CN116227009B - Method, device and equipment for estimating bias of BIM model and point cloud model of tunnel - Google Patents
Method, device and equipment for estimating bias of BIM model and point cloud model of tunnel Download PDFInfo
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Abstract
The invention relates to the field of computer modeling, and discloses a method, a device and equipment for estimating bias of a tunnel BIM model and a point cloud model, wherein the method comprises the following steps: writing pile number information corresponding to each model vertex in the complete BIM tunnel model into the model grid; performing point cloud scanning on the tunnel entity based on a preset pile number range to acquire tunnel point cloud data; determining target model vertexes falling in a preset pile number range from the complete BIM tunnel model through pile number information, and extracting target model grids corresponding to the target model vertexes; constructing a local BIM tunnel model based on the target model vertexes and the target model grids; and determining model deviation through comparison of the local BIM tunnel model and tunnel point cloud data. According to the technical scheme provided by the invention, the deviation estimation efficiency of the tunnel BIM model and the point cloud model is improved.
Description
Technical Field
The invention relates to the field of computer modeling, in particular to a method, a device and equipment for estimating bias of a tunnel BIM model and a point cloud model.
Background
At present, with the continuous improvement of the informatization degree of engineering, the building information model (BuildingInformation Models, BIM) technology is widely popularized and implemented in the building field. In the field of tunnel engineering, when a constructor needs to construct at a certain position of a tunnel (for example, an illuminating lamp is installed at a certain position of the tunnel), a BIM model of the complete tunnel needs to be built on a computer in advance, then three-dimensional laser scanning is performed on the tunnel site within a range of a front section and a rear section of the position to be constructed, point cloud data are obtained, and a point cloud model of the tunnel is obtained. And then carrying out coordinate deviation analysis on a point cloud model and a BIM model of the position to be constructed through BIM software, acquiring key quality information such as tunnel overexcavation, underexcavation, support, lining thickness and the like according to a deviation analysis result, and realizing accurate engineering quantity calculation on the basis.
When the coordinate deviation analysis is carried out, the deviation analysis algorithm currently executed by the software is to compare each point of the point cloud data with the model grids of each position of the BIM model, and determine a deviation value based on the obtained shortest distance. The BIM model has more nodes and grid units (generally more than ten thousand levels), and the calculated distance from a single point to the BIM model is calculated more; more prominently, the point cloud data is generally in centimeter-level density, the single-collected data is very large in scale, millions and tens of millions of point cloud data are calculated in sequence according to the traditional method, and the conditions of long calculation process and low efficiency are brought. Therefore, a new deviation calculation method is needed, and the deviation calculation efficiency of the tunnel BIM model and the point cloud model is improved.
Disclosure of Invention
In view of the above, the embodiment of the invention provides a method, a device and equipment for estimating the deviation of a tunnel BIM model and a point cloud model, so that the deviation calculation efficiency of the tunnel BIM model and the point cloud model is improved.
According to a first aspect, an embodiment of the present invention provides a method for estimating bias of a tunnel BIM model and a point cloud model, the method including: writing pile number information corresponding to each model vertex in the complete BIM tunnel model into a model grid, wherein the model vertex is the vertex of the model grid, and the pile number information is used for representing the distance between each position on a tunnel control axis along the advancing direction of the tunnel and the initial position; performing point cloud scanning on the tunnel entity based on a preset pile number range to acquire tunnel point cloud data; determining a target model vertex falling into the preset pile number range from the complete BIM tunnel model through the pile number information, and extracting a target model grid corresponding to the target model vertex; and constructing a local BIM tunnel model based on the target model vertexes and the target model grids, and determining model deviation through comparison of the local BIM tunnel model and the tunnel point cloud data.
Optionally, the determining, by the pile number information, a target model vertex falling within the preset pile number range from the complete BIM tunnel model, and extracting a target model mesh corresponding to the target model vertex, includes: extracting all model vertexes falling into the preset pile number range from the complete BIM tunnel model through the pile number information to serve as the target model vertexes; and extracting a target model grid where the vertex of the target model is located.
