Multi-source high-precision positioning method and system for dam surface of double-curvature arch dam

Abstract

The invention provides a double-curvature arch dam surface multi-source high-precision positioning method and system, and relates to the technical field of arch dam inspection. The method comprises the following steps: matching point cloud data acquired in the inspection process of an unmanned aerial vehicle under different coordinate systems by utilizing a random sampling consensus algorithm RANSAC of double-curvature arch dam surface environment constraint, and determining a fitting attitude of the unmanned aerial vehicle; and in combination with the fitting attitude of the unmanned aerial vehicle, the position information of the unmanned aerial vehicle and the space coordinate of the IMU center in an object space coordinate system obtained by performing attitude calculation on the IMU data, performing multi-source data loose coupling positioning by using a Kalman filter, and performing real-time correction on the IMU data. The attitude of the unmanned aerial vehicle for inspection of the double-curvature arch dam is positioned and corrected through the fitting attitude, the position information and the space coordinates of the unmanned aerial vehicle and the Kalman filtering method, subsequent dam surface defect detection work based on the dam surface image acquired by the unmanned aerial vehicle is facilitated, and the defect detection accuracy of the dam surface of the double-curvature arch dam can be improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of arch dam inspection, and in particular to a multi-source high-precision positioning method and system for the dam surface of a hyperbolic arch dam. Background Art

[0002] With the rapid development of artificial intelligence (AI) technology, intelligent inspection methods such as robots and drones have become an alternative to manual surface inspections of dams. The widespread use of drones and robots has also resulted in a surge in massive amounts of image data, providing strong data support for dam safety assessments. However, the position and attitude data provided by drone platform interfaces cannot meet the requirements of precise photogrammetry, often failing to accurately locate target geometry when identifying defects on arch dam surfaces. Summary of the Invention

[0003] The present disclosure aims to solve one of the technical problems in the related art at least to a certain extent.

[0004] To this end, the first embodiment of the present disclosure proposes a multi-source high-precision positioning method for the dam surface of a hyperbolic arch dam, comprising:

[0005] Acquiring IMU data collected by an inertial navigation measurement unit (IMU) mounted on a drone, wherein the inertial navigation measurement unit (IMU) is mounted on the drone;

[0006] The IMU data is subjected to attitude calculation using the Euler angle method to determine the spatial coordinates of the center of the inertial navigation measurement unit (IMU) in the object coordinate system;

[0007] Obtaining point cloud data of the dam surface of the hyperbolic arch dam collected by a laser radar carried on the UAV;

[0008] The point cloud data in different coordinate systems are matched using the random sampling consensus algorithm RANSAC with environmental constraints on the dam surface of the hyperbolic arch dam to determine the fitting posture of the UAV;

[0009] Obtaining location information of the drone collected by a location sensor on the drone;

[0010] Combining the fitted posture of the UAV, the position information and the spatial coordinates, a Kalman filter is used to perform multi-source data loosely coupled positioning, and the IMU data is corrected in real time.

[0011] In some embodiments of the present disclosure, performing attitude calculation on the IMU data using the Euler angle method to determine the spatial coordinates of the center of the inertial navigation measurement unit (IMU) in the object coordinate system includes:

[0012] The Northeast Celestial Coordinate System (ENU) is used as the initial coordinate system of the IMU data, and the three coordinate axes of the inertial navigation measurement unit (IMU) point to the east, north and zenith directions respectively;

[0013] Solving the IMU data by using the Euler angle differential equation to obtain the three attitude angles ψ, θ, and γ of the drone;

[0014] The three attitude angles are used to perform attitude update to obtain the spatial coordinates of the center of the inertial navigation measurement unit (IMU) in the object coordinate system.

