CN115512067A - Structural component parameter extraction and deformation measurement method based on laser point cloud - Google Patents
Structural component parameter extraction and deformation measurement method based on laser point cloud Download PDFInfo
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Abstract
A method for extracting parameters and measuring deformation of a structural member based on laser point cloud comprises the following steps: collecting laser point cloud data; preprocessing laser point cloud data; integrally calibrating laser point cloud data and a BIM model; dividing the laser point cloud data according to the floor axis network information, and registering the layered point cloud data with the floor axis network; extracting structural members with rectangular and I-shaped cross sections from the calibrated floor point cloud models; extracting and optimizing geometric parameters of the section of the structural member; structural member inclination and camber calculations. The cross-sectional dimension of the structural component of the actually built structural system can be checked, and whether design changes without files exist or not can be checked; the invention can find structural members with abnormal inclination and bending rates, and can modify the BIM model based on the comparison results, so that the BIM model can reflect the current real situation of a structural system.
Description
Technical Field
The invention relates to the field of structural engineering measurement, in particular to a method for extracting parameters and measuring deformation of a structural member based on laser point cloud.
Background
The built large-scale structural engineering is usually different from a design drawing, and is subjected to multiple times of modification and maintenance in the long-term service process. When a structure needs to be modified in a large scale, design drawings and technical modification data cannot generally reflect the current actual situation of the structure comprehensively, and difficulty is brought to modification design.
The traditional surveying and mapping modes such as theodolite and total station are long in measuring time consumption, and surveying and mapping quality is restricted by observation visual angles and positions. For complex structural engineering with densely arranged components, when the measurement time is short and the task is heavy, all the conditions cannot be captured and recorded. The quality improvement effect of the surveying and mapping result on the improved design is not large.
Disclosure of Invention
In view of the above, the present invention has been made in order to provide a laser point cloud based method of extracting parameters of a structural member and measuring deformation that overcomes or at least partially solves the above problems.
In order to solve the technical problem, the embodiment of the application discloses the following technical scheme:
a method for extracting parameters and measuring deformation of a structural member based on laser point cloud comprises the following steps:
s100, carrying out panoramic coverage type scanning on a target structure system by adopting laser scanning equipment to obtain point cloud data of the target structure system;
s200, preprocessing the laser point cloud data, eliminating noise and redundant data irrelevant to structural members, thinning and simplifying the point cloud data, and ensuring the uniform spacing of the sampling point clouds;
s300, carrying out integral registration on the preprocessed laser point cloud data and the BIM three-dimensional model, and unifying origin positioning and coordinates;
s400, partitioning the point cloud model after integral registration according to floors, registering the point cloud model with a floor axis network, and adjusting the coordinates of each layer of point cloud model;
s500, extracting point cloud data belonging to a structural member from the point cloud model after each layer of calibration, wherein the cross section of the structural member comprises a rectangle and an I shape;
s600, fitting a three-dimensional outer contour plane of the structural member and fitting a section geometric boundary to obtain a member section geometric parameter;
s700, calculating the inclination rate and the bending rate of the structural member according to the acquired geometric parameters of the section of the structural member.
Further, in S100, the laser scanning device adopts a ground type laser scanning system, and the scanning precision is not lower than 1mm; the scan range covers all the major structural members and connecting nodes of the target architecture.
Further, in S200, denoising the point cloud data by an euclidean distance outlier, and removing discrete interference noise points; redundant point cloud data including portrait and sundries are removed through manual intervention selection; and thinning and simplifying the point cloud data by using an even grid method, and ensuring that the distance between the sampled point clouds is even and is not more than 1mm.
Further, in S300, performing overall registration on the preprocessed laser point cloud data and the BIM three-dimensional model, including: and adjusting the global origin and the three-dimensional coordinate direction of the actually measured laser point cloud data according to the global origin and the three-dimensional coordinate direction of the BIM model, calculating the contact ratio of the actually measured laser point cloud data and the BIM model after each adjustment, and giving a new three-dimensional coordinate value to the actually measured laser point cloud according to the coordinate change parameter when the contact ratio of the two models is the maximum.