Optionally, the constructing a local BIM tunnel model based on the target model vertices and the target model mesh includes: adding the vertex of the target model into a vertex list; adding the target model grid into a grid list; adding other vertexes except the vertex of the target model in the target model grid into the vertex list; and constructing the local BIM tunnel model based on the vertex list and the grid list.
Optionally, the determining the model bias by comparing the local BIM tunnel model and the tunnel point cloud data includes: extracting a target point from the tunnel point cloud data; making a vertical line to the tunnel control axis through the target point, and determining a target pile number corresponding to the target point; extracting a sub-model vertex corresponding to the target pile number from the local BIM tunnel model, and extracting a sub-model grid corresponding to the sub-model vertex; and comparing the sub-model grids with tunnel point cloud data corresponding to the target pile numbers, and determining the model deviation.
Optionally, the determining the model deviation by comparing the sub-model grid with the tunnel point cloud data corresponding to the target pile number includes: and comparing the sub-model grids with tunnel point cloud data corresponding to the target pile numbers, and taking the minimum value in the comparison result as the model deviation.
Optionally, the method further comprises: traversing each point in the tunnel point cloud data to obtain model deviation corresponding to each point of the tunnel point cloud data by comparing the step of extracting a target point from the tunnel point cloud data with the tunnel point cloud data corresponding to the sub-model grid and the target pile number and taking the minimum value in the comparison result as the model deviation; and storing all the obtained model deviations into a model deviation set.
Optionally, the determining, by the target point, a target pile number corresponding to the target point, includes: reading intersection point pile number information corresponding to the intersection point of the vertical line and the tunnel control axis; and rounding the intersection pile number information to obtain the target pile number.
According to a second aspect, an embodiment of the present invention provides an apparatus for estimating bias of a tunnel BIM model and a point cloud model, the apparatus including: the pile number information initialization module is used for writing pile number information corresponding to each model vertex in the complete BIM tunnel model into the model grid, wherein the model vertex is the vertex of the model grid, and the pile number information is used for representing the distance between each position on a tunnel control axis along the advancing direction of the tunnel and the initial position; the point cloud scanning module is used for carrying out point cloud scanning on the tunnel entity based on a preset pile number range to acquire tunnel point cloud data; the model grid extraction module is used for determining a target model vertex falling into the preset pile number range from the complete BIM tunnel model through the pile number information, and extracting a target model grid corresponding to the target model vertex; the local BIM model construction module is used for constructing a local BIM tunnel model based on the target model vertexes and the target model grids; and the deviation calculation module is used for determining model deviation through comparison of the local BIM tunnel model and the tunnel point cloud data.
According to a third aspect, an embodiment of the present invention provides an apparatus for estimating bias of a tunnel BIM model and a point cloud model, including: the system comprises a memory and a processor, wherein the memory and the processor are in communication connection, the memory stores computer instructions, and the processor executes the computer instructions, thereby executing the method in the first aspect or any optional implementation manner of the first aspect.
According to a fourth aspect, embodiments of the present invention provide a computer readable storage medium storing computer instructions for causing a computer to perform the method of the first aspect, or any one of the alternative embodiments of the first aspect.
The technical scheme that this application provided has following advantage:
according to the technical scheme, based on the characteristics of the tunnel, the local extraction of the BIM model is realized by utilizing the pile number information of the tunnel. Firstly, pre-writing pile number information corresponding to each model vertex in a complete BIM tunnel model into a model grid; then determining a preset pile number range near the position to be constructed, and carrying out point cloud scanning on tunnel entities on the construction site based on the preset pile number range to acquire tunnel point cloud data; and determining a target model vertex falling in a preset pile number range from the complete BIM tunnel model through the pile number information written in the first step, extracting a target model grid corresponding to the target model vertex, constructing a local BIM tunnel model based on the target model grid, and finally determining model deviation through comparison of the local BIM tunnel model and tunnel point cloud data. Therefore, BIM model simplification and point cloud deviation value rapid calculation are performed based on the pile number of the tunnel control axis through the steps, and the deviation analysis efficiency of the point cloud data and the BIM model is remarkably improved.
