CN115619969A - Building damage trace measuring method based on laser point cloud difference - Google Patents
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Abstract
The invention discloses a method for measuring damage traces of a building based on laser point cloud difference, which relates to the field of measuring damage traces of building weapons and evaluating damage effects.
Description
Technical Field
The invention relates to the field of building weapon damage trace measurement and damage effect evaluation, in particular to a method for measuring a building damage trace based on laser point cloud difference, and specifically relates to a typical building damage trace extraction and damage characteristic multi-parameter description method based on multi-platform repeated scanning laser point cloud data difference technology.
Background
It is known that high-precision extraction and measurement of damage traces of a building are always the basic methods for evaluating the damage effect of the building and the damage capability of a weapon, and the building is used for various engineering facilities for avoiding and reducing the damage effect of the weapon, such as protective building facilities, shelters, command centers and the like, and generally comprises various urban basic building facilities such as residences, markets, hospitals, airports, pipelines, railways and the like. The important building is exposed to the risk of being hit and damaged by weapons in war, especially various protection projects with important military value, which are used as important support and guarantee strength in modern war and are also main targets of weapon hitting and damage, while damage evaluation is a main method for analyzing damage degree of building, can scientifically reflect damage condition of building and damage force of weapon system, provide comprehensive evaluation for the damage degree of building core function after damage, and provide necessary basic data for analyzing damage efficiency of weapon system and optimizing hit strategy. Therefore, the high-precision quantitative extraction and the characteristic parameter analysis of the damage traces are important supports for the evaluation of the damage performance of the building damage and the weapon system.
Generally speaking, field and field measurement, image acquisition and expert evaluation of a damaged area of a building are the main means for analyzing and judging the damage of a building weapon at present. The main performance characteristics of the damage of the building structure are craters and openings caused by the attack of weapons, structural damage caused by the craters and the openings, the extraction and analysis of characteristics such as gray information, shape distribution, size, texture and the like of corresponding typical characteristics of the damage are the main ways for identifying and analyzing relevant characteristics based on image processing methods at present, the method is mainly applied to identifying craters of the damage of the weapons, such as craters on runways and the like, and the damage degrees of different attacks are evaluated through statistics of corresponding characteristic points, so that the method can also play a role in extracting damage traces of the building structure, such as identifying the central area of the damage, the damage influence range and the like.
However, the above-mentioned damage identification and evaluation algorithm based on images has the problem of low extraction precision of damage traces, and relies on external light sources, generally only can identify the information of the position and size of the damage, lacks the sufficient mining of the three-dimensional information of the damaged area, and has a deficiency in extracting the volume information of the damage traces. Moreover, the traditional field investigation method is easily limited by disasters such as complex scene damage and potential collapse, and has the problems of high operation risk, low efficiency, poor precision and the like. In addition, the traditional method relying on manual work or image recognition usually pays more attention to large-scale damage traces, and has defects in measurement precision and reliability, because the damage regions are difficult to reach or have potential accident risks, partial damage regions can not be observed deeply inside, so that the loss of key damage information is caused, the accuracy of interpretation of a damage expert is greatly influenced, besides, the damage of the building is reflected in the damage of components, the spattered gravels formed along with the damage are also important references for judging the damage traces and the size, but the small-scale damage information can be easily ignored in the traditional damage judgment, so that the related characteristic distribution of the damage can not be completely and accurately carved, and the like.
