CN115017578A - Intelligent actual measurement method and device for building, UGV and storage medium - Google Patents
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Abstract
The embodiment of the invention discloses an intelligent actual measurement method and device for a building, UGV and a storage medium. The method comprises the following steps: importing a BIM model of a building, and establishing a grid map of an indoor detection environment based on the BIM model; planning a path of the UGV based on the raster map to generate a global optimal path; acquiring point cloud data of the indoor detection environment based on the global optimal path by adopting a three-dimensional laser scanning method, and registering the point cloud data; and calculating actual measurement indexes of the building according to the registered point cloud data. According to the technical scheme provided by the embodiment of the invention, the BIM is used for automatically planning the path, UGV and three-dimensional laser scanning technology are used for automatically acquiring point cloud data, and then the point cloud data is automatically processed and calculated to obtain the actually measured actual quantity index of the building, so that the integrated measurement of the indoor space is realized, the measurement efficiency and the coverage rate are improved, and the labor cost is greatly saved.
Description
Technical Field
The embodiment of the invention relates to the technical field of building detection, in particular to an intelligent actual measurement method and device for a building, a UGV (Unigraphics) and a storage medium.
Background
The traditional actual measurement of the building is mainly completed by manually adopting measuring instruments such as a total station, an electronic warrior, a level gauge, a steel tape, a vernier caliper, a positive and negative angle square, a suspension wire and the like, the measured data mainly adopts paper management and is sampled and detected, comprehensive detection of the house cannot be achieved, meanwhile, unstable errors caused by manual intervention are difficult to remove, data statistics is complicated, and repeated checking is needed.
Disclosure of Invention
The embodiment of the invention provides an intelligent actual measurement method and device for a building, UGV and a storage medium, and aims to solve the problems of large measurement error, low efficiency, high cost and the like in the traditional method.
In a first aspect, an embodiment of the present invention provides an intelligent actual measurement method for a building, where the method includes:
importing a BIM model of a building, and establishing a grid map of an indoor detection environment based on the BIM model;
planning a UGV path based on the raster map to generate a global optimal path;
acquiring point cloud data of the indoor detection environment based on the global optimal path by adopting a three-dimensional laser scanning method, and registering the point cloud data;
and calculating actual measurement indexes of the building according to the registered point cloud data.
Optionally, the importing a building BIM model, and establishing a grid map of an indoor detection environment based on the BIM model includes:
extracting entity type identification and boundary information from the BIM based on semantic segmentation to obtain a semantic BIM, and generating a grid plane corresponding to the semantic BIM;
and performing three-dimensional space semantic recognition of the indoor detection environment based on the semantic BIM model, and generating the grid map by combining the grid plane.
Optionally, the acquiring point cloud data of the indoor detection environment based on the global optimal path includes:
and positioning the UGV in real time, and adjusting the error between the actual pose of the UGV and the reference pose based on the global optimal path in real time.
Optionally, the positioning the UGV in real time includes:
building a wireless local area network in a building room;
and based on the wireless local area network, adopting an RSSI indoor positioning algorithm to position the UGV in real time.
Optionally, the calculating an actual measurement indicator of the building according to the registered point cloud data includes:
preprocessing the point cloud data, wherein the preprocessing comprises denoising, point cloud segmentation, thinning and point cloud classification;
and calculating the actual measurement index by adopting a virtual guiding rule algorithm and a virtual angle square algorithm based on the preprocessed point cloud data.
Optionally, after the calculating an actually measured quantity index of the building according to the registered point cloud data, the method further includes:
and carrying out index matching on the actually measured indexes in the BIM model, and calculating index errors to generate a geometric quality detection analysis report.
Optionally, the method further includes:
synchronizing the actually measured actual quantity index to a cloud end; and/or the presence of a gas in the gas,
and exporting and uploading the geometric quality detection analysis report to a terminal.
In a second aspect, an embodiment of the present invention further provides an intelligent actual measurement device for a building, where the device includes:
the grid map building module is used for importing a building BIM model and building a grid map of an indoor detection environment based on the BIM model;
the path planning module is used for planning paths of the UGV based on the raster map so as to generate a global optimal path;
the point cloud data acquisition module is used for acquiring point cloud data of the indoor detection environment based on the global optimal path by adopting a three-dimensional laser scanning method and registering the point cloud data;
and the index calculation module is used for calculating the actual measurement index of the building according to the registered point cloud data.