Drawings
The features and advantages of the present invention will be more clearly understood by reference to the accompanying drawings, which are illustrative and should not be construed as limiting the invention in any way, in which:
FIG. 1 shows a schematic step diagram of a method for estimating bias of a tunnel BIM model and a point cloud model in one embodiment of the invention;
FIG. 2 is a flow chart of a method for estimating bias of a tunnel BIM model and a point cloud model in one embodiment of the invention;
FIG. 3 shows a schematic structural diagram of a tunnel BIM model in one embodiment of the invention;
FIG. 4 shows another schematic structural diagram of a tunnel BIM model in accordance with one embodiment of the invention;
FIG. 5 shows a schematic structural diagram of an apparatus for estimating bias of a tunnel BIM model and a point cloud model in an embodiment of the present invention;
fig. 6 shows a schematic structural diagram of an apparatus for estimating bias of a tunnel BIM model and a point cloud model in an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, based on the embodiments of the invention, which a person skilled in the art would obtain without making any inventive effort, are within the scope of the invention.
Referring to fig. 1 and 2, in one embodiment, a method for estimating bias of a tunnel BIM model and a point cloud model specifically includes the following steps:
step S101: pile number information corresponding to each model vertex in the complete BIM tunnel model is written into the model mesh, the model vertex is the vertex of the model mesh, and the pile number information is used for representing the distance between each position on the tunnel control axis along the tunnel advancing direction and the initial position.
Specifically, in the field of engineering construction, the tunnel has unique properties, as shown in fig. 3, when an engineer excavates the tunnel, the control axis of the tunnel needs to be determined, the control axis is the center line of the tunnel, and the control axis generally extends at the center position of the bottom surface of the section of the tunnel and along the advancing direction of the tunnel, so as to reflect the specific trend of the tunnel. In a practical scenario, the excavated tunnel usually needs to turn, the corresponding control axis generally comprises a plurality of straight line segments, circular curve segments and mild curve segments, and pile number information indicates a distance or mileage represented by a certain position along the advancing direction relative to a starting position on the control axis, and is usually expressed in meters. For example, the starting position is set as k, the pile number information of the starting position is k+0, and the pile number information of the position extending m meters in sequence is k+m.
For the BIM tunnel model, in order to facilitate stress-strain calculation of tunnel engineering, the BIM tunnel model is generally divided into a large number of model meshes, including but not limited to rectangular meshes and triangular meshes, generally triangular meshes, and model vertices are vertices of rectangular or triangular meshes, and the tunnel model shown in fig. 3 is exemplified by a small number of rectangular meshes. Based on the attribute characteristics of the tunnels, the embodiment writes the pile number information corresponding to the model vertexes of all model grids in the complete BIM tunnel model into the model grids in advance, and prepares for the localized extraction of the BIM model by taking the pile number information as an index in the subsequent step, thereby improving the efficiency of deviation analysis.
Step S102: and carrying out point cloud scanning on the tunnel entity based on a preset pile number range to acquire tunnel point cloud data.
Specifically, when an engineering person determines a certain position of a tunnel as a construction position, and obtains laser scanning data for a certain time by utilizing high-precision three-dimensional laser scanning data based on the position, a plurality of targets are placed in a scanning area, engineering coordinates where the targets are located are positioned by utilizing equipment such as a total station, and the like, so that the scanned tunnel point cloud data can be converted into engineering actual coordinates corresponding to point cloud according to the relation between the engineering actual coordinates of the targets and the corresponding point positions, and the engineering actual coordinates are kept consistent with a coordinate system of a BIM (building information modeling) model, and start-stop pile number information corresponding to the tunnel point cloud data is obtained. In the embodiment of the present invention, a single scanning start-stop pile number range of 30 meters may be adopted, i.e. the preset pile number range is 30 meters, which is only used as an example and not limited thereto.
Step S103: and determining the target model vertexes falling in the range of the preset pile numbers from the complete BIM tunnel model through the pile number information, and extracting target model grids corresponding to the target model vertexes.
Step S104: and constructing a local BIM tunnel model based on the target model vertexes and the target model grids.
Step S105: and determining model deviation through comparison of the local BIM tunnel model and tunnel point cloud data.
