An intelligent method, device, UGV and storage medium for actual measurement of a building

Abstract

An embodiment of the present invention discloses an intelligent actual measurement method, device, UGV and storage medium for a building. The method includes: importing a BIM model of the building and establishing a grid map of the indoor detection environment based on the BIM model; performing path planning on the UGV based on the grid map to generate a globally optimal path; adopting a three-dimensional laser scanning method to collect point cloud data of the indoor detection environment based on the globally optimal path and registering the point cloud data; calculating the actual measurement indexes of the building according to the registered point cloud data. The technical solution provided by the embodiment of the present invention realizes the integrated measurement of the indoor space, improves the measurement efficiency and coverage rate, and greatly saves the labor cost by automatically planning the path using the BIM model, automatically collecting the point cloud data using the UGV and the three-dimensional laser scanning technology, and then automatically processing and calculating the point cloud data to obtain the actual measurement indexes of the building.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of building detection, and in particular, to an intelligent actual measurement method, device, UGV and storage medium for buildings. Background Art

[0002] Traditional actual measurement of buildings is mainly completed by manual use of measuring instruments such as total stations, electronic theodolites, levels, steel tapes, vernier calipers, inside and outside corner rulers, and plumb lines. The measurement data is mainly managed in paper form, and only sampling inspections are carried out, which cannot achieve a comprehensive inspection of the house. At the same time, the unstable errors caused by human intervention are difficult to remove, and the data statistics are cumbersome and complex, requiring repeated verification. Summary of the Invention

[0003] The embodiments of the present invention provide an intelligent actual measurement method, device, UGV and storage medium for buildings to solve the problems of large traditional measurement errors, low efficiency and high costs.

[0004] In a first aspect, the embodiments of the present invention provide an intelligent actual measurement method for buildings, the method comprising:

[0005] Import a BIM model of a building and establish a grid map of the indoor detection environment based on the BIM model;

[0006] Perform path planning for the UGV based on the grid map to generate a globally optimal path;

[0007] Adopt a three-dimensional laser scanning method to collect point cloud data of the indoor detection environment based on the globally optimal path, and register the point cloud data;

[0008] Calculate the actual measurement indexes of the building according to the registered point cloud data.

[0009] Optionally, the importing a BIM model of a building and establishing a grid map of the indoor detection environment based on the BIM model includes:

[0010] Extract entity type identifiers and boundary information from the BIM model based on semantic segmentation to obtain a semantic BIM model, and generate a grid plane corresponding to the semantic BIM model;

[0011] Perform three-dimensional space semantic recognition of the indoor detection environment based on the semantic BIM model, and generate the grid map in combination with the grid plane.

[0012] Optionally, the collecting point cloud data of the indoor detection environment based on the globally optimal path includes:

[0013] Perform real-time positioning on the UGV, and adjust the error between the actual pose of the UGV and the reference pose based on the global optimal path in real time.

[0014] Optionally, the performing real-time positioning on the UGV includes:

[0015] Build a wireless local area network (WLAN) indoors in the building;

[0016] Based on the WLAN, perform real-time positioning on the UGV using the RSSI indoor positioning algorithm.

[0017] Optionally, the calculating the actual measurement indicators of the building according to the registered point cloud data includes:

[0018] Preprocess the point cloud data, where the preprocessing includes denoising, point cloud segmentation, thinning, and point cloud classification;

[0019] Based on the preprocessed point cloud data, calculate the actual measurement indicators using the virtual straightedge algorithm and the virtual square algorithm.

[0020] Optionally, after the calculating the actual measurement indicators of the building according to the registered point cloud data, it further includes:

[0021] Match the actual measurement indicators in the BIM model, and calculate the indicator error to generate a geometric quality inspection and analysis report.

[0022] Optionally, the method further includes:

[0023] Synchronize the actual measurement indicators to the cloud; and / or,

[0024] Export and upload the geometric quality inspection and analysis report to the terminal.

