A prism rod device, a ground detail point measurement method, equipment and medium

Abstract

The application discloses a prism rod device, a ground object detail point measurement method, equipment and a medium, and the method comprises the following steps: calibrating the inertial error of the MEMS of the prism rod device by using a six-position method; the prism rod device is arranged at a known control point, the attitude angle thereof is determined and is converted into an initial attitude matrix, the prism rod device is controlled to start moving, the observation data of each time closed loop correction is used, a mechanical arrangement algorithm is used to obtain navigation parameters, and correction is performed according to the distance between the center of the MEMS and the center of the prism; the corrected navigation parameter error and the inertial error are used as state quantities, the zero-speed pseudo-observation value of the MEMS when the MEMS is static at the known control point or the to-be-measured detail point, the attitude angle calculated by the magnetometer and the position information of the known control point are used as observation values, the extended Kalman filtering algorithm is used for state estimation, and the RTS smoothing filtering algorithm is used to obtain the position information of the to-be-measured detail point according to the state estimation result. The application can realize the measurement of the ground object detail point without satisfying the visibility between the prism and the total station.

Description

Technical Field

[0001] This invention relates to the field of surveying and mapping technology, and in particular to a prism rod device, a method for measuring ground feature detail points, a terminal device, and a computer-readable storage medium. Background Technology

[0002] In existing technologies, detail point measurement in complex urban environments typically employs total station surveying and RTK surveying. Total station surveying calculates the coordinates of the detail point from known point coordinates, while RTK surveying obtains the coordinates of the detail point through real-time differential analysis of GNSS satellite signals. However, in densely built-up environments, total station surveying often requires multiple stations deployed within a short distance to meet line-of-sight requirements and detail point measurement needs, impacting efficiency and introducing significant errors in detail point measurement. Furthermore, RTK surveying is susceptible to environmental influences, and in densely built-up environments, ambiguity fixation is often impossible, hindering accurate detail point measurement. Summary of the Invention

[0003] This invention provides a prism rod device, a method, equipment, and medium for measuring ground feature details. By integrating a nine-axis MEMS sensor based on inertial technology with the photoelectric ranging technology of a total station, the measurement of ground feature details is unaffected by the measurement environment. It can achieve the measurement of ground feature details without requiring the prism and total station to have a line of sight, which helps to reduce the number of total stations deployed and significantly improves the measurement efficiency and accuracy of ground feature details.

[0004] To address the aforementioned technical problems, a first aspect of the present invention provides a prism rod device, comprising a prism rod, a prism, and a nine-axis MEMS sensor;

[0005] The prism is located at the top of the prism rod, and the nine-axis MEMS sensor is located on the prism rod. The nine-axis MEMS sensor includes a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer.

[0006] As a preferred embodiment, the prism rod device further includes a first connecting rod and a second connecting rod; one end of the first connecting rod and one end of the second connecting rod are both disposed on the prism rod, and the other ends of the first connecting rod and the second connecting rod are both connected to the nine-axis MEMS sensor.

[0007] As a preferred embodiment, the prism rod device further includes a power module; the power supply terminal of the power module is connected to the power receiving terminal of the nine-axis MEMS sensor.

[0008] A second aspect of the present invention provides a method for measuring ground feature detail points, using a prism rod device as described in any of the first aspects, comprising the following steps:

[0009] The inertial error of the nine-axis MEMS sensor of the prism rod device was calibrated using the six-position method, and several calibration inertial errors were determined.

[0010] The prism rod device is set at a preset known control point. Based on the position information of the known control point, the attitude angle of the prism rod device at the known control point is determined by the nine-axis MEMS sensor, and the attitude angle is converted into an initial attitude matrix.

[0011] The prism rod device is controlled to move from the known control point. The specific force, angular velocity, and magnetic flux density values ​​at each moment during the movement are detected by the nine-axis MEMS sensor. Based on the initial attitude matrix and the closed-loop corrected specific force, angular velocity, and magnetic flux density values ​​at each moment, the navigation parameters of the prism rod device at each moment are obtained using a mechanical arrangement algorithm. Specifically, the specific force, angular velocity, and magnetic flux density values ​​at the initial moment are corrected by eliminating the calibrated inertial error to achieve closed-loop correction, while the specific force, angular velocity, and magnetic flux density values ​​at non-initial moments are corrected by eliminating the inertial error estimated by the filtering at the previous moment to achieve closed-loop correction.

[0012] Based on the distance between the center of the nine-axis MEMS sensor and the center of the prism of the prism rod device, the navigation parameters at each moment are corrected to obtain the corrected navigation parameters at each moment.

[0013] The corrected navigation parameter error and inertial error at each moment are used as state variables. The zero-velocity pseudo-observation value obtained by the nine-axis MEMS sensor when the known control point or the part to be measured is stationary, the attitude angle calculated by the three-axis magnetometer of the nine-axis MEMS sensor, and the position information of the known control point are used as observation values. The extended Kalman filter algorithm is used to obtain the state matrix, state covariance matrix and state transition matrix at each moment.

[0014] Based on the state matrix, state covariance matrix, and state transition matrix at each time step, the location information of the fragment points to be measured is obtained using the RTS smoothing filtering algorithm.

[0015] As a preferred embodiment, determining the attitude angle of the prism rod device at the known control point based on the position information of the known control point using the nine-axis MEMS sensor specifically includes the following steps:

[0016] Based on the position information of the known control point, the pitch angle and roll angle of the prism rod device at the known control point are determined by the triaxial accelerometer of the nine-axis MEMS sensor.