Specifically, based on the preset pile number range determined in step S102, sequentially querying in each model grid of the complete BIM tunnel model, taking pile number information as an index, and judging whether pile number information of each model vertex written in advance in the model grid falls within the preset pile number range. For example, the preset pile number range is 0-30 m, and whether pile number information of each model vertex written in advance in each model grid of the complete BIM tunnel model falls into the range of 0-30 m is inquired. And then judging and extracting a target model grid corresponding to the vertex of the target model according to the set standard: for example, all model vertices of a certain model mesh fall within a range of 0-30 meters, the model mesh is included in a range of deviation calculation consideration, or at least one model vertex of a certain model mesh falls within a range of 0-30 meters, and the model mesh is included in a range of deviation calculation consideration. And then constructing a local BIM tunnel model based on the target model grid, so that the localized extraction of the BIM model is realized, and the obtained local BIM tunnel model can be accurately matched with tunnel point cloud data. And finally, determining model deviation through comparison of the simplified local BIM tunnel model and tunnel point cloud data. Through the steps, a set of flow of the BIM tunnel model is simplified by taking pile number information as an index and referring to the tunnel point cloud data based on the pile number information, so that BIM software performs deviation analysis through the simplified BIM tunnel model and the simplified tunnel point cloud data, the problem that the traditional BIM software needs to compare each point of the complete BIM tunnel model and the complete tunnel point cloud data one by one when performing deviation analysis is avoided, a large number of redundant calculation processes are eliminated, and the deviation analysis efficiency of the point cloud data and the BIM model is remarkably improved.
Specifically, in this embodiment, the specific step of extracting the model mesh includes: and extracting model vertexes with pile number information falling into a preset pile number range as target model vertexes, and extracting target model grids with the target model vertexes. In other words, the model mesh extraction means adopted in this embodiment are: all model vertexes are determined within a preset pile number range, and then a model grid is included in a deviation calculation consideration range as long as one model vertex of the model grid falls within the preset pile number range. For example, if a triangle mesh has a vertex that falls within a predetermined pile number range, the triangle mesh is included in the deviation calculation consideration range. Through the processing of the embodiment, the integrity of the data is ensured to the greatest extent on the basis of simplifying the BIM model, and the phenomenon of grid loss does not occur.
Based on this, in an embodiment, the step S104 specifically includes the following steps:
step one: adding the target model vertices to the vertex list.
Step two: adding the target model mesh to the mesh list.
Step three: adding other vertexes except the vertex of the target model in the target model grid to the vertex list.
Step four: and constructing a local BIM tunnel model based on the vertex list and the grid list.
Specifically, a local BIM tunnel model is constructed, a required model mesh and model vertices need to be determined, the target model mesh extracted in the embodiment is added to the mesh list, and when the target model mesh is extracted in the embodiment, as long as one model vertex of a certain model mesh falls within a preset pile number range, the model mesh is included in a deviation calculation consideration range, so that the target model mesh generally further comprises other vertices, and the other model vertices are close to the preset pile number range but not within the preset pile number range. According to the embodiment of the invention, firstly, the vertexes of the target model are added into a vertex list, then, other vertexes except the vertexes of the target model are added into the vertex list, and finally, the vertex list and the grid list are utilized to construct the local BIM tunnel model. On the basis of simplifying the BIM model, the integrity of data is further ensured, and half grid phenomenon does not occur.
Specifically, in one embodiment, the step S105 specifically includes the following steps:
step five: and extracting a target point from the tunnel point cloud data.
Step six: and (5) making a vertical line to the tunnel control axis through the target point, and determining a target pile number corresponding to the target point.
Step seven: and extracting sub-model vertexes corresponding to the target pile numbers from the local BIM tunnel model, and extracting sub-model grids corresponding to the sub-model vertexes.
Step eight: and comparing the sub-model grids with tunnel point cloud data corresponding to the target pile numbers, and determining model deviation.
