Construction progress management method and device based on aerial photography, medium and equipment

Abstract

The invention provides a construction progress management method and device based on aerial photography, a medium and equipment, and the method comprises the steps: carrying out the oblique photography through employing an unmanned plane, obtaining image sequences of a construction site in different periods, and enabling the image sequences to comprise GNSS data and IMU data; establishing a three-dimensional point cloud of the target building based on the image sequence; based on the two groups of point cloud data in different periods, generating difference point cloud data of the target building; determining a construction progress based on the difference point cloud data, the GNSS data and the IMU data; according to the method, images of a construction site in different periods are shot through an unmanned aerial vehicle, a three-dimensional point cloud of a target building is established based on the images, difference point cloud data of the target building in different periods are generated through two sets of point cloud data in different periods, and the construction progress of the target building is determined according to the difference point cloud data, GNSS data and IMU data. Therefore, unmanned supervision of the construction progress of the target building is realized, and meanwhile, three-dimensional image information with a good visualization effect can be generated by utilizing the three-dimensional point cloud data.

Description

Technical Field

[0001] The present application relates to the technical field of construction progress management, and specifically to a construction progress management method, device, medium and equipment based on aerial photography. Background Art

[0002] Construction progress monitoring is an important part of construction project management. By monitoring the construction progress, it can help project managers discover problems in a timely manner and take appropriate measures to ensure that the construction process proceeds as planned. Therefore, it is of great significance.

[0003] However, at present, most construction units control the overall progress of the project by registering the arrival of raw materials, and conducting statistics by comprehensively analyzing the usage of steel bars and concrete combined with construction experience. This method can basically solve the problem of general civil construction projects with low construction technology content and low construction difficulty. However, it is difficult to quickly and accurately obtain the construction progress information of buildings for larger engineering projects, and the accuracy cannot be guaranteed. In addition, a single display data cannot fully reflect the actual progress in the construction process, resulting in incomplete construction data and difficulty in accurate construction progress management. Therefore, there is an urgent need for a method that can efficiently, safely and comprehensively obtain construction progress. Summary of the invention

[0004] In order to solve the above technical problems, the present application is proposed. The embodiments of the present application provide a construction progress management method, device, medium and equipment based on aerial photography.

[0005] According to one aspect of the present application, a construction progress management method based on aerial photography is provided, including: using a drone to perform oblique photography to obtain an image sequence of a construction site at different periods; wherein the image sequence includes GNSS (Global Navigation Satellite System) data and IMU (Inertial Measurement Unit) data; based on the image sequence, establishing a three-dimensional point cloud of a target building; based on two sets of point cloud data of the target building at different periods, generating difference point cloud data of the target building at different periods; based on the difference point cloud data, the GNSS data and the IMU data, determining the construction progress of the target building.

[0006] In one embodiment, the use of a drone to perform oblique photography to obtain a sequence of images of the construction site at different periods includes: correcting the exposure delay time of the drone's camera according to a cubic spline interpolation method; differentiating the GNSS data according to the camera exposure delay time to obtain a relationship between antenna phase center coordinates and camera station coordinates; based on the relationship between the antenna phase center coordinates and the camera station coordinates, using the drone to perform oblique photography to obtain a sequence of images of the construction site at different periods.

[0007] In one embodiment, the method of correcting the camera exposure delay time of the UAV according to the cubic spline interpolation method includes: obtaining the flight sampling points of the UAV according to GNSS sampling; based on the flight sampling points, using a cubic spline function to perform cubic polynomial fitting to obtain the flight trajectory of the UAV; according to an interpolation calculation method, obtaining the exposure time for obtaining the antenna phase center coordinates of the UAV; based on the exposure time for obtaining the antenna phase center coordinates of the UAV, correcting the camera exposure delay time of the UAV.

[0008] In one embodiment, the generating of differential point cloud data of the target building at different periods based on the two sets of point cloud data of the target building at different periods includes: extracting feature data of the two sets of point cloud data; based on the feature data, performing feature matching on the two sets of point cloud data to generate a matching relationship set; based on the matching relationship set, calculating the difference between the two sets of point cloud data to generate the differential point cloud data.

