Method, device, equipment and storage medium for determining centering rod error

Abstract

The embodiment of the present application provides a method, device, equipment and storage medium for determining the error of a centering rod, the method comprising: obtaining multiple groups of real-time feature data of the centering rod under different postures while the tip of the centering rod remains in contact with the ground contact point, wherein the multiple groups of real-time feature data include multiple groups of first real-time feature data of the centering rod and multiple groups of second real-time feature data of the centering rod; based on the multiple groups of first feature data and the multiple groups of second feature data, using a preset fusion filtering algorithm to calculate multiple groups of centering rod posture information; based on the multiple groups of centering rod posture information and the multiple groups of second real-time feature data, using a preset Kalman filtering algorithm to calculate the three-dimensional vector error of the centering rod; the embodiment of the present application can solve the problem of poor accuracy of the three-dimensional vector error of the centering rod determined by the prior art.

Description

Technical Field

[0001] The present application belongs to the field of measurement technology, and in particular, relates to a method, device, equipment and storage medium for determining a centering rod error. Background Art

[0002] In the fields of construction surveying, topographic mapping, and photogrammetry and image control point layout, satellite navigation (Global Navigation Satellite System, GNSS) real-time dynamic differential (Real Time Kinematic, RTK) measurement technology is widely used. The GNSS RTK receiver measures the position of the phase center of the antenna placed on the top of the centering pole. In actual operations, the centering pole must be strictly perpendicular to the horizontal plane to accurately convert the coordinates of the phase center point of the receiver antenna to the point to be measured. This requires engineering surveyors to always observe the horizontal bubble of the centering pole and keep the bubble centered during measurement to ensure measurement accuracy. In traditional RTK measurement, the operation efficiency is limited and measurement errors are easily introduced due to human operation factors. At the same time, the operation scenes are limited. For scenes such as corners and pipelines, it is difficult to keep the centering pole vertical to the ground for measurement.

[0003] To solve the above problems, tilt measurement technology came into being. The essence of tilt measurement is to obtain real-time high-precision attitude information of the receiver by introducing other sensors, so that the phase center coordinates of the RTK receiver antenna can be accurately converted to the point to be measured when the centering pole is tilted. Tilt measurement technology was initially based on the magnetometer + accelerometer solution. Since the magnetometer is easily interfered and the calibration process is cumbersome, it has gradually been replaced by the solution that only uses the inertial measurement unit (IMU).

[0004] The three-dimensional vector error of the centering rod is an important error source of the IMU-based tilt measurement scheme. Only by accurately determining the three-dimensional vector error of the centering rod can the accuracy of the tilt measurement be guaranteed. However, the three-dimensional vector error of the centering rod determined by the existing technology may result in poor accuracy due to imperfect modeling and quality control. Summary of the invention

[0005] The embodiments of the present application provide a method, device, equipment and storage medium for determining a centering rod error, which can solve the problem of poor accuracy of the three-dimensional vector error of the centering rod determined by the prior art.

[0006] In a first aspect, an embodiment of the present application provides a method for determining a centering rod error, the method comprising:

[0007] When the tip of the centering rod is in contact with the ground contact point, multiple sets of real-time feature data under different postures of the centering rod are obtained, wherein the multiple sets of real-time feature data include multiple sets of first real-time feature data of the centering rod measured by the inertial sensor and multiple sets of second real-time feature data of the centering rod measured by the global navigation satellite system receiver; wherein the inertial sensor, the global navigation satellite system, and the centering rod are fixedly connected, and the multiple sets of real-time feature data correspond to multiple moments respectively;

[0008] According to the multiple sets of first feature data and the multiple sets of second feature data, a preset fusion filtering algorithm is used to calculate and obtain multiple sets of centering pole posture information;

[0009] According to the multiple sets of centering pole posture information and the multiple sets of second real-time feature data, a preset Kalman filter algorithm is used to calculate the three-dimensional vector error of the centering pole;

[0010] The state vector of the preset Kalman filter algorithm includes the three-dimensional vector error of the centering rod and the three-dimensional coordinates of the contact point between the tip of the centering rod and the ground.

[0011] Further, in one embodiment, the first real-time characteristic data includes: angular velocity and acceleration of the inertial sensor installation point on the centering pole;

[0012] The second real-time characteristic data includes: the velocity and position coordinates of the antenna phase center of the receiver;

[0013] According to the multiple sets of first feature data and the multiple sets of second feature data, a preset fusion filtering algorithm is used to calculate multiple sets of centering pole posture information, including:

[0014] According to the angular velocity and acceleration of the inertial sensor installation point on the centering pole, and the velocity and position coordinates of the antenna phase center of the receiver, a preset fusion filtering algorithm is used to calculate multiple sets of centering pole attitude information.

[0015] Further, in one embodiment, according to the multiple sets of centering pole posture information and the multiple sets of second real-time feature data, a preset Kalman filter algorithm is used to calculate the three-dimensional vector error of the centering pole, including:

[0016] According to the attitude information of the centering pole and the position coordinates of the antenna phase center of the receiver, a preset Kalman filter algorithm is used to construct and calculate the equation group to obtain the three-dimensional vector error of the centering pole.

[0017] Further, in one embodiment, the system of equations is constructed by the following formula:

[0018]

[0019] Where b represents the three-axis orthogonal coordinate system of the inertial sensor, e represents the Earth-centered Earth-fixed coordinate system, and I is the three-dimensional unit matrix. is the centering pole attitude information at time i, To preset the three-dimensional vector of the centering rod, is the three-dimensional coordinate of the contact point between the centering rod tip and the ground, δl b is the three-dimensional vector error of the centering rod, The measurement noise that characterizes the position coordinates of the receiver's antenna phase center, is the position coordinate of the receiver's antenna phase center in the Earth-centered Earth-fixed coordinate system at time i.

[0020] Furthermore, in one embodiment, the method further comprises:

[0021] According to the multiple groups of first characteristic data and the multiple groups of second characteristic data, a preset fusion filtering algorithm is used to calculate the diagonal elements of the error covariance matrix corresponding to the misalignment angle of the centering rod;

[0022] According to multiple sets of centering pole attitude information and multiple sets of second real-time feature data, a preset Kalman filter algorithm is used to calculate the three-dimensional vector error of the centering pole, including:

[0023] When the diagonal elements of the error covariance matrix corresponding to the misalignment angle are not greater than the preset misalignment angle parameter threshold, the three-dimensional vector error of the centering pole is calculated using a preset Kalman filter algorithm based on multiple sets of centering pole posture information and multiple sets of second real-time feature data.