Specifically, the present embodiment performs secondary hierarchical simplification based on the BIM model simplified in the above embodiment, thereby further improving the efficiency of bias estimation. And (3) for the tunnel point cloud data in the preset pile number range, randomly extracting a target point from the tunnel point cloud data, then making a vertical line to a tunnel control axis through the target point, and determining a target pile number corresponding to the target point, wherein the target pile number is a uniquely determined numerical value. Specifically, in the embodiment, the sixth step further rounds the information of the pile number of the intersection point of the read vertical line and the tunnel control axis, and the output target pile number is an integer, so that the accuracy of positioning the vertex of the sub-model in the following step is ensured.
Then, as shown in fig. 4, the target pile number is used as index information, and all model vertices (i.e., sub-model vertices) corresponding to the target pile number are found from the local BIM tunnel model, so as to extract a circle of sub-model meshes corresponding to the sub-model vertices from the local BIM tunnel model. And finally, only one circle of tunnel point cloud data corresponding to the extracted circle of sub-model grids and the target pile number is subjected to one-to-one comparison to determine the model deviation, so that the calculation amount of model deviation estimation can be obviously reduced, and the calculation efficiency is improved.
Specifically, in this embodiment, a circle of extracted sub-model grids and a circle of tunnel point cloud data corresponding to the target pile number are compared, the distance between the point cloud and each grid unit is calculated, and the minimum value (i.e., the shortest distance) in the comparison result is used as model deviation, so that the randomness influence of the deviation is reduced, and the accuracy of the model deviation is further ensured.
Specifically, in an embodiment, through the fifth step to the eighth step, each point in the tunnel point cloud data is traversed, and a model deviation corresponding to each point in the tunnel point cloud data is obtained; the deviation of the BIM model and the point cloud model of any point nearby the position to be constructed can be calculated by engineering personnel in a short time. And finally, storing all the obtained model deviations into a model deviation set so as to facilitate later inquiry of engineering personnel at any time, and obviously improving the use experience of model analysis software.
Through the steps, the technical scheme provided by the application realizes the local extraction of the BIM model by utilizing the pile number information of the tunnel based on the characteristics of the tunnel. Firstly, pre-writing pile number information corresponding to each model vertex in a complete BIM tunnel model into a model grid; then determining a preset pile number range near the position to be constructed, and carrying out point cloud scanning on tunnel entities on the basis of the preset pile number range to a construction site to acquire tunnel point cloud data; and determining a target model vertex falling in a preset pile number range from the complete BIM tunnel model through the pile number information written in the first step, extracting a target model grid corresponding to the target model vertex, constructing a local BIM tunnel model based on the target model grid, and finally determining model deviation through comparison of the local BIM tunnel model and tunnel point cloud data. Therefore, the BIM model simplification and the point cloud deviation value rapid calculation method are carried out based on the pile number of the tunnel control axis through the steps, and the deviation analysis efficiency of the point cloud data and the BIM model is remarkably improved.
As shown in fig. 5, the embodiment further provides an apparatus for estimating bias of a tunnel BIM model and a point cloud model, where the apparatus includes:
the pile number information initializing module 101 is configured to write pile number information corresponding to each model vertex in the complete BIM tunnel model into the model mesh, where the model vertex is a vertex of the model mesh, and the pile number information is used to represent a distance between each position on the tunnel control axis along the tunnel advancing direction and the initial position. For details, refer to the related description of step S101 in the above method embodiment, and no further description is given here.
The point cloud scanning module 102 is configured to perform point cloud scanning on the tunnel entity based on a preset pile number range, and obtain tunnel point cloud data. For details, refer to the related description of step S102 in the above method embodiment, and no further description is given here.
The model mesh extraction module 103 is configured to determine, from the complete BIM tunnel model, a target model vertex that falls within a preset pile number range according to the pile number information, and extract a target model mesh corresponding to the target model vertex. For details, see the description of step S103 in the above method embodiment, and the details are not repeated here.
The local BIM model construction module 104 is configured to construct a local BIM tunnel model based on the target model vertices and the target model mesh. For details, refer to the related description of step S104 in the above method embodiment, and no further description is given here.
The deviation calculation module 105 is used for determining model deviation through comparison of the local BIM tunnel model and tunnel point cloud data. For details, see the description of step S105 in the above method embodiment, and the details are not repeated here.
