section to be detected from the point cloud data corresponding to the wall surface to be detected; and determining the section size of the section to be measured at any preset height according to the point cloud data corresponding to the section to be measured.
The embodiment of the application also provides a processor, and the processor is used for running the program stored in the memory, wherein the program is run to execute the above method for measuring the section size of the wall surface.
The processor is used for running a program for executing the following functions: acquiring original point cloud data containing a wall surface to be detected; extracting point cloud data corresponding to a wall surface to be detected from the original point cloud data, wherein the wall surface to be detected comprises a section to be detected; extracting point cloud data corresponding to a section to be detected from the point cloud data corresponding to the wall surface to be detected; and determining the section size of the section to be measured at any preset height according to the point cloud data corresponding to the section to be measured.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U disk, a read-Only Memory (ROM), a random access Memory (RBZLJM), a removable hard disk, a magnetic disk, or an optical disk, which can store program codes.
The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.
Claims (13)
1. A method for measuring the sectional dimension of a wall surface is characterized by comprising the following steps:
acquiring original point cloud data containing a wall surface to be detected, wherein the wall surface to be detected comprises a section to be detected;
extracting point cloud data corresponding to the wall surface to be detected from the original point cloud data;
extracting point cloud data corresponding to the section to be detected from the point cloud data corresponding to the wall surface to be detected;
determining the section size of the section to be detected at any preset height according to the point cloud data corresponding to the section to be detected;
extracting point cloud data corresponding to the wall surface to be detected from the original point cloud data, wherein the point cloud data comprises the following steps: screening out point cloud data of a building surface from the original point cloud data; extracting point cloud data of the wall surface to be detected from the point cloud data of the building surface;
extracting point cloud data corresponding to the section to be detected from the point cloud data corresponding to the wall surface to be detected, wherein the point cloud data comprises: traversing point cloud data corresponding to the wall surface to be detected, comparing point cloud width data in the point cloud data of the wall surface to be detected with a preset cross section size range, and taking the point cloud data of the wall surface to be detected corresponding to the point cloud width data falling into the preset cross section size range as point cloud data of a candidate cross section to be detected, wherein the point cloud width data is point cloud data of a short edge in the wall surface to be detected;
after the point cloud data of the wall surface to be detected corresponding to the point cloud width data falling into the preset section size range is used as the point cloud data of the candidate section to be detected, the method further comprises the following steps: selecting point cloud data with the largest cross section height from the point cloud data of the candidate cross sections to be measured as point cloud data of a target cross section to be measured, searching point cloud data which are perpendicular to and adjacent to the point cloud data of the target cross section to be measured from a set of the point cloud data of the wall surface to be measured, and using the point cloud data as point cloud data of a reference surface, wherein the point cloud data of the reference surface is used for measuring the size of the candidate cross section to be measured;
determining the section size of the section to be detected at any preset height according to the point cloud data corresponding to the section to be detected, wherein the step comprises the following steps: screening point cloud data, the distance between which and the intersection line of the target section to be measured and the reference surface is within a preset range, from the point cloud data of the reference surface, and taking the point cloud data as first reference surface point cloud data; extracting edge contour data of point cloud data of the target section to be measured, extracting two edge contour data along the height direction of the target section to be measured from the edge contour data, and selecting edge contour data far away from the first datum plane point cloud data from the two edge contour data as first edge contour data; intercepting second edge profile data from the first edge profile data according to a preset height range, and intercepting second datum plane point cloud data from the first datum plane point cloud data, wherein the preset height range is a measurement range determined by selecting heights of preset values upwards and downwards respectively on the basis of the preset height;
fitting the second datum plane point cloud data to obtain a plane equation of the second datum plane point cloud data in a preset coordinate system, wherein the preset coordinate system takes a ground wall angle position as an origin, and the wall surface direction of the datum plane is a coordinate system determined by a coordinate axis direction; and determining the distance between the point cloud data corresponding to the second edge profile data and the plane equation, and taking the distance as the section size of the section to be measured at the preset height.
2. The method of claim 1, wherein before extracting the point cloud data corresponding to the wall surface to be measured from the original point cloud data, the method further comprises:
performing down-sampling processing on the original point cloud data; and
and carrying out filtering and denoising treatment on the original point cloud data.
3. The method of claim 1, wherein screening point cloud data for a building surface from the raw point cloud data comprises:
carrying out plane segmentation processing on the original point cloud data, and extracting point cloud data of different planes;
and screening out point cloud data corresponding to three planes which are perpendicular to each other in pairs from the point cloud data of the different planes as the point cloud data of the building surface.