In a third aspect, an embodiment of the present invention further provides a UGV, where the UGV includes:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the intelligent real-world measurement method for a building provided by any of the embodiments of the present invention.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for intelligently and actually measuring actual quantities of a building provided in any embodiment of the present invention.
The embodiment of the invention provides an intelligent actual measurement method of a building, which comprises the steps of firstly introducing a BIM model of the building, establishing a raster map of an indoor detection environment based on the model, then carrying out path planning on UGV based on the raster map to generate a global optimal path, then adopting a three-dimensional laser scanning method, collecting point cloud data of the indoor detection environment based on the global optimal path, and carrying out registration on the collected point cloud data, thereby calculating an actual measurement index of the building according to the registered point cloud data. According to the intelligent actual measurement method for the building, provided by the embodiment of the invention, the path is automatically planned by using the BIM, the point cloud data is automatically acquired by using UGV and a three-dimensional laser scanning technology, and the actual measurement indexes of the building are obtained by automatically processing and calculating the point cloud data, so that the integrated measurement of the indoor space is realized, the measurement efficiency and the coverage rate are improved, and the labor cost is greatly saved.
Drawings
Fig. 1 is a flowchart of an intelligent actual measurement method for a building according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an intelligent actual measurement device for a building according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of the UGV provided in the third embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Before discussing exemplary embodiments in greater detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the steps as a sequential process, many of the steps can be performed in parallel, concurrently or simultaneously. In addition, the order of the steps may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.
Example one
Fig. 1 is a flowchart of an intelligent actual measurement method for a building according to an embodiment of the present invention. The embodiment is applicable to the actual measurement of the indoor building, and the method can be executed by the intelligent actual measurement device for the building provided by the embodiment of the invention, and the device can be realized by hardware and/or software and can be generally integrated in UGV. As shown in fig. 1, the method specifically comprises the following steps:
and S11, importing a BIM model of the building, and establishing a grid map of the indoor detection environment based on the BIM model.
Specifically, the BIM model of the building to be detected currently may be first introduced into a UGV (Unmanned Ground Vehicle), where the UGV may be a quadruped robot. The UGV can comprise an embedded computer module for executing the method, and after the BIM model is obtained, the UGV can be subjected to environment semantic recognition analysis based on the BIM model, so that a single-layer grid map containing three-dimensional information, coordinates and other attributes is constructed. The BIM model contains abundant design geometric and semantic information and can digitally express physical and functional characteristics of a target object.
Optionally, the importing a building BIM model, and establishing a grid map of an indoor detection environment based on the BIM model includes: extracting entity type identification and boundary information from the BIM based on semantic segmentation to obtain a semantic BIM, and generating a grid plane corresponding to the semantic BIM; and performing three-dimensional space semantic recognition of the indoor detection environment based on the semantic BIM model, and generating the grid map by combining the grid plane. Specifically, semantic recognition of an indoor environment can be performed based on an imported BIM, entity type identification and boundary information in the BIM are obtained based on semantic segmentation extraction, and then the entity type identification and the boundary information can be recognized and marked in the BIM according to the extracted entity type identification and boundary information, so that a semantic BIM is obtained, and a grid plane corresponding to the semantic BIM can be generated. And then, constructing a single-layer grid map based on the semantic BIM, specifically traversing the entity model in a corresponding grid plane according to the semantic BIM and matching semantic information, thereby obtaining the required grid map.
And S12, planning the UGV path based on the grid map to generate a global optimal path.
Specifically, after the grid map of the indoor detection environment is obtained, a travel path of the UGV can be planned based on the grid map, so that the UGV acquires all point cloud data of the required indoor detection environment step by step according to the path. The global path planning can be specifically carried out by adopting an A-star algorithm, and the local path planning can be carried out by combining with a dynamic window algorithm so as to obtain a finally required global optimal path, so that the problems of scene identification and a traveling path of UGV intelligent data acquisition are solved, and the UGV can be ensured to accurately and efficiently acquire data.
And S13, acquiring point cloud data of the indoor detection environment based on the global optimal path by adopting a three-dimensional laser scanning method, and registering the point cloud data.