[0024] Furthermore, in one embodiment, the method further comprises:

[0025] According to the multiple sets of centering pole attitude information and the multiple sets of second real-time feature data, a Kalman filter algorithm is used to calculate the state vector of the centering pole and the diagonal elements of the error covariance matrix corresponding to the three-dimensional vector error of the centering pole;

[0026] According to multiple sets of centering pole attitude information and multiple sets of second real-time feature data, a preset Kalman filter algorithm is used to calculate the three-dimensional vector error of the centering pole, including:

[0027] When the diagonal elements of the error covariance matrix corresponding to the three-dimensional vector error of the centering rod are not greater than the preset centering rod error parameter threshold, and the modulus of the difference between the state vector at the current moment and the state vector at the previous moment is less than the preset state vector threshold, the three-dimensional vector error of the centering rod in the state vector at the current moment is used as the output.

[0028] Furthermore, in one embodiment, the method further comprises:

[0029] According to the multiple sets of first characteristic data and the multiple sets of second characteristic data, a preset fusion filtering algorithm is used to calculate the filtering new information statistical parameters that characterize whether the multiple sets of second pairs of center rod real-time characteristic data have abnormalities;

[0030] According to multiple sets of centering pole attitude information and multiple sets of second real-time feature data, a preset Kalman filter algorithm is used to calculate the three-dimensional vector error of the centering pole, including:

[0031] When the filtering innovation statistical parameter is not greater than the preset filtering innovation statistical parameter threshold, the three-dimensional vector error of the centering rod is calculated using a preset Kalman filter algorithm based on multiple sets of centering rod posture information and multiple sets of second real-time feature data.

[0032] In a second aspect, an embodiment of the present application provides a device for determining a centering rod error, the device comprising:

[0033] An acquisition module is used to acquire multiple sets of real-time feature data of the centering rod in different postures when the tip of the centering rod is in contact with the ground contact point, wherein the multiple sets of real-time feature data include multiple sets of first real-time feature data of the centering rod measured by the inertial sensor and multiple sets of second real-time feature data of the centering rod measured by the global navigation satellite system receiver; wherein the inertial sensor, the global navigation satellite system, and the centering rod are fixedly connected, and the multiple sets of real-time feature data correspond to multiple moments respectively;

[0034] A calculation module, used for calculating multiple sets of centering pole posture information using a preset fusion filtering algorithm according to multiple sets of first feature data and multiple sets of second feature data;

[0035] A calculation module, used for calculating the three-dimensional vector error of the centering rod by using a preset Kalman filter algorithm according to the multiple sets of centering rod posture information and the multiple sets of second real-time feature data;

[0036] The state vector of the preset Kalman filter algorithm includes the three-dimensional vector error of the centering rod and the three-dimensional coordinates of the contact point between the tip of the centering rod and the ground.

[0037] Further, in one embodiment, the first real-time characteristic data includes: angular velocity and acceleration of the inertial sensor installation point on the centering pole;

[0038] The second real-time characteristic data includes: the velocity and position coordinates of the antenna phase center of the receiver;

[0039] Computing module, specifically used for:

[0040] According to the angular velocity and acceleration of the inertial sensor installation point on the centering pole, and the velocity and position coordinates of the antenna phase center of the receiver, a preset fusion filtering algorithm is used to calculate multiple sets of centering pole attitude information.

[0041] Furthermore, in one embodiment, the computing module is specifically configured to:

[0042] According to the attitude information of the centering pole and the position coordinates of the antenna phase center of the receiver, a preset Kalman filter algorithm is used to construct and calculate the equation group to obtain the three-dimensional vector error of the centering pole.

[0043] Further, in one embodiment, the system of equations is constructed by the following formula:

[0044]

[0045] Where b represents the three-axis orthogonal coordinate system of the inertial sensor, e represents the Earth-centered Earth-fixed coordinate system, is the centering pole attitude information at time i, To preset the three-dimensional vector of the centering rod, is the three-dimensional coordinate of the contact point between the centering rod tip and the ground, δl b is the three-dimensional vector error of the centering rod, is the measurement noise of the position coordinates of the receiver antenna phase center, is the position coordinate of the receiver's antenna phase center in the Earth-centered Earth-fixed coordinate system at time i.

[0046] Furthermore, in one embodiment, the computing module is further configured to:

[0047] According to the multiple groups of first characteristic data and the multiple groups of second characteristic data, a preset fusion filtering algorithm is used to calculate the diagonal elements of the error covariance matrix corresponding to the misalignment angle of the centering rod;

[0048] When the diagonal elements of the error covariance matrix corresponding to the misalignment angle are not greater than the preset misalignment angle parameter threshold, the three-dimensional vector error of the centering pole is calculated using a preset Kalman filter algorithm based on multiple sets of centering pole posture information and multiple sets of second real-time feature data.

[0049] In a third aspect, an embodiment of the present application provides a device for determining a centering rod error, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the above-mentioned method for determining the centering rod error when executed by the processor.

[0050] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a program for implementing information transmission is stored, and when the program is executed by a processor, the method for determining the centering rod error mentioned above is implemented.

[0051] The method, device, equipment and storage medium for determining the centering pole error of the embodiments of the present application uniformly model various error factors that may cause the three-dimensional vector of the centering pole to change, such as the installation angle, the installation position of the centering pole, and the deformation of the centering pole, that is, the three-dimensional vector error of the centering pole is directly estimated, and the inertial navigation attitude error is controlled by the error covariance matrix characterizing the inertial navigation attitude error output by the Kalman filtering algorithm and the fusion filtering algorithm, and the estimation accuracy of the centering pole vector is dynamically evaluated to ensure the stability and accuracy of the three-dimensional vector error estimation of the centering pole. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solution of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0053] Figure 1 It is a flowchart of a method for determining a centering rod error provided in an embodiment of the present application;

[0054] Figure 2 It is a structural schematic diagram of a device for determining a centering rod error provided in an embodiment of the present application;

[0055] Figure 3 It is a structural schematic diagram of a device for determining a centering rod error provided in an embodiment of the present application. DETAILED DESCRIPTION

[0056] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only configured to explain the present application and are not configured to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0057] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0058] At present, the IMU-based tilt measurement solution has become the standard configuration of mainstream surveying and mapping RTK products in the market. In the IMU-based tilt measurement solution, in order to ensure the measurement accuracy, the three-dimensional vector error of the centering rod is an important error source. It is generally believed that the axis of the centering rod and the phase center of the GNSS antenna are in a straight line, and there is no error in the horizontal direction. However, in fact, due to factors such as the installation angle, the installation position of the centering rod, and the deformation of the centering rod, the centering rod may have errors in the three dimensions. Only by accurately calibrating the errors in the three dimensions at the same time can the accuracy of the tilt measurement be guaranteed.