4. The method of claim 3, wherein screening out point cloud data corresponding to three planes perpendicular to each other from the point cloud data of the different planes as the point cloud data of the building surface comprises:
carrying out plane fitting processing on the point cloud data of different planes to obtain the direction of a plane normal vector corresponding to each plane, and classifying the planes based on the direction of the plane normal vector to obtain point cloud data corresponding to the planes in different directions;
and screening out point cloud data corresponding to three planes which are perpendicular in pairs from the point cloud data corresponding to the planes in different directions to serve as the point cloud data of the building surface.
5. The method of claim 4, wherein extracting the point cloud data of the wall surface to be detected from the point cloud data of the building surface comprises:
according to the data acquisition angle and the coordinate direction of the sensor, point cloud data parallel to the ground are screened out from the point cloud data of the building surface, and the point cloud data of the ground are extracted from the screened point cloud data parallel to the ground;
and taking two groups of point cloud data which are perpendicular to each other except the point cloud data parallel to the ground in the point cloud data of the building surface as the point cloud data forming the wall surface to be detected.
6. The method according to claim 5, wherein before two sets of point cloud data perpendicular to each other, excluding point cloud data parallel to the ground, in the point cloud data of the building surface are used as point cloud data constituting the wall surface to be measured, the method further comprises:
judging the concavity and convexity formed by the two groups of point cloud data which are perpendicular to each other;
if the concavity and the convexity formed by the two groups of point cloud data which are perpendicular to each other are convex, taking the two groups of point cloud data which are perpendicular to each other as the point cloud data of the wall surface to be detected;
and if the concavity and the convexity formed by the two groups of point cloud data which are perpendicular to each other are concave, taking the two groups of point cloud data which are perpendicular to each other as interference point cloud data.
7. The method of claim 5, wherein extracting the point cloud data of the ground from the screened point cloud data parallel to the ground comprises:
and the Z direction of the data collected by the sensor faces upwards, and the point cloud data with the minimum Z value in the screened point cloud data of the building surface parallel to the ground is the ground point cloud data.
8. The method of claim 5, wherein after extracting the point cloud data corresponding to the wall surface to be measured from the original point cloud data, the method further comprises:
calculating the inclination angle of the point cloud data of the wall surface to be detected relative to the gravity surface;
and performing rotation calibration on the point cloud data of the building surface according to the inclination angle, so that a plane normal vector corresponding to the point cloud data of the ground is vertically upward.
9. The method of claim 1, wherein determining the section size of the section to be measured at any preset height according to the point cloud data corresponding to the section to be measured comprises:
and determining the section size of the section to be measured at any preset height according to the point cloud data of the target section to be measured and the point cloud data of the reference surface.
10. A device for measuring the cross-sectional dimension of a wall surface, comprising:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring original point cloud data containing a to-be-detected wall surface, and the to-be-detected wall surface comprises a to-be-detected section;
the first extraction module is used for extracting point cloud data corresponding to the wall surface to be detected from the original point cloud data;
the second extraction module is used for extracting point cloud data corresponding to the section to be detected from the point cloud data corresponding to the wall surface to be detected;
the determining module is used for determining the section size of the section to be detected at any preset height according to the point cloud data corresponding to the section to be detected;
the first extraction module is also used for screening out point cloud data of a building surface from the original point cloud data; extracting point cloud data of the wall surface to be detected from the point cloud data of the building surface;
the second extraction module is further configured to traverse point cloud data corresponding to the wall surface to be detected, compare point cloud width data in the point cloud data of the wall surface to be detected with a preset cross-sectional dimension range, and use the point cloud data of the wall surface to be detected corresponding to the point cloud width data falling within the preset cross-sectional dimension range as point cloud data of a candidate cross-section to be detected, where the point cloud width data is point cloud data of a short side in the wall surface to be detected;
after point cloud data of a wall surface to be measured corresponding to the point cloud width data falling into the preset cross section size range is used as point cloud data of a candidate cross section to be measured, point cloud data with the largest cross section height is selected from the point cloud data of the candidate cross section to be measured and used as point cloud data of a target cross section to be measured, point cloud data which are perpendicular to and adjacent to the point cloud data of the target cross section to be measured are searched from a set of the point cloud data of the wall surface to be measured and used as point cloud data of a reference surface, wherein the point cloud data of the reference surface is used for measuring the size of the cross section to be measured;
the determining module is further used for screening out point cloud data, of which the distance from the intersection line of the target section to be measured and the reference surface is within a preset range, from the point cloud data of the reference surface to serve as first reference surface point cloud data; extracting edge contour data of point cloud data of the target section to be measured, extracting two edge contour data along the height direction of the target section to be measured from the edge contour data, and selecting edge contour data far away from the first datum plane point cloud data from the two edge contour data as first edge contour data; intercepting second edge profile data from the first edge profile data according to a preset height range, and intercepting second datum plane point cloud data from the first datum plane point cloud data, wherein the preset height range is a measurement range determined by selecting heights of preset values upwards and downwards respectively on the basis of the preset height;
fitting the second datum plane point cloud data to obtain a plane equation of the second datum plane point cloud data in a preset coordinate system, wherein the preset coordinate system takes a ground wall angle position as an origin, and the wall surface direction of the datum plane is a coordinate system determined by a coordinate axis direction; and determining the distance between the point cloud data corresponding to the second edge profile data and the plane equation, and taking the distance as the section size of the section to be measured at the preset height.