Disclosure of Invention
In order to overcome the defects in the background technology, the invention provides a building damage trace measuring method based on laser point cloud difference, the invention utilizes a multi-platform three-dimensional monitoring method to comprehensively obtain point cloud data of building geometric structure information of a target area, three-dimensional point cloud data before and after the building damage and a segmentation model are registered at high precision, and the difference, segmentation and feature description method provided by the invention is utilized to excavate damage-related three-dimensional geometric feature information, thereby realizing accurate extraction and feature analysis of the building damage trace, ensuring the scientificity and reasonability of damage assessment and the like.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a method for measuring damage traces of a building based on laser point cloud difference specifically comprises the following steps:
firstly, according to the geometric structure, scale and surrounding environment of a building, a three-dimensional laser radar platform is built, a corresponding base station position is selected, multi-platform three-dimensional monitoring on the building is formed, and high-precision point cloud data of the building in a target area are obtained;
secondly, performing data preprocessing on the three-dimensional point cloud acquired by the laser radars of different platforms, specifically comprising outlier deletion, noise point removal, three-dimensional point cloud adaptive filtering, point cloud down-sampling, and feature line and surface extraction;
thirdly, performing high-precision registration of multi-platform point cloud data based on the overlapping area;
fourthly, based on the integrated three-dimensional point cloud data after the fusion, three-dimensional model reconstruction is carried out on the building, and the segmentation and the weighting of the building structure main body are carried out by utilizing the geometric form information and the regular repetitive characteristics of the reconstruction model, so as to represent the importance of each segmentation component;
fifthly, acquiring a three-dimensional point cloud model and a segmentation model before the damage of the building, repeatedly utilizing the steps 1-4 to finish the three-dimensional point cloud model and the segmentation result after the damage, and then registering three-dimensional point cloud data and the segmentation model before and after the damage of the building with high precision according to the line-plane and other characteristics (reference points) of the undamaged area and the minimum optimization method of point cloud distance;
carrying out differential processing on the three-dimensional point cloud and the segmentation model based on the three-dimensional point cloud and the segmentation model before and after the registered building is damaged under the same three-dimensional coordinate system, quantitatively calculating the volume change of the three-dimensional model of the internal structure and the external structure of the building before and after the damage based on a differential result, obtaining a three-dimensional model differential body brought after the damage by setting a minimum effective available damage volume threshold value, carrying out differential body segmentation, and judging the attribute of the three-dimensional model differential body to be an added body or a subtracted body according to the volume change trend;
seventhly, respectively describing characteristic parameters of the reduction body and the increase body by respectively utilizing a cuboid envelope and a Weir distribution function according to the three-dimensional space position distribution and volume quantity information of the difference body, and respectively obtaining a cuboid parameter corresponding to the increase body and an asymmetric Gaussian function parameter corresponding to the reduction body by utilizing a maximum likelihood optimization estimation method;
and eighthly, comprehensively calculating the corresponding damage amount of the building according to the cuboid parameter (subtraction), the Weir distribution (addition) function and the characteristic parameters thereof and the weighted volume amount of the addition and the subtraction, thereby accurately reflecting the damage effect caused by explosion.
The method for measuring the damage traces of the building based on the laser point cloud difference comprises the steps that when a three-dimensional laser radar platform is built in the first step, according to the internal and external geometric characteristics of the current building, an unmanned aerial vehicle laser radar platform P1, a ground laser radar platform P2 and a mobile laser radar platform P3 are selected and used for obtaining a building upper point cloud data set D1, an external vertical face point cloud data set D2 and an internal structure point cloud data set D3 respectively, wherein each point cloud data set is composed of one or more subsets, and each point cloud subset corresponds to an observation result.
According to the method for measuring the damage traces of the building based on the laser point cloud difference, when the corresponding base station position is selected in the first step, in order to reduce the workload of subsequent point cloud data registration, the corresponding observation station needs to be selected according to the geometric characteristics of the building structure, so that the station moving times are reduced, and meanwhile, the cloud data set at each observation time has certain repeatability, so that the precision and the reliability of point cloud data set registration are improved.
According to the building damage trace measuring method based on the laser point cloud difference, when the multi-platform three-dimensional monitoring of the building is carried out in the first step, indoor and outdoor combination for the building is formed, and then the multi-platform three-dimensional monitoring method assisted by the unmanned aerial vehicle laser radar platform P1 is added, so that the point cloud data of the geometric structure information of the building in the target area can be comprehensively obtained, meanwhile, the point cloud data of different platforms are required to have certain repeatability, and a foundation is provided for splicing of the complete point cloud data of the subsequent target area.
According to the method for measuring the damage traces of the building based on the laser point cloud difference, when the pretreatment of the three-dimensional laser point cloud data is performed in the second step, due to the fact that the density and the precision of the point cloud data obtained by laser radars of different platforms are different, corresponding parameters need to be selected in a differentiation mode during the pretreatment of the point cloud data, and therefore a better filtering effect is achieved.
According to the method for measuring the damage traces of the building based on the laser point cloud difference, when the high-precision registration of multi-platform point cloud data based on the registration area is carried out in the third step, the advantages of different platforms can be exerted through multi-platform combination, the defect that only point clouds outside or inside the building are obtained through a traditional single platform is overcome, in order to unify the point cloud data of the different platforms to the same coordinate system, the high-precision registration and the unification of the coordinate system of the point clouds of the different platforms are achieved through the optimal methods such as point cloud characteristics of the registration parts of the different platforms, weighted least squares and the like, and therefore the global fusion point cloud data reflecting the internal and external geometric characteristics of the building are obtained.
According to the method for measuring the damage traces of the building based on the laser point cloud difference, the partition component in the fourth step comprises a wall body, a floor slab, a cross beam, a supporting beam or an internal object, and the weight of the bearing component related to the structural stability is the largest.