[0015] In some embodiments of the present disclosure, the use of the random sampling consensus algorithm RANSAC with environmental constraints on the dam surface of a hyperbolic arch dam to match the point cloud data in different coordinate systems and determine the fitting pose of the drone includes:

[0016] The random sampling consensus algorithm RANSAC with environmental constraints on the surface of the hyperbolic arch dam is used to eliminate abnormal data in the point cloud data, and a cylindrical surface is fitted to the point cloud after the rotation transformation. The error equation is calculated and the fitting result is optimized to obtain the fitting posture of the UAV;

[0017] Among them, the environmental constraints of the hyperbolic arch dam surface include the characteristic that the distance between the dam surface points and the central axis of the dam surface curve is equal. The dam surface curve is determined by 7 parameters, including the central axis direction vector (a, b, c), the starting point coordinates (x0, y0, z0) and the radius R.

[0018] In some embodiments of the present disclosure, combining the fitted attitude of the drone, the position information, and the spatial coordinates, using a Kalman filter to perform multi-source data loosely coupled positioning, and performing real-time correction on the IMU data, includes:

[0019] Establish the state equation of the combined system;

[0020] Designing a first sub-filter to process the fitting posture and the position information, designing a second sub-filter to process the spatial coordinates, and transmitting the estimated values ​​output by the first sub-filter and the second sub-filter to the main filter for optimal fusion;

[0021] The first sub-filter and the second sub-filter are fed back and reset using the information allocation coefficient.

[0022] In some embodiments of the present disclosure, the combined system state equation includes: an attitude angle error equation, a velocity error equation, a position error equation, an inertial instrument error equation, an INS error equation, and a multi-source positioning system error equation.

[0023] In some embodiments of the present disclosure, the following steps are further included:

[0024] The drone is equipped with multi-mode GNSS RTK and high-precision MEMS to perform decimeter-level positioning and obtain positioning data;

[0025] The positioning data is fused with high-definition images and the point cloud data, and combined with image solution of aerial triangulation adjustment to achieve centimeter-level positioning and registration of the hyperbolic arch dam image.

[0026] The second embodiment of the present disclosure provides a multi-source high-precision attitude positioning system for the dam surface of a hyperbolic arch dam, comprising:

[0027] A first acquisition module is used to acquire IMU data collected by an inertial navigation measurement unit (IMU) mounted on the drone, where the IMU is mounted on the drone;

[0028] A first determination module is configured to perform attitude calculation on the IMU data using the Euler angle method to determine the spatial coordinates of the center of the inertial navigation measurement unit (IMU) in an object coordinate system;

[0029] A second acquisition module is used to acquire point cloud data of the dam surface of the hyperbolic arch dam collected by the laser radar carried by the UAV;

[0030] The second determination module is used to match the point cloud data in different coordinate systems using the random sampling consensus algorithm RANSAC with environmental constraints on the dam surface of the hyperbolic arch dam to determine the fitting posture of the UAV;

[0031] A third acquisition module is used to obtain the position information of the drone collected by the position sensor on the drone;

[0032] A positioning correction module is used to combine the fitting posture of the UAV, the position information and the spatial coordinates, use a Kalman filter to perform multi-source data loose coupling positioning, and perform real-time correction on the IMU data.

[0033] In some embodiments of the present disclosure, the second determining module is specifically configured to:

[0034] The random sampling consensus algorithm RANSAC with environmental constraints on the surface of the hyperbolic arch dam is used to eliminate abnormal data in the point cloud data, and a cylindrical surface is fitted to the point cloud after the rotation transformation. The error equation is calculated and the fitting result is optimized to obtain the fitting posture of the UAV;

[0035] Among them, the environmental constraints of the hyperbolic arch dam surface include the characteristic that the distance between the dam surface points and the central axis of the dam surface curve is equal. The dam surface curve is determined by 7 parameters, including the central axis direction vector (a, b, c), the starting point coordinates (x0, y0, z0) and the radius R.

[0036] A third embodiment of the present disclosure provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0037] The memory stores computer-executable instructions;

[0038] The processor executes the computer-executable instructions stored in the memory to implement the method described in the first aspect.