Specifically, after the global optimal path is determined, the UGV can be controlled to perform data acquisition along the global optimal path. Specifically, a three-dimensional laser scanner can be carried on the UGV, so that the point cloud data of the indoor detection environment can be acquired by using a three-dimensional laser scanning technology. The three-dimensional laser scanning technology can acquire the spatial point cloud data of the building in a non-contact and high-speed and accurate mode, so that a real three-dimensional building model is established, a single-point measurement method is broken through, and the method has the unique advantages of high efficiency and high precision. More than, through being based on BIM to carry three-dimensional laser scanner and carry on perception and the data acquisition of indoor actual measurement real quantity environment in UGV, can realize the scanning automation, compensatied that traditional point cloud data acquisition is inefficient, gather with computational analysis process discretization etc. not enough. Since the three-dimensional laser scanner always establishes a coordinate system by taking the three-dimensional laser scanner as an origin, point clouds in different coordinate systems may be acquired for an indoor detection environment, after point cloud data of the indoor detection environment is acquired, the point clouds in different coordinate systems need to be unified to the same coordinate system, that is, the point cloud data also needs to be registered, and then after the point cloud data is acquired, global automatic registration of the point cloud data can be performed to acquire subsequent available point cloud data. The registration process specifically comprises coarse registration and fine registration, wherein the coarse registration refers to registration of point clouds under the condition that the relative pose of the point clouds is completely unknown, a good initial value can be provided for the fine registration, and the purpose of the fine registration is to minimize the space position difference between the point clouds on the basis of the coarse registration. In this embodiment, the coarse registration and the fine registration may adopt any existing coarse registration algorithm and fine registration algorithm, for example, a four-point fast matching algorithm may be adopted to obtain an optimal transformation, then coordinate transformation is performed on the point cloud data based on the optimal transformation to realize coarse registration, and then Hessian matrix optimization and an improved newton iteration algorithm are used to perform iteration to realize fine registration.
Optionally, the acquiring point cloud data of the indoor detection environment based on the global optimal path includes: and positioning the UGV in real time, and adjusting the error between the actual pose of the UGV and the reference pose based on the global optimal path in real time. Specifically, in the process that the UGV moves and collects data based on the global optimal path, the UGV can be positioned in real time, and the deviation between the actual pose and the reference pose is adjusted in real time, so that the UGV can automatically collect point cloud data based on the station of the global optimal path, and the point cloud data is accurate and complete. The UGV can be subjected to motion trail tracking control, a prediction control algorithm is adopted to optimize a control strategy, a fuzzy prediction control algorithm is adopted to perform feedback correction, and error adjustment is performed according to a control pose (namely a reference pose) and an actual pose.
Further optionally, the locating the UGV in real time includes: building a wireless local area network in a building room; and based on the wireless local area network, adopting an RSSI indoor positioning algorithm to position the UGV in real time. Specifically, routers can be arranged in the building room in a global mode and coordinate positions are added to complete the building of the wireless local area network, UGV signals can be collected through the routers, Kalman filtering methods can be adopted to filter the signals to remove noise and interference, and then an RSSI indoor positioning algorithm is adopted to accurately position the path of the UGV according to the filtered signals.
And S14, calculating the actual measurement index of the building according to the point cloud data which is registered.
Specifically, after the point cloud data is registered, the actual measurement index can be automatically calculated according to the registered point cloud data. The actual measurement indexes can comprise wall surface flatness, perpendicularity, level range, internal and external angles, depth/interval, clear height and the like, so that integrated measurement of an indoor space is achieved, and intelligent conversion from manual local sampling detection to actual measurement based on BIM is achieved.