According to the method for measuring the damage trace of the building based on the laser point cloud difference, in the sixth step, the volume increase volume and the volume decrease volume are basically consistent in the total volume, but are different in spatial distribution, the increase volume is relatively discrete, and the decrease volume is relatively concentrated.
According to the method for measuring the damage trace of the building based on the laser point cloud difference, the description parameters of the outer enveloping cuboid of the reducer in the seventh step mainly comprise: the maximum and minimum values of the subtracted volume in the three XYZ axes are formed to form a corresponding cuboid, taking a wall damage notch perpendicular to the X axis as an example, the cuboid corresponding to the corresponding subtracted volume is a yellow region, the area of influence is an enveloping rectangle (Zmin, zmax, ymin, ymax) in the ZY plane at a point perpendicular to the X axis and intersecting at Xi point, and the thickness of the wall is the thickness (H) of the corresponding cuboid, so that a corresponding subtracted enveloping cuboid (Xi, ymin, zmin) (Xi, ymin, zmax) (Xi, ymax, zmax) (Xi-H, ymin, zmax) (Xi-H, ymax, zmax-H, ymin, zmax) is formed, the other corresponding subtracted enveloping cuboids are obtained in the same way, and the description of the added volume is described by using a significant asymmetry, and the corresponding functions of the added volume are described by using the weill distribution formula as follows,
wherein β is a shape parameter of the augmented body distributed along the distance to the volume, η is a corresponding scaling coefficient, γ is a position parameter, and a value is generally 0, it can be seen that the augmented body volume distribution of a certain area is counted by taking Xc and Yc coordinates of the damage center as the center, taking the distance horizontal axis of the augmented body from the point and taking the augmented body volume as the vertical axis, and the weil distribution function and the corresponding parameters can be used for describing the distribution characteristics.
In the eighth step, for cuboid parameters (reduction), the corresponding total damage volume Vneg is obtained through weighting calculation by using the weight and the volume of each member, and the cuboid volume Vs is obtained through calculation by using an outer envelope cuboid; for Weir distribution (volume increase), the volume increase accumulated value Vpos of a corresponding function is calculated on the basis of obtaining corresponding Weir distribution parameters, the volume increase total volume Vz can be calculated according to volume increase segmentation results, and high-precision fine measurement and analysis can be performed on damage based on the parameters and information.
By adopting the technical scheme, the invention has the following advantages:
the invention solves the problems of high operation risk, low efficiency, poor precision and the like, fully excavates and extracts three-dimensional information of a building and a component damage area (including small-scale damage information such as splashed broken stones) of the building, comprehensively obtains detailed point cloud information of the building, efficiently and accurately constructs a three-dimensional point cloud model of the building, objectively describes damage traces of the building by using the difference, segmentation and feature description method, provides a corresponding description function, can provide more reliable data base support for quantitatively analyzing the damage degree of the building, and the like, and is suitable for large-scale popularization and application.
Drawings
FIG. 1 is a schematic diagram of a multi-platform point cloud acquisition and model construction of a construction according to the present invention;
FIG. 2 is a schematic view of a defect-related debulking and debulking of the present invention;
FIG. 3 is a defect-related augmented reality scoring coordinate system of the present invention;
FIG. 4 is a schematic illustration of the statistical range distribution of the lesion minus in vitro envelope and the augmented volume in the present invention;
FIG. 5 is a statistical distribution (distance and volume coordinate system) of the augmented volume in the present invention.
Detailed Description
The present invention will be explained in more detail by the following examples, which are not intended to limit the invention;
in the invention, fig. 1 is a schematic diagram of building multi-platform point cloud acquisition and model construction, wherein (1) represents various three-dimensional laser radar platforms which generally need to be reasonably selected according to the geometric distribution of a target building, and the common structures mainly include an unmanned aerial vehicle platform, a ground fixed platform and a mobile platform as shown in the figure. (2) The method is mainly used for indicating the wall, indicating the three-dimensional point cloud and the model constructed based on the point cloud through a simplified wall model, and describing the thickness information of the wall through the point cloud model as the point cloud after registration is positioned in a uniform coordinate system. (3) Used to illustrate the ground point cloud. (4) And representing a unified coordinate system after multi-platform point cloud registration.
Fig. 2 is a schematic diagram of a damage-related reduction and increase, which is mainly used for illustrating wall gaps caused by explosion and fragments scattered around due to wall damage, wherein a missing part on an original building object is called a reduction, as shown in (5) in the diagram, and a part increased on an original geometric structure caused by explosion is called an increase, as shown in (6) in the diagram, wherein the increases caused by explosion on the inner side and the outer side of the wall are not communicated in spatial distribution and are segmented by the wall.
