Building measurement method and building measurement system based on three-dimensional point cloud model

Abstract

The invention provides a building measurement method and system based on a three-dimensional point cloud model, and the method comprises the steps: enabling a target three-dimensional laser scanner to carry out the three-dimensional laser scanning after a target robot reaches a scanning point, obtaining the local point cloud data corresponding to the scanning point, and transmitting the local point cloud data to a data processing module; the target robot moves to a next scanning point location based on the planned path until local point cloud data acquisition of all scanning point locations is completed; the data processing module obtains a three-dimensional point cloud model corresponding to the to-be-detected area; the parameter measurement module calculates building parameters represented by each building component in the three-dimensional point cloud model based on the geometric distribution of each building component in the three-dimensional point cloud model. According to the method and the system, the efficiency and the precision of building parameter measurement are improved.

Description

Technical Field

[0001] This application relates to the field of three-dimensional data processing technology, and in particular to a building measurement method and system based on a three-dimensional point cloud model. Background Technology

[0002] During the construction process, conducting actual measurements of the building's interior space is a crucial quality control step to ensure that the project meets design specifications and acceptance standards. In recent years, with the rapid development of 3D laser scanning technology, this technology has been increasingly widely used in the field of building surveying, and spatial measurement methods based on 3D models have become a commonly adopted technique in the industry.

[0003] Currently, a complete indoor 3D model can be constructed by collecting data at multiple stations using a static 3D laser scanner and then stitching the data together. However, the existing application model still heavily relies on on-site operation by technicians, requiring manual handling of equipment, setting up tripods, leveling and centering, scanning station by station and recording location information. This process is time-consuming, labor-intensive, and limited by the station location, easily resulting in scanning blind spots, especially in complex construction site environments. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a building measurement method and system based on a three-dimensional point cloud model, which realizes the fully automated operation from the autonomous planning of scanning points, automatic data acquisition, automatic point cloud processing to the generation of high-precision measured data, improves work efficiency, significantly reduces labor costs and labor intensity, avoids human error, and not only improves the accuracy of the constructed three-dimensional point cloud model, but also improves the efficiency and accuracy of building parameter measurement.

[0005] In a first aspect, embodiments of this application provide a building measurement method based on a three-dimensional point cloud model. The building measurement method is applied to a building measurement system based on a three-dimensional point cloud model. The building measurement system includes a collaborative control module, a data processing module, a parameter measurement module, multiple mobile robots, and a three-dimensional laser scanner mounted on the mobile robots. The building measurement method includes: The collaborative control module determines the area to be measured in the target building, determines multiple scanning points covering the area to be measured based on the three-dimensional environment map corresponding to the target building, and generates a planned path based on the multiple scanning points. The collaborative control module identifies the target robot from multiple mobile robots to perform the current task, identifies the 3D laser scanner installed on the target robot as the target 3D laser scanner, and sends the planned path to the target robot. The target robot moves based on the planned path and sends an arrival signal to the collaborative control module after reaching the scanning point, so that the collaborative control module sends a scanning command to the target 3D laser scanner based on the arrival signal. The target 3D laser scanner performs 3D laser scanning based on the scanning command, acquires local point cloud data corresponding to the scanning point, and sends the local point cloud data to the data processing module; The target robot moves to the next scanning point based on the planned path and returns to execute the step of sending an arrival signal to the collaborative control module after reaching the scanning point, until the local point cloud data acquisition of all scanning points is completed; The data processing module stitches together the local point cloud data corresponding to all scanning points to obtain a three-dimensional point cloud model corresponding to the area to be tested. The parameter measurement module identifies multiple building components from the three-dimensional point cloud model based on a plane fitting algorithm, and calculates the building parameters represented by each building component in the three-dimensional point cloud model according to the geometric distribution of each building component in the three-dimensional point cloud model.

[0006] Furthermore, before the data processing module stitches together the local point cloud data corresponding to all scanned points, the building measurement method further includes: The data processing module performs integrity verification on the local point cloud data corresponding to all scanning points. If the integrity verification result is unsuccessful, the rescanning points are determined from the multiple scanning points and the rescanning points are sent to the collaborative control module. The collaborative control module generates a rescanning path based on the rescanning point and sends the rescanning path to the target robot to control the target robot to move to the rescanning point.

[0007] Furthermore, after the parameter measurement module determines the building parameters represented by each building component in the three-dimensional point cloud model, the building measurement method further includes: Multiple measurement parameters are compared with the BIM 3D model of the target building to generate a deviation color cloud map based on the 3D point cloud model.

[0008] Furthermore, the building components include the walls, floor, and ceiling within the area to be measured; the parameter measurement module determines the building parameters represented by each building component in the three-dimensional point cloud model through the following steps: The three-dimensional point cloud model is used to calculate the ground levelness of each ground surface, the roof levelness of each roof slab, and the measurement results for each wall surface; wherein, the measurement results include flatness, verticality, and squareness. For each floor level, the net height of the room to which the floor level belongs is determined based on the height between the floor level and the corresponding ceiling.