11. A system for measuring the cross-sectional dimension of a wall, comprising: a vision sensor and a processor, wherein,
the vision sensor is used for acquiring original point cloud data of a wall surface to be detected and sending the original point cloud data to the processor, wherein the wall surface to be detected comprises a section to be detected;
the processor is in communication connection with the visual sensor and is used for extracting point cloud data corresponding to the wall surface to be detected from the original point cloud data; extracting point cloud data corresponding to the section to be detected from the point cloud data corresponding to the wall surface to be detected; determining the section size of the section to be detected at any preset height according to the point cloud data corresponding to the section to be detected;
the processor is also used for screening out point cloud data of a building surface from the original point cloud data; extracting point cloud data of the wall surface to be detected from the point cloud data of the building surface;
the processor is further used for traversing point cloud data corresponding to the wall surface to be detected, comparing point cloud width data in the point cloud data of the wall surface to be detected with a preset cross section size range, and taking the point cloud data of the wall surface to be detected corresponding to the point cloud width data falling into the preset cross section size range as point cloud data of a candidate cross section to be detected, wherein the point cloud width data is point cloud data of a short side in the wall surface to be detected;
after point cloud data of a wall surface to be measured corresponding to the point cloud width data falling into the preset cross section size range is used as point cloud data of a candidate cross section to be measured, point cloud data with the largest cross section height is selected from the point cloud data of the candidate cross section to be measured and used as point cloud data of a target cross section to be measured, point cloud data which are perpendicular to and adjacent to the point cloud data of the target cross section to be measured are searched from a set of the point cloud data of the wall surface to be measured and used as point cloud data of a reference surface, wherein the point cloud data of the reference surface is used for measuring the size of the cross section to be measured;
screening point cloud data, the distance between which and the intersection line of the target section to be measured and the reference surface is within a preset range, from the point cloud data of the reference surface, and taking the point cloud data as first reference surface point cloud data; extracting edge contour data of point cloud data of the target section to be measured, extracting two edge contour data along the height direction of the target section to be measured from the edge contour data, and selecting edge contour data far away from the first datum plane point cloud data from the two edge contour data as first edge contour data; intercepting second edge profile data from the first edge profile data according to a preset height range, and intercepting second datum plane point cloud data from the first datum plane point cloud data, wherein the preset height range is a measurement range determined by selecting heights of preset values upwards and downwards respectively on the basis of the preset height;
fitting the second datum plane point cloud data to obtain a plane equation of the second datum plane point cloud data in a preset coordinate system, wherein the preset coordinate system takes a ground wall angle position as an origin, and the wall surface direction of the datum plane is a coordinate system determined by a coordinate axis direction; and determining the distance between the point cloud data corresponding to the second edge profile data and the plane equation, and taking the distance as the section size of the section to be measured at the preset height.
12. A non-volatile storage medium, characterized in that the non-volatile storage medium includes a stored program, and when the program runs, the non-volatile storage medium is controlled to execute the method for measuring the cross-sectional dimension of the wall surface according to any one of claims 1 to 9.
13. A processor for running a program stored in a memory, wherein the program when run performs the method of measuring a cross-sectional dimension of a wall surface of any one of claims 1 to 9.
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| CN202010275617.XA CN111412842B (en) | 2020-04-09 | 2020-04-09 | Method, device and system for measuring cross-sectional dimension of wall surface |
| PCT/CN2020/121261 WO2021203664A1 (en) | 2020-04-09 | 2020-10-15 | Wall cross-sectional size measurement method, apparatus, and system |
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| CN111862315A (en) * | 2020-07-25 | 2020-10-30 | 南开大学 | Human body multi-size measuring method and system based on depth camera |
| CN113983966B (en) * | 2021-10-27 | 2023-07-21 | 北京半导体专用设备研究所（中国电子科技集团公司第四十五研究所） | Chip flatness measuring method and device, electronic equipment and storage medium |
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| CN116030103B (en) * | 2023-03-07 | 2024-02-27 | 北京百度网讯科技有限公司 | Method, device, apparatus and medium for determining masonry quality |
| CN116258969B (en) * | 2023-05-12 | 2023-08-25 | 宁波市天一测绘设计研究有限公司 | Structural member measuring method and device based on point cloud data |
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