Fig. 3 is a damage-related augmented statistical distribution coordinate system, which mainly takes one side of a wall as an example for explaining the constructed augmented statistical distribution, and as shown in (7) in the figure, the augmented statistical distribution on both sides of the wall is counted respectively with the projection O' point of the damage center on the XY plane as the origin, the distance of the augmented object from the origin as the horizontal axis, and the volume of the augmented object as the vertical axis, wherein the semi-circles of the dotted lines are corresponding equidistant lines.
FIG. 4 is a schematic diagram of the statistical range distribution of the outer envelope and the volume increase of the damaged wall, wherein the cuboid of the outer envelope of the damaged wall is shown as (8) in the figure.
Fig. 5 is a statistical distribution of bulks (distance and volume coordinate system), where (9) represents the distribution of bulks on both sides of the wall, and the vertical axis is the cumulative volume at the same distance.
The three-dimensional laser scanning technology has the characteristics of rapidness, high accuracy and high automation degree, is an operation mode for efficiently and accurately acquiring the three-dimensional point cloud model of the building, and can provide an important method and technical support for accurately and efficiently acquiring the three-dimensional point cloud model of the building. Compared with the traditional damage assessment method based on images, the laser radar has the characteristics of high point cloud density, high measurement precision, capability of multi-platform carrying and the like, personnel do not need to enter the damaged building, the detailed point cloud information of the building can be comprehensively obtained, the all-day measurement can be carried out on the damaged area, the damage feature extraction efficiency and reliability can be greatly improved, high-precision three-dimensional models of the building under different scenes can be formed by means of the point cloud data of the building, the accurate carving of different structures forming the building can be realized, and compared with the traditional method, the building feature carving capability based on the point cloud is stronger, and the scientificity and reasonability of the damage assessment can be ensured. Therefore, based on the three-dimensional point cloud models of the building before and after damage, the three-dimensional geometric characteristic information related to damage is mined, a reasonable evaluation model is built, more accurate data support can be provided for building damage evaluation and weapon damage efficiency evaluation, and a method system for building damage characteristic extraction and application research can be further enriched.
By means of the characteristics of large scanning scene, long working distance and high cloud density of data acquisition precision Gao Hedian of laser radar, on the basis of establishing high-precision three-dimensional high-precision models before and after the damage of a building by utilizing point cloud filtering smoothness, point cloud segmentation, point cloud-based three-dimensional modeling and the like, accurate extraction and characteristic analysis of damage traces of the building are realized by utilizing the difference, segmentation and characteristic description method provided by the invention. The three-dimensional space distribution characteristics of the damage characteristics and the corresponding volume measurement can objectively and accurately describe the damage trace of the building, provide a corresponding description function and provide more reliable data base support for quantitatively analyzing the damage degree of the building.
With reference to fig. 1 to 5, the method for measuring a damage trace of a building based on laser point cloud difference specifically comprises the following steps:
firstly, as shown in fig. 1, according to the geometric structure, scale and surrounding environment of a building, constructing a three-dimensional laser radar platform and selecting a corresponding base station position to form multi-platform three-dimensional monitoring of the building and acquire high-precision point cloud data of the building in a target area;
in specific implementation, when a three-dimensional laser radar platform is constructed, an unmanned aerial vehicle laser radar platform P1, a ground laser radar platform P2 and a mobile laser radar platform P3 are selected according to internal and external geometric characteristics of a current constructed object and are respectively used for acquiring an upper point cloud data set D1, an external facade point cloud data set D2 and an internal structure point cloud data set D3 of the constructed object, wherein each point cloud data set consists of one or more subsets, and each point cloud subset corresponds to an observation result;
furthermore, when a corresponding base station position is selected, in order to reduce the workload of subsequent point cloud data registration, a corresponding observation station needs to be selected according to the geometric characteristics of a building structure, so that the station moving times are reduced, and meanwhile, the point cloud data set is ensured to have certain repeatability in each observation, so that the precision and the reliability of point cloud data set registration are improved;
further, when the multi-platform three-dimensional monitoring of the building is carried out, a multi-platform three-dimensional monitoring method which aims at indoor and outdoor combination of the building and is assisted by an unmanned aerial vehicle laser radar platform P1 is formed, point cloud data of geometric structure information of the building in a target area are comprehensively obtained, meanwhile, the point cloud data of different platforms are required to have certain repeatability, and a foundation is laid for splicing of complete point cloud data of a subsequent target area;