[0039] The fourth aspect of the present disclosure provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores computer-executable instructions, which are used to implement the method described in the first aspect when executed by a processor.

[0040] The multi-source high-precision positioning method for the surface of a hyperbolic arch dam provided by the present disclosure solves the instantaneous attitude of the UAV through laser radar and inertial navigation measurement unit (IMU). The position and attitude data can be further combined and solved to achieve the effect of mutual correction and improved accuracy, that is, the attitude of the UAV used for the inspection of the hyperbolic arch dam is corrected and positioned through multi-source data. The RANSAC fitting positioning method constrained by the environmental constraints of the surface of the hyperbolic arch dam is used to solve the problem that the texture of the dam surface is relatively simple and the features between point clouds in different coordinate systems cannot be matched. The Kalman filtering method is applied to the loosely coupled positioning of multi-source data, and the fitting attitude, position information and spatial coordinates of the UAV are simultaneously used in the process of solving the positioning solution, so that the IMU data is continuously corrected in the filtering process, thereby improving the accuracy of the attitude of the UAV used for the inspection of the hyperbolic arch dam, facilitating the subsequent dam surface defect detection work based on the dam surface images collected by the UAV, and improving the accuracy of defect detection.

[0041] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The above and / or additional aspects and advantages of the present disclosure will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0043] Figure 1 A schematic flow chart of a multi-source high-precision positioning method for the dam surface of a hyperbolic arch dam provided in an embodiment of the present disclosure;

[0044] Figure 2 A point cloud matching result diagram provided by an embodiment of the present disclosure;

[0045] Figure 3 A structural diagram of a multi-source combined positioning system combined with a Kalman filter provided in an embodiment of the present disclosure;

[0046] Figure 4 A schematic diagram of a multi-source high-precision attitude positioning system for the dam surface of a hyperbolic arch dam provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0047] The following describes in detail embodiments of the present disclosure, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0048] Specifically, the multi-source high-precision positioning method and system for the dam surface of a hyperbolic arch dam according to an embodiment of the present disclosure will be described below with reference to the accompanying drawings. Figure 1 This is a flow chart of a multi-source high-precision positioning method for the dam surface of a hyperbolic arch dam provided by an embodiment of the present disclosure. Figure 1 As shown, the multi-source high-precision positioning method for the dam surface of a hyperbolic arch dam may include the following steps:

[0049] Step 101: Acquire IMU data collected by an inertial navigation measurement unit (IMU) mounted on a drone. The inertial navigation measurement unit (IMU) is mounted on the drone.

[0050] Step 102: Use the Euler angle method to perform attitude calculation on the IMU data to determine the spatial coordinates of the center of the inertial navigation measurement unit (IMU) in the object coordinate system.

[0051] To convert the coordinates of both the laser point cloud data and the CCD image pixels into the object-space coordinate system, the spatial coordinates of the IMU center in this coordinate system must first be determined. The IMU includes three accelerometers and three gyroscopes, with the three coordinate axes pointing due east (e), due north (n), and the zenith (u), also known as the northeast celestial coordinate system. Solving the IMU data using the Euler angle differential equation yields the drone's three attitude angles (ψ, θ, and γ). These three attitude angles are used to perform attitude updates, yielding the spatial coordinates of the IMU center in the object-space coordinate system.

[0052] When the initial coordinate system O-XnYnZn is transformed to the next coordinate system O-XbYbZb, the angular velocity vector relative to the navigation system can be expressed as:

[0053]

[0054] Written in projective form:

[0055]

[0056] Matrix inversion can obtain the Euler differential equation, and solving this differential equation can obtain the three attitude angles ψ, θ, and γ of the carrier.

[0057] Step 103: Obtain point cloud data of the dam surface of the hyperbolic arch dam collected by the laser radar carried by the UAV.