[0059] For the calibration problem of the centering rod vector, the existing technology estimates the two horizontal installation angles and the length of the centering rod, usually using conventional least squares estimation, lacks a quality control strategy, and has certain defects in both the estimation state and the estimation method.

[0060] When estimating the centering pole vector, the existing technology usually considers the centering pole installation angle (pitch angle and roll angle) error; or, on the basis of considering the centering pole installation angle, adds the centering pole length error estimation. Both schemes mainly consider the horizontal installation angle error in error modeling, and cannot fully and accurately characterize the three-dimensional vector error of the centering pole. Both schemes do not consider the influence of the inertial navigation attitude error, resulting in low centering pole calibration accuracy.

[0061] In order to solve the problems of the prior art, the embodiments of the present application provide a method, device, equipment and storage medium for determining the error of a centering rod. The embodiments of the present application uniformly model various error factors that may cause the three-dimensional vector of the centering rod to change, such as the installation angle, the installation position of the centering rod, and the deformation of the centering rod, that is, directly estimate the three-dimensional vector error of the centering rod, and control the inertial navigation attitude error through the error covariance matrix that characterizes the inertial navigation attitude error output by the Kalman filter algorithm and the fusion filter algorithm, and dynamically evaluate the estimation accuracy of the centering rod vector to ensure the stability and accuracy of the three-dimensional vector error estimation of the centering rod. The following first introduces the method for determining the error of the centering rod provided in the embodiments of the present application.

[0062] Figure 1 FIG. 1 is a flow chart of a method for determining a centering rod error provided by an embodiment of the present application. Figure 1 As shown, the method may include the following steps:

[0063] S10, acquiring multiple groups of real-time feature data of the centering rod in different postures while the tip of the centering rod is in contact with the ground contact point.

[0064] The multiple sets of real-time characteristic data include multiple sets of first real-time characteristic data of the centering rod measured by the inertial sensor and multiple sets of second real-time characteristic data of the centering rod measured by the global navigation satellite system receiver. The inertial sensor, the global navigation satellite system, and the centering rod are fixedly connected, and the multiple sets of real-time characteristic data correspond to multiple moments respectively.

[0065] S12, according to the multiple groups of first feature data and the multiple groups of second feature data, a preset fusion filtering algorithm is used to calculate and obtain multiple groups of centering pole posture information.

[0066] In one embodiment, S12 may include:

[0067] According to the angular velocity and acceleration of the inertial sensor installation point on the centering pole, and the velocity and position coordinates of the antenna phase center of the receiver, a preset fusion filtering algorithm is used to calculate multiple sets of centering pole attitude information.

[0068] S14, according to the multiple sets of centering rod posture information and the multiple sets of second real-time feature data, a preset Kalman filter algorithm is used to calculate the three-dimensional vector error of the centering rod.

[0069] The state vector x of the preset Kalman filter algorithm includes the three-dimensional vector error of the centering rod and the three-dimensional coordinates of the contact point between the centering rod tip and the ground, that is,

[0070] Preset the initial state vector of the Kalman filter algorithm represents the estimated value of the state vector under the initial conditions and can be set as:

[0071]

[0072] The initial error covariance matrix P0 represents the The estimated value of the mean square error can be set as:

[0073] P0=diag[4 4 4 0.01 0.01 0.01]

[0074] Where diag represents a diagonal matrix constructed with the given row vector elements as the main diagonal elements.

[0075] Measurement noise R k Characterize the observed quantity z k The noise can be set as:

[0076] z k =diag[0.0001 0.0001 0.0001]

[0077] Process noise Q k Represents model noise and can be set to a zero matrix. The above parameter settings only represent a set of reference values ​​that can be used in conventional applications. The specific parameter settings can be adjusted according to actual applications.

[0078] In one embodiment, S14 may include:

[0079] According to the attitude information of the centering pole and the position coordinates of the antenna phase center of the receiver, a preset Kalman filter algorithm is used to construct and calculate the equation group to obtain the three-dimensional vector error of the centering pole.

[0080] In one embodiment, the system of equations is constructed as follows:

[0081]

[0082] Where b represents the three-axis orthogonal coordinate system of the inertial sensor, e represents the Earth-centered Earth-fixed coordinate system, is the centering pole attitude information at time i, To preset the three-dimensional vector of the centering rod, Among them, l is the z-axis vector of the centering pole, including the nominal pole height and antenna height of the centering pole. The nominal pole height is the scale value directly read from the centering pole (such as 1.8m), which needs to be set and input into the device by the user. The antenna height is the distance from the antenna phase center of the receiver to the reference point of the device. For general handheld RTK surveying and mapping equipment, the antenna height is generally 5 to 15 cm, which is a parameter value determined inside the device. is the three-dimensional coordinate of the contact point between the centering rod tip and the ground, δl b is the three-dimensional vector error of the centering rod, The measurement noise that characterizes the position coordinates of the receiver's antenna phase center, is the position coordinate of the receiver's antenna phase center in the Earth-centered Earth-fixed coordinate system at time i.

[0083] The basic original formula for tilt measurement compensation is: Where n is the navigation coordinate system, which represents the geographic coordinate system where the inertial sensor is located. The x-axis, y-axis, and z-axis of the navigation coordinate system point to the local east, north, and celestial direction, respectively. is the position coordinate of the receiver's antenna phase center in the navigation coordinate system at time i, To align the three-dimensional coordinates of the contact point between the rod tip and the ground in the navigation coordinate system, is the theoretical attitude information of the centering rod in the navigation coordinate system at time i, l b is the theoretical three-dimensional vector of the centering rod.

[0084] Since there is an inertial navigation attitude error in the inertial navigation output attitude Centering rod three-dimensional vector l b The relative true value may change due to the installation angle, installation position, deformation, etc. Regardless of the factors causing the change, it is uniformly modeled as the three-dimensional vector error of the centering rod, recorded as δl b , then the original formula can be transformed into formula 2: in, is the attitude information of the centering rod in the navigation coordinate system at time i, and the three-dimensional vector of the centering rod is accurately calibrated, that is, δl b Make an estimate.