[0009] In one embodiment, the feature matching of the two groups of point cloud data based on the feature data to generate a matching relationship set includes: for any first point cloud in one group of point cloud data in the two groups of point cloud data, querying the second point cloud closest to the first point cloud in the other group of point cloud data in the two groups of point cloud data, and adding the first point cloud and the corresponding second point cloud as a point cloud pair to the candidate set; for any pair of point cloud pairs in the candidate set, calculating the first distance value between the second point cloud of the point cloud pair and all the first point clouds in the candidate set; if the first distance value is equal to the distance between the first point cloud and the second point cloud of the point cloud pair, determining that the point cloud pair is a candidate point cloud pair; selecting any three pairs of candidate point cloud pairs, calculating the side lengths of the first triangle and the second triangle; wherein the first triangle is composed of the first point clouds of the three pairs of candidate point cloud pairs, and the second triangle is composed of the second point clouds of the three pairs of candidate point cloud pairs; if the side lengths of the first triangle and the second triangle meet the preset conditions, adding the three pairs of candidate point cloud pairs to the matching relationship set; wherein the preset conditions are: ;in, For a given threshold, , i=1,2,3, are the lengths of the three sides of the first triangle, are the lengths of the three sides of the second triangle respectively.

[0010] In one embodiment, the calculating the difference between the two sets of point cloud data based on the matching relationship set to generate the difference point cloud data includes: for each point cloud pair in the matching relationship set, calculating the Euclidean distance between the first point cloud and the second point cloud; wherein the calculation formula of the Euclidean distance is: ,in, is the first point cloud, is the second point cloud, is the scaling factor, is the rotation matrix, is the translation vector; the error contribution of the point cloud pair is calculated by the error metric function; wherein the error metric function is , the error contribution is calculated as: ,in, is the scaling factor of the error metric function; the error contributions of all point cloud pairs in the matching relationship set are accumulated to obtain the total error; wherein the calculation formula of the total error is: ; Based on the minimization of the total error, the optimal scaling factor, the optimal rotation matrix and the optimal translation vector are calculated; based on the optimal scaling factor, the optimal rotation matrix and the optimal translation vector, the second point cloud of all point cloud pairs in the matching relationship set is transformed into a transformed point cloud in the first point cloud coordinate system; wherein the calculation formula of the transformed point cloud is: ,in, To transform the point cloud, is a second point cloud set; calculating a second distance value between the transformed point cloud and the corresponding first point cloud; if the second distance value is greater than a preset distance threshold, taking the corresponding first point cloud and the transformed point cloud as a difference point cloud pair.

[0011] In one embodiment, determining the construction progress of the target building based on the difference point cloud data, the GNSS data and the IMU data includes: aligning the difference point cloud data, the GNSS data and the IMU data; and determining the construction progress of the target building based on the aligned difference point cloud data, the GNSS data and the IMU data.

[0012] According to another aspect of the present application, a construction progress management device based on aerial photography is provided, including: an image acquisition module, used to use a drone to perform oblique photography to acquire an image sequence of a construction site at different periods; wherein the image sequence includes GNSS data and IMU data; a point cloud establishment module, used to establish a three-dimensional point cloud of a target building based on the image sequence; a difference generation module, used to generate difference point cloud data of the target building at different periods based on two sets of point cloud data of the target building at different periods; and a progress determination module, used to determine the construction progress of the target building based on the difference point cloud data, the GNSS data and the IMU data.

[0013] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and the computer program is used to execute any of the above methods.

[0014] According to another aspect of the present application, an electronic device is provided, comprising: a processor; a memory for storing instructions executable by the processor; and the processor is used to execute any of the above-described methods.

[0015] The present application provides a construction progress management method, device, medium and equipment based on aerial photography, which uses a drone to perform oblique photography to obtain an image sequence of a construction site at different periods; wherein the image sequence includes GNSS data and IMU data; based on the image sequence, a three-dimensional point cloud of a target building is established; based on two sets of point cloud data of the target building at different periods, differential point cloud data of the target building at different periods is generated; based on the differential point cloud data, GNSS data and IMU data, the construction progress of the target building is determined; that is, images of the construction site at different periods are taken by a drone, and a three-dimensional point cloud of the target building is established based on the images, differential point cloud data of the target building at different periods is generated through the two sets of point cloud data at different periods, and the construction progress of the target building is determined according to the differential point cloud data, GNSS data and IMU data, thereby realizing unmanned supervision of the construction progress of the target building, and at the same time, the three-dimensional point cloud data can be used to generate three-dimensional image information with good visualization effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0017] Figure 1It is a flowchart of a construction progress management method based on aerial photography provided by an exemplary embodiment of the present application.