[0058] Step 104 , using the random sampling consensus algorithm RANSAC with environmental constraints on the surface of the hyperbolic arch dam, the point cloud data in different coordinate systems are matched to determine the fitting posture of the UAV.

[0059] The general least squares algorithm includes all sample points in the final fit. In some cases, the widespread distribution of noise can directly affect the fitting accuracy. Within a fixed sample, the RANSAC algorithm considers the sample to include both accurate and outliers. Accurate points can be used to estimate model parameters, while outliers may be points that are unsuitable for fitting model parameters due to noise or systematic errors. Figure 2 A point cloud matching result diagram provided by an embodiment of the present disclosure.

[0060] In some embodiments of the present disclosure, a random sampling consensus algorithm (RANSAC) for hyperbolic arch dam surface environmental constraints can be used to remove abnormal data from point cloud data. A cylindrical surface is then fitted to the rotated point cloud, and the error equation is calculated and the fitting result is optimized to obtain the drone's fitted pose. The hyperbolic arch dam surface environmental constraints include the requirement that all points on the dam surface be equally distant from the central axis of the dam surface curve. The dam surface curve is determined by seven parameters: the central axis direction vector (a, b, c), the starting point coordinates (x0, y0, z0), and the radius R.

[0061] In one implementation, a subset of samples is randomly selected, and the required model parameters are calculated using the minimum variance estimation algorithm. The deviation between all samples and the calculated model parameters is then calculated and compared to a set threshold. If the deviation is less than the threshold, the selected subset is considered a correct point and included in the final model calculation. If the deviation is greater than the threshold, the selected subset is considered an outlier. All points on the dam surface are equidistant from its central axis. Based on this characteristic, a surface can be defined using seven parameters: the direction vector (a, b, c) of the central axis, the coordinates of one of the starting points on the line (x0, y0, z0), and the radius R. Assume that the direction vector (a, b, c) is a unit vector pointing in the positive direction, with a² + b² + c² = 1, and let a > 0; when a = 0, b > 0; and when a = 0 and b = 0, let c > 0. x0 is taken as the average x-coordinate of all points.

[0062] Assume that Pi(xi,yi,zi) is a point of sample data, P0(x0,y0,z0) is a starting point of the central axis, P is the projection point of Pi on the central axis, α is the angle between PPi and the axis, and R is the radius of the surface.

[0063] Then we have:

[0064]

[0065] The error equation is:

[0066] V=P i P 2 -R 2 =(X i -X0) 2 +(y i -y0) 2 +(z i -z0) 2

[0067] -[a(X i -X0)+b(y i -y0)+c(z i -z0)]-R

[0068] The fitting results include the axis direction vector, the three-dimensional coordinates and surface coordinates of any point on the axis, and the cylindrical surface is fitted to the point cloud after rotation transformation.

[0069] Step 105: Acquire the position information of the drone collected by the position sensor on the drone.

[0070] Step 106 , combining the fitted attitude, position information and spatial coordinates of the UAV, using a Kalman filter to perform multi-source data loosely coupled positioning, and correcting the IMU data in real time.

[0071] The drone's fitted attitude, position information, and spatial coordinates obtained through the above steps can be further combined and solved to achieve mutual correction and improved accuracy. Because the inertial navigation system (INS) relies on integral calculations to infer navigation status from initial conditions, the accuracy of a pure inertial navigation system is very high at the beginning or for a short period of time, but its error accumulates over time. This section applies the Kalman filter method to loosely coupled positioning with multi-source data. RANSAC fitting positioning, position sensors, and IMUs are simultaneously used in the positioning solution process, allowing the INS state to be continuously corrected during the filtering process.