The device for estimating the bias of the BIM model and the point cloud model of the tunnel provided by the embodiment of the invention is used for executing the method for estimating the bias of the BIM model and the point cloud model of the tunnel provided by the embodiment of the invention, the implementation mode and the principle are the same, and details are referred to the related description of the embodiment of the method and are not repeated.
Through the cooperation of the components, the technical scheme provided by the application realizes the local extraction of the BIM model by utilizing the pile number information of the tunnel based on the characteristics of the tunnel. Firstly, pre-writing pile number information corresponding to each model vertex in a complete BIM tunnel model into a model grid; then determining a preset pile number range near the position to be constructed, and carrying out point cloud scanning on tunnel entities on the basis of the preset pile number range to a construction site to acquire tunnel point cloud data; and determining a target model vertex falling in a preset pile number range from the complete BIM tunnel model through the pile number information written in the first step, extracting a target model grid corresponding to the target model vertex, constructing a local BIM tunnel model based on the target model grid, and finally determining model deviation through comparison of the local BIM tunnel model and tunnel point cloud data. Therefore, the BIM model simplification and the point cloud deviation value rapid calculation method are carried out based on the pile number of the tunnel control axis through the steps, and the deviation analysis efficiency of the point cloud data and the BIM model is remarkably improved.
FIG. 6 illustrates an apparatus for estimating bias of a tunnel BIM model and a point cloud model according to an embodiment of the invention, the apparatus comprising a processor 901 and a memory 902, which may be connected by a bus or otherwise, the connection being exemplified by a bus in FIG. 4.
The processor 901 may be a central processing unit (Central Processing Unit, CPU). The processor 901 may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination thereof.
The memory 902 is used as a non-transitory computer readable storage medium for storing non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the methods in the method embodiments described above. The processor 901 executes various functional applications of the processor and data processing, i.e., implements the methods in the above-described method embodiments, by running non-transitory software programs, instructions, and modules stored in the memory 902.
The memory 902 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for a function; the storage data area may store data created by the processor 901, and the like. In addition, the memory 902 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 902 optionally includes memory remotely located relative to processor 901, which may be connected to processor 901 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
One or more modules are stored in the memory 902 that, when executed by the processor 901, perform the methods of the method embodiments described above.
The specific details of the device for estimating the deviation between the tunnel BIM model and the point cloud model can be correspondingly understood by referring to the corresponding related description and effect in the above method embodiment, and will not be repeated here.
It will be appreciated by those skilled in the art that implementing all or part of the above-described methods in the embodiments may be implemented by a computer program for instructing relevant hardware, and the implemented program may be stored in a computer readable storage medium, and the program may include the steps of the embodiments of the above-described methods when executed. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a Flash Memory (Flash Memory), a Hard Disk (HDD), or a Solid State Drive (SSD); the storage medium may also comprise a combination of memories of the kind described above.
Although embodiments of the present invention have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the invention, and such modifications and variations are within the scope of the invention as defined by the appended claims.
Claims (7)
1. A method of estimating bias of a tunnel BIM model and a point cloud model, the method comprising:
writing pile number information corresponding to each model vertex in the complete BIM tunnel model into a model grid, wherein the model vertex is the vertex of the model grid, and the pile number information is used for representing the distance between each position on a tunnel control axis along the advancing direction of the tunnel and the initial position;
performing point cloud scanning on the tunnel entity based on a preset pile number range to acquire tunnel point cloud data;
determining a target model vertex falling into the preset pile number range from the complete BIM tunnel model through the pile number information, and extracting a target model grid corresponding to the target model vertex;
constructing a local BIM tunnel model based on the target model vertices and the target model grids;
determining model deviation through comparison of the local BIM tunnel model and the tunnel point cloud data;
determining a target model vertex falling into the preset pile number range from the complete BIM tunnel model through the pile number information, and extracting a target model grid corresponding to the target model vertex, wherein the method comprises the following steps:
extracting all model vertexes falling into the preset pile number range from the complete BIM tunnel model through the pile number information to serve as the target model vertexes;
extracting a target model grid where the vertex of the target model is located;
the constructing a local BIM tunnel model based on the target model vertices and the target model mesh includes:
adding the vertex of the target model into a vertex list;
adding the target model grid into a grid list;
adding other vertexes except the vertex of the target model in the target model grid into the vertex list;
constructing the local BIM tunnel model based on the vertex list and the grid list;
determining model bias by comparing the local BIM tunnel model and the tunnel point cloud data comprises the following steps:
extracting a target point from the tunnel point cloud data;
making a vertical line to the tunnel control axis through the target point, and determining a target pile number corresponding to the target point;
extracting a sub-model vertex corresponding to the target pile number from the local BIM tunnel model, and extracting a sub-model grid corresponding to the sub-model vertex;
and comparing the sub-model grids with tunnel point cloud data corresponding to the target pile numbers, and determining the model deviation.