Further, in S400, the point cloud model after the integral registration is segmented according to floors, and registered with a floor axis network, and coordinates of the point cloud model of each layer are adjusted, which specifically includes: according to the elevation information of each floor of the building, partitioning point cloud data in the height direction of the floor; and (3) picking up the section centroids of the main frame columns from the partitioned floor point cloud data, connecting the section centroids of the frame columns, and calibrating the section centroids with the floor axis network through the section centroid coordinates and the connecting direction of the frame columns.
Further, in S500, extracting point cloud data of the target structural member from the point cloud models after each layer calibration includes: for a structural member existing in the BIM, extracting point cloud data of the structural member in a certain range of space occupation and periphery according to the space coordinate and cross section geometric design parameters of the structural member in the BIM; and (3) finding a newly added structural component which does not exist in the BIM model by comparing the BIM model with the actually measured point cloud model, and directly performing frame selection in the point cloud model for the newly added structural component to obtain point cloud data of the newly added structural component.
Further, in S600, fitting the three-dimensional outer contour plane of the structural member and fitting the cross-sectional geometric boundary to obtain the cross-sectional geometric parameters of the structural member specifically includes: fitting a three-dimensional outer contour plane of the structural member by using an RANSAC method, determining the accurate space occupation of the structural member, and filtering point cloud data which do not belong to the structural member; averagely dividing the structural member into a plurality of sections according to the axial length, intercepting each section, fitting boundary lines of the section of the member by using an RANSAC method, measuring the geometric design parameters of the section of the member, and calculating the average value and the mean square error of the geometric design parameters of the section according to the measurement results of the plurality of sections; wherein, for a rectangular cross-section, the geometric design parameters include the length and width of the rectangular cross-section; for an i-section, the geometric design parameters include the length and thickness of the flanges and webs.
Further, the specific steps of fitting the single plane include:
randomly selecting 3 non-collinear seed points from the point cloud data set of the member to establish an initial plane L 0 :α 0 x+β 0 y+γ 0 z+δ 0 ＝0；
Calculating the point cloud data set of the component by removing the rest points of the 3 seed points to the plane L 0 The distance of (d); when distance is measuredLess than a given distance threshold d ε Then count the point into the plane L 0 The in-plane point set of (3);
repeating the steps for n times, comparing the points of the point set in the current plane with the points of the point set in the previous plane each time, and reserving the plane with larger points;
and after n times of circulation is finished, recalculating optimal plane parameters alpha, beta, gamma and delta according to the point cloud data with the maximum point set number in the plane.
Further, the specific step of fitting the boundary line on the section of the single component comprises the following steps:
randomly selecting 2 seed points which are not collinear from a point cloud data set of a cross section, and establishing an initial straight line l 0 :A 0 x+B 0 y+C 0 ＝0；
Calculating the point cloud data set of the cross section, removing the rest points of the 2 seed points to the straight line l 0 The vertical distance of (c); when the distance is less than a given distance threshold d ∈ Then count the point into the straight line l 0 The linear inner point set of (2);
repeating the steps m times, comparing the number of points in the current straight line inner point set with the number of points in the previous straight line inner point set each time, and reserving a straight line with larger number of points;
after m cycles are finished, the optimal straight line parameter A is recalculated according to the point cloud data with the maximum point set number in the straight line 0 、B 0 And C 0 。
Further, calculating the tilt rate and the bending rate of the extracted structural member includes: calculating the inclination rate of the structural member in two directions of the section centroid main shaft according to the centroid coordinates of the end surfaces of the two ends of the structural member and the length of the structural member; and calculating the bending rate of the structural member in two directions of the centroid main shaft of the cross section according to the centroid coordinates of the cross section and the cross section of one end of the structural member.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
the invention discloses a method for extracting parameters and measuring deformation of a structural member based on laser point cloud, which comprises the following steps: collecting laser point cloud data; preprocessing laser point cloud data; integrally calibrating the laser point cloud data and the BIM model; dividing the laser point cloud data according to the floor axis network information, and registering the layered point cloud data with the floor axis network; extracting structural members with rectangular and I-shaped cross sections from the calibrated floor point cloud models; extracting and optimizing geometric parameters of the section of the structural member; structural member inclination and camber calculations. The cross-sectional dimension of the structural component of the actually built structural system can be checked, and whether design changes without files exist or not can be checked; the invention can find structural members with abnormal inclination and bending rates, and can modify the BIM model based on the comparison results, so that the BIM model can reflect the current real situation of a structural system.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a flowchart of a method for extracting parameters of a structural member and measuring deformation based on laser point cloud in embodiment 1 of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
In order to solve the problems in the prior art, the embodiment of the invention provides a structural member parameter extraction and deformation measurement method based on laser point cloud.