Optionally, the calculating an actual measurement indicator of the building according to the registered point cloud data includes: preprocessing the point cloud data, wherein the preprocessing comprises denoising, point cloud segmentation, thinning and point cloud classification; and calculating the actual measurement index by adopting a virtual guiding rule algorithm and a virtual angle square algorithm based on the preprocessed point cloud data. Specifically, the obtained registered point cloud data may be preprocessed, which may specifically include denoising, point cloud segmentation, thinning, point cloud classification, and the like, where denoising may employ a conditional filtering method, and thinning may employ a random sampling method. The point cloud segmentation is to divide the point cloud according to characteristic points such as space, geometry, texture and the like, the point cloud in the same division has similar characteristics, and the point cloud segmentation aims at blocking, so that the point cloud segmentation is convenient to process independently and can be realized by adopting a clustering algorithm. The point cloud classification is to distribute a semantic mark for each point, so that the point cloud is classified into different point cloud sets, the same point cloud set has similar or same attributes, and the point cloud classification aims to semantically mark the whole scene, so that subsequent index calculation is facilitated, and the point cloud classification can be specifically realized by adopting a PointNet network. After the preprocessing is finished, the BIM model and the point cloud model can be compared based on the preprocessed point cloud data, and the automatic measurement of indoor space integration is realized by utilizing a virtual guiding rule algorithm, a virtual angle square algorithm and the like, so that various required actual measurement indexes are obtained.
On the basis of the above technical solution, optionally, after the calculating an actual measurement index of the building according to the registered point cloud data, the method further includes: and carrying out index matching on the actually measured indexes in the BIM model, and calculating index errors to generate a geometric quality detection analysis report. Specifically, after each actually measured actual quantity index is obtained through calculation, the corresponding preset index is matched with each actually measured actual quantity index through comparison with the BIM model, and index errors are calculated, so that a geometric quality detection analysis report can be generated according to each actually measured actual quantity index and each index error, actual measurement results are comprehensively reflected, and reference is provided for further decision making of a user.
Further optionally, the method further comprises: synchronizing the actually measured actual quantity index to a cloud end; and/or exporting and uploading the geometric quality detection analysis report to a terminal. Specifically, after various actual measurement indexes are obtained, the actual measurement indexes can be synchronized to the cloud, and after a geometric quality detection analysis report is generated, the geometric quality detection analysis report can be exported and uploaded to a terminal, so that data can be made to be open and transparent for multiple parties to use, and meanwhile, the actual measurement results can be visually reflected, and a user can obtain better information.
According to the technical scheme provided by the embodiment of the invention, a BIM model of a building is firstly introduced, a raster map of an indoor detection environment is established based on the BIM model, UGV is subjected to path planning based on the raster map to generate a global optimal path, a three-dimensional laser scanning method is adopted, point cloud data of the indoor detection environment are collected based on the global optimal path, the collected point cloud data are registered, and therefore the actually measured actual quantity index of the building is calculated according to the registered point cloud data. The BIM model is used for automatically planning a path, UGV and three-dimensional laser scanning technology are used for automatically acquiring point cloud data, and then the point cloud data are automatically processed and calculated to obtain the actual measurement index of the building, so that the integrated measurement of the indoor space is realized, the measurement efficiency and the coverage rate are improved, and the labor cost is greatly saved.
Example two
Fig. 2 is a schematic structural diagram of an intelligent actual measurement device for a building according to a second embodiment of the present invention, where the device may be implemented in a hardware and/or software manner, and may be generally integrated in a UGV, and is used to execute the intelligent actual measurement method for a building according to any embodiment of the present invention. As shown in fig. 2, the apparatus includes:
the grid map building module 21 is used for importing a building BIM (building information modeling) model and building a grid map of an indoor detection environment based on the BIM model;
a path planning module 22, configured to perform path planning on the UGV based on the raster map to generate a global optimal path;
a point cloud data acquisition module 23, configured to acquire point cloud data of the indoor detection environment based on the global optimal path by using a three-dimensional laser scanning method, and perform registration on the point cloud data;
and the index calculation module 24 is used for calculating the actual measurement index of the building according to the registered point cloud data.
According to the technical scheme provided by the embodiment of the invention, a BIM model of a building is firstly introduced, a raster map of an indoor detection environment is established based on the BIM model, UGV is subjected to path planning based on the raster map to generate a global optimal path, a three-dimensional laser scanning method is adopted, point cloud data of the indoor detection environment are collected based on the global optimal path, the collected point cloud data are registered, and therefore the actually measured actual quantity index of the building is calculated according to the registered point cloud data. The BIM model is used for automatically planning a path, UGV and three-dimensional laser scanning technology are used for automatically acquiring point cloud data, and then the point cloud data are automatically processed and calculated to obtain the actual measurement index of the building, so that the integrated measurement of the indoor space is realized, the measurement efficiency and the coverage rate are improved, and the labor cost is greatly saved.