2. The method of claim 1, wherein the comparing the sub-model mesh with the tunnel point cloud data corresponding to the target pile number to determine the model bias comprises:
and comparing the sub-model grids with tunnel point cloud data corresponding to the target pile numbers, and taking the minimum value in the comparison result as the model deviation.
3. The method according to claim 2, wherein the method further comprises:
traversing each point in the tunnel point cloud data to obtain model deviation corresponding to each point of the tunnel point cloud data by comparing the step of extracting a target point from the tunnel point cloud data with the tunnel point cloud data corresponding to the sub-model grid and the target pile number and taking the minimum value in the comparison result as the model deviation;
and storing all the obtained model deviations into a model deviation set.
4. The method of claim 1, wherein determining a target pile number corresponding to the target point by making a perpendicular to the tunnel control axis with the target point comprises:
reading intersection point pile number information corresponding to the intersection point of the vertical line and the tunnel control axis;
and rounding the intersection pile number information to obtain the target pile number.
5. An apparatus for estimating bias of a tunnel BIM model and a point cloud model, the apparatus comprising:
the pile number information initialization module is used for writing pile number information corresponding to each model vertex in the complete BIM tunnel model into the model grid, wherein the model vertex is the vertex of the model grid, and the pile number information is used for representing the distance between each position on a tunnel control axis along the advancing direction of the tunnel and the initial position;
the point cloud scanning module is used for carrying out point cloud scanning on the tunnel entity based on a preset pile number range to acquire tunnel point cloud data;
the model grid extraction module is used for determining a target model vertex falling into the preset pile number range from the complete BIM tunnel model through the pile number information, and extracting a target model grid corresponding to the target model vertex;
the local BIM model construction module is used for constructing a local BIM tunnel model based on the target model vertexes and the target model grids;
the deviation calculation module is used for determining model deviation through comparison of the local BIM tunnel model and the tunnel point cloud data;
determining a target model vertex falling into the preset pile number range from the complete BIM tunnel model through the pile number information, and extracting a target model grid corresponding to the target model vertex, wherein the method comprises the following steps:
extracting all model vertexes falling into the preset pile number range from the complete BIM tunnel model through the pile number information to serve as the target model vertexes;
extracting a target model grid where the vertex of the target model is located;
the constructing a local BIM tunnel model based on the target model vertices and the target model mesh includes:
adding the vertex of the target model into a vertex list;
adding the target model grid into a grid list;
adding other vertexes except the vertex of the target model in the target model grid into the vertex list;
constructing the local BIM tunnel model based on the vertex list and the grid list;
determining model bias by comparing the local BIM tunnel model and the tunnel point cloud data comprises the following steps:
extracting a target point from the tunnel point cloud data;
making a vertical line to the tunnel control axis through the target point, and determining a target pile number corresponding to the target point;
extracting a sub-model vertex corresponding to the target pile number from the local BIM tunnel model, and extracting a sub-model grid corresponding to the sub-model vertex;
and comparing the sub-model grids with tunnel point cloud data corresponding to the target pile numbers, and determining the model deviation.
6. An apparatus for estimating bias of a tunnel BIM model and a point cloud model, comprising:
a memory and a processor in communication with each other, the memory having stored therein computer instructions, the processor executing the computer instructions to perform the method of any of claims 1-4.
7. A computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1-4.
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