secondly, performing data preprocessing on the three-dimensional point cloud acquired by the laser radars of different platforms, specifically comprising outlier deletion, noise point removal, three-dimensional point cloud adaptive filtering, point cloud down-sampling, feature line and surface extraction, reducing the influence of environmental noise and system noise, and laying a high-quality data base for the subsequent registration of the point cloud data of different platforms;
in specific implementation, when the preprocessing of the three-dimensional laser point cloud data is carried out, due to the fact that the density and the precision of the point cloud data acquired by the laser radars of different platforms are different, corresponding parameters need to be selected in a differentiation mode during the preprocessing of the point cloud data, and therefore a better filtering effect is achieved;
thirdly, performing high-precision registration of multi-platform point cloud data based on the overlapping area;
in specific implementation, when multi-platform point cloud data based on a superposition area are subjected to high-precision registration, multi-platform combination can exert the advantages of different platforms, the defect that only point clouds outside or inside a building are obtained by a traditional single platform is overcome, in order to unify the point cloud data of different platforms to the same coordinate system, the high-precision registration and the coordinate system unification of the point cloud data of different platforms are realized by using the optimal methods such as point cloud characteristics and weighted least squares of the superposition parts of different platforms, and therefore global fusion point cloud data reflecting the internal and external geometric characteristics of the building are obtained;
in the implementation, because the external and internal structures of the building are generally complex, complete point cloud information of a corresponding target is difficult to obtain comprehensively only by depending on a single observation station, the initial references of three-dimensional point cloud data of multiple platforms and multiple observation stations are different, in order to enable the point cloud data to accurately reflect the geometric characteristics of the target, corresponding rotation matrix Rij and offset matrix Tij need to be obtained by depending on the point cloud data of each observation repeated region, i and j represent the point cloud data with a common part obtained at the i and j stations, and in order to further improve the precision and reliability of registration, the constraint of the corresponding point cloud line-surface characteristics is also added to the final target function min | Rij | Di-Tij-Dj | + line or surface characteristic superposition constraint, so as to obtain coordinate transformation parameters between different point clouds, thereby realizing the operational registration of the point clouds and obtaining the complete point cloud reflecting the geometric characteristics of the building;
fourthly, based on the integrated three-dimensional point cloud data after fusion, three-dimensional model reconstruction is carried out on the building, and the segmentation and weighting of the building structure main body are carried out by utilizing the geometric form information and the regular repetition characteristics of the reconstruction model, so as to represent the importance of each segmentation component;
in specific implementation, the partition components comprise walls, floors, beams, support beams or internal objects, and the weight of the bearing component related to the structural stability is the largest;
fifthly, acquiring a three-dimensional point cloud model and a segmentation model before the damage of the building, repeatedly utilizing the steps 1-4 to finish the three-dimensional point cloud model and the segmentation result after the damage, and then registering three-dimensional point cloud data and the segmentation model before and after the damage of the building with high precision according to the line-plane and other characteristics (reference points) of the undamaged area and the minimum optimization method of point cloud distance;
sixthly, as shown in fig. 2, based on the three-dimensional point cloud and the segmentation model before and after the damage of the registered building, carrying out differential processing on the three-dimensional point cloud and the segmentation model under the same three-dimensional coordinate system, quantitatively calculating the volume change of the three-dimensional models of the internal and external structures of the building before and after the damage based on the differential result, obtaining a three-dimensional model differential body brought after the damage by setting a minimum effective available damage volume threshold value, carrying out differential body segmentation, and judging the attribute to be an increase body or a decrease body according to the volume change trend;
in specific implementation, the volume increase volume and the volume decrease volume are basically consistent in volume total amount, but the spatial distribution is different, the volume increase is relatively discrete, and the volume decrease is relatively concentrated;
seventhly, as shown in fig. 3, 4 and 5, according to the three-dimensional spatial position distribution of the difference body and the volume quantity information thereof, respectively using a cuboid envelope and a Weir distribution function to respectively describe characteristic parameters of the added body and the subtracted body, and respectively obtaining a cuboid parameter corresponding to the added body and an asymmetric Gaussian function parameter corresponding to the subtracted body by using a maximum likelihood optimization estimation method;