On the basis of the above technical solution, optionally, the grid map building module 21 includes:
the grid plane generating unit is used for extracting entity type identification and boundary information from the BIM based on semantic segmentation to obtain a semantic BIM and generating a grid plane corresponding to the semantic BIM;
and the grid map generating unit is used for carrying out three-dimensional space semantic recognition on the indoor detection environment based on the semantic BIM model and generating the grid map by combining the grid plane.
On the basis of the above technical solution, optionally, the point cloud data collecting module 23 includes:
and the pose adjusting unit is used for positioning the UGV in real time and adjusting the error between the actual pose of the UGV and the reference pose based on the global optimal path in real time.
On the basis of the above technical solution, optionally, the pose adjusting unit includes:
the local area network building subunit is used for building a wireless local area network in a building room;
and the real-time positioning subunit is used for positioning the UGV in real time by adopting an RSSI (received signal strength indicator) indoor positioning algorithm based on the wireless local area network.
On the basis of the above technical solution, optionally, the index calculating module 24 includes:
the preprocessing unit is used for preprocessing the point cloud data, and the preprocessing comprises denoising, point cloud segmentation, thinning and point cloud classification;
and the index calculation unit is used for calculating the actually measured actual quantity index by adopting a virtual guiding rule algorithm and a virtual angle square algorithm based on the preprocessed point cloud data.
On the basis of the above technical scheme, optionally, this real device of intelligent actual measurement of building still includes:
and the report generation module is used for performing index matching on the actual measurement indexes in the BIM after the actual measurement indexes of the building are calculated according to the registered point cloud data, and calculating index errors so as to generate a geometric quality detection analysis report.
On the basis of the above technical scheme, optionally, this real device of intelligent actual measurement of building still includes:
the index synchronization module is used for synchronizing the actually measured actual quantity indexes to a cloud end; and/or the presence of a gas in the atmosphere,
and the report uploading module is used for exporting the geometric quality detection analysis report and uploading the geometric quality detection analysis report to the terminal.
The intelligent actual measurement device for the building provided by the embodiment of the invention can execute the intelligent actual measurement method for the building provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
It should be noted that, in the embodiment of the intelligent actual measurement device for a building, each included unit and module are only divided according to functional logic, but are not limited to the above division, as long as the corresponding function can be realized; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a UGV provided in a third embodiment of the present invention, and shows a block diagram of an exemplary UGV suitable for implementing an embodiment of the present invention. The UGV shown in fig. 3 is only an example and should not bring any limitation to the function and use range of the embodiment of the present invention. As shown in FIG. 3, the UGV includes a processor 31, a memory 32, an input device 33, and an output device 34; the number of the processors 31 in the UGV may be one or more, one processor 31 is taken as an example in fig. 3, the processor 31, the memory 32, the input device 33 and the output device 34 in the UGV may be connected by a bus or other means, and the connection by the bus is taken as an example in fig. 3.
The memory 32 is used as a computer readable storage medium for storing software programs, computer executable programs, and modules, such as program instructions/modules corresponding to the method for building intelligent actual measurement in the embodiment of the present invention (for example, the grid map building module 21, the path planning module 22, the point cloud data collecting module 23, and the index calculating module 24 in the intelligent actual measurement device for building). The processor 31 executes various functional applications and data processing of the UGV by executing software programs, instructions and modules stored in the memory 32, that is, implements the above-described intelligent real-world measurement method of the building.
The memory 32 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of UGV, and the like. Further, the memory 32 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some instances, the memory 32 may further include memory located remotely from the processor 31, which may be connected to the UGV 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.
The input device 33 may be used to acquire a BIM model of a building, and to generate key signal inputs related to user settings and function control of UGVs, and the like. The output device 34 may be used to synchronize the actual measurement metrics to the cloud, etc.
Example four
A fourth embodiment of the present invention further provides a storage medium containing computer-executable instructions, which when executed by a computer processor, perform a method for intelligently measuring actual quantities of a building, the method comprising:
importing a BIM model of a building, and establishing a grid map of an indoor detection environment based on the BIM model;
planning a path of the UGV based on the raster map to generate a global optimal path;
acquiring point cloud data of the indoor detection environment based on the global optimal path by adopting a three-dimensional laser scanning method, and registering the point cloud data;
and calculating actual measurement indexes of the building according to the registered point cloud data.