[0009] Furthermore, the step of comparing multiple measurement parameters with the BIM 3D model of the target building to generate a deviation color cloud map based on the 3D point cloud model includes: For each building parameter, standard parameters are determined from the BIM 3D model based on the building components corresponding to that building parameter, and the building parameter is compared with the standard parameters. If the difference between the building parameter and the standard parameter is greater than or equal to the difference threshold, then the difference point cloud data of the building component corresponding to the building parameter is extracted from the three-dimensional point cloud model, and the difference point cloud data is color-rendered in the three-dimensional point cloud model to obtain the deviation color cloud map.

[0010] Furthermore, after reaching the scanning point, the target robot sends an arrival signal to the collaborative control module, including: After reaching the scanning point, the attitude tilt information detected by the inertial measurement unit determines whether the carrying platform is currently tilted. If so, the joint motor is controlled by inverse kinematics until the tilt angle deviation of the bearing platform relative to the ground is reduced to within the angle threshold, then the arrival signal is generated and sent to the cooperative control module.

[0011] Furthermore, the collaborative control module determines the target robot to perform the current task from among multiple mobile robots, including: Identify at least one idle robot from a plurality of mobile robots; For each idle robot, obtain the current remaining power of the idle robot, and determine the weighted score corresponding to the idle robot based on the current remaining power and the distance between the idle robot and the starting point in the planned path; The idle robot corresponding to the highest weighted score among multiple weighted scores is identified as the target robot.

[0012] Secondly, this application also provides a building measurement system based on a three-dimensional point cloud model. The building measurement system includes a collaborative control module, a data processing module, a parameter measurement module, multiple mobile robots, and a three-dimensional laser scanner installed on the mobile robots. The collaborative control module is used to determine the area to be measured in the target building, determine multiple scanning points covering the area to be measured based on the three-dimensional environment map corresponding to the target building, and generate a planned path based on the multiple scanning points. The collaborative control module is also used to identify the target robot that performs the current task from among multiple mobile robots, identify the 3D laser scanner installed on the target robot as the target 3D laser scanner, and send the planned path to the target robot; The target robot is used to move based on the planned path, and after reaching the scanning point, it sends an arrival signal to the collaborative control module, so that the collaborative control module sends a scanning command to the target 3D laser scanner based on the arrival signal. The target 3D laser scanner is used to perform 3D laser scanning based on the scanning command, obtain local point cloud data corresponding to the scanning point, and send the local point cloud data to the data processing module. The target robot is also used to move to the next scanning point based on the planned path, and return to execute the step of sending an arrival signal to the collaborative control module after reaching the scanning point, until the local point cloud data acquisition of all scanning points is completed; The data processing module is used to stitch together the local point cloud data corresponding to all scanning points to obtain a three-dimensional point cloud model corresponding to the area to be tested. The parameter measurement module is used to identify multiple building components from the three-dimensional point cloud model based on a plane fitting algorithm, and to calculate the building parameters represented by each building component in the three-dimensional point cloud model according to the geometric distribution of each building component in the three-dimensional point cloud model.

[0013] Thirdly, embodiments of this application also provide an electronic device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, the steps of the building measurement method based on a three-dimensional point cloud model as described above are performed.

[0014] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the building measurement method based on a three-dimensional point cloud model as described above.

[0015] This application achieves full automation of building parameter measurement by constructing an integrated system comprising a collaborative control module, a data processing module, a parameter measurement module, multiple mobile robots, and a 3D laser scanner. The collaborative control module intelligently plans the scanning points and paths covering the area to be measured based on a 3D environment map, scheduling the target robot and the target 3D laser scanner to automatically execute movement and scanning tasks. Simultaneously, the data processing module automatically processes the acquired 3D point cloud data to generate corresponding 3D point cloud models. The parameter measurement module uses these 3D point cloud models to measure the parameters of building components within the building. Compared to traditional methods relying on manual handling, station setup, and operation, this application achieves full automation of the entire process, from autonomous planning of scanning points, automatic data acquisition, automatic point cloud processing to the generation of high-precision measured data. This improves work efficiency, significantly reduces labor costs and intensity, avoids human error, and enhances not only the accuracy of the constructed 3D point cloud models but also the efficiency and accuracy of building parameter measurement. Furthermore, the robots possess autonomous navigation capabilities, allowing them to flexibly navigate complex construction environments, effectively reducing blind spots and improving spatial coverage.

[0016] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a building measurement method based on a 3D point cloud model, provided as an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a building measurement system based on a three-dimensional point cloud model provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. Based on the embodiments of this application, every other embodiment obtained by those skilled in the art without inventive effort falls within the scope of protection of this application.

[0020] First, the applicable scenarios for this application will be introduced. This application can be applied to the field of three-dimensional data processing technology.

[0021] During the construction process, conducting actual measurements of the building's interior space is a crucial quality control step to ensure that the project meets design specifications and acceptance standards. In recent years, with the rapid development of 3D laser scanning technology, this technology has been increasingly widely used in the field of building surveying, and spatial measurement methods based on 3D models have become a commonly adopted technique in the industry.