[0085] Theoretically, if the true coordinates of the antenna phase center can be obtained The real coordinates of the measuring point and inertial navigation attitude error Then we can directly calculate δl b In fact, the antenna phase center coordinates can only be solved using RTK. Approximately, let the RTK solution error be but Inertial navigation attitude error It cannot be obtained accurately, only Approximate substitution; measuring point true coordinate value Although unknown, it can be the same as δl b The RTK solution error is estimated as an unknown term. The inertial navigation attitude error can be smoothed to a certain extent through the observation values ​​at multiple times. It can only be controlled to minimize its effect on δl b Estimated impact.

[0086] By taking multiple measurements at the same point, using RTK observations and It can realize δl b Estimation of the inertial navigation attitude error, temporarily ignoring Term, then equation 2 can be transformed into equation 3:

[0087] Formula 3: Both sides of the left coordinate system transformation matrix It is used to convert the navigation coordinate system into the earth coordinate system, and the above formula 1 can be obtained by sorting:

[0088] in,

[0089] λ and L are the longitude and latitude of the inertial sensor installation point, respectively; and The following relationship exists:

[0090] In one embodiment, the first real-time characteristic data includes: angular velocity and acceleration of an inertial sensor installation point on the centering pole.

[0091] The second real-time characteristic data includes: the speed and position coordinates of the antenna phase center of the receiver.

[0092] In one embodiment, the method further comprises:

[0093] S16, according to the multiple groups of first feature data and the multiple groups of second feature data, a preset fusion filtering algorithm is used to calculate the diagonal elements of the error covariance matrix corresponding to the misalignment angle of the centering rod.

[0094] In one embodiment, S14 may include:

[0095] When the diagonal elements of the error covariance matrix corresponding to the misalignment angle are not greater than the preset misalignment angle parameter threshold, the three-dimensional vector error of the centering pole is calculated using a preset Kalman filter algorithm based on multiple sets of centering pole posture information and multiple sets of second real-time feature data.

[0096] The accurate estimation of the three-dimensional vector error of the centering rod needs to be performed after the inertial navigation attitude converges to ensure the estimation accuracy. The estimation of the inertial navigation attitude error is generally performed in the INS / GNSS preset fusion filter algorithm. The preset fusion filter algorithm error state includes the velocity error δV, misalignment angle (inertial navigation attitude error) Position error (δλ, δh), gyro drift error ε, plus zero bias error Gyro scale coefficient error δK g,For the main filter model of integrated navigation, it is easy to find in various ,relevant papers and books in the field of integrated navigation, and will not ,be repeated here.

[0097] The embodiment of the present application mainly provides a method for judging the convergence of inertial navigation attitude, which is based on the error covariance corresponding to the misalignment angle of the main filter of the integrated navigation. The corresponding error covariance matrix diagonal elements are The criterion for judging posture convergence is:

[0098]

[0099] and Respectively represent the horizontal misalignment angle in the preset misalignment angle parameter threshold Horizontal misalignment angle and the azimuth misalignment angle Corresponding convergence threshold. After judging that the misalignment angle or attitude error has converged, the preset Kalman filter algorithm is used to calculate the three-dimensional vector error of the centering pole.

[0100] In one embodiment, the method further comprises:

[0101] S18, based on the multiple groups of first characteristic data and the multiple groups of second characteristic data, a preset fusion filtering algorithm is used to calculate the filtering new information statistical parameters that characterize whether there are abnormalities in the multiple groups of second pair center rod real-time characteristic data.

[0102] In one embodiment, S14 may include:

[0103] When the filtering innovation statistical parameter β k Not greater than the preset filtering innovation statistical parameter threshold When the centering rod is in the center, the three-dimensional vector error of the centering rod is calculated by using a preset Kalman filter algorithm according to multiple sets of centering rod posture information and multiple sets of second real-time feature data.

[0104] When the Kalman filter algorithm is used to calculate the state vector of the centering rod, the filtering new information r k Defined as:

[0105]

[0106] r k The variance of is:

[0107]

[0108] When the fixed solution quality is normal, theoretically r k Obey Gaussian distribution. According to the hypothesis testing theory in mathematical statistics, the statistical parameter β of the filter innovation can be output by the preset fusion filter algorithm. k :

[0109]

[0110] β k Subject to χ with m degrees of freedom 2 Distribution, that is:

[0111] β k ~x 2 (m)

[0112] Where m is the dimension of the measurement vector. For the preset fusion filter algorithm, m = 6. Select an appropriate false alarm probability α according to χ 2 The distribution function value can be calculated to obtain the critical value corresponding to α Under normal circumstances The probability of a small probability event is less than α. Then it can be determined that the system is faulty, that is, it is considered that there is an abnormality in the RTK fixed solution data. The judgment criteria are as follows: If the measurement data is judged to be abnormal, no measurement update will be performed; Determine that the measurement data is normal and perform measurement update.

[0113] In one embodiment, the method further comprises:

[0114] S19, according to the multiple sets of centering rod posture information and the multiple sets of second real-time feature data, a Kalman filter algorithm is used to calculate the state vector of the centering rod and the diagonal elements of the error covariance matrix corresponding to the three-dimensional vector error of the centering rod.

[0115] In one embodiment, S14 may include:

[0116] When the diagonal elements of the error covariance matrix corresponding to the three-dimensional vector error of the centering rod are not greater than the preset centering rod error parameter threshold, and the modulus of the difference between the state vector at the current moment and the state vector at the previous moment is less than the preset state vector threshold, the three-dimensional vector error of the centering rod in the state vector at the current moment is used as the output.

[0117] The embodiment of the present application needs to obtain multiple sets of real-time feature data of the centering rod in different postures while the tip of the centering rod is in contact with the ground contact point, so as to estimate the three-dimensional vector error of the centering rod, and at the same time, the preset fusion filter algorithm performs convergence judgment in real time to determine whether the calibration of the centering rod is completed. The embodiment of the present application judges the error covariance matrix and the volatility of the state estimate corresponding to the three-dimensional vector error of the centering rod based on the preset fusion filter algorithm.

[0118] Assume that the three-dimensional error vector of the centering rod is The corresponding error covariance matrix diagonal elements are

[0119] The criterion 1 for judging whether the centering rod calibration is completed is:

[0120]

[0121] and They represent the preset centering rod error parameter thresholds in each direction respectively.