[0025] In a second aspect, an embodiment of the present invention further provides an intelligent actual measurement device for a building, and the device includes:

[0026] A grid map building module, configured to import the BIM model of the building and build a grid map of the indoor detection environment based on the BIM model;

[0027] A path planning module, configured to perform path planning on the UGV based on the grid map to generate a global optimal path;

[0028] A point cloud data acquisition module, configured to use a three-dimensional laser scanning method to acquire point cloud data of the indoor detection environment based on the global optimal path, and register the point cloud data;

[0029] An index calculation module, configured to calculate the actual measurement indicators of the building according to the registered point cloud data.

[0030] In a third aspect, an embodiment of the present invention further provides a UGV, which includes:

[0031] One or more processors;

[0032] A memory for storing one or more programs;

[0033] When the one or more programs are executed by the one or more processors, the one or more processors implement the intelligent actual measurement method for buildings provided in any embodiment of the present invention.

[0034] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the intelligent actual measurement method for buildings provided in any embodiment of the present invention.

[0035] An embodiment of the present invention provides an intelligent actual measurement method for buildings. First, the BIM model of the building is imported, and a grid map of the indoor detection environment is established based on this model. Then, based on this grid map, path planning is performed on the UGV to generate a globally optimal path. Next, the three-dimensional laser scanning method is used to collect point cloud data of the indoor detection environment based on this globally optimal path, and the collected point cloud data is registered. Thus, the actual measurement indicators of the building are calculated according to the registered point cloud data. The intelligent actual measurement method for buildings provided by the embodiments of the present invention realizes the integrated measurement of the indoor space by automatically planning the path using the BIM model, automatically collecting point cloud data using the UGV and three-dimensional laser scanning technology, and then automatically processing and calculating the point cloud data to obtain the actual measurement indicators of the building, improving the measurement efficiency and coverage rate, and greatly saving the labor cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a flowchart of the intelligent actual measurement method for buildings provided in Embodiment 1 of the present invention;

[0037] Figure 2 It is a schematic structural diagram of the intelligent actual measurement device for buildings provided in Embodiment 2 of the present invention;

[0038] Figure 3 It is a schematic structural diagram of the UGV provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] The present invention will be further described in detail below with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the present invention, rather than limiting the present invention. Additionally, it should be noted that for the sake of description, only parts related to the present invention are shown in the drawings, rather than all the structures.

[0040] Before discussing the exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts depict the steps as sequential processes, many of the steps can be performed in parallel, concurrently, or simultaneously. In addition, the order of the steps can be rearranged. The process can be terminated when its operations are completed, but it can also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, and so on.

[0041] Embodiment 1

[0042] Figure 1 The following is a flowchart of the intelligent actual measurement method for a building provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation of actual measurement of the interior of a building. This method can be executed by the intelligent actual measurement device for a building provided in the embodiments of the present invention. The device can be implemented in a hardware and / or software manner and is generally integrated into a UGV. As Figure 1 shown, the specific steps are as follows:

[0043] S11. Import the BIM model of the building and establish a grid map of the indoor detection environment based on the BIM model.

[0044] Specifically, the BIM model of the building to be detected currently can be imported into a UGV (Unmanned Ground Vehicle). The UGV can specifically be a quadruped robot. The UGV can include an embedded computer module for executing this method. After obtaining the BIM model, the UGV can perform environmental semantic recognition and analysis based on the BIM model to construct a single-layer grid map containing three-dimensional information and attributes such as coordinates. The BIM model contains rich design geometry and semantic information and can digitally represent the physical and functional characteristics of the target object.

[0045] Optionally, import the BIM model of the building and establish a grid map of the indoor detection environment based on the BIM model, including: extracting entity type identifiers and boundary information from the BIM model based on semantic segmentation to obtain a semantic BIM model, and generating a grid plane corresponding to the semantic BIM model; performing three-dimensional space semantic recognition of the indoor detection environment based on the semantic BIM model, and generating the grid map in combination with the grid plane. Specifically, first, semantic recognition of the indoor environment can be performed based on the imported BIM model, and entity type identifiers and boundary information in the BIM model can be extracted based on semantic segmentation. Then, identification and marking can be performed in the BIM model according to the extracted entity type identifiers and boundary information to obtain a semantic BIM model, and at the same time, its corresponding grid plane can be generated. Then, the construction of a single-layer grid map can be performed based on the semantic BIM model. Specifically, the entity model can be traversed in the corresponding grid plane according to the semantic BIM model and semantic information can be matched to obtain the required grid map.