[0022] Research has shown that currently, a complete indoor 3D model can be constructed by collecting data from multiple stations using a static 3D laser scanner and then stitching the data together. However, the current application model still heavily relies on on-site operation by technicians, requiring manual handling of equipment, setting up tripods, leveling and centering, scanning station by station and recording location information. This process is time-consuming, labor-intensive, and limited by the station locations, easily resulting in scanning blind spots, especially noticeable in complex construction site environments.

[0023] Based on this, the embodiments of this application provide a building measurement method based on a three-dimensional point cloud model, which realizes the fully automated operation from the autonomous planning of scanning points, automatic data acquisition, automatic point cloud processing to the generation of high-precision measured data. This improves work efficiency, significantly reduces labor costs and labor intensity, avoids human error, and not only improves the accuracy of the constructed three-dimensional point cloud model, but also improves the efficiency and accuracy of building parameter measurement.

[0024] Please see Figure 1 , Figure 1This is a flowchart illustrating a building measurement method based on a 3D point cloud model, provided as an embodiment of this application. The building measurement method provided in this embodiment is applied to a building measurement system based on a 3D point cloud model. The building measurement system includes a collaborative control module, a data processing module, a parameter measurement module, multiple mobile robots, and a 3D laser scanner mounted on the mobile robots.

[0025] like Figure 1 As shown in the embodiments of this application, the building measurement method includes: S101, the collaborative control module determines the area to be measured in the target building, determines multiple scanning points covering the area to be measured based on the three-dimensional environment map corresponding to the target building, and generates a planned path based on the multiple scanning points.

[0026] Here, the collaborative control center communicates with each mobile robot and 3D laser scanner to plan the global inspection path, issue movement commands to the mobile robots, and issue measurement commands to the 3D laser scanner after the mobile robots arrive at the measurement points.

[0027] Regarding step S101 above, in specific implementation, the collaborative control module first determines the area to be measured in the target building. Here, the area to be measured can be selected by the user on the graphical interface or automatically triggered according to a preset inspection plan; this application does not specifically limit this. Then, based on the 3D environment map corresponding to the target building, the collaborative control module plans multiple scanning points that can fully cover the area to be measured, and generates the robot's planned path based on these multiple scanning points. Here, the 3D environment map can be constructed through a pre-deployed SLAM system, or generated from historical scanning tasks and stored in a cloud server. The layout of the scanning points adopts a meshable algorithm, comprehensively considering the effective field of view and maximum ranging radius of the 3D laser scanner, as well as the point cloud overlap rate requirements between adjacent scanning points, to ensure the success rate of subsequent automatic registration and the integrity of the overall model.

[0028] S102, the collaborative control module determines the target robot to perform the current task from multiple mobile robots, identifies the 3D laser scanner installed on the target robot as the target 3D laser scanner, and sends the planned path to the target robot.

[0029] In specific implementation of step S102 above, the collaborative control module determines the target robot to perform the current task from among multiple mobile robots, identifies the 3D laser scanner installed on the target robot as the target 3D laser scanner, and sends the planned path to the target robot.

[0030] As an optional embodiment, regarding step S102 above, the collaborative control module determines the target robot to perform the current task from among multiple mobile robots, including: Step 1021: Identify at least one idle robot from among the multiple mobile robots.

[0031] Step 1022: For each idle robot, obtain the current remaining power of the idle robot, and determine the weighted score corresponding to the idle robot based on the current remaining power and the distance between the idle robot and the starting point in the planned path.

[0032] Step 1023: Identify the idle robot corresponding to the highest weighted score among multiple weighted scores as the target robot.

[0033] Regarding steps 1021-1023 above, in specific implementation, firstly, at least one idle robot without an active task is identified from among the multiple mobile robots. For each idle robot, its current remaining battery power is obtained, and the distance between its current position and the starting point of the planned path is calculated. Based on the current remaining battery power and the distance from the idle robot to the starting point of the planned path, a weighted score is determined for that idle robot. All weighted scores are compared, and the idle robot corresponding to the highest weighted score among the multiple weighted scores is identified as the target robot.

[0034] S103, the target robot moves based on the planned path and sends an arrival signal to the collaborative control module after reaching the scanning point, so that the collaborative control module sends a scanning command to the target 3D laser scanner based on the arrival signal.

[0035] Here, the target robot serves as a mobile base, possessing autonomous navigation and dynamic stabilization capabilities. It is equipped with sensors such as LiDAR, IMU, and depth cameras for environmental perception and SLAM mapping. As an example, the target robot can utilize a robot dog mobile platform or can be replaced with other types of quadruped robots; this application does not impose specific limitations on this.

[0036] Regarding step S103 above, in practical implementation, the target robot moves to the first scanning point based on the planned path. During the movement, it simultaneously utilizes laser SLAM technology to dynamically adjust its local trajectory in complex environments to avoid obstacles. In addition to laser SLAM, visual SLAM, UWB ultra-wideband positioning, or QR code navigation technology can be combined. After the target robot reaches the scanning point, it sends an arrival signal to the cooperative control module. The cooperative control module responds to this arrival signal by sending a scanning command to the target 3D laser scanner.