Example 1
The embodiment discloses a method for extracting parameters of a structural member and measuring deformation based on laser point cloud, as shown in fig. 1, comprising the following steps:
s100, carrying out panoramic coverage type scanning on a target structure system by adopting laser scanning equipment to obtain point cloud data of the target structure system; specifically, in this embodiment S100, the laser scanning device adopts a ground-based laser scanning system, and the scanning precision is not lower than 1mm; the scan range covers all the major structural members and connecting nodes of the target architecture.
S200, preprocessing laser point cloud data, eliminating noise points and redundant data irrelevant to structural members, thinning and simplifying the point cloud data, and ensuring uniform spacing of sampled point clouds;
in S200 of this embodiment, the laser point cloud data is preprocessed, which includes: denoising the point cloud data by an Euclidean distance outlier method, and eliminating discrete interference noise points; redundant point cloud data including portrait, sundries and the like are removed through manual intervention selection; and thinning and simplifying the point cloud data by using an even grid method, and ensuring that the distance between the sampled point clouds is even and is not more than 1mm.
S300, carrying out integral registration on the preprocessed laser point cloud data and the BIM three-dimensional model, and unifying origin positioning and coordinates;
in S300 of this embodiment, the overall registration of the preprocessed laser point cloud data and the BIM three-dimensional model includes: and adjusting the global origin and the three-dimensional coordinate direction of the actually-measured laser point cloud data according to the global origin and the three-dimensional coordinate direction of the BIM model, calculating the contact ratio of the actually-measured laser point cloud data and the BIM model after each adjustment, and giving a new three-dimensional coordinate value to the actually-measured laser point cloud according to the coordinate change parameter when the contact ratio of the two models is maximum.
Specifically, in this embodiment, the contact ratio calculation needs to be performed on the actual measurement laser point cloud data subjected to the denoising processing and the BIM model, and the specific method is as follows: and adjusting the global origin and the three-dimensional coordinate direction of the actually-measured laser point cloud data according to the global origin and the three-dimensional coordinate direction of the BIM model to maximize the contact ratio of the two models. Wherein the registration objective function is
m is the number of the total point clouds; q. q of i For actually measured laser point clouds p i Projection points on the nearest plane or curved surface of the BIM model; r is a coordinate rotation transformation matrix of the actually measured laser point cloud, and t is a coordinate translation vector.
And giving a new three-dimensional coordinate value to the actually measured laser point cloud according to the origin and the three-dimensional coordinate when the overlapping degree is maximum, and finishing model registration.
The accuracy of the BIM model should ensure that the spatial coordinate information and cross-sectional geometry of the structural member can be obtained therefrom, including but not limited to: the x, y and z direction coordinates of the section centroids at two ends of the member, the length and width of the rectangular section, and the length and width of the web and the flange of the I-shaped section. The data precision of the BIM model is ensured to be not less than 0.1mm
S400, partitioning the point cloud model after integral registration according to floors, registering the point cloud model with a floor axis network, and adjusting the coordinates of each layer of point cloud model;
in S400 of this embodiment, the step of segmenting the point cloud model after the integral registration according to floors, registering the point cloud model with a floor axis network, and adjusting coordinates of each layer of point cloud model specifically includes: according to the elevation information of each floor of the building, partitioning point cloud data in the height direction of the floor; and (3) picking up the section centroids of the main frame columns from the partitioned floor point cloud data, connecting the section centroids of the frame columns, and calibrating the section centroids with the floor axis network through the section centroid coordinates and the connecting direction of the frame columns.