[0018] Figure 2 It is a schematic diagram of an oblique photography method provided by an exemplary embodiment of the present application.

[0019] Figure 3 It is a schematic diagram of a point cloud structure provided by an exemplary embodiment of the present application.

[0020] Figure 4 It is a structural schematic diagram of a construction progress management device based on aerial photography provided by an exemplary embodiment of the present application.

[0021] Figure 5 It is a structural diagram of an electronic device provided by an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0022] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0023] Figure 1 FIG. 1 is a flow chart of a construction progress management method based on aerial photography provided by an exemplary embodiment of the present application. Figure 1 As shown, the construction progress management method based on aerial photography includes the following steps: Step 110: Use a drone to perform oblique photography to obtain image sequences of the construction site at different times.

[0024] The image sequence includes GNSS data and IMU data. This application uses drones for oblique photography to obtain image sequences of the construction site at different times. Since oblique photography can provide richer surface information and improve the accuracy of three-dimensional point clouds, this application uses drones to cruise around the construction site regularly or as needed to capture image sequences of the construction site (such as Figure 2 As shown in the figure), the image information of the construction building in the construction site is obtained to realize unmanned monitoring of the construction progress.

[0025] Step 120: Create a three-dimensional point cloud of the target building based on the image sequence.

[0026] After acquiring the image sequence of the construction site, this application uses differential GNSS and IMU data based on the acquired image sequence to generate a high-precision three-dimensional model through image registration, attitude estimation and optimization, and auxiliary aerial triangulation to establish a three-dimensional point cloud of the target building.

[0027] Step 130: Based on the two sets of point cloud data of the target building at different periods, differential point cloud data of the target building at different periods are generated.

[0028] After establishing the three-dimensional point cloud of the target building, the present application compares the point cloud data of the target building at different periods to obtain the difference point cloud data of the target building at different periods, thereby knowing the construction progress of the target building in the time period between different periods.

[0029] Step 140: Determine the construction progress of the target building based on the difference point cloud data, GNSS data and IMU data.

[0030] This application comprehensively determines the construction progress of the target building based on difference point cloud data, GNSS data and IMU data to achieve real-time and accurate monitoring of the target building.

[0031] The present application provides a construction progress management method based on aerial photography, which uses a drone to perform oblique photography to obtain an image sequence of a construction site at different periods; wherein the image sequence includes GNSS data and IMU data; based on the image sequence, a three-dimensional point cloud of a target building is established; based on two sets of point cloud data of the target building at different periods, differential point cloud data of the target building at different periods is generated; based on the differential point cloud data, GNSS data and IMU data, the construction progress of the target building is determined; that is, images of the construction site at different periods are taken by a drone, and a three-dimensional point cloud of the target building is established based on the images, differential point cloud data of the target building at different periods is generated through the two sets of point cloud data at different periods, and the construction progress of the target building is determined according to the differential point cloud data, GNSS data and IMU data, thereby realizing unmanned supervision of the construction progress of the target building, and at the same time, the three-dimensional point cloud data can be used to generate three-dimensional image information with good visualization effect.

[0032] In one embodiment, the specific implementation method of the above-mentioned step 110 can be: according to the cubic spline interpolation method, correct the camera exposure delay time of the drone; according to the camera exposure delay time differential GNSS data, obtain the relationship between the antenna phase center coordinates and the camera station coordinates; based on the relationship between the antenna phase center coordinates and the camera station coordinates, use the drone to perform oblique photography to obtain image sequences of different periods of the construction site.

[0033] This application uses the cubic spline interpolation method to correct the exposure delay time of the drone's camera to determine the actual camera exposure time of the drone, and combines the GNSS data to obtain the relationship between the antenna phase center coordinates and the camera station coordinates. After using the drone for oblique photography, the captured image is converted according to the relationship between the antenna phase center coordinates and the camera station coordinates to obtain an image sequence of different periods at the construction site.