[0122] At the same time, since the estimated state is a constant value, the criterion 2 for judging whether the centering rod calibration is completed is:

[0123]

[0124] That is, the state vector at the current moment With the state vector at the previous moment The modulus of the difference is less than the preset state vector threshold T d .

[0125] When criteria 1 and 2 are met at the same time, it indicates that the three-dimensional error vector of the centering rod converges and can be output externally, and the centering rod calibration is completed.

[0126] The method for determining the centering rod error in the embodiment of the present application uniformly models various error factors that may cause the three-dimensional vector of the centering rod to change, such as the installation angle, the installation position of the centering rod, and the deformation of the centering rod, that is, directly estimates the three-dimensional vector error of the centering rod, and controls the inertial navigation attitude error through the error covariance matrix characterizing the inertial navigation attitude error output by the Kalman filtering algorithm and the fusion filtering algorithm, and dynamically evaluates the estimation accuracy of the centering rod vector to ensure the stability and accuracy of the three-dimensional vector error estimation of the centering rod.

[0127] Figure 1 The method for determining the centering rod error is described below. Figure 2 and attached Figure 3 Describe the device provided in the embodiment of the present application.

[0128] Figure 2 A schematic diagram of the structure of a device for determining a centering rod error provided by an embodiment of the present application is shown. Figure 2 Each module in the device shown has the function of realizing Figure 1 The functions of each step in the process can achieve the corresponding technical effects. Figure 2 As shown, the device may include an acquisition module 20 and a calculation module 22 .

[0129] The acquisition module 20 is used to acquire multiple groups of real-time feature data of the centering rod in different postures when the tip of the centering rod keeps in contact with the ground contact point.

[0130] The multiple sets of real-time characteristic data include multiple sets of first real-time characteristic data of the centering rod measured by the inertial sensor and multiple sets of second real-time characteristic data of the centering rod measured by the global navigation satellite system receiver. The inertial sensor, the global navigation satellite system, and the centering rod are fixedly connected, and the multiple sets of real-time characteristic data correspond to multiple moments respectively.

[0131] The calculation module 22 is used to calculate multiple sets of centering pole posture information using a preset fusion filtering algorithm according to multiple sets of first feature data and multiple sets of second feature data.

[0132] In one embodiment, the calculation module 22 may be specifically configured to:

[0133] According to the angular velocity and acceleration of the inertial sensor installation point on the centering pole, and the velocity and position coordinates of the antenna phase center of the receiver, a preset fusion filtering algorithm is used to calculate multiple sets of centering pole attitude information.

[0134] The calculation module 22 is further used to calculate the three-dimensional vector error of the centering rod using a preset Kalman filter algorithm according to the multiple sets of centering rod posture information and the multiple sets of second real-time feature data.

[0135] The state vector x of the preset Kalman filter algorithm includes the three-dimensional vector error of the centering rod and the three-dimensional coordinates of the contact point between the centering rod tip and the ground, that is,

[0136] Preset the initial state vector of the Kalman filter algorithm represents the estimated value of the state vector under the initial conditions and can be set as:

[0137]

[0138] The initial error covariance matrix P0 represents the The estimated value of the mean square error can be set as:

[0139] P0=diag[4 4 4 0.01 0.01 0.01]

[0140] Where diag represents a diagonal matrix constructed with the given row vector elements as the main diagonal elements.

[0141] Measurement noise R k Characterize the observed quantity z k The noise can be set as:

[0142] z k =diag[0.0001 0.0001 0.0001]

[0143] Process noise Q kRepresents model noise and can be set to a zero matrix. The above parameter settings only represent a set of reference values ​​that can be used in conventional applications. The specific parameter settings can be adjusted according to actual applications.

[0144] In one embodiment, the calculation module 22 may be specifically configured to:

[0145] According to the attitude information of the centering pole and the position coordinates of the antenna phase center of the receiver, a preset Kalman filter algorithm is used to construct and calculate the equation group to obtain the three-dimensional vector error of the centering pole.

[0146] In one embodiment, the system of equations is constructed as follows:

[0147]

[0148] Where b represents the three-axis orthogonal coordinate system of the inertial sensor, e represents the Earth-centered Earth-fixed coordinate system, is the centering pole attitude information at time i, To preset the three-dimensional vector of the centering rod, Among them, l is the z-axis vector of the centering pole, including the nominal pole height and antenna height of the centering pole. The nominal pole height is the scale value directly read from the centering pole (such as 1.8m), which needs to be set and input into the device by the user. The antenna height is the distance from the antenna phase center of the receiver to the reference point of the device. For general handheld RTK surveying and mapping equipment, the antenna height is generally 5 to 15 cm, which is a parameter value determined inside the device. is the three-dimensional coordinate of the contact point between the centering rod tip and the ground, δl b is the three-dimensional vector error of the centering rod, The measurement noise that characterizes the position coordinates of the receiver's antenna phase center, is the position coordinate of the receiver's antenna phase center in the Earth-centered Earth-fixed coordinate system at time i.

[0149] The basic original formula for tilt measurement compensation is: Where n is the navigation coordinate system, which represents the geographic coordinate system where the inertial sensor is located. The x-axis, y-axis, and z-axis of the navigation coordinate system point to the local east, north, and celestial direction, respectively. is the position coordinate of the antenna phase center of the receiver in the navigation coordinate system at time i, To align the three-dimensional coordinates of the contact point between the rod tip and the ground in the navigation coordinate system, is the theoretical attitude information of the centering rod in the navigation coordinate system at time i, l b is the theoretical three-dimensional vector of the centering rod.

[0150] Since there is an inertial navigation attitude error in the inertial navigation output attitude Centering rod three-dimensional vector l bThe relative true value may change due to the installation angle, installation position, deformation, etc. Regardless of the factors causing the change, it is uniformly modeled as the three-dimensional vector error of the centering rod, recorded as δl b , then the original formula can be transformed into formula 2: in, is the attitude information of the centering rod in the navigation coordinate system at time i, and the three-dimensional vector of the centering rod is accurately calibrated, that is, δl b Make an estimate.