[0037] As an optional embodiment, regarding step S103 above, the target robot sends an arrival signal to the collaborative control module after reaching the scanning point, including: Step 1031: After reaching the scanning point, determine whether the bearing platform is currently tilted based on the attitude tilt information detected by the inertial measurement unit.

[0038] Step 1032, if yes, then the joint motor is controlled by inverse kinematics until the tilt angle deviation of the bearing platform relative to the ground is reduced to within the angle threshold, then the arrival signal is generated and sent to the cooperative control module.

[0039] Regarding steps 1031-1032 above, in specific implementation, after the target robot arrives at the scanning point, it first performs an attitude detection and leveling process. The inertial measurement unit (IMU) collects the attitude tilt information of the carrier platform in real time, which can include pitch and roll angles. If any angle exceeds a preset angle threshold, it is determined that the carrier platform is not in a stable horizontal state. At this time, the target robot initiates an inverse kinematics control algorithm to calculate the target rotation angle of each leg joint motor, driving the robot to fine-tune the position of the foot contact point, gradually reducing the tilt angle deviation until the tilt angle deviation of the carrier platform relative to the ground is reduced to within the angle threshold. At this point, an arrival signal is generated and sent to the cooperative control module. Here, as an optional embodiment, in addition to the fine-tuning of the target robot joints, an electric leveling gimbal can be added to the back of the target robot to achieve secondary stabilization, or a hydraulic leveling system can be used.

[0040] Thus, according to steps 1031-1032 above, after the target robot arrives at the scanning point, it first uses an inertial measurement unit to detect the tilt of the supporting platform. If the tilt angle exceeds the allowable range, it activates an inverse kinematics algorithm to adjust the joint motors and actively adjust the foot position to achieve self-balancing and leveling until the platform is nearly level before sending an arrival signal and triggering scanning. This effectively overcomes the risk of equipment instability caused by uneven ground and undulating slopes at the construction site, providing the necessary stable environment for high-precision scanning and significantly improving the accuracy of point cloud acquisition and the success rate of subsequent point cloud registration.

[0041] S104, the target 3D laser scanner performs 3D laser scanning based on the scanning command, obtains local point cloud data corresponding to the scanning point, and sends the local point cloud data to the data processing module.

[0042] Here, the target 3D laser scanner is fixedly mounted on the target robot's support platform via a quick-release shock-absorbing bracket. It is used to acquire panoramic 3D point cloud data of the surrounding environment at multiple scanning points through 360° 3D laser scanning.

[0043] Regarding step S104 above, in specific implementation, after receiving the scanning command, the target 3D laser scanner performs a 360° 3D laser scan to obtain local point cloud data corresponding to the scanned points, and sends the local point cloud data to the data processing module.

[0044] Furthermore, a dedicated shock absorption and automatic leveling mechanism can be installed between the target robot and the target 3D laser scanner to provide a stable measurement reference for the target 3D laser scanner when the target robot is stationary.

[0045] S105, the target robot moves to the next scanning point based on the planned path, and returns to execute the step of sending an arrival signal to the collaborative control module after reaching the scanning point, until the local point cloud data acquisition of all scanning points is completed.

[0046] Regarding step S105 above, in specific implementation, after the local point cloud data of the current scanning point is scanned, the target robot moves to the next scanning point based on the planned path, and repeats steps S103 to S105 above until the target robot traverses all scanning stations and collects local point cloud data of all scanning points.

[0047] S106, the data processing module stitches together the local point cloud data corresponding to all scanning points to obtain a three-dimensional point cloud model corresponding to the area to be tested.

[0048] Regarding step S106 above, in specific implementation, after all scanning points have been scanned, the data processing module stitches together the local point cloud data corresponding to all scanning points to obtain a 3D point cloud model corresponding to the area to be measured. Here, the data processing module can use the ICP (Iterative Closest Point) algorithm during the stitching process, utilizing the overlapping features in the local point cloud data of each scanning point, such as door frames and wall corners, to perform initial coarse registration, followed by fine registration optimization, and finally outputting a complete 3D point cloud model in a unified coordinate system. Besides ICP, point cloud registration algorithms can also use NDT, Feature-based Matching, etc., and this application does not specifically limit the specific methods used.

[0049] As an optional embodiment, before the data processing module stitches together the local point cloud data corresponding to all scanned points, the building measurement method further includes: A: The data processing module performs integrity verification on the local point cloud data corresponding to all scan points. If the integrity verification result is unsuccessful, the rescanned points are determined from the multiple scan points and sent to the collaborative control module.

[0050] Regarding step A above, in practical implementation, before the data processing module stitches together the local point cloud data corresponding to all scanned points, it also needs to perform integrity verification on the local point cloud data corresponding to all scanned points. This integrity verification may include whether the point cloud density of each local point cloud data is lower than a threshold, and whether there are large missing areas in the local point cloud data. If the data verification result for any scanned point fails, it is marked as a point to be rescanned. Subsequently, these points are fed back to the collaborative control module.