[0046] S12. Perform path planning for the UGV based on the grid map to generate a globally optimal path.

[0047] Specifically, after obtaining the grid map of the indoor detection environment, the traveling path of the UGV can be planned based on this grid map so that the UGV can gradually collect all the point cloud data of the required indoor detection environment according to this path. Specifically, the A* algorithm can be used for global path planning, and the dynamic window algorithm can be combined for local path planning to obtain the final required globally optimal path. In this way, the scenario recognition and traveling path problems of UGV intelligent data collection are solved, thereby ensuring that the UGV can accurately and efficiently collect data.

[0048] S13. Adopt the three-dimensional laser scanning method to collect the point cloud data of the indoor detection environment based on the globally optimal path and register the point cloud data.

[0049] Specifically, after determining the global optimal path, the UGV can be controlled to perform data collection along the global optimal path. Specifically, a three-dimensional laser scanner can be mounted on the UGV to obtain the point cloud data of the indoor detection environment by using the three-dimensional laser scanning technology. The three-dimensional laser scanning technology can obtain the spatial point cloud data of the building non-contact, high-speed and accurately, so as to establish a real three-dimensional building model, breaking through the single-point measurement method, and having the unique advantages of high efficiency and high precision. Above, by based on BIM and mounting the three-dimensional laser scanner on the UGV to sense and collect data in the indoor actual measurement environment, the scanning automation can be realized, making up for the deficiencies of the traditional point cloud data collection with low efficiency, and the discretization of the collection and calculation analysis process. Since the three-dimensional laser scanner always establishes a coordinate system with itself as the origin, for the indoor detection environment, point clouds in different coordinate systems may be collected. Therefore, after obtaining the point cloud data of the indoor detection environment, the point clouds in different coordinate systems need to be unified into the same coordinate system, that is, the point cloud data also needs to be registered. Then, in this embodiment, after collecting the point cloud data, the global automatic registration of the point cloud data can also be performed to obtain the subsequent available point cloud data. The registration process can specifically include rough registration and fine registration. Rough registration refers to registering the point cloud when the relative pose of the point cloud is completely unknown, which can provide a good initial value for the fine registration. The purpose of the fine registration is to minimize the spatial position difference between the point clouds on the basis of the rough registration. In this embodiment, the processes of rough registration and fine registration can adopt any existing rough registration algorithm and fine registration algorithm. Exemplarily, the four-point fast matching algorithm can be used to obtain the optimal transformation, and then the coordinate transformation of the point cloud data is performed based on the optimal transformation to achieve rough registration, and then the Hessian matrix optimization and the improved Newton iteration algorithm are used for iteration to achieve fine registration.

[0050] Optionally, collecting the point cloud data of the indoor detection environment based on the global optimal path includes: performing real-time positioning on the UGV 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, during the process of the UGV moving and collecting 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 can be adjusted in real time to ensure that the UGV can automatically collect the point cloud data based on the stations of the global optimal path, so as to ensure the accuracy and integrity of the point cloud data. Specifically, the motion trajectory tracking control of the UGV can be performed, the control strategy can be optimized by using the predictive control algorithm, and the feedback correction can be performed by using the fuzzy predictive control algorithm, and the error adjustment is performed according to the control pose (i.e., the reference pose) and the actual pose.

[0051] Further optionally, the real-time positioning of the UGV includes: building a wireless local area network indoors in a building; based on the wireless local area network, using the RSSI indoor positioning algorithm to position the UGV in real time. Specifically, routers can be globally arranged indoors in the building and coordinate positions can be added to complete the construction of the wireless local area network. Then, the UGV signal can be collected through each router, and the Kalman filtering method can be used to filter the signal to remove noise and interference, and then the RSSI indoor positioning algorithm is used to accurately locate the path of the UGV based on the filtered signal.