in specific implementation, the parameters describing parameters of the external envelope cuboid of the reducer mainly comprise: the maximum and minimum values of the subtracted volume in the three XYZ axes are formed to form a corresponding rectangular volume, for example, a wall damage notch perpendicular to the X axis (as shown in fig. 4), the rectangular volume corresponding to the corresponding subtracted volume is a yellow region, the area of influence is an enveloping rectangle (Zmin, zmax, ymin, ymax) in the ZY plane at a point perpendicular to the X axis and intersecting Xi, and the thickness of the wall is the thickness (H) of the corresponding rectangular volume, thereby forming a corresponding subtracted enveloping rectangular volume (Xi, ymin, zmin) (Xi, ymax, zmax) (Xi-H, ymin, zmin) (Xi-H, ymin, zmax) (Xi-H, ymax, zmax) (Xi-H, ymax, zmax), other corresponding subtracted enveloping rectangular volume obtaining methods are the same as above, and the description of the added volume is described by using a significant asymmetry distribution, and the added volume is described by a function corresponding to the weier distribution formula (5) as shown below,
wherein beta is a shape parameter of the volume distribution of the added body along the distance, eta is a corresponding scaling coefficient, gamma is a position parameter, and the value is generally 0, therefore, the added body volume distribution of a certain area is counted by taking the coordinates of Xc and Yc of a damage center as the center, the distance horizontal axis of the added body from the point and the added body volume as the vertical axis, and a Weir distribution function and corresponding parameters can be used for describing the distribution characteristics;
eighthly, based on the cuboid parameters (reduction), the Weir distribution (increase) functions and the characteristic parameters thereof, and combining the weighted volume quantities of the increase and the decrease, comprehensively calculating the corresponding damage quantity of the building, thereby accurately reflecting the damage effect caused by explosion;
in specific implementation, for cuboid parameters (reduction), the corresponding total damaged volume Vneg is obtained through weighting calculation by using the weight and the volume of each member, and meanwhile, the cuboid volume Vs is obtained through calculation by using an outer enveloping cuboid; for Weir distribution (volume increase), the volume increase accumulated value Vpos of a corresponding function is calculated on the basis of obtaining corresponding Weir distribution parameters, the volume increase total volume Vz can be calculated according to volume increase segmentation results, and high-precision fine measurement and analysis can be performed on damage based on the parameters and information.
When the method is implemented specifically, the influence of the explosion damage area on the registration of the point cloud and the model of the building before and after explosion can be isolated by means of the constructed mask file, only the characteristics of the point cloud, the line surface and the like of the area which is less affected by damage or not affected by damage, namely the stable characteristics of the overlapping area are utilized to construct the target function similar to the step 3, and the rotation and offset matrixes Rqh and Tqh are obtained through optimized calculation, so that the registration of the multi-stage three-dimensional point cloud and the model before and after the building damage is realized.
Further, based on the registered three-dimensional models before and after the damage of the building, model differential processing under a three-dimensional coordinate system is carried out, the change of the three-dimensional models of the internal and external structures of the building before and after the damage is calculated quantitatively, the influence of measurement errors is avoided by setting a minimum effective available damage volume threshold value, so that a three-dimensional model differential body caused by the damage is obtained, the differential body is segmented according to the position and the change trend, and corresponding added body and subtracted body are judged, generally speaking, the total volume of the two bodies is basically consistent, but the distribution is different.
Further, in order to further clarify the attributes of the added body and the subtracted body obtained after the difference, according to the point cloud segmentation model, the segmentation and the weighting of the main body of the structure are carried out by utilizing the geometrical form information and the repeatability characteristics of the point cloud segmentation model, the importance of each component is represented, and the weight information of the corresponding component is utilized to supplement the weight information to the added body and the subtracted body. The weight parameters need to be adjusted in due time according to different types and purposes of the building, generally speaking, the weight of the structural component with the load bearing property is great, the weight of the inter-floor partition is inferior, the weight of the general wall is smaller, and the weight of the internal facilities of the building is minimum. If the basis for determining the damage effect is different, the weight parameters of the corresponding components are adjusted appropriately.
And further, according to the three-dimensional space position distribution and volume information of the differential body, and by using the weight information obtained in the last step, on the basis of a cuboid and an asymmetric Gaussian function (Weir function) respectively, and by using a maximum likelihood method, on the basis of obtaining the volume of the added and subtracted body through calculation, calculating the parameters of the external envelope cuboid of the subtracted body.
Further, aiming at the augmented part, the projection of the center of the main reduced body on the plane is taken as the center O ', the distance R of the augmented body from the center is taken as the horizontal axis, the weighted volume quantity of the augmented body is taken as the vertical axis, a corresponding distribution histogram is drawn, if the augmented part is divided by a wall body and the like, the augmented body of each divided area is respectively drawn according to the method, the ground projection O ' of the explosion center is taken as the center, the distance between the augmented body and the O ' is taken as the horizontal axis, the volume of the corresponding augmented body is taken as the vertical axis, and then the corresponding model parameters are estimated by utilizing a Weir distribution function and a maximum likelihood method.