[0051] B: The collaborative control module generates a rescanning path based on the rescanning point and sends the rescanning path to the target robot to control the target robot to move to the rescanning point.

[0052] Regarding step B above, in specific implementation, the collaborative control module generates a short path containing only the failed points, i.e., the rescanning path, based on the target robot's current position and the rescanning points, and assigns the target robot to the rescanning points. The target 3D laser scanner reacquires the 3D data of the point and synchronizes the scanned point data to the data processing module.

[0053] Thus, following steps A-B above, after the initial scan, the data processing module verifies the integrity of all local point cloud data and automatically identifies scanned points with insufficient point cloud density or large areas of missing data, marking them as points requiring rescanning. The collaborative control module then generates a targeted rescanning path and schedules the robot to perform the rescanning task. This ensures the integrity and reliability of the point cloud data used for the final stitching, avoiding overall model distortion or registration failure due to individual point data quality issues, thereby improving the modeling success rate.

[0054] S107, the parameter measurement module identifies multiple building components from the three-dimensional point cloud model based on a plane fitting algorithm, and calculates the building parameters represented by each building component in the three-dimensional point cloud model according to the geometric distribution of each building component in the three-dimensional point cloud model.

[0055] Regarding step S107 above, in specific implementation, after the data processing module obtains the 3D point cloud model corresponding to the area to be measured, the parameter measurement module identifies multiple building components from the 3D point cloud model based on the RANSAC plane fitting algorithm. Here, as an optional embodiment, the building components include walls, floors, and roofs within the area to be measured. The building parameters represented by each building component in the 3D point cloud model are calculated based on the geometric distribution of each component. Here, in addition to RANSAC, the platform fitting algorithm can also employ Hough transform, region growing, etc., and this application does not specifically limit its application to these algorithms.

[0056] Specifically, regarding step S107 above, the architectural parameters represented by each architectural component in the three-dimensional point cloud model are determined through the following steps: The three-dimensional point cloud model is used to calculate the ground levelness of each floor, the ceiling levelness of each ceiling, and the measurement results corresponding to each wall surface; wherein, the measurement results include flatness, verticality, and squareness; for each floor, the net height of the room to which the floor belongs is determined based on the height value between the floor and the ceiling corresponding to the floor.

[0057] For the two steps mentioned above, in specific implementation, for each identified wall surface, the deviation between the wall surface point cloud and its best-fit plane is calculated using a 3D point cloud model to determine the wall surface's flatness. Specifically, plane fitting is performed on the wall surface point cloud data to obtain the best-fit plane for the wall surface. The distances from all points in the wall surface point cloud to the best-fit plane are calculated, and the maximum, minimum, or standard deviation of these distances are used as quantitative indicators of the wall surface's flatness. The angle between the best-fit plane of the wall surface and the gravity direction (Z-axis) or adjacent walls is calculated to determine the wall surface's verticality and squareness. Specifically, plane fitting is performed on the wall surface point cloud data to obtain the first best-fit plane for the wall surface. The gravity direction vector or the normal vector of the second best-fit plane of the adjacent walls is obtained. The angle between the normal vector of the first best-fit plane and the normal vector of the gravity direction vector or the second best-fit plane is calculated, and this angle is used as a quantitative indicator of the wall surface's verticality. When calculating the squareness of a wall surface, plane fitting is performed on two adjacent walls intersecting at the same corner to obtain their respective best-fit planes and their normal vectors. The angle between these two normal vectors is then calculated, and the difference between this angle and 90 degrees is used as a quantitative indicator of the squareness of the corner. For the identified floor and ceiling components, the levelness of the floor and ceiling is calculated using a plane fitting algorithm. For a segmented floor point cloud region of a room, the net height of the room to which the floor belongs is determined based on the height value between the floor and the corresponding ceiling.

[0058] As an optional embodiment, the parameter measurement module determines the building parameters represented by each building component in the three-dimensional point cloud model. The building measurement method provided in this application further includes: Multiple measurement parameters are compared with the BIM 3D model of the target building to generate a deviation color cloud map based on the 3D point cloud model.

[0059] In the specific implementation of the above steps, the multiple measurement parameters determined in step I are compared with the BIM 3D model of the target building to generate a visualized deviation color cloud map based on the 3D point cloud model.

[0060] Specifically, the step of comparing multiple measurement parameters with the BIM 3D model of the target building to generate a deviation color cloud map based on the 3D point cloud model includes: i: For each building parameter, standard parameters are determined from the BIM 3D model based on the building components corresponding to that building parameter, and the building parameter is compared with the standard parameters.

[0061] ii: If the difference between the building parameter and the standard parameter is greater than or equal to the difference threshold, then the difference point cloud data of the building component corresponding to the building parameter is extracted from the three-dimensional point cloud model, and the difference point cloud data is color-rendered in the three-dimensional point cloud model to obtain the deviation color cloud map.