[0034] In one embodiment, the specific implementation method of the above step 110 may be: based on GNSS sampling, obtain the flight sampling points of the UAV; based on the flight sampling points, use a cubic spline function to perform cubic polynomial fitting to obtain the flight trajectory of the UAV; according to the interpolation calculation method, obtain the exposure time for obtaining the antenna phase center coordinates of the UAV; based on the exposure time for obtaining the antenna phase center coordinates of the UAV, correct the exposure delay time of the UAV's camera.

[0035] The present application obtains multiple flight sampling points of the UAV according to GNSS sampling, and uses a cubic spline function to perform cubic polynomial fitting based on the multiple flight sampling points to obtain the flight trajectory of the UAV, and obtains the exposure time of the UAV's antenna phase center coordinates through an interpolation calculation method. According to the exposure time of the UAV's antenna phase center coordinates, the exposure delay time of the UAV's camera is corrected to obtain an accurate correspondence between time and space position in the image sequence.

[0036] In one embodiment, the specific implementation method of the above step 130 can be: extracting feature data of two sets of point cloud data; based on the feature data, performing feature matching on the two sets of point cloud data to generate a matching relationship set; based on the matching relationship set, calculating the difference between the two sets of point cloud data to generate difference point cloud data.

[0037] This application extracts feature data of two sets of point cloud data from different periods and performs feature matching to calculate the difference between the two sets of point cloud data. Specifically, the two sets of point cloud data are point cloud groups P and point cloud group Q , extract point cloud group P and point cloud group Q SHOT features, point cloud group P Point Cloud p The characteristic data is , Point Cloud Group Q Point Cloud q The characteristic data is .

[0038] In one embodiment, the specific implementation method of the above step 130 may be: for any first point cloud in one set of point cloud data in the two sets of point cloud data, query the second point cloud closest to the first point cloud in the other set of point cloud data in the two sets of point cloud data, and add the first point cloud and the corresponding second point cloud as a point cloud pair to the candidate set; for any pair of point cloud pairs in the candidate set, calculate the first distance value between the second point cloud of the point cloud pair and all the first point clouds in the candidate set; if the first distance value is equal to the distance between the first point cloud and the second point cloud of the point cloud pair, determine that the point cloud pair is a candidate point cloud pair; select any three pairs of candidate point cloud pairs, and calculate the side lengths of the first triangle and the second triangle; wherein the first triangle is composed of the first point clouds of the three pairs of candidate point cloud pairs, and the second triangle is composed of the second point clouds of the three pairs of candidate point cloud pairs; if the side lengths of the first triangle and the second triangle meet the preset conditions, add the three pairs of candidate point cloud pairs to the matching relationship set; wherein the preset conditions are: ;in, For a given threshold, , i =1,2,3, are the lengths of the three sides of the first triangle, are the lengths of the three sides of the second triangle respectively.

[0039] Specifically, for the point cloud group P Each point cloud p , in the point cloud group Q Query and point cloud in the feature set p The nearest , all such points Add to collection , for each ,calculate With point cloud group The eigenvectors of all points in Find the point with the smallest distance ,if , then Add the selected candidate point cloud pair set , in the collection Three pairs of points randomly sampled from , constructed by point pairs The two triangles formed are denoted as and , and the corresponding edges are recorded as , calculate the ratio between the corresponding side lengths , i =1,2,3, if satisfied , that is, if the ratio of the square of any edge to the product of the other two edges satisfies the above range, then all three pairs of points are added to the final matching relationship set .