The storage medium may be any of various types of memory devices or storage devices. The term "storage medium" is intended to include: mounting media such as CD-ROM, floppy disk, or tape devices; computer system memory or random access memory such as DRAM, DDR RAM, SRAM, EDO RAM, Lambda (Rambus) RAM, etc.; non-volatile memory such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. The storage medium may also include other types of memory or combinations thereof. In addition, the storage medium may be located in the computer system in which the program is executed, or may be located in a different second computer system connected to the computer system through a network (such as the internet). The second computer system may provide the program instructions to the computer for execution. The term "storage medium" may include two or more storage media that may reside in different locations, such as in different computer systems that are connected via a network. The storage medium may store program instructions (e.g., embodied as a computer program) that are executable by one or more processors.
Of course, the storage medium provided by the embodiment of the present invention contains computer executable instructions, and the computer executable instructions are not limited to the operations of the method described above, and may also perform related operations in the intelligent actual measurement method for a building provided by any embodiment of the present invention.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly can be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the methods according to the embodiments of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. Those skilled in the art will appreciate that the present invention is not limited to the particular embodiments described herein, and that various obvious changes, rearrangements and substitutions will now be apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. An intelligent actual measurement method for a building, comprising:
importing a BIM model of a building, and establishing a grid map of an indoor detection environment based on the BIM model;
planning a path of the UGV based on the raster map to generate a global optimal path;
acquiring point cloud data of the indoor detection environment based on the global optimal path by adopting a three-dimensional laser scanning method, and registering the point cloud data;
and calculating actual measurement indexes of the building according to the registered point cloud data.
2. The method of claim 1, wherein the importing a BIM model of a building and building a grid map of an indoor detection environment based on the BIM model comprises:
extracting entity type identification and boundary information from the BIM based on semantic segmentation to obtain a semantic BIM, and generating a grid plane corresponding to the semantic BIM;
and performing three-dimensional space semantic recognition of the indoor detection environment based on the semantic BIM model, and generating the grid map by combining the grid plane.
3. The method of claim 1, wherein the collecting point cloud data of the indoor detection environment based on the globally optimal path comprises:
and positioning the UGV in real time, and adjusting the error between the actual pose of the UGV and the reference pose based on the global optimal path in real time.
4. The intelligent actual measurement method for the building according to claim 3, wherein the real-time positioning of the UGV comprises:
building a wireless local area network in a building room;
and based on the wireless local area network, adopting an RSSI indoor positioning algorithm to position the UGV in real time.
5. The method of claim 1, wherein the calculating actual measurement indicators of the building from the registered point cloud data comprises:
preprocessing the point cloud data, wherein the preprocessing comprises denoising, point cloud segmentation, thinning and point cloud classification;
and calculating the actual measurement index by adopting a virtual guiding rule algorithm and a virtual angle square algorithm based on the preprocessed point cloud data.
6. The method of claim 1, further comprising, after the calculating a measured actual measure index of the building from the registered point cloud data:
and carrying out index matching on the actually measured indexes in the BIM model, and calculating index errors to generate a geometric quality detection analysis report.
7. The method of claim 6, further comprising:
synchronizing the actually measured actual quantity indexes to a cloud end; and/or the presence of a gas in the gas,
and exporting and uploading the geometric quality detection analysis report to a terminal.
8. The utility model provides an actual measurement device in intelligence of building which characterized in that includes:
the grid map building module is used for importing a BIM (building information model) of a building and building a grid map of an indoor detection environment based on the BIM;
the path planning module is used for planning paths of the UGV based on the raster map so as to generate a global optimal path;
the point cloud data acquisition module is used for acquiring point cloud data of the indoor detection environment based on the global optimal path by adopting a three-dimensional laser scanning method and registering the point cloud data;
and the index calculation module is used for calculating the actual measurement indexes of the building according to the registered point cloud data.
9. A UGV, comprising:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the smart real estate method of a building of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method of intelligently measuring actual quantities of a building according to any one of claims 1 to 7.
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