S500, extracting point cloud data belonging to a structural member from the point cloud model after each layer of calibration, wherein the cross section of the structural member comprises a rectangle and an I shape;
in S500 of this embodiment, extracting point cloud data of the target structural member from the point cloud models after each layer calibration includes: for a structural member existing in the BIM, extracting point cloud data of the structural member in a certain range of space occupation and periphery according to the space coordinate and cross section geometric design parameters of the structural member in the BIM; and (3) finding a newly added structural component which does not exist in the BIM model by comparing the BIM model with the actually measured point cloud model, and directly performing frame selection in the point cloud model for the newly added structural component to obtain point cloud data of the newly added structural component.
Specifically, in the present embodiment, the extracted member cross-sectional form includes a rectangular shape and an i-shape. The approaches include two types: firstly, selecting an interested structural member through a BIM model, and extracting point cloud data of the member in a certain range of space occupation and periphery according to space coordinates and cross section geometric parameters of the member in the BIM model; and secondly, directly framing and selecting new structural components which do not exist in the BIM found after the BIM is compared with the actually measured point cloud data model. And carrying out smooth denoising on the selected point cloud data by a bilateral filtering method.
S600, fitting a three-dimensional outer contour plane of the structural member and fitting a section geometric boundary to obtain a member section geometric parameter;
in S600 of this embodiment, fitting the three-dimensional outer contour plane of the structural member and fitting the geometric boundary of the cross section to obtain geometric parameters of the cross section of the structural member specifically includes: fitting a three-dimensional outer contour plane of the structural member by using an RANSAC method, determining the accurate space occupation of the structural member, and filtering point cloud data which do not belong to the structural member; averagely dividing the structural member into a plurality of sections according to the axial length, cutting each section, fitting the boundary lines of the section of the structural member by using a RANSAC method, measuring the geometric design parameters of the section of the structural member, and calculating the average value and the mean square error of the geometric design parameters of the section according to a plurality of sections of measurement results; wherein, for a rectangular cross-section, the geometric design parameters include the length and width of the rectangular cross-section; for an i-section, the geometric design parameters include the length and thickness of the flanges and webs.
In some preferred embodiments, the specific steps of fitting the single plane include:
randomly selecting 3 non-collinear seed points from the point cloud data set of the component to establish an initial plane L 0 :α 0 x+β 0 y+γ 0 z+δ 0 ＝0；
Calculating the point cloud data set of the component by removing the rest points of the 3 seed points to the plane L 0 The distance of (a); when the distance is less than a given distance threshold d ε Then count the point into the plane L 0 The in-plane point set of (3);
repeating the steps for n times, comparing the points of the point set in the current plane with the points of the point set in the previous plane each time, and reserving the plane with larger points;
and after n times of circulation is finished, recalculating optimal plane parameters alpha, beta, gamma and delta according to the point cloud data with the maximum point set number in the plane.
For structural members with rectangular sections, carrying out plane fitting for 4 times in total; carrying out plane fitting for 12 times on the structural member with the I-shaped section; and after each plane is fitted, removing the point cloud data in the plane, and then fitting the next plane. d is a radical of ε The value-vision mapping precision and the sampling density are adjusted, and are not more than 5cm; and n is determined according to the point cloud data set number of the view component and is not less than 30. And determining two end surfaces of the component through the plane and point cloud data which are fitted in the axial length direction of the component.