[0072] In some embodiments of the present disclosure, a combined system state equation can be established, including: an attitude angle error equation, a velocity error equation, a position error equation, an inertial instrument error equation, an INS error equation, and a multi-source positioning system error equation; a first sub-filter is designed to process the fitted attitude and position information, and a second sub-filter is designed to process the spatial coordinates, and the estimated values ​​output by the first sub-filter and the second sub-filter are transmitted to the main filter for optimal fusion; the first sub-filter and the second sub-filter are feedback reset through the information distribution coefficient to improve the estimation accuracy of the system state.

[0073] In one implementation, the project primarily uses a position and velocity combination to study the system. The system under study can essentially be considered to be composed of its own errors. When filtering the system, the parameter error estimate is the primary component of the filter estimate. The error equations are divided into platform error angle equations, position error equations, and velocity error equations. The combined system state equation is expressed as follows:

[0074] ①Attitude angle error equation

[0075] The attitude angle error can be expressed as:

[0076]

[0077] The coordinate transformation matrix between the geographic system and the platform system can be expressed as follows, taking a first-order approximation:

[0078]

[0079] The equation vector of the platform's error angle is:

[0080]

[0081] ②Speed ​​error equation

[0082] From the basic equation of inertial navigation:

[0083]

[0084] The velocity error equation is:

[0085]

[0086] ③Position error equation

[0087] Assume that the position error x, y, h, and the velocity error of the carrier in the east, north, and sky directions are δV E , δV N , δV U , then:

[0088]

[0089] ④Inertial instrument error equation

[0090] The most typical error in an inertial navigation system is the inertial instrument error, which comes in two main types: accelerometer error ▽ and gyroscope drift error ε. This article focuses on the random errors of accelerometer error and gyroscope drift error. Gyroscope drift is generally expressed as:

[0091] ε=ε b +ε r +ω g

[0092] Where ε is a first-order Markov process, ε b is the gyroscope random constant drift, ω g is Gaussian white noise.

[0093] The gyroscope error model of INS is expressed as:

[0094]

[0095] In the formula is ε r The relevant time constant.

[0096] Random noise and zero bias cause measurement errors in the accelerometer as shown below:

[0097]

[0098] Where T a is the relevant time constant.

[0099] ⑤INS error equation

[0100] The INS error equation mainly combines the inertial instrument error equation, position error equation, velocity error equation, platform error angle equation and accelerometer measurement error. The INS system error state equation is as follows:

[0101]

[0102] Where F N It is a 9×9 order principle, representing the system dynamic matrix of position error, velocity error and attitude angle error; F S is the transformation matrix between the 9 basic navigation parameters and the gyro and accelerometer drift; F M is the system matrix corresponding to the gyro and accelerometer drift.

[0103] ⑥ Error equation of multi-source positioning system

[0104] The position and velocity calculated by RANSAC fitting and barometric altimeter have certain errors, so the multi-source position calculation error equation selects position error and velocity error, and a state space model is used to describe the position and velocity equation calculated by multi-source data, and the state equation can be expressed as follows:

[0105]

[0106] In the formula, X G is an n-dimensional state vector, F G is an n*n dynamic matrix, W G is an n-dimensional system noise, G G is a unit matrix.

[0107] Further, the combined system observation equation can be established. The number and type of the state variables of the subsystem are mainly determined by the combination method. At the same time, the error state variables of the multi-source positioning system (including the fitted attitude and position information of the unmanned aerial vehicle) and the INS system (IMU data) jointly constitute the state variables of the combined positioning system. The corresponding Kalman filter is designed for the subsystem, and the state equations of the two sub-filters are established separately. Figure 3 FIG. 1 is a structure diagram of a multi-source combined positioning system joint Kalman filter provided by the embodiment of the present disclosure.

[0108] The position observation vector in the INS system is as follows:

[0109]

[0110] The position observation vector of the multi-source positioning system is as follows:

[0111]

[0112] In the formula, N x , N y is the position error obtained by RANSAC fitting positioning, N h is the height error of the barometric altimeter.

[0113] The velocity observation vector in the INS system is as follows:

[0114]

[0115] In the formula, δv N , δv E , δv U is the error of the corresponding velocity of the system.