[0052] S14. Calculate the actual measured quantity indicators of the building according to the registered point cloud data.

[0053] Specifically, after completing the registration of the point cloud data, the actual measured quantity indicators can be automatically calculated based on the registered point cloud data. The actual measured quantity indicators can include wall flatness, verticality, horizontal range, yin and yang angles, depth / span, and clear height, etc., so as to achieve integrated measurement of indoor space and complete the transformation from manual local sampling detection to BIM-based actual measurement and intelligentization.

[0054] Optionally, the method of calculating the measured quantity index of the building according to the registered point cloud data includes: preprocessing the point cloud data, the preprocessing includes denoising, point cloud segmentation, thinning and point cloud classification; based on the preprocessed point cloud data, using a virtual ruler algorithm and a virtual angle ruler algorithm to calculate the measured quantity index. Specifically, the registered point cloud data obtained can be preprocessed first, which can include denoising, point cloud segmentation, thinning and point cloud classification, etc., wherein denoising can use a conditional filtering method, and thinning can use a random sampling method. Point cloud segmentation is to divide according to feature points such as space, geometry and texture. The point clouds in the same division have similar features. The purpose of point cloud segmentation is to divide into blocks, so as to facilitate separate processing. Specifically, a clustering algorithm can be used to achieve this. Point cloud classification is to assign a semantic tag to each point, so as to classify the point cloud into different point cloud sets. The same point cloud set has similar or identical attributes. The purpose of point cloud classification is to semantically mark the entire scene, so as to facilitate subsequent calculation of indicators. Specifically, a PointNet network can be used to achieve this. After completing the preprocessing, the BIM model can be compared with the point cloud model based on the preprocessed point cloud data, and the virtual ruler algorithm and virtual angle ruler algorithm can be used to realize automatic measurement of the integrated indoor space to obtain the required actual measured quantity indicators.

[0055] On the basis of the above technical solution, optionally, after calculating the actual measurement indicators of the building according to the registered point cloud data, the method further includes: matching the actual measurement indicators in the BIM model and calculating the indicator error to generate a geometric quality inspection and analysis report. Specifically, after calculating each actual measurement indicator, by comparing with the BIM model, corresponding preset indicators can be matched for each actual measurement indicator and the indicator error can be calculated. Thus, a geometric quality inspection and analysis report can be generated according to each actual measurement indicator and each indicator error to comprehensively reflect the actual measurement results and provide a reference for the user's further decision-making.

[0056] Further optionally, the method further includes: synchronizing the actual measurement indicators to the cloud; and / or exporting and uploading the geometric quality inspection and analysis report to the terminal. Specifically, after obtaining each actual measurement indicator, the actual measurement indicators can be synchronized to the cloud. After generating the geometric quality inspection and analysis report, the geometric quality inspection and analysis report can also be exported and uploaded to the terminal, so that the data can be made public and transparent for multiple parties to use, and at the same time, it can also visually reflect the actual measurement results for the user to better obtain information.

[0057] The technical solution provided by the embodiment of the present invention first imports the BIM model of the building, establishes a grid map of the indoor detection environment based on this model, then performs path planning for the UGV based on this grid map to generate a globally optimal path, and then uses the three-dimensional laser scanning method to collect the point cloud data of the indoor detection environment based on this globally optimal path and register the collected point cloud data. Thus, the actual measurement indicators of the building are calculated according to the registered point cloud data. By automatically planning the path using the BIM model, automatically collecting the point cloud data using the UGV and three-dimensional laser scanning technology, and then automatically processing the point cloud data to calculate the actual measurement indicators of the building, the integrated measurement of the indoor space is realized, the measurement efficiency and coverage rate are improved, and the labor cost is also greatly saved.