[0062] For steps i-ii above, in specific implementation, for each building parameter, the standard parameter of the corresponding building component is found in the BIM 3D model, and the building parameter is compared with the standard parameter. It is determined whether the difference between the building parameter and the standard parameter is greater than or equal to a preset difference threshold. If so, the difference point cloud extraction process begins. Difference point cloud data of the building component corresponding to the building parameter is extracted from the 3D point cloud model, and color values ​​are assigned to the difference point cloud data according to the magnitude of its deviation from the ideal plane. For example, positive deviation (convexity) is mapped to a red gradient; negative deviation (concavity) is mapped to a blue gradient. The rendered point cloud forms a deviation color cloud map, which can be superimposed on the original 3D point cloud model for display, forming a quality defect heatmap with clear spatial location and color coding.

[0063] Thus, following the steps outlined above, after obtaining a complete 3D point cloud model, a plane fitting algorithm is used to automatically identify key building components such as walls, floors, and ceilings, and extract their corresponding building parameters, achieving an intelligent conversion from the original point cloud to structured engineering indicators. Subsequently, the measured parameters are compared and analyzed with the standard parameters in the BIM design model to generate visualized deviation results, significantly shortening the quality inspection cycle and providing data support for engineering quality assessment.

[0064] As an optional implementation, after the above process is completed, all calculated building parameters can be automatically filled into the report template, and the 3D point cloud model and deviation color cloud map can be visualized to generate an inspection report, which will then be sent to relevant management personnel. This achieves an end-to-end automated integrated solution from path planning, data collection, processing and analysis to report generation.

[0065] Furthermore, according to the method provided in this application, the system can also receive user input for report type selection. As an example, report types may include parameter pass rate statistical reports, construction cycle quality trend reports, and regional quality distribution reports, etc., which are not specifically limited in this application. Users can select the desired report type and further set filtering conditions, such as specifying floors, room numbers, component categories, inspection time periods, or quality standard levels. The data processing module automatically retrieves the corresponding database records based on the user's report type selection. For example, if the user selects a parameter pass rate statistical report, the system will count the number of parameters that meet the preset tolerance range among all inspected wall flatness, verticality, squareness, and ground clearance parameters, calculate their proportion of the total inspected items, generate a pie chart or bar chart of the pass rate for each parameter category, and mark the specific location and deviation value of the non-conforming items. If the user selects a cycle pass rate report, the system will combine the timestamp of the current scanning task with data from multiple historical scanning tasks, divide the time intervals by week, month, or construction stage, and calculate the average pass rate of various building parameters within each cycle.

[0066] This application achieves full automation of building parameter measurement by constructing an integrated system comprising a collaborative control module, a data processing module, a parameter measurement module, multiple mobile robots, and a 3D laser scanner. The collaborative control module intelligently plans the scanning points and paths covering the area to be measured based on a 3D environment map, scheduling the target robot and the target 3D laser scanner to automatically execute movement and scanning tasks. Simultaneously, the data processing module automatically processes the acquired 3D point cloud data to generate corresponding 3D point cloud models. The parameter measurement module uses these 3D point cloud models to measure the parameters of building components within the building. Compared to traditional methods relying on manual handling, station setup, and operation, this application achieves full automation of the entire process, from autonomous planning of scanning points, automatic data acquisition, automatic point cloud processing to the generation of high-precision measured data. This improves work efficiency, significantly reduces labor costs and intensity, avoids human error, and enhances not only the accuracy of the constructed 3D point cloud models but also the efficiency and accuracy of building parameter measurement. Furthermore, the robots possess autonomous navigation capabilities, allowing them to flexibly navigate complex construction environments, effectively reducing blind spots and improving spatial coverage.

[0067] Please see Figure 2 , Figure 2 This is a structural schematic diagram of a building measurement system based on a three-dimensional point cloud model, provided as an embodiment of this application. Figure 2 As shown, the building measurement system 200 includes a collaborative control module A, a data processing module B, multiple mobile robots C1…Cn, and a three-dimensional laser scanner D1…Dn and a parameter measurement module E installed on the mobile robots; The collaborative control module A is used to determine the area to be measured in the target building, determine multiple scanning points covering the area to be measured based on the three-dimensional environment map corresponding to the target building, and generate a planned path based on the multiple scanning points. The collaborative control module A is also used to determine the target robot Ci from multiple mobile robots C1...Cn to perform the current task, identify the three-dimensional laser scanner installed on the target robot as the target three-dimensional laser scanner Di, and send the planned path to the target robot Ci; The target robot Ci is used to move based on the planned path and send an arrival signal to the collaborative control module after reaching the scanning point, so that the collaborative control module sends a scanning command to the target 3D laser scanner Di based on the arrival signal. The target 3D laser scanner Di is used to perform 3D laser scanning based on the scanning command, obtain local point cloud data corresponding to the scanning point, and send the local point cloud data to the data processing module; The target robot Ci is also used to move to the next scanning point based on the planned path, and return to execute the step of sending an arrival signal to the cooperative control module after reaching the scanning point, until the local point cloud data acquisition of all scanning points is completed; The data processing module B is used to stitch together the local point cloud data corresponding to all scanning points to obtain the three-dimensional point cloud model corresponding to the area to be tested. The parameter measurement module E is used to identify multiple building components from the three-dimensional point cloud model based on a plane fitting algorithm, and to calculate the building parameters represented by each building component in the three-dimensional point cloud model according to the geometric distribution of each building component in the three-dimensional point cloud model.