[0116] The velocity observation vector of the multi-source positioning system is as follows:

[0117]

[0118] Where M N , M E It is the velocity error obtained by RANSAC fitting positioning.

[0119] The application of joint Kalman filtering technology in the multi-source combined positioning system can effectively fuse the data of the combined system. On the one hand, it can reduce the system combination error and improve the positioning accuracy of the system; on the other hand, it can make the entire system easier to implement when combined.

[0120] First, the first sub-filter processes the fitted pose and position information. A second sub-filter is designed to process the spatial coordinates, and the resulting estimated values ​​are transmitted to the main filter. The main filter then optimally integrates these estimated values ​​to improve the accuracy of the system state estimate. Finally, the information allocation coefficients are used to reset the sub-filters through feedback, further improving the accuracy of each sub-filter. This significantly reduces the computational workload of the main system, making the overall system processing faster than feedback filtering, and achieving the most efficient system design. The information allocation coefficients corresponding to the multi-source positioning subsystem and the INS subsystem are β1 and β2, respectively.

[0121] The observation equation of the INS sub-filter is:

[0122]

[0123] Where ω is the gyro output angular rate, s is the odometer change value, vω and vs are the measurement noise of the angular velocity gyro and odometer, and these two variables are and The Gaussian white noise is measured, and the covariance matrix of the noise is R1(k).

[0124] The observation equation of the sub-filter of the multi-source positioning system is:

[0125]

[0126] Where H1(k) is the identity matrix of the state variables, v e (k) and v n (k) is the observation noise of the system in the east and north directions, and these two variables are and Gaussian white noise, the measurement noise covariance matrix is ​​R2(k)

[0127] The multi-source combined positioning main filter fuses the estimated values ​​of the sub-filters and obtains the optimal estimated value, and finally realizes the information feedback of the main filter to each sub-filter to complete the filtering process.

[0128] Optionally, in some embodiments of the present disclosure, decimeter-level positioning can be performed by using a drone equipped with multi-mode GNSS RTK and high-precision MEMS to obtain positioning data; the positioning data is fused with high-definition images and point cloud data through multi-source data fusion, and combined with image solution of aerial triangulation adjustment to achieve centimeter-level positioning and registration of hyperbolic arch dam images.

[0129] By implementing this embodiment, the instantaneous attitude of the UAV is solved by using a laser radar and an inertial navigation measurement unit (IMU). The position and attitude data can be further combined and solved to achieve the effect of mutual correction and improved accuracy, that is, the attitude of the UAV used for the inspection of the hyperbolic arch dam can be corrected and positioned by using multi-source data. The RANSAC fitting positioning method constrained by the dam surface environment of the hyperbolic arch dam is used to solve the problem that the dam surface texture is relatively simple and the features between point clouds in different coordinate systems cannot be matched. The Kalman filtering method is applied to the loosely coupled positioning of multi-source data, and the fitting attitude, position information and spatial coordinates of the UAV are simultaneously used in the process of solving the positioning solution, so that the IMU data is continuously corrected in the filtering process, thereby improving the accuracy of the attitude of the UAV used for the inspection of the hyperbolic arch dam, facilitating the subsequent dam surface defect detection work based on the dam surface images collected by the UAV, and improving the accuracy of defect detection.

[0130] Figure 4 This is a schematic diagram of a multi-source high-precision attitude positioning system for a hyperbolic arch dam surface provided by an embodiment of the present disclosure. Figure 4 As shown, the multi-source high-precision attitude positioning system for the dam surface of a hyperbolic arch dam may include: a first acquisition module 401 , a first determination module 402 , a second acquisition module 403 , a second determination module 404 , a third acquisition module 405 and a positioning correction module 406 .

[0131] The first acquisition module 401 is used to acquire IMU data collected by an inertial navigation measurement unit (IMU) mounted on a drone. The inertial navigation measurement unit (IMU) is mounted on the drone.