[0058] Embodiment 2

[0059] Figure 2 FIG. is a schematic structural diagram of an intelligent actual measurement device for a building provided by Embodiment 2 of the present invention. This device can be implemented in a hardware and / or software manner and is generally integrated into the UGV for executing the intelligent actual measurement method for a building provided by any embodiment of the present invention. As Figure 2 shown, the device includes:

[0060] A grid map establishment module 21, configured to import the BIM model of the building and establish a grid map of the indoor detection environment based on the BIM model;

[0061] The path planning module 22 is used to perform path planning for the UGV based on the grid map to generate a globally optimal path;

[0062] The point cloud data acquisition module 23 is used to collect the point cloud data of the indoor detection environment based on the globally optimal path by using the three-dimensional laser scanning method, and register the point cloud data;

[0063] The index calculation module 24 calculates the actual measurement indexes of the building according to the registered point cloud data.

[0064] In the technical solution provided by the embodiment of the present invention, first, the BIM model of the building is imported, and a grid map of the indoor detection environment is established based on this model. Then, path planning for the UGV is performed based on this grid map to generate a globally optimal path. Next, the three-dimensional laser scanning method is used to collect the point cloud data of the indoor detection environment based on this globally optimal path, and the collected point cloud data is registered. Thus, the actual measurement indexes of the building are calculated according to the registered point cloud data. By automatically planning the path using the BIM model, automatically collecting the point cloud data using the UGV and the three-dimensional laser scanning technology, and then automatically processing and calculating the point cloud data to obtain the actual measurement indexes of the building, the integrated measurement of the indoor space is realized, the measurement efficiency and coverage rate are improved, and the labor cost is greatly saved.

[0065] On the basis of the above technical solution, optionally, the grid map establishment module 21 includes:

[0066] The grid plane generation unit is used to extract the entity type identifier and boundary information from the BIM model based on semantic segmentation to obtain a semantic BIM model, and generate a grid plane corresponding to the semantic BIM model;

[0067] The grid map generation unit is used to perform three-dimensional space semantic recognition of the indoor detection environment based on the semantic BIM model, and generate the grid map in combination with the grid plane.

[0068] On the basis of the above technical solution, optionally, the point cloud data acquisition module 23 includes:

[0069] The pose adjustment unit is used to perform real-time positioning on the UGV, and adjust the error between the actual pose of the UGV and the reference pose based on the globally optimal path in real time.

[0070] On the basis of the above technical solution, optionally, the pose adjustment unit includes:

[0071] The local area network building sub-unit is used to build a wireless local area network in the building interior;

[0072] A real-time positioning subunit, configured to perform real-time positioning on the UGV based on the wireless local area network by using the RSSI indoor positioning algorithm.

[0073] Based on the above technical solution, optionally, the index calculation module 24 includes:

[0074] A preprocessing unit, configured to preprocess the point cloud data, where the preprocessing includes denoising, point cloud segmentation, thinning, and point cloud classification;

[0075] An index calculation unit, configured to calculate the actual measurement index based on the preprocessed point cloud data by using a virtual reference ruler algorithm and a virtual angle ruler algorithm.

[0076] Based on the above technical solution, optionally, the intelligent actual measurement device for a building further includes:

[0077] A report generation module, configured to, after calculating the actual measurement index of the building according to the registered point cloud data, perform index matching on the actual measurement index in the BIM model and calculate the index error to generate a geometric quality detection analysis report.

[0078] Based on the above technical solution, optionally, the intelligent actual measurement device for a building further includes:

[0079] An index synchronization module, configured to synchronize the actual measurement index to the cloud; and / or,

[0080] A report upload module, configured to export and upload the geometric quality detection analysis report to a terminal.

[0081] The intelligent actual measurement device for a building provided in an embodiment of the present invention can execute the intelligent actual measurement method for a building provided in any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method.

[0082] It should be noted that in the embodiment of the intelligent actual measurement device for a building, the included units and modules are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of the functional units are only for the convenience of mutual distinction and do not limit the protection scope of the present invention.

[0083] Embodiment III

[0084] Figure 3 FIG. is a schematic structural diagram of a UGV provided in Embodiment III of the present invention, showing a block diagram of an exemplary UGV suitable for implementing the embodiment of the present invention. Figure 3 The shown UGV is only an example and should not bring any limitation to the functions and usage scope of the embodiment of the present invention. As Figure 3As shown, the UGV includes a processor 31, a memory 32, an input device 33, and an output device 34; the number of processors 31 in the UGV can be one or more. Figure 3 Taking one processor 31 as an example, the processor 31, the memory 32, the input device 33, and the output device 34 in the UGV can be connected by a bus or other means. Figure 3 Taking connection by bus as an example.