In some preferred embodiments, the specific step of fitting the boundary line on the section of the single component comprises:
randomly selecting 2 seed points which are not collinear from a point cloud data set of a cross section, and establishing an initial straight line l 0 :A 0 x+B 0 y+C 0 ＝0；
Calculating the point cloud data set of the cross section, removing the rest points of the 2 seed points to the straight line l 0 The vertical distance of (d); when the distance is less than a given distance threshold d ∈ Then count the point into the straight line l 0 The linear inner point set of (2);
repeating the steps m times, comparing the number of points in the current straight line inner point set with the number of points in the previous straight line inner point set each time, and reserving a straight line with larger number of points;
after m cycles are finished, the optimal straight line parameter A is recalculated according to the point cloud data with the maximum point set number in the straight line 0 、B 0 And C 0 。
For a structural member with a rectangular section, carrying out boundary line fitting on each section for 4 times; for junctions with I-shaped cross-sectionA structural member, which is subjected to 12-time boundary line fitting; after each straight line is fitted, point cloud data in the straight line is removed, and then the next straight line is fitted; d ∈ The value-taking visual mapping precision and the sampling density are adjusted, and are not suitable to be larger than 5cm; and the number of point cloud data collection points of the value view component of m is determined and is not less than 30.
And for a single component, averagely dividing the component into w sections according to the axial length, performing boundary fitting of w +1 sections, measuring the boundary dimension length of each section, and calculating the average value and the mean square error of the geometric design parameters of the section of the component. The value of w is adjusted according to the length of the component, and an integer between 8 and 20 is recommended. For rectangular cross sections, the geometric design parameters include the length and width of the rectangle; for rectangular cross sections, the geometric design parameters include the length and thickness of the flanges, webs.
S700, calculating the inclination rate and the bending rate of the structural member according to the acquired geometric parameters of the section of the structural member.
In S700 of the present embodiment, calculating the inclination rate and the curvature rate of the extracted structural member includes: calculating the inclination rates of the structural member in two directions of the section centroid main shaft according to the centroid coordinates of the end surfaces of the two ends of the structural member and the length of the structural member; and calculating the bending rate of the structural member in two directions of the centroid main shaft of the cross section according to the centroid coordinates of the cross section and the cross section of one end of the structural member.
Specifically, the inclination rate of the member in two directions of the cross-section centroid main shaft is calculated through centroid coordinates of two end faces of the member, and the calculation formula of the inclination rate of the member is as follows:
deltax and deltay represent the rate of tilt of the member in the x-and y-axis directions, respectively, of the principal axis of the section centroid, deltax 1 And Δ y 1 The coordinate difference of the coordinates of the centroids of the cross sections of the two ends of the member in the x and y directions is shown. X 1 And Y 1 Is the centroid coordinate, X, of the component end face 1 2 And Y 2 Is the centroid coordinate of the component end face 2. L is the length of the member and is the centroid of the cross section of the two ends of the memberAbsolute value of coordinate difference | Z in out-of-plane normal phase direction 1 -Z 2 And l is determined.
Specifically, the bending ratios of the member in two directions of the section centroid main axis are calculated through the centroid coordinates of one end surface and the midspan section of the member, and the calculation formula of the bending ratio of the member is as follows:
ε x and ε y represent the bending ratios of the member in the directions of the x-axis and y-axis of the centroid of the cross section, respectively, Δ x 2 、Δy 2 The difference in coordinates of the centroid coordinates of the member end face and the cross-section in the x, y and z directions is taken. X 3 And Y 3 Is the centroid coordinate of the member across the mid-section 3.
The embodiment discloses a method for extracting parameters of a structural member and measuring deformation of the structural member based on laser point cloud, which comprises the following steps: carrying out panoramic coverage type scanning on a target structure system by adopting laser scanning equipment to obtain point cloud data of the target structure system; preprocessing the laser point cloud data, eliminating noise points and redundant data irrelevant to structural members, thinning and simplifying the point cloud data, and ensuring the uniform spacing of the sampling point clouds; carrying out integral registration on the preprocessed laser point cloud data and the BIM three-dimensional model, and unifying the original point positioning and the coordinates; dividing the point cloud model after integral registration according to floors, registering the point cloud model with a floor axis network, and adjusting the coordinates of each layer of point cloud model; extracting point cloud data belonging to a structural member from the point cloud model after each layer of calibration, wherein the section form of the structural member comprises a rectangle and an I shape; fitting a three-dimensional outer contour plane of the structural member and fitting a section geometric boundary to obtain a member section geometric parameter; and calculating the inclination rate and the bending rate of the structural member according to the acquired geometric parameters of the section of the structural member.