[0132] The first determination module 402 is configured to perform attitude calculation on the IMU data using the Euler angle method to determine the spatial coordinates of the center of the inertial navigation measurement unit (IMU) in the object coordinate system.

[0133] The second acquisition module 403 is used to acquire point cloud data of the dam surface of the hyperbolic arch dam collected by a laser radar mounted on a drone.

[0134] The second determination module 404 is configured to match point cloud data in different coordinate systems using a random sampling consensus algorithm (RANSAC) constrained by the dam surface environment of a hyperbolic arch dam to determine the fitting posture of the UAV.

[0135] The third acquisition module 405 is used to obtain the position information of the drone collected by the position sensor on the drone.

[0136] The positioning correction module 406 is used to combine the fitted attitude, position information and spatial coordinates of the UAV, use the Kalman filter to perform multi-source data loose coupling positioning, and perform real-time correction on the IMU data.

[0137] In some embodiments of the present disclosure, the second determination module 404 is specifically used to: use the random sampling consistency algorithm RANSAC of the hyperbolic arch dam surface environmental constraints to eliminate abnormal data in the point cloud data, and fit the cylinder to the point cloud after the rotation transformation, calculate the error equation and optimize the fitting result to obtain the fitting posture of the UAV; wherein, the hyperbolic arch dam surface environmental constraints include the characteristic that the distance from the hyperbolic arch dam surface point to the central axis of the dam surface curve is equal, and the dam surface curve is determined by 7 parameters, and the 7 parameters include the central axis direction vector (a, b, c), the starting point coordinates (x0, y0, z0) and the radius R.

[0138] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0139] In order to implement the above embodiments, the present disclosure also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided by the above embodiments.

[0140] In order to implement the above embodiments, the present disclosure further proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.

[0141] In order to implement the above embodiments, the present disclosure further provides a computer program product, including a computer program, which implements the methods provided in the above embodiments when executed by a processor.

[0142] In the descriptions of the aforementioned embodiments, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are mutually inconsistent.

[0143] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. Throughout the present disclosure, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0144] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present disclosure includes additional implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present disclosure belong.

[0145] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0146] It should be understood that various parts of the present disclosure can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0147] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0148] In addition, the functional units in the various embodiments of the present disclosure may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0149] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present disclosure have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. A person of ordinary skill in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A multi-source high-precision positioning method for the dam surface of a hyperbolic arch dam, characterized in that: The following steps are involved: Acquiring IMU data collected by an inertial navigation measurement unit (IMU) mounted on a drone, wherein the inertial navigation measurement unit (IMU) is mounted on the drone; The IMU data is subjected to attitude calculation using the Euler angle method to determine the spatial coordinates of the center of the inertial navigation measurement unit (IMU) in the object coordinate system; Obtaining point cloud data of the dam surface of the hyperbolic arch dam collected by a laser radar carried on the UAV; The point cloud data in different coordinate systems are matched using the random sampling consensus algorithm RANSAC with environmental constraints on the dam surface of the hyperbolic arch dam to determine the fitting posture of the UAV; Obtaining location information of the drone collected by a location sensor on the drone; Combining the fitted posture of the UAV, the position information and the spatial coordinates, a Kalman filter is used to perform multi-source data loosely coupled positioning, and the IMU data is corrected in real time.

2. The method according to claim 1, characterized in that The method of performing attitude calculation on the IMU data using the Euler angle method to determine the spatial coordinates of the center of the inertial navigation measurement unit (IMU) in the object coordinate system includes: The Northeast Celestial Coordinate System (ENU) is used as the initial coordinate system of the IMU data, and the three coordinate axes of the inertial navigation measurement unit (IMU) point to the east, north and zenith directions respectively; Solving the IMU data by using the Euler angle differential equation to obtain the three attitude angles ψ, θ, and γ of the drone; The three attitude angles are used to perform attitude update to obtain the spatial coordinates of the center of the inertial navigation measurement unit (IMU) in the object coordinate system.