Furthermore, based on the external envelope cuboid (reduction body), the Weir distribution function (increase body) and characteristic parameters thereof, high-precision monitoring of the damage traces of the building is carried out, so that the damage effect of the explosion on the building is quantitatively measured.
Compared with the traditional damage evaluation method, the method solves the problems of high operation risk, low efficiency, poor precision and the like, fully excavates and extracts three-dimensional information of the damaged area (including small-scale damage information such as splashed broken stones) of the building and the component of the building, comprehensively obtains detailed point cloud information of the building, efficiently and accurately constructs a three-dimensional point cloud model of the building, objectively and accurately describes damage traces of the building by using the difference, segmentation and characteristic description method provided by the invention, and provides a corresponding description function, thereby providing more reliable data base support for quantitatively analyzing damage and damage efficiency evaluation of the building.
The present invention is not described in detail in the prior art.
The embodiments chosen for the purpose of disclosure of the invention are presently considered to be suitable, however, it is to be understood that the invention is intended to cover all variations and modifications of the embodiments which fall within the spirit and scope of the invention.
Claims (10)
1. A method for measuring a damage trace of a building based on laser point cloud difference is characterized by comprising the following steps: the measuring method specifically comprises the following steps:
firstly, according to the geometric structure, scale and surrounding environment of a building, a three-dimensional laser radar platform is built, a corresponding base station position is selected, multi-platform three-dimensional monitoring on the building is formed, and high-precision point cloud data of the building in a target area are obtained;
secondly, performing data preprocessing on the three-dimensional point cloud acquired by the laser radars of different platforms, specifically comprising outlier deletion, noise point removal, three-dimensional point cloud adaptive filtering, point cloud down-sampling, and feature line and surface extraction;
thirdly, performing high-precision registration of multi-platform point cloud data based on the overlapping area;
fourthly, based on the integrated three-dimensional point cloud data after the fusion, three-dimensional model reconstruction is carried out on the building, and the segmentation and the weighting of the building structure main body are carried out by utilizing the geometric form information and the regular repetitive characteristics of the reconstruction model, so as to represent the importance of each segmentation component;
fifthly, acquiring a three-dimensional point cloud model and a segmentation model before the damage of the building, repeatedly utilizing the three-dimensional point cloud model and the segmentation result after the damage in the steps 1-4, and then registering three-dimensional point cloud data and the segmentation model before and after the damage of the building with high precision according to the line-surface and other characteristics (reference points) of the undamaged area and the minimum optimization method of point cloud distance;
carrying out differential processing on the three-dimensional point cloud and the segmentation model based on the three-dimensional point cloud before and after the registered building is damaged in the same three-dimensional coordinate system, quantitatively calculating the volume change of the three-dimensional models of the internal and external structures of the building before and after the damage based on a differential result, obtaining a three-dimensional model differential body brought after the damage by setting a minimum effective available damage volume threshold, carrying out differential body segmentation, and judging the attribute of the three-dimensional model to be an increase body or a decrease body according to the volume change trend;
seventhly, respectively utilizing a cuboid envelope and a Weir distribution function to respectively describe characteristic parameters of the added body and the subtracted body according to the three-dimensional spatial position distribution and the volume quantity information of the difference body, and respectively obtaining a cuboid parameter corresponding to the added body and an asymmetric Gaussian function parameter corresponding to the subtracted body by utilizing a maximum likelihood optimization estimation method;
and eighthly, comprehensively calculating the corresponding damage amount of the building according to the cuboid parameter (subtraction), the Weir distribution (addition) function and the characteristic parameters thereof and the weighted volume amount of the addition and the subtraction, thereby accurately reflecting the damage effect caused by explosion.
2. The method for measuring the damage trace of the building based on the laser point cloud difference as claimed in claim 1, wherein: when the three-dimensional laser radar platform is constructed in the first step, the unmanned aerial vehicle laser radar platform P1, the ground laser radar platform P2 and the mobile laser radar platform P3 are selected according to the internal and external geometric characteristics of the current constructed object and are respectively used for acquiring an upper point cloud data set D1, an external facade point cloud data set D2 and an internal structure point cloud data set D3 of the constructed object, wherein each point cloud data set is composed of one or more subsets, and each point cloud subset corresponds to an observation result.
3. The method for measuring the damage trace of the building based on the laser point cloud difference as claimed in claim 1, wherein: when the corresponding base station position is selected in the first step, in order to reduce the workload of subsequent point cloud data registration, the corresponding observation station needs to be selected according to the geometric characteristics of the building structure, so that the station moving times are reduced, and meanwhile, the point cloud data set at each observation time has certain repeatability, so that the precision and the reliability of point cloud data set registration are improved.