[0040] In one embodiment, the specific implementation of the above step 130 may be: for each point cloud pair in the matching relationship set, the Euclidean distance between the first point cloud and the second point cloud is calculated; wherein the calculation formula of the Euclidean distance is: ,in, is the first point cloud, is the second point cloud, is the scaling factor, is the rotation matrix, is the translation vector; the error contribution of the point cloud pair is calculated by the error metric function; where the error metric function is , the error contribution is calculated as: ,in, is the scaling factor of the error metric function; the error contributions of all point cloud pairs in the matching relationship set are accumulated to obtain the total error; the calculation formula of the total error is: ; Based on the minimization of the total error, the optimal scaling factor, the optimal rotation matrix and the optimal translation vector are calculated; based on the optimal scaling factor, the optimal rotation matrix and the optimal translation vector, the second point cloud of all point cloud pairs in the matching relationship set is transformed into the transformed point cloud in the first point cloud coordinate system; wherein the calculation formula of the transformed point cloud is: ,in, To transform the point cloud, is a second point cloud set; calculating a second distance value between the transformed point cloud and the corresponding first point cloud; if the second distance value is greater than a preset distance threshold, taking the corresponding first point cloud and the transformed point cloud as a difference point cloud pair.

[0041] Specifically, for the set Each point cloud pair in , calculate the transformed point cloud With point cloud The Euclidean distance between ; Through the error measurement function Calculate the error contribution of each point pair , where the scaling factor Used to control the scaling of the error metric function; the error contributions of all point cloud pairs are accumulated to get the total error: ; Based on the minimization of the total error, the optimal scaling factor, optimal rotation matrix and optimal translation vector are obtained through optimization calculation; the point cloud group Transform to Point Cloud Group In the coordinate system of: , the transformed point cloud group is recorded as ; For point cloud group Each point cloud , find the transformed point cloud group The point cloud closest to it , calculate the distance between each point cloud , set the distance threshold If the distance between point clouds is greater than the distance threshold, the point is considered and If there are significant differences, a difference point cloud pair set is generated, where the difference point cloud pair set includes all point pairs with significant differences. .

[0042] In one embodiment, the specific implementation method of the above step 140 may be: aligning the difference point cloud data, GNSS data and IMU data; and determining the construction progress of the target building based on the aligned difference point cloud data, GNSS data and IMU data.

[0043] Specifically, this application obtains geographic information data of the construction area through the GIS platform, including topographic maps, planning maps, etc., and aligns the GIS data with the point cloud data. The aligned GIS data can be used as a benchmark to provide a geographic reference for subsequent analysis; and the difference point cloud pair set is integrated with the GIS data, and aligned through the geographic coordinate system to ensure that all data is in the same reference frame. Specifically, this application converts the point cloud data from the local coordinate system to the global coordinate system using the following conversion formula: ； in, is the global coordinate, are local coordinates, is the rotation matrix, is the translation vector; further fine alignment is performed on the point cloud data and GIS data. The specific alignment formula is as follows: ； in, For point cloud data, is the corresponding GIS data, N is the total number of point cloud data.

[0044] After the data is processed, the superimposed 3D model will be displayed on the GIS platform (such as Figure 3As shown in the figure, the changed parts of the construction progress are highlighted, and the difference point cloud pairs in the difference point cloud set are color-coded according to the difference size. For example, the part with the largest change is marked in red, the part with the smallest change is marked in yellow, and the unchanged part is marked in green. The GIS tool is used to perform spatial analysis on the superimposed 3D model, and the point cloud differences at different locations in the construction area are analyzed, so that construction managers can intuitively see the changes in the construction progress.

[0045] Figure 4 FIG. 1 is a schematic diagram of a construction progress management device based on aerial photography provided by an exemplary embodiment of the present application. Figure 4 As shown, the construction progress management device 40 based on aerial photography includes: an image acquisition module 41, which is used to use a drone to perform oblique photography to obtain an image sequence of the construction site at different periods; wherein the image sequence includes GNSS data and IMU data; a point cloud establishment module 42, which is used to establish a three-dimensional point cloud of the target building based on the image sequence; a difference generation module 43, which is used to generate difference point cloud data of the target building at different periods based on two sets of point cloud data of the target building at different periods; a progress determination module 44, which is used to determine the construction progress of the target building based on the difference point cloud data, GNSS data and IMU data.