It should be understood that the specific order or hierarchy of steps in the processes disclosed is an example of exemplary approaches. Based upon design preferences, it is understood that the specific order or hierarchy of steps in the processes may be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order, and are not intended to be limited to the specific order or hierarchy presented.
In the foregoing detailed description, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments of the subject matter require more features than are expressly recited in each claim. Rather, as the following claims reflect, invention lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. Of course, the processor and the storage medium may reside as discrete components in a user terminal.
For a software implementation, the techniques described herein may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes may be stored in memory units and executed by processors. The memory unit may be implemented within the processor or external to the processor, in which case it can be communicatively coupled to the processor via various means as is known in the art.
What has been described above includes examples of one or more embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the aforementioned embodiments, but one of ordinary skill in the art may recognize that many further combinations and permutations of various embodiments are possible. Accordingly, the embodiments described herein are intended to embrace all such alterations, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent that the term "includes" is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term "comprising" as "comprising" is interpreted when employed as a transitional word in a claim. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or".
Claims (10)
1. A method for extracting parameters and measuring deformation of a structural member based on laser point cloud is characterized by comprising the following steps:
s100, adopting laser scanning equipment to carry out panoramic coverage type scanning on a target structural system to obtain point cloud data of the target structural system;
s200, preprocessing the laser point cloud data, eliminating noise and redundant data irrelevant to structural members, thinning and simplifying the point cloud data, and ensuring the uniform spacing of the sampling point clouds;
s300, carrying out integral registration on the preprocessed laser point cloud data and the BIM three-dimensional model, and unifying origin positioning and coordinates;
s400, partitioning the point cloud model after integral registration according to floors, registering the point cloud model with a floor axis network, and adjusting the coordinates of each layer of point cloud model;
s500, extracting point cloud data belonging to a structural member from the point cloud model after each layer of calibration, wherein the section form of the structural member comprises a rectangle and an I shape;
s600, fitting a three-dimensional outer contour plane of the structural member and fitting a section geometric boundary to obtain a member section geometric parameter;
s700, calculating the inclination rate and the bending rate of the structural member according to the acquired geometric parameters of the section of the structural member.
2. The method for extracting parameters and measuring deformation of a structural member based on laser point cloud as claimed in claim 1, wherein in S100, the laser scanning device adopts a ground-based laser scanning system, and the scanning precision is not lower than 1mm; the scan range covers all the major structural members and connecting nodes of the target architecture.
3. The method for extracting parameters and measuring deformation of a structural member based on laser point cloud as claimed in claim 1, wherein in S200, point cloud data is denoised by an euclidean distance outlier to remove discrete interference noise points; removing redundant point cloud data including human images and sundries through manual intervention selection; and thinning and simplifying the point cloud data by using an even grid method, and ensuring that the distance between the sampled point clouds is even and is not more than 1mm.
4. The method for extracting parameters and measuring deformation of a structural member based on laser point cloud as claimed in claim 1, wherein the step S300 of integrally registering the preprocessed laser point cloud data with the BIM three-dimensional model comprises: and adjusting the global origin and the three-dimensional coordinate direction of the actually measured laser point cloud data according to the global origin and the three-dimensional coordinate direction of the BIM model, calculating the contact ratio of the actually measured laser point cloud data and the BIM model after each adjustment, and giving a new three-dimensional coordinate value to the actually measured laser point cloud according to the coordinate change parameter when the contact ratio of the two models is the maximum.