3. The method according to claim 1, characterized in that The method of using the random sampling consensus algorithm RANSAC with environmental constraints on the dam surface of a hyperbolic arch dam to match the point cloud data in different coordinate systems and determine the fitting posture of the UAV includes: The random sampling consensus algorithm RANSAC with environmental constraints on the surface of the hyperbolic arch dam is used to eliminate abnormal data in the point cloud data, and a cylindrical surface is fitted to the point cloud after the rotation transformation. The error equation is calculated and the fitting result is optimized to obtain the fitting posture of the UAV; Among them, the environmental constraints of the hyperbolic arch dam surface include the characteristic that the distance between the dam surface points and the central axis of the dam surface curve is equal. The dam surface curve is determined by 7 parameters, including the central axis direction vector (a, b, c), the starting point coordinates (x0, y0, z0) and the radius R.

4. The method according to claim 1, wherein The method combines the fitted posture of the UAV, the position information and the spatial coordinates, performs multi-source data loose coupling positioning using a Kalman filter, and corrects the IMU data in real time, including: Establish the state equation of the combined system; Designing a first sub-filter to process the fitting posture and the position information, designing a second sub-filter to process the spatial coordinates, and transmitting the estimated values ​​output by the first sub-filter and the second sub-filter to the main filter for optimal fusion; The first sub-filter and the second sub-filter are fed back and reset using the information allocation coefficient.

5. The method according to claim 4, characterized in that The combined system state equations include: attitude angle error equation, velocity error equation, position error equation, inertial instrument error equation, INS error equation and multi-source positioning system error equation.

6. The method according to claim 1, characterized in that The following steps are also included: The drone is equipped with multi-mode GNSS RTK and high-precision MEMS to perform decimeter-level positioning and obtain positioning data; The positioning data is fused with high-definition images and the point cloud data, and combined with image solution of aerial triangulation adjustment to achieve centimeter-level positioning and registration of the hyperbolic arch dam image.

7. A multi-source high-precision attitude positioning system for the dam surface of a hyperbolic arch dam, characterized by: include: A first acquisition module is used to acquire IMU data collected by an inertial navigation measurement unit (IMU) mounted on the drone, where the IMU is mounted on the drone; A first determination module is configured to perform attitude calculation on the IMU data using the Euler angle method to determine the spatial coordinates of the center of the inertial navigation measurement unit (IMU) in an object coordinate system; A second acquisition module is used to acquire point cloud data of the dam surface of the hyperbolic arch dam collected by the laser radar carried by the UAV; The second determination module is used to match the point cloud data in different coordinate systems using the random sampling consensus algorithm RANSAC with environmental constraints on the dam surface of the hyperbolic arch dam to determine the fitting posture of the UAV; A third acquisition module is used to obtain the position information of the drone collected by the position sensor on the drone; A positioning correction module is used to combine the fitting posture of the UAV, the position information and the spatial coordinates, use a Kalman filter to perform multi-source data loose coupling positioning, and perform real-time correction on the IMU data.

8. The system according to claim 7, characterized in that The second determining module is specifically configured to: The random sampling consensus algorithm RANSAC with environmental constraints on the surface of the hyperbolic arch dam is used to eliminate abnormal data in the point cloud data, and a cylindrical surface is fitted to the point cloud after the rotation transformation. The error equation is calculated and the fitting result is optimized to obtain the fitting posture of the UAV; Among them, the environmental constraints of the hyperbolic arch dam surface include the characteristic that the distance between the dam surface points and the central axis of the dam surface curve is equal. The dam surface curve is determined by 7 parameters, including the central axis direction vector (a, b, c), the starting point coordinates (x0, y0, z0) and the radius R.

9. An electronic device, characterized in that: include: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

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