[0046] The present application provides a construction progress management device based on aerial photography, in which an image acquisition module 41 uses a drone to perform oblique photography to acquire an image sequence of a construction site at different periods; wherein the image sequence includes GNSS data and IMU data; a point cloud establishment module 42 establishes a three-dimensional point cloud of a target building based on the image sequence; a difference generation module 43 generates difference point cloud data of a target building at different periods based on two sets of point cloud data of the target building at different periods; a progress determination module 44 determines the construction progress of the target building based on the difference point cloud data, GNSS data and IMU data; that is, images of the construction site at different periods are taken by a drone, and a three-dimensional point cloud of the target building is established based on the images, difference point cloud data of the target building at different periods is generated by two sets of point cloud data at different periods, and the construction progress of the target building is determined according to the difference point cloud data, GNSS data and IMU data, thereby realizing unmanned supervision of the construction progress of the target building, and at the same time, the three-dimensional point cloud data can be used to generate three-dimensional image information with good visualization effect.

[0047] In one embodiment, the above-mentioned image acquisition module 41 can be further configured as follows: according to the cubic spline interpolation method, the camera exposure delay time of the drone is corrected; according to the camera exposure delay time differential GNSS data, the relationship between the antenna phase center coordinates and the camera station coordinates is obtained; based on the relationship between the antenna phase center coordinates and the camera station coordinates, the drone is used for oblique photography to obtain image sequences of different periods of the construction site.

[0048] In one embodiment, the above-mentioned image acquisition module 41 can be further configured as follows: based on GNSS sampling, the flight sampling points of the UAV are acquired; based on the flight sampling points, a cubic spline function is used to perform cubic polynomial fitting to obtain the flight trajectory of the UAV; based on the interpolation calculation method, the exposure time for acquiring the antenna phase center coordinates of the UAV is obtained; based on the exposure time for acquiring the antenna phase center coordinates of the UAV, the exposure delay time of the camera of the UAV is corrected.

[0049] In one embodiment, the above-mentioned difference generation module 43 can be further configured to: extract feature data of two sets of point cloud data; based on the feature data, perform feature matching on the two sets of point cloud data to generate a matching relationship set; based on the matching relationship set, calculate the difference between the two sets of point cloud data to generate difference point cloud data.

[0050] In one embodiment, the difference generation module 43 can be further configured as follows: for any first point cloud in one set of point cloud data in the two sets of point cloud data, query the second point cloud closest to the first point cloud in the other set of point cloud data in the two sets of point cloud data, and add the first point cloud and the corresponding second point cloud as a point cloud pair to the candidate set; for any pair of point cloud pairs in the candidate set, calculate the first distance value between the second point cloud of the point cloud pair and all the first point clouds in the candidate set; if the first distance value is equal to the distance between the first point cloud and the second point cloud of the point cloud pair, determine that the point cloud pair is a candidate point cloud pair; select any three pairs of candidate point cloud pairs, and calculate the side lengths of the first triangle and the second triangle; wherein the first triangle is composed of the first point clouds of the three pairs of candidate point cloud pairs, and the second triangle is composed of the second point clouds of the three pairs of candidate point cloud pairs; if the side lengths of the first triangle and the second triangle meet the preset conditions, add the three pairs of candidate point cloud pairs to the matching relationship set; wherein the preset conditions are: ;in, For a given threshold, , i =1,2,3, are the lengths of the three sides of the first triangle, are the lengths of the three sides of the second triangle respectively.

[0051] In one embodiment, the difference generation module 43 may be further configured to: for each point cloud pair in the matching relationship set, calculate the Euclidean distance between the first point cloud and the second point cloud; wherein the calculation formula of the Euclidean distance is: ,in, is the first point cloud, is the second point cloud, is the scaling factor, is the rotation matrix, is the translation vector; the error contribution of the point cloud pair is calculated by the error metric function; where the error metric function is , the error contribution is calculated as: ,in, is the scaling factor of the error metric function; the error contributions of all point cloud pairs in the matching relationship set are accumulated to obtain the total error; the calculation formula of the total error is: ; Based on the minimization of the total error, the optimal scaling factor, the optimal rotation matrix and the optimal translation vector are calculated; based on the optimal scaling factor, the optimal rotation matrix and the optimal translation vector, the second point cloud of all point cloud pairs in the matching relationship set is transformed into the transformed point cloud in the first point cloud coordinate system; wherein the calculation formula of the transformed point cloud is: ,in, To transform the point cloud, is a second point cloud set; calculating a second distance value between the transformed point cloud and the corresponding first point cloud; if the second distance value is greater than a preset distance threshold, taking the corresponding first point cloud and the transformed point cloud as a difference point cloud pair.