[0068] Furthermore, before the data processing module B stitches together the local point cloud data corresponding to all scanned points: The data processing module B is also used to perform integrity verification on the local point cloud data corresponding to all scanning points. If the integrity verification result is unsuccessful, the rescanning points are determined from the multiple scanning points and the rescanning points are sent to the collaborative control module. The collaborative control module A is also used to generate a rescanning path based on the rescanning point and send the rescanning path to the target robot Ci to control the target robot Ci to move to the rescanning point.

[0069] Furthermore, after determining the building parameters represented by each building component in the three-dimensional point cloud model, the parameter measurement module E is also used for: Multiple measurement parameters are compared with the BIM 3D model of the target building to generate a deviation color cloud map based on the 3D point cloud model.

[0070] Furthermore, the building components include the walls, floor, and ceiling within the area to be measured; the parameter measurement module E is also used to determine the building parameters represented by each building component in the three-dimensional point cloud model through the following steps: The three-dimensional point cloud model is used to calculate the ground levelness of each floor, the ceiling levelness of each ceiling, and the measurement results corresponding to each wall surface; wherein, the measurement results include flatness, verticality, and squareness; for each floor, the net height of the room to which the floor belongs is determined based on the height value between the floor and the ceiling corresponding to the floor.

[0071] Furthermore, when the data processing module B compares multiple measurement parameters with the BIM 3D model of the target building to generate a deviation color cloud map based on the 3D point cloud model, the data processing module B is also used for: For each building parameter, standard parameters are determined from the BIM 3D model based on the building components corresponding to that building parameter, and the building parameter is compared with the standard parameters. If the difference between the building parameter and the standard parameter is greater than or equal to the difference threshold, then the difference point cloud data of the building component corresponding to the building parameter is extracted from the three-dimensional point cloud model, and the difference point cloud data is color-rendered in the three-dimensional point cloud model to obtain the deviation color cloud map.

[0072] Furthermore, when the target robot Ci sends an arrival signal to the cooperative control module after reaching the scanning point, the target robot Ci is also used to: After reaching the scanning point, the attitude tilt information detected by the inertial measurement unit determines whether the carrying platform is currently tilted. If so, the joint motor is controlled by inverse kinematics until the tilt angle deviation of the bearing platform relative to the ground is reduced to within the angle threshold, then the arrival signal is generated and sent to the cooperative control module.

[0073] Furthermore, when the collaborative control module A determines the target robot to perform the current task from among multiple mobile robots, the collaborative control module A is also used to: Identify at least one idle robot from a plurality of mobile robots; For each idle robot, obtain the current remaining power of the idle robot, and determine the weighted score corresponding to the idle robot based on the current remaining power and the distance between the idle robot and the starting point in the planned path; The idle robot corresponding to the highest weighted score among multiple weighted scores is identified as the target robot.

[0074] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 3 As shown, the electronic device 300 includes a processor 310, a memory 320, and a bus 330.

[0075] The memory 320 stores machine-readable instructions executable by the processor 310. When the electronic device 300 is running, the processor 310 and the memory 320 communicate via the bus 330. When the machine-readable instructions are executed by the processor 310, they can perform the operations described above. Figure 1 The steps of the building measurement method based on a 3D point cloud model in the method embodiment shown are described in detail in the method embodiment, and will not be repeated here.

[0076] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can perform the above-described actions. Figure 1 The steps of the building measurement method based on a 3D point cloud model in the method embodiment shown are described in detail in the method embodiment, and will not be repeated here.

[0077] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0078] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0079] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0080] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0081] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0082] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The scope of protection of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A building measurement method based on a three-dimensional point cloud model, characterized in that, The building measurement is applied to a building measurement system based on a three-dimensional point cloud model. The building measurement system includes a collaborative control module, a data processing module, a parameter measurement module, multiple mobile robots, and a three-dimensional laser scanner installed on the mobile robots. The building surveying method includes: The collaborative control module determines the area to be measured in the target building, determines multiple scanning points covering the area to be measured based on the three-dimensional environment map corresponding to the target building, and generates a planned path based on the multiple scanning points. The collaborative control module identifies the target robot from multiple mobile robots to perform the current task, identifies the 3D laser scanner installed on the target robot as the target 3D laser scanner, and sends the planned path to the target robot. The target robot moves based on the planned path and sends an arrival signal to the collaborative control module after reaching the scanning point, so that the collaborative control module sends a scanning command to the target 3D laser scanner based on the arrival signal. The target 3D laser scanner performs 3D laser scanning based on the scanning command, acquires local point cloud data corresponding to the scanning point, and sends the local point cloud data to the data processing module; The target robot moves to the next scanning point based on the planned path and returns to execute the step of sending an arrival signal to the collaborative control module after reaching the scanning point, until the local point cloud data acquisition of all scanning points is completed; The data processing module stitches together the local point cloud data corresponding to all scanning points to obtain a three-dimensional point cloud model corresponding to the area to be tested. The parameter measurement module identifies multiple building components from the three-dimensional point cloud model based on a plane fitting algorithm, and calculates the building parameters represented by each building component in the three-dimensional point cloud model according to the geometric distribution of each building component in the three-dimensional point cloud model.