[0085] The memory 32, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the intelligent actual measurement method of the building in the embodiments of the present invention (for example, the grid map establishment module 21, the path planning module 22, the point cloud data acquisition module 23, and the index calculation module 24 in the intelligent actual measurement device of the building). The processor 31 executes various functional applications and data processing of the UGV by running the software programs, instructions, and modules stored in the memory 32, that is, implements the above-mentioned intelligent actual measurement method of the building.

[0086] The memory 32 mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the UGV, etc. In addition, the memory 32 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some instances, the memory 32 can further include a memory remotely set relative to the processor 31, and these remote memories can be connected to the UGV through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0087] The input device 33 can be used to obtain the BIM model of the building, and generate key signal inputs related to the user settings and function control of the UGV, etc. The output device 34 can be used to synchronize the actual measurement index to the cloud, etc.

[0088] Embodiment Four

[0089] Embodiment Four of the present invention further provides a storage medium containing computer-executable instructions, and the computer-executable instructions are used to execute an intelligent actual measurement method of a building when executed by a computer processor. The method includes:

[0090] Import the BIM model of the building, and establish a grid map of the indoor detection environment based on the BIM model;

[0091] Perform path planning on the UGV based on the grid map to generate a globally optimal path;

[0092] Adopt a three-dimensional laser scanning method to collect point cloud data of the indoor detection environment based on the global optimal path, and register the point cloud data.

[0093] Calculate the actual measurement indexes of the building according to the registered point cloud data.

[0094] The storage medium can be any of various types of memory devices or storage devices. The term "storage medium" is intended to include: installation media such as CD-ROMs, floppy disks or tape devices; computer system memory or random access memory such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; non-volatile memory such as flash memory, magnetic media (such as hard disks or optical storage); registers or other similar types of memory elements, etc. The storage medium can also include other types of memory or combinations thereof. Additionally, the storage medium can be located in the computer system in which the program is executed, or can be located in a different second computer system that is connected to the computer system through a network (such as the Internet). The second computer system can provide program instructions to the computer for execution. The term "storage medium" can include two or more storage media that can reside in different locations (such as in different computer systems connected through a network). The storage medium can store program instructions (such as specifically implemented as a computer program) executable by one or more processors.

[0095] Certainly, the storage medium containing computer-executable instructions provided by the embodiments of the present invention is not limited to the method operations as described above, and can also execute related operations in the intelligent actual measurement method of the building provided by any embodiment of the present invention.

[0096] The computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries the computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0097] The program code contained on the computer-readable medium can be transmitted by any appropriate medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination of the above.

[0098] Through the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software and necessary general hardware. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk or optical disc of a computer, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments of the present invention.