[0052] In one embodiment, the progress determination module 44 may be further configured to: align the difference point cloud data, GNSS data and IMU data; and determine the construction progress of the target building based on the aligned difference point cloud data, GNSS data and IMU data.

[0053] Below, reference Figure 5 The electronic device according to the embodiment of the present application is described. The electronic device may be any one or both of the first device and the second device, or a stand-alone device independent of them, and the stand-alone device may communicate with the first device and the second device to receive the collected input signal from them.

[0054] Figure 5 A block diagram of an electronic device according to an embodiment of the present application is illustrated.

[0055] like Figure 5 As shown, the electronic device 10 includes one or more processors 11 and a memory 12 .

[0056] The processor 11 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.

[0057] The memory 12 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, a random access memory (RAM) and / or a cache memory (cache), etc. The non-volatile memory may include, for example, a read-only memory (ROM), a hard disk, a flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 11 may run the program instructions to implement the methods of the various embodiments of the present application described above and / or other desired functions. Various contents such as input signals, signal components, noise components, etc. may also be stored in the computer-readable storage medium.

[0058] In one example, the electronic device 10 may further include: an input device 13 and an output device 14 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0059] When the electronic device is a stand-alone device, the input device 13 may be a communication network connector, which is used to receive the collected input signals from the first device and the second device.

[0060] In addition, the input device 13 may also include, for example, a keyboard, a mouse, and the like.

[0061] The output device 14 can output various information to the outside, including the determined distance information, direction information, etc. The output device 14 can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, and the like.

[0062] Of course, to simplify, Figure 5 Only some of the components related to the present application in the electronic device 10 are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, according to specific application situations, the electronic device 10 may also include any other appropriate components.

[0063] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present application described in the above-mentioned "Exemplary Method" section of this specification.

[0064] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0065] In addition, an embodiment of the present application may also be a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the processor executes the steps of the method according to various embodiments of the present application described in the above "Exemplary Method" section of this specification.

[0066] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0067] The basic principles of the present application are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present application are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present application. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, not for limitation, and the above details do not limit the present application to being implemented by adopting the above specific details.

[0068] The block diagrams of the devices, apparatuses, equipment, and systems involved in this application are only illustrative examples and do not require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The word "such as" used here refers to the phrase "such as but not limited to", and can be used interchangeably with it.

[0069] It should also be noted that in the apparatus, device and method of the present application, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0070] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not limited to the aspects shown herein, but is in accordance with the widest scope consistent with the principles and novel features disclosed herein.

[0071] The above description has been given for the purpose of illustration and description. In addition, this description does not limit the embodiments of the present application to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A construction progress management method based on aerial photography, characterized in that: include: Using a drone to perform oblique photography to obtain image sequences of the construction site at different times; wherein the image sequence includes GNSS data and IMU data; Based on the image sequence, establishing a three-dimensional point cloud of the target building; Based on the two sets of point cloud data of the target building at different periods, generating difference point cloud data of the target building at different periods; The construction progress of the target building is determined based on the difference point cloud data, the GNSS data and the IMU data.

2. The construction progress management method based on aerial photography according to claim 1 is characterized in that: The use of a drone for oblique photography to obtain image sequences of the construction site at different times includes: Correcting the exposure delay time of the camera of the UAV according to the cubic spline interpolation method; Differentiate the GNSS data according to the camera exposure delay time to obtain the relationship between the antenna phase center coordinates and the camera station coordinates; Based on the relationship between the antenna phase center coordinates and the camera station coordinates, the drone is used to perform oblique photography to obtain image sequences of the construction site at different times.

3. The construction progress management method based on aerial photography according to claim 2 is characterized in that: The method of correcting the exposure delay time of the camera of the drone according to the cubic spline interpolation method includes: According to GNSS sampling, the flight sampling points of the UAV are obtained; Based on the flight sampling points, a cubic spline function is used to perform cubic polynomial fitting to obtain the flight trajectory of the UAV; According to the interpolation calculation method, the exposure time for obtaining the antenna phase center coordinates of the UAV is obtained; Based on the exposure time of the antenna phase center coordinates of the drone, the camera exposure delay time of the drone is corrected.