5. The method for extracting parameters and measuring deformation of a structural member based on laser point cloud as claimed in claim 1, wherein in S400, the point cloud model after the integral registration is segmented according to floors and registered with a floor axis network, and coordinates of each layer of point cloud model are adjusted, specifically comprising: according to the elevation information of each floor of the building, partitioning point cloud data in the height direction of the floor; and picking up the section centroids of the main frame columns from the partitioned floor point cloud data, connecting the section centroids of the frame columns, and calibrating the section centroids with the floor axis network through the section centroid coordinates and the connecting direction of the frame columns.
6. The method for extracting parameters and measuring deformation of a structural member based on laser point cloud as claimed in claim 1, wherein in S500, extracting point cloud data of a target structural member from each layer of calibrated point cloud model comprises: for a structural member existing in the BIM, extracting point cloud data of the structural member in a certain range of space occupation and periphery according to the space coordinate and cross section geometric design parameters of the structural member in the BIM; and (3) finding a newly added structural component which does not exist in the BIM model by comparing the BIM model with the actually measured point cloud model, and directly performing frame selection in the point cloud model for the newly added structural component to obtain point cloud data of the newly added structural component.
7. The method for extracting parameters and measuring deformation of a structural member based on laser point cloud as claimed in claim 1, wherein in S600, fitting the three-dimensional outer contour plane and the cross-sectional geometric boundary of the structural member to obtain the cross-sectional geometric parameters of the structural member specifically comprises: fitting a three-dimensional outer contour plane of the structural member by using an RANSAC method, determining the accurate space occupation of the structural member, and filtering point cloud data which do not belong to the structural member; averagely dividing the structural member into a plurality of sections according to the axial length, cutting each section, fitting the boundary lines of the section of the structural member by using a RANSAC method, measuring the geometric design parameters of the section of the structural member, and calculating the average value and the mean square error of the geometric design parameters of the section according to a plurality of sections of measurement results; wherein, for a rectangular cross-section, the geometric design parameters include the length and width of the rectangular cross-section; for an i-section, the geometric design parameters include the length and thickness of the flanges and webs.
8. The method for extracting parameters and measuring deformation of a structural member based on laser point cloud as claimed in claim 7, wherein the step of fitting a single plane comprises:
randomly selecting 3 non-collinear seed points from the point cloud data set of the member to establish an initial plane L 0 :α 0 x+β 0 y+γ 0 z+δ 0 ＝0；
Calculating the point cloud data set of the component by removing the rest points of the 3 seed points to the plane L 0 The distance of (d); when the distance is less than a given distance threshold d ε Then the point is counted into the plane L 0 The in-plane point set of (1);
repeating the steps for n times, comparing the points of the point set in the current plane with the points of the point set in the previous plane each time, and reserving the plane with larger points;
and after n times of circulation is finished, recalculating optimal plane parameters alpha, beta, gamma and delta according to the point cloud data with the maximum point set number in the plane.
9. The method for extracting parameters and measuring deformation of a structural member based on laser point cloud as claimed in claim 7, wherein the specific step of fitting the boundary lines on the section of a single member comprises:
randomly selecting 2 seed points which are not collinear from a point cloud data set of a cross section, and establishing an initial straight line l 0 :A 0 x+B 0 y+C 0 ＝0；
Calculating the point cloud data set of the cross section, removing the rest points of the 2 seed points to the straight line l 0 The vertical distance of (c); when the distance is less than a given distance threshold d ∈ Then count the point into the straight line l 0 The linear inner point set of (2);
repeating the steps m times, comparing the number of points in the current straight line inner point set with the number of points in the previous straight line inner point set each time, and reserving a straight line with larger number of points;
after m cycles are finished, the optimal straight line parameter A is recalculated according to the point cloud data with the maximum point set number in the straight line 0 、B 0 And C 0 。
10. The method of claim 1, wherein calculating the tilt rate and the bend rate of the extracted structural member comprises: calculating the inclination rate of the structural member in two directions of the section centroid main shaft according to the centroid coordinates of the end surfaces of the two ends of the structural member and the length of the structural member; and calculating the bending rate of the structural member in two directions of the centroid main shaft of the cross section according to the centroid coordinates of the cross section and the cross section of one end of the structural member.
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