[0099] Note that the above is only the preferred embodiment of the present invention and the technical principles applied. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein. Various obvious changes, re-adjustments and substitutions can be made by those skilled in the art without departing from the protection scope of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments. Without departing from the concept of the present invention, more other equivalent embodiments can be included, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. An intelligent on-site measurement method for buildings, characterized in that, Including: Import the BIM model of the building and establish a grid map of the indoor detection environment based on the BIM model; Perform path planning for the UGV based on the grid map to generate a globally optimal path; Adopt the three-dimensional laser scanning method to collect the point cloud data of the indoor detection environment based on the globally optimal path and register the point cloud data; Calculate the actual measurement indicators of the building according to the registered point cloud data; Among them, the collecting the point cloud data of the indoor detection environment based on the globally optimal path includes: Perform real-time positioning on the UGV and adjust the error between the actual pose of the UGV and the reference pose based on the globally optimal path in real time; Among them, the performing real-time positioning on the UGV includes: Build a wireless local area network in the building interior and perform real-time positioning on the UGV based on the wireless local area network using the RSSI indoor positioning algorithm; Among them, the building a wireless local area network in the building interior and performing real-time positioning on the UGV based on the wireless local area network using the RSSI indoor positioning algorithm includes: Globally arrange routers in the building interior and add coordinate positions to complete the construction of the wireless local area network; collect the UGV signals through each router, filter the UGV signals using the Kalman filter, and determine the UGV path according to the filtered UGV signals through the RSSI indoor positioning algorithm; Among them, the importing the BIM model of the building and establishing a grid map of the indoor detection environment based on the BIM model includes: Extract entity type identifiers and boundary information from the BIM model based on semantic segmentation to obtain a semantic BIM model and generate a grid plane corresponding to the semantic BIM model; Traverse the entity model in the corresponding grid plane according to the semantic BIM model and match the semantic information to obtain a single-layer grid map containing three-dimensional information and coordinates; Among them, the calculating the actual measurement indicators of the building according to the registered point cloud data includes: Perform preprocessing on the point cloud data, and the preprocessing includes denoising, point cloud segmentation, thinning, and point cloud classification; Based on the preprocessed point cloud data, calculate the actual measurement indicators using the virtual straightedge algorithm and the virtual angle ruler algorithm.

2. The intelligent on-site measurement method for buildings according to claim 1, characterized in that, After the calculating the actual measurement indicators of the building according to the registered point cloud data, it further includes: Match the actual measurement indicators in the BIM model and calculate the indicator error to generate a geometric quality inspection and analysis report.

3. The intelligent on-site measurement method for buildings according to claim 2, characterized in that, The method further includes: Synchronize the actual measurement indicators to the cloud; and / or, Export and upload the geometric quality inspection and analysis report to the terminal.

4. An intelligent on-site measurement device for buildings, characterized in that, Including: A grid map establishment module for importing the BIM model of the building and establishing a grid map of the indoor detection environment based on the BIM model; A path planning module for performing path planning on the UGV based on the grid map to generate a globally optimal path; A point cloud data acquisition module, which is used to collect the point cloud data of the indoor detection environment based on the globally optimal path by using the three-dimensional laser scanning method, and register the point cloud data; An index calculation module, which calculates the actual measurement indexes of the building according to the registered point cloud data; Among them, the point cloud data acquisition module includes: A pose adjustment unit, which is used to perform real-time positioning on the UGV, and adjust the error between the actual pose of the UGV and the reference pose based on the globally optimal path in real time; Among them, the pose adjustment unit includes: A local area network building sub-unit, which is used to build a wireless local area network in the building interior; A real-time positioning sub-unit, which is used to perform real-time positioning on the UGV based on the wireless local area network by using the RSSI indoor positioning algorithm; Among them, the local area network building sub-unit is specifically used for: globally arranging routers in the building interior and adding coordinate positions to complete the construction of the wireless local area network; The real-time positioning sub-unit is specifically used for: collecting UGV signals through each router, filtering the UGV signals by using Kalman filtering, and determining the UGV path according to the filtered UGV signals by using the RSSI indoor positioning algorithm; Among them, the grid map building module includes: A grid plane generation unit, which is used to extract entity type identifiers and boundary information from the BIM model based on semantic segmentation to obtain a semantic BIM model, and generate a grid plane corresponding to the semantic BIM model; A grid map generation unit, which is used to traverse the entity model in the corresponding grid plane according to the semantic BIM model and match semantic information, so as to obtain a single-layer grid map containing three-dimensional information and coordinates; Among them, the index calculation module includes: A preprocessing unit, which is used to preprocess the point cloud data, and the preprocessing includes denoising, point cloud segmentation, thinning and point cloud classification; An index calculation unit, which is used to calculate the actual measurement indexes by using the virtual straightedge algorithm and the virtual square algorithm based on the preprocessed point cloud data.

5. A UGV, characterized in that, It includes: One or more processors; A memory, which is used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the intelligent actual measurement method of the building as described in any one of claims 1-3.

6. A computer-readable storage medium, on which a computer program is stored, characterized in that, When the program is executed by the processor, it implements the intelligent actual measurement method of the building as described in any one of claims 1-3.

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