4. The construction progress management method based on aerial photography according to claim 1 is characterized in that: The generating of difference point cloud data of the target building at different periods based on the two sets of point cloud data of the target building at different periods comprises: Extracting feature data of the two sets of point cloud data; Based on the feature data, feature matching is performed on the two sets of point cloud data to generate a matching relationship set; Based on the matching relationship set, the difference between the two sets of point cloud data is calculated to generate the difference point cloud data.

5. The construction progress management method based on aerial photography according to claim 4 is characterized in that: The performing feature matching on the two sets of point cloud data based on the feature data to generate a matching relationship set includes: For any first point cloud in one of the two sets of point cloud data, query the other set of point cloud data in the two sets of point cloud data for a second point cloud that is closest to the first point cloud, and add the first point cloud and the corresponding second point cloud as a point cloud pair to a candidate set; For any pair of point cloud pairs in the candidate set, calculating a first distance value between a second point cloud of the point cloud pair and all first point clouds in the candidate set; If the first distance value is equal to the distance between the first point cloud and the second point cloud of the point cloud pair, determining the point cloud pair as a candidate point cloud pair; Select any three pairs of candidate point cloud pairs, and calculate the side lengths of the first triangle and the second triangle; wherein the first triangle is composed of the first point clouds of the three pairs of candidate point cloud pairs, and the second triangle is composed of the second point clouds of the three pairs of candidate point cloud pairs; If the side lengths of the first triangle and the second triangle meet a preset condition, the three pairs of candidate point cloud pairs are added to the matching relationship set; wherein the preset condition is: ;in, For a given threshold, , i =1,2,3, are the lengths of the three sides of the first triangle, are the lengths of the three sides of the second triangle respectively.

6. The construction progress management method based on aerial photography according to claim 4 is characterized in that: The calculating the difference between the two sets of point cloud data based on the matching relationship set to generate the difference point cloud data comprises: For each point cloud pair in the matching relationship set, the Euclidean distance between the first point cloud and the second point cloud is calculated; wherein the calculation formula of the Euclidean distance is: ,in, is the first point cloud, is the second point cloud, is the scaling factor, is the rotation matrix, is the translation vector; The error contribution of the point cloud pair is calculated by the error metric function; wherein the error metric function is , the error contribution is calculated as: ,in, is the scaling factor of the error metric function; The error contributions of all point cloud pairs in the matching relationship set are accumulated to obtain the total error; wherein the calculation formula of the total error is: ； Based on the minimization of the total error, an optimal scaling factor, an optimal rotation matrix and an optimal translation vector are calculated; Based on the optimal scaling factor, the optimal rotation matrix and the optimal translation vector, the second point clouds of all point cloud pairs in the matching relationship set are transformed into transformed point clouds in the first point cloud coordinate system; wherein the calculation formula of the transformed point cloud is: ,in, To transform the point cloud, is the second point cloud set; Calculating a second distance value between the transformed point cloud and the corresponding first point cloud; If the second distance value is greater than a preset distance threshold, the corresponding first point cloud and the transformed point cloud are taken as a difference point cloud pair.

7. The construction progress management method based on aerial photography according to claim 1 is characterized in that: The determining the construction progress of the target building based on the difference point cloud data, the GNSS data and the IMU data includes: Aligning the difference point cloud data, the GNSS data, and the IMU data; Based on the aligned difference point cloud data, the GNSS data and the IMU data, the construction progress of the target building is determined.

8. A construction progress management device based on aerial photography, characterized in that: include: An image acquisition module is used to use a drone to perform oblique photography to acquire image sequences of the construction site at different times; wherein the image sequence includes GNSS data and IMU data; A point cloud building module, used to build a three-dimensional point cloud of the target building based on the image sequence; A difference generation module, used for generating difference point cloud data of the target building at different periods based on the two sets of point cloud data of the target building at different periods; A progress determination module is used to determine the construction progress of the target building based on the difference point cloud data, the GNSS data and the IMU data.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and the computer program is used to execute the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is used to execute the method described in any one of claims 1 to 7.

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