4. The method for measuring the damage trace of the building based on the laser point cloud difference as claimed in claim 1, wherein: when the multi-platform three-dimensional monitoring of the building is carried out in the first step, a multi-platform three-dimensional monitoring method which aims at indoor and outdoor combination of the building and is assisted by an unmanned aerial vehicle laser radar platform P1 is formed, point cloud data of geometric structure information of the building in a target area are comprehensively obtained, meanwhile, certain repeatability of point cloud data of different platforms is required to be ensured, and a foundation is provided for splicing of complete point cloud data of a subsequent target area.
5. The method for measuring the damage trace of the building based on the laser point cloud difference as claimed in claim 1, wherein: when the preprocessing of the three-dimensional laser point cloud data is performed in the second step, due to the fact that the density and the precision of the point cloud data acquired by the laser radars of different platforms are different, corresponding parameters need to be selected in a differentiation mode during the preprocessing of the point cloud data, and therefore a better filtering effect is achieved.
6. The method for measuring the damage trace of the building based on the laser point cloud difference as claimed in claim 1, wherein: when the high-precision registration of the multi-platform point cloud data based on the registration area is carried out in the third step, the multi-platform combination can exert the advantages of different platforms, the defect that only point clouds outside or inside the building are obtained by a traditional single platform is overcome, in order to unify the point cloud data of different platforms to the same coordinate system, the high-precision registration and the coordinate system unification of the point cloud data of different platforms are realized by using the optimal methods such as point cloud characteristics of the registration parts of different platforms, weighted least squares and the like, and therefore the global fusion point cloud data reflecting the internal and external geometric characteristics of the building are obtained.
7. The method for measuring the damage trace of the building based on the laser point cloud difference as claimed in claim 1, wherein: the fourth step is that the partitioning component comprises a wall, a floor slab, a beam, a supporting beam or an internal object, and the weight of the bearing component related to the structural stability is the largest.
8. The method for measuring the damage trace of the building based on the laser point cloud difference as claimed in claim 1, wherein: in the sixth step, the volume increase volume and the volume decrease volume are basically consistent in the total volume, but the spatial distribution is different, the increase volume is relatively discrete, and the decrease volume is relatively concentrated.
9. The method for measuring the damage trace of the building based on the laser point cloud difference as claimed in claim 1, wherein: the parameter description parameters of the external envelope cuboid of the subtraction body in the seventh step mainly comprise: the maximum value and the minimum value of the reducer on the XYZ three axes form a corresponding cuboid, taking a damage notch of the wall body vertical to the X axis as an example, the cuboid corresponding to the reducer is a yellow area, the influence area of the cuboid is an enveloping rectangle (Zmin, zmax, ymin, ymax) in a ZY plane at a point which is vertical to the X axis and intersects at an Xi point, and the thickness of the wall body is the thickness (H) of the corresponding cuboid, so that the corresponding reducer enveloping cuboid (Xi, ymin, zmin) (Xi, ymin, zmax) (Xi, ymax, zmin) is formed
(Xi, ymax, zmax) (Xi-H, ymin, zmin) (Xi-H, ymin, zmax) (Xi-H, ymax, zmin) (Xi-H, ymax, zmax), the other corresponding methods for obtaining the cuboid enveloping the outside of the body are the same as above, while for the description of the body, because of the obvious asymmetry, the body is described by the Weir distribution, and the corresponding formula functions are as follows,
wherein beta is a shape parameter of the volume distribution of the added body along the distance, eta is a corresponding scaling coefficient, gamma is a position parameter, and the value is generally 0, therefore, the added body volume distribution of a certain area is counted by taking the Xc and Yc coordinates of the damage center as the center, the distance of the added body from the point as the horizontal axis and the added body volume as the vertical axis, and a Weir distribution function and corresponding parameters can be used for describing the distribution characteristics.
10. The method for measuring the damage trace of the building based on the laser point cloud difference as claimed in claim 1, wherein: in the eighth step, for the cuboid parameters (reduction), the weight and the volume of each member are utilized to obtain the corresponding total damaged volume Vneg through weighting calculation, and meanwhile, the outer envelope cuboid is utilized to obtain the cuboid volume Vs through calculation; for Weir distribution (volume increase), the volume increase accumulated value Vpos of a corresponding function is calculated on the basis of obtaining corresponding Weir distribution parameters, the volume increase total volume Vz can be calculated according to volume increase segmentation results, and high-precision fine measurement and analysis can be performed on damage based on the parameters and information.
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