[0151] Theoretically, if the true coordinates of the antenna phase center can be obtained The real coordinates of the measuring point and inertial navigation attitude error Then we can directly calculate δl b In fact, the antenna phase center coordinates can only be solved using RTK. Approximately, let the RTK solution error be but Inertial navigation attitude error It cannot be obtained accurately, only Approximate substitution; measuring point true coordinate value Although unknown, it can be the same as δl b The RTK solution error is estimated as an unknown term. The inertial navigation attitude error can be smoothed to a certain extent through the observation values ​​at multiple times. It can only be controlled to minimize its effect on δl b Estimated impact.

[0152] By taking multiple measurements at the same point, using RTK observations and It can realize δl b Estimation of the inertial navigation attitude error, temporarily ignoring Term, then equation 2 can be transformed into equation 3:

[0153] Formula 3: Both sides of the left coordinate system transformation matrix It is used to convert the navigation coordinate system into the earth coordinate system, and the above formula 1 can be obtained by sorting:

[0154] in,

[0155] λ and L are the longitude and latitude of the inertial sensor installation point, respectively; and The following relationship exists:

[0156] In one embodiment, the first real-time characteristic data includes: angular velocity and acceleration of an inertial sensor installation point on the centering pole.

[0157] The second real-time characteristic data includes: the speed and position coordinates of the antenna phase center of the receiver.

[0158] In one embodiment, the calculation module 22 may also be used to:

[0159] According to the multiple groups of first feature data and the multiple groups of second feature data, a preset fusion filtering algorithm is used to calculate the diagonal elements of the error covariance matrix corresponding to the misalignment angle of the centering rod.

[0160] In one embodiment, the calculation module 22 may be specifically configured to:

[0161] When the diagonal elements of the error covariance matrix corresponding to the misalignment angle are not greater than the preset misalignment angle parameter threshold, the three-dimensional vector error of the centering pole is calculated using a preset Kalman filter algorithm based on multiple sets of centering pole posture information and multiple sets of second real-time feature data.

[0162] The accurate estimation of the three-dimensional vector error of the centering rod needs to be performed after the inertial navigation attitude converges to ensure the estimation accuracy. The estimation of the inertial navigation attitude error is generally performed in the INS / GNSS preset fusion filter algorithm. The preset fusion filter algorithm error state includes the velocity error δV, misalignment angle (inertial navigation attitude error) Position error (δλ, δh), gyro drift error ε, plus zero bias error Gyro scale coefficient error δK g ,For the main filter model of integrated navigation, it is easy to find in various ,relevant papers and books in the field of integrated navigation, and will not ,be repeated here.

[0163] The embodiment of the present application mainly provides a method for judging the convergence of inertial navigation attitude, which is based on the error covariance corresponding to the misalignment angle of the main filter of the integrated navigation. The corresponding error covariance matrix diagonal elements are The criterion for judging posture convergence is

[0164]

[0165] and Respectively represent the horizontal misalignment angle in the preset misalignment angle parameter threshold Horizontal misalignment angle and the azimuth misalignment angle Corresponding convergence threshold. After judging that the misalignment angle or attitude error has converged, the preset Kalman filter algorithm is used to calculate the three-dimensional vector error of the centering pole.

[0166] In one embodiment, the calculation module 22 may also be specifically configured to:

[0167] According to the multiple groups of first characteristic data and the multiple groups of second characteristic data, a preset fusion filtering algorithm is used to calculate the filtering new information statistical parameters that characterize whether there are abnormalities in the multiple groups of second pair center rod real-time characteristic data.

[0168] In one embodiment, the calculation module 22 may also be specifically configured to:

[0169] When the filtering innovation statistical parameter β k Not greater than the preset filtering innovation statistical parameter threshold When the centering rod is in the center, the three-dimensional vector error of the centering rod is calculated by using a preset Kalman filter algorithm according to multiple sets of centering rod posture information and multiple sets of second real-time feature data.

[0170] When the Kalman filter algorithm is used to calculate the state vector of the centering rod, the filtering new information r k Defined as:

[0171]

[0172] r k The variance of is:

[0173]

[0174] When the fixed solution quality is normal, theoretically r k Obey Gaussian distribution. According to the hypothesis testing theory in mathematical statistics, the statistical parameter β of the filter innovation can be output by the preset fusion filter algorithm. k :

[0175]

[0176] β k Subject to χ with m degrees of freedom 2 Distribution, that is

[0177] β k ~x 2 (m)

[0178] Where m is the dimension of the measurement vector. For the preset fusion filter algorithm, m = 6. Select an appropriate false alarm probability α according to χ 2 The distribution function value can be calculated to obtain the critical value corresponding to α Under normal circumstances The probability of a small probability event is less than α. Then it can be determined that the system is faulty, that is, it is considered that there is an abnormality in the RTK fixed solution data. The judgment criteria are as follows: If the measurement data is judged to be abnormal, no measurement update will be performed; Determine that the measurement data is normal and perform measurement update.

[0179] In one embodiment, the calculation module 22 may also be specifically configured to:

[0180] According to multiple sets of centering rod posture information and multiple sets of second real-time feature data, a Kalman filter algorithm is used to calculate the state vector of the centering rod and the diagonal elements of the error covariance matrix corresponding to the three-dimensional vector error of the centering rod.

[0181] In one embodiment, the calculation module 22 may also be specifically configured to:

[0182] When the diagonal elements of the error covariance matrix corresponding to the three-dimensional vector error of the centering rod are not greater than the preset centering rod error parameter threshold, and the modulus of the difference between the state vector at the current moment and the state vector at the previous moment is less than the preset state vector threshold, the three-dimensional vector error of the centering rod in the state vector at the current moment is used as the output.

[0183] The embodiment of the present application needs to obtain multiple sets of real-time feature data of the centering rod in different postures while the tip of the centering rod is in contact with the ground contact point, so as to estimate the three-dimensional vector error of the centering rod, and at the same time, the preset fusion filter algorithm performs convergence judgment in real time to determine whether the calibration of the centering rod is completed. The embodiment of the present application judges the error covariance matrix and the volatility of the state estimate corresponding to the three-dimensional vector error of the centering rod based on the preset fusion filter algorithm.

[0184] Assume that the three-dimensional error vector of the centering rod is The corresponding error covariance matrix diagonal elements are

[0185] The criterion 1 for judging whether the centering rod calibration is completed is

[0186]

[0187] and They represent the preset centering rod error parameter thresholds in each direction respectively.

[0188] At the same time, since the estimated state is a constant value, the criterion 2 for judging whether the centering rod calibration is completed is:

[0189]

[0190] That is, the state vector at the current moment With the state vector at the previous moment The modulus of the difference is less than the preset state vector threshold T d .