2. The building surveying method according to claim 1, characterized in that, Before the data processing module stitches together the local point cloud data corresponding to all scanned points, the building measurement method further includes: The data processing module performs integrity verification on the local point cloud data corresponding to all scanning points. If the integrity verification result is unsuccessful, the rescanning points are determined from the multiple scanning points and the rescanning points are sent to the collaborative control module. The collaborative control module generates a rescanning path based on the rescanning point and sends the rescanning path to the target robot to control the target robot to move to the rescanning point.

3. The building surveying method according to claim 1, characterized in that, After the parameter measurement module determines the building parameters represented by each building component in the three-dimensional point cloud model, the building measurement method further includes: Multiple measurement parameters are compared with the BIM 3D model of the target building to generate a deviation color cloud map based on the 3D point cloud model.

4. The building surveying method according to claim 1, characterized in that, The building components include the walls, floor, and ceiling within the area to be measured; the parameter measurement module determines the building parameters represented by each building component in the three-dimensional point cloud model through the following steps: The three-dimensional point cloud model is used to calculate the ground levelness of each ground surface, the roof levelness of each roof slab, and the measurement results for each wall surface; wherein, the measurement results include flatness, verticality, and squareness. For each floor level, the net height of the room to which the floor level belongs is determined based on the height between the floor level and the corresponding ceiling.

5. The building surveying method according to claim 3, characterized in that, The step of comparing multiple measurement parameters with the BIM 3D model of the target building to generate a deviation color cloud map based on the 3D point cloud model includes: For each building parameter, standard parameters are determined from the BIM 3D model based on the building components corresponding to that building parameter, and the building parameter is compared with the standard parameters. If the difference between the building parameter and the standard parameter is greater than or equal to the difference threshold, then the difference point cloud data of the building component corresponding to the building parameter is extracted from the three-dimensional point cloud model, and the difference point cloud data is color-rendered in the three-dimensional point cloud model to obtain the deviation color cloud map.

6. The building surveying method according to claim 1, characterized in that, After reaching the scanning point, the target robot sends an arrival signal to the collaborative control module, including: After reaching the scanning point, the attitude tilt information detected by the inertial measurement unit determines whether the carrying platform is currently tilted. If so, the joint motor is controlled by inverse kinematics until the tilt angle deviation of the bearing platform relative to the ground is reduced to within the angle threshold, then the arrival signal is generated and sent to the cooperative control module.

7. The building surveying method according to claim 1, characterized in that, The collaborative control module determines the target robot from among multiple mobile robots to perform the current task, including: Identify at least one idle robot from a plurality of mobile robots; For each idle robot, obtain the current remaining power of the idle robot, and determine the weighted score corresponding to the idle robot based on the current remaining power and the distance between the idle robot and the starting point in the planned path; The idle robot corresponding to the highest weighted score among multiple weighted scores is identified as the target robot.

8. A building measurement system based on a three-dimensional point cloud model, characterized in that, The building measurement system includes a collaborative control module, a data processing module, a parameter measurement module, multiple mobile robots, and a 3D laser scanner mounted on the mobile robots. The collaborative control module is used to determine the area to be measured in the target building, determine multiple scanning points covering the area to be measured based on the three-dimensional environment map corresponding to the target building, and generate a planned path based on the multiple scanning points. The collaborative control module is also used to identify the target robot that performs the current task from among multiple mobile robots, identify the 3D laser scanner installed on the target robot as the target 3D laser scanner, and send the planned path to the target robot; The target robot is used to move based on the planned path, and after reaching the scanning point, it sends an arrival signal to the collaborative control module, so that the collaborative control module sends a scanning command to the target 3D laser scanner based on the arrival signal. The target 3D laser scanner is used to perform 3D laser scanning based on the scanning command, obtain local point cloud data corresponding to the scanning point, and send the local point cloud data to the data processing module. The target robot is also used to move to the next scanning point based on the planned path, and return to execute the step of sending an arrival signal to the collaborative control module after reaching the scanning point, until the local point cloud data acquisition of all scanning points is completed; The data processing module is used to stitch together the local point cloud data corresponding to all scanning points to obtain a three-dimensional point cloud model corresponding to the area to be tested. The parameter measurement module is used to identify multiple building components from the three-dimensional point cloud model based on a plane fitting algorithm, and to calculate the building parameters represented by each building component in the three-dimensional point cloud model according to the geometric distribution of each building component in the three-dimensional point cloud model.

9. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. The machine-readable instructions are executed by the processor to perform the steps of the building measurement method based on a three-dimensional point cloud model as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the building measurement method based on a three-dimensional point cloud model as described in any one of claims 1 to 7.

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