[0191] When criteria 1 and 2 are met at the same time, it indicates that the three-dimensional error vector of the centering rod converges and can be output externally, and the centering rod calibration is completed.

[0192] The device for determining the centering rod error of the embodiment of the present application uniformly models various error factors that may cause the three-dimensional vector of the centering rod to change, such as the installation angle, the installation position of the centering rod, and the deformation of the centering rod, that is, directly estimates the three-dimensional vector error of the centering rod, and controls the inertial navigation attitude error through the error covariance matrix characterizing the inertial navigation attitude error output by the Kalman filtering algorithm and the fusion filtering algorithm, and dynamically evaluates the estimation accuracy of the centering rod vector to ensure the stability and accuracy of the three-dimensional vector error estimation of the centering rod.

[0193] Figure 3 FIG. 1 is a schematic diagram showing a structure of a device for determining a centering rod error provided by an embodiment of the present application. Figure 3 As shown, the device may include a processor 301 and a memory 302 storing computer program instructions.

[0194] Specifically, the processor 301 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0195] The memory 302 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 302 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In one example, the memory 302 may include a removable or non-removable (or fixed) medium, or the memory 302 is a non-volatile solid-state memory. The memory 302 may be inside or outside the integrated gateway disaster recovery device.

[0196] In one example, the memory 302 may be a read-only memory (ROM). In one example, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or a flash memory, or a combination of two or more of these.

[0197] The processor 301 reads and executes the computer program instructions stored in the memory 302 to implement Figure 1 The method in the embodiment shown in the figure is achieved Figure 1 The corresponding technical effects achieved by executing the method in the example shown are not repeated here for the sake of brevity.

[0198] In one example, the centering rod error determination device may further include a communication interface 303 and a bus 310. Figure 3 As shown, the processor 301, the memory 302, and the communication interface 303 are connected via a bus 310 and communicate with each other.

[0199] The communication interface 303 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0200] Bus 310 includes hardware, software or both, and the components of online data flow billing equipment are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (Accelerated Graphics Port, AGP) or other graphics bus, enhanced industry standard architecture (Extended Industry Standard Architecture, EISA) bus, front side bus (Front Side Bus, FSB), Hyper Transport (Hyper Transport, HT) interconnection, industry standard architecture (Industry Standard Architecture, ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 310 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the present application considers any suitable bus or interconnection.

[0201] The centering rod error determination device can execute the centering rod error determination method in the embodiment of the present application, thereby achieving Figure 1 The corresponding technical effects of the method for determining the centering rod error are described.

[0202] In addition, in combination with the method for determining the centering rod error in the above embodiment, the embodiment of the present application can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any method for determining the centering rod error in the above embodiment is implemented.

[0203] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0204] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), appropriate firmware, plug-in, function card, etc. When implemented in software, the elements of the present application are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or communication link by a data signal carried in a carrier. "Machine-readable medium" may include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (Radio Frequency, RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0205] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0206] The above reference is according to the method of the embodiment of the present application, the flow chart of the device (system) and the computer program product and / or the block diagram described various aspects of the present application.It should be understood that each square box in the flow chart and / or the block diagram and the combination of each square box in the flow chart and / or the block diagram can be realized by computer program instructions.These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the realization of the function / action specified in one or more square boxes of the flow chart and / or the block diagram.Such a processor can be but is not limited to a general-purpose processor, a special-purpose processor, a special application processor or a field programmable logic circuit.It can also be understood that each square box in the block diagram and / or the flow chart and the combination of the square boxes in the block diagram and / or the flow chart can also be realized by the dedicated hardware that performs the specified function or action, or can be realized by the combination of dedicated hardware and computer instructions.

[0207] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A method for determining a centering rod error, characterized in that: include: When the tip of the centering rod is in contact with the ground contact point, multiple groups of real-time feature data under different postures of the centering rod are obtained, wherein the multiple groups of real-time feature data include multiple groups of first real-time feature data of the centering rod measured by an inertial sensor and multiple groups of second real-time feature data of the centering rod measured by a global navigation satellite system receiver; wherein the inertial sensor, the global navigation satellite system, and the centering rod are fixedly connected, and the multiple groups of real-time feature data correspond to multiple moments respectively; According to the multiple sets of first real-time feature data and the multiple sets of second real-time feature data, a preset fusion filtering algorithm is used to calculate and obtain multiple sets of centering pole posture information; According to the multiple sets of centering rod posture information and the multiple sets of second real-time feature data, a preset Kalman filter algorithm is used to calculate the three-dimensional vector error of the centering rod; The state vector of the preset Kalman filter algorithm is set to include the three-dimensional vector error of the centering rod and the three-dimensional coordinates of the contact point between the tip of the centering rod and the ground; The method further comprises: According to the multiple groups of centering rod posture information and the multiple groups of second real-time feature data, a Kalman filter algorithm is used to calculate the state vector of the centering rod and the diagonal elements of the error covariance matrix corresponding to the three-dimensional vector error of the centering rod; The method of calculating the three-dimensional vector error of the centering rod using a preset Kalman filter algorithm according to the multiple sets of centering rod posture information and the multiple sets of second real-time feature data includes: When the diagonal elements of the error covariance matrix corresponding to the three-dimensional vector error of the centering rod are not greater than the preset centering rod error parameter threshold, and the modulus of the difference between the state vector at the current moment and the state vector at the previous moment is less than the preset state vector threshold, the three-dimensional vector error of the centering rod in the state vector at the current moment is used as the output.

2. The method for determining the centering rod error according to claim 1, characterized in that: The first real-time characteristic data includes: angular velocity and acceleration of the inertial sensor installation point on the centering rod; The second real-time characteristic data includes: the velocity and position coordinates of the antenna phase center of the receiver; The method of calculating multiple sets of centering rod posture information based on the multiple sets of first real-time feature data and the multiple sets of second real-time feature data using a preset fusion filtering algorithm includes: According to the angular velocity and acceleration of the inertial sensor installation point on the centering pole, and the velocity and position coordinates of the antenna phase center of the receiver, a preset fusion filtering algorithm is used to calculate multiple sets of centering pole attitude information.

3. The method for determining the centering rod error according to claim 2, characterized in that: The method of calculating the three-dimensional vector error of the centering rod using a preset Kalman filter algorithm according to the multiple sets of centering rod posture information and the multiple sets of second real-time feature data includes: According to the centering pole attitude information and the position coordinates of the antenna phase center of the receiver, a preset Kalman filter algorithm is used to construct and calculate a group of equations to obtain a three-dimensional vector error of the centering pole.

4. The method for determining the centering rod error according to claim 3, characterized in that: The system of equations is constructed by the following formula: Wherein, b represents the three-axis orthogonal coordinate system of the inertial sensor, e represents the earth-centered earth-fixed coordinate system, is the centering pole attitude information at time i, To preset the three-dimensional vector of the centering rod, is the three-dimensional coordinate of the contact point between the tip of the centering rod and the ground, δl b is the three-dimensional vector error of the centering rod, characterizes the measurement noise of the position coordinates of the receiver's antenna phase center, are the position coordinates of the antenna phase center of the receiver in the Earth-centered Earth-fixed coordinate system at time i.

5. The method for determining the centering rod error according to claim 1, characterized in that: The method further comprises: According to the plurality of groups of first real-time feature data and the plurality of groups of second real-time feature data, a preset fusion filtering algorithm is used to calculate the diagonal elements of the error covariance matrix corresponding to the misalignment angle of the centering rod; The method of calculating the three-dimensional vector error of the centering rod using a preset Kalman filter algorithm according to the multiple sets of centering rod posture information and the multiple sets of second real-time feature data includes: When the diagonal elements of the error covariance matrix corresponding to the misalignment angle are not greater than the preset misalignment angle parameter threshold, the three-dimensional vector error of the centering rod is calculated using a preset Kalman filter algorithm based on the multiple sets of centering rod posture information and the multiple sets of second real-time feature data.

6. The method for determining the centering rod error according to claim 1, characterized in that: The method further comprises: According to the plurality of groups of first real-time feature data and the plurality of groups of second real-time feature data, a preset fusion filtering algorithm is used to calculate and obtain filtering innovation statistical parameters that characterize whether the plurality of groups of second pair centering rod real-time feature data have abnormalities; The method of calculating the three-dimensional vector error of the centering rod using a preset Kalman filter algorithm according to the multiple sets of centering rod posture information and the multiple sets of second real-time feature data includes: When the filtering innovation statistical parameter is not greater than a preset filtering innovation statistical parameter threshold, the three-dimensional vector error of the centering rod is calculated using a preset Kalman filtering algorithm based on the multiple sets of centering rod posture information and the multiple sets of second real-time feature data.

7. A device for determining centering rod error, characterized in that: include: An acquisition module is used to acquire multiple groups of real-time feature data of the centering rod in different postures when the tip of the centering rod is in contact with the ground contact point, wherein the multiple groups of real-time feature data include multiple groups of first real-time feature data of the centering rod measured by an inertial sensor and multiple groups of second real-time feature data of the centering rod measured by a global navigation satellite system receiver; wherein the inertial sensor, the global navigation satellite system, and the centering rod are fixedly connected, and the multiple groups of real-time feature data correspond to multiple moments respectively; A calculation module, configured to calculate and obtain multiple sets of centering rod posture information using a preset fusion filtering algorithm according to the multiple sets of first real-time feature data and the multiple sets of second real-time feature data; A calculation module, configured to calculate the three-dimensional vector error of the centering rod by using a preset Kalman filter algorithm according to the multiple sets of centering rod posture information and the multiple sets of second real-time feature data; The state vector of the preset Kalman filter algorithm is set to include the three-dimensional vector error of the centering rod and the three-dimensional coordinates of the contact point between the tip of the centering rod and the ground; The calculation module is also used to: calculate the state vector of the centering rod and the diagonal elements of the error covariance matrix corresponding to the three-dimensional vector error of the centering rod using a Kalman filter algorithm according to the multiple sets of centering rod posture information and the multiple sets of second real-time feature data; When the diagonal elements of the error covariance matrix corresponding to the three-dimensional vector error of the centering rod are not greater than the preset centering rod error parameter threshold, and the modulus of the difference between the state vector at the current moment and the state vector at the previous moment is less than the preset state vector threshold, the three-dimensional vector error of the centering rod in the state vector at the current moment is used as the output.

8. The device for determining the centering rod error according to claim 7, characterized in that: The first real-time characteristic data includes: angular velocity and acceleration of the inertial sensor installation point on the centering rod; The second real-time characteristic data includes: the velocity and position coordinates of the antenna phase center of the receiver; The computing module is specifically used for: According to the angular velocity and acceleration of the inertial sensor installation point on the centering pole, and the velocity and position coordinates of the antenna phase center of the receiver, a preset fusion filtering algorithm is used to calculate multiple sets of centering pole attitude information.

9. The device for determining the centering rod error according to claim 8, characterized in that: The computing module is specifically used for: According to the centering pole attitude information and the position coordinates of the antenna phase center of the receiver, a preset Kalman filter algorithm is used to construct and calculate a group of equations to obtain a three-dimensional vector error of the centering pole.

10. The device for determining the centering rod error according to claim 9, characterized in that: The system of equations is constructed by the following formula: Wherein, b represents the three-axis orthogonal coordinate system of the inertial sensor, e represents the earth-centered earth-fixed coordinate system, is the centering pole attitude information at time i, To preset the three-dimensional vector of the centering rod, is the three-dimensional coordinate of the contact point between the tip of the centering rod and the ground, δl b is the three-dimensional vector error of the centering rod, is the measurement noise of the position coordinate of the receiver antenna phase center, are the position coordinates of the antenna phase center of the receiver in the Earth-centered Earth-fixed coordinate system at time i.

11. The device for determining the centering rod error according to claim 7, characterized in that: The computing module is further used for: According to the plurality of groups of first real-time feature data and the plurality of groups of second real-time feature data, a preset fusion filtering algorithm is used to calculate the diagonal elements of the error covariance matrix corresponding to the misalignment angle of the centering rod; When the diagonal elements of the error covariance matrix corresponding to the misalignment angle are not greater than the preset misalignment angle parameter threshold, the three-dimensional vector error of the centering rod is calculated using a preset Kalman filter algorithm based on the multiple sets of centering rod posture information and the multiple sets of second real-time feature data.

12. A device for determining centering rod error, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the method for determining a centering rod error according to any one of claims 1 to 6 is implemented.

13. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an implementation program for information transmission, and when the program is executed by a processor, the method for determining a centering rod error according to any one of claims 1 to 6 is implemented.

Images