[0017] The magnetic declination of the prism rod device at the known control point is determined by the three-axis magnetometer of the nine-axis MEMS sensor, and the heading angle in the geographic coordinate system is obtained based on the magnetic declination.

[0018] The attitude angle of the prism rod device at the known control point is determined based on the pitch angle, the roll angle, and the heading angle.

[0019] As a preferred embodiment, the step of obtaining the navigation parameters of the prism rod device at each moment using a mechanical arrangement algorithm based on the initial attitude matrix, the closed-loop corrected specific force value, angular velocity value, and magnetic induction intensity value at each moment specifically includes the following steps:

[0020] The attitude matrix at time k is obtained by multiplying the attitude matrix at time k-1, the navigation coordinate system transformation matrix from time k-1 to time k, and the vehicle coordinate system transformation matrix; where k is an integer greater than 0; when k = 1, the attitude matrix at time k-1 is the initial attitude matrix.

[0021] Based on the attitude matrix at time k-1, the specific force and angular velocity values ​​after closed-loop correction at time k-1, and the specific force and angular velocity values ​​after closed-loop correction at time k, determine the specific force-velocity increment and Coriolis velocity increment at time k. Then, based on the velocity value at time k-1, the sum of the specific force-velocity increment and the Coriolis velocity increment at time k, obtain the velocity value at time k.

[0022] Based on the velocity value at time k and the velocity value at time k-1, obtain the position information of the prism rod device at time k.

[0023] Based on the attitude matrix, velocity value, and position information of the prism rod device at each moment, the navigation parameters of the prism rod device at each moment are determined.

[0024] As a preferred embodiment, the inertial error calibration of the nine-axis MEMS sensor of the prism rod device using the six-position method to determine several calibration inertial errors specifically includes the following steps:

[0025] With the six planes of the nine-axis MEMS sensor facing upwards and stationary, a set of output values ​​from a three-axis accelerometer, a set of output values ​​from a three-axis gyroscope, and a set of output values ​​from a three-axis magnetometer are obtained.

[0026] Based on the obtained output values ​​of the triaxial accelerometer, the following expression is solved using the Gauss-Newton iteration method to obtain the accelerometer inertial error:

[0027]

[0028] Where, θ aIndicates the accelerometer inertial error; k represents time; g represents the local gravitational acceleration; h(a s ,θ a The expression represents the output value of the triaxial accelerometer after error correction; the accelerometer inertial error includes accelerometer zero bias error, accelerometer mounting angle error, and accelerometer scaling factor error.

[0029] Based on the obtained output values ​​of the triaxial accelerometer and the triaxial gyroscope, the following expression is solved using the Gauss-Newton iteration method to obtain the gyroscope inertial error:

[0030]

[0031] Where, θ gry This indicates the inertial error of the gyroscope; μ represents the output value of the triaxial accelerometer at time k after error correction. k This represents the acceleration value after attitude transformation; the gyroscope inertial error includes gyroscope zero bias error, gyroscope mounting angle error, and gyroscope scaling factor error.

[0032] Based on the obtained output value of the triaxial magnetometer, the following expression is solved using the Gauss-Newton iteration method to obtain the magnetometer's inertial error:

[0033]

[0034] Where, θ m The magnetometer's inertial error is represented by m; the local magnetic field strength is represented by h(m). s ,θ m The expression represents the output value of the triaxial magnetometer after error correction; the magnetometer inertial error includes the magnetometer zero bias error, the magnetometer mounting angle error, and the magnetometer scaling factor error.

[0035] A third aspect of the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the ground feature detail measurement method as described in any of the second aspects.

[0036] A fourth aspect of the present invention provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the ground feature detail measurement method as described in any of the second aspects.

[0037] Compared with the prior art, the beneficial effect of the embodiments of the present invention is that by integrating the nine-axis MEMS sensor based on inertial technology with the photoelectric ranging technology of the total station, the measurement of ground feature details is not affected by the measurement environment. The measurement of ground feature details can be realized without the need for the prism and the total station to have a line of sight, which helps to reduce the number of total stations deployed and significantly improves the measurement efficiency and accuracy of ground feature details. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the prism rod device in an embodiment of the present invention;

[0039] Figure 2 This is a flowchart illustrating the method for measuring ground feature detail points in an embodiment of the present invention;

[0040] Figure 3 This is a data processing architecture diagram of the nine-axis MEMS processor in an embodiment of the present invention;

[0041] Among them, 1. Prism rod; 2. Prism; 3. Nine-axis MEMS sensor; 4. First connecting rod; 5. Second connecting rod. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] See Figure 1 The first aspect of the present invention provides a prism rod device, including a prism rod 1, a prism 2 and a nine-axis MEMS sensor 3;

[0044] The prism 2 is located at the top of the prism rod 1, and the nine-axis MEMS sensor 3 is located on the prism rod 1. The nine-axis MEMS sensor 3 includes a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer.

[0045] It is worth noting that the prism 2, located at the top of the prism rod 1, can be used to measure ground feature details using the photoelectric ranging technology of the total station. When ground feature details need to be measured in densely built-up areas, the location information of known control points can be used to measure ground feature details using a nine-axis MEMS sensor 3 based on inertial technology. This makes the measurement of ground feature details unaffected by the measurement environment. It is not necessary to have line of sight between the prism 2 and the total station to achieve the measurement of ground feature details, which helps to reduce the number of total stations deployed and significantly improves the measurement efficiency and accuracy of ground feature details.

[0046] As a preferred embodiment, the prism rod device further includes a first connecting rod 4 and a second connecting rod 5; one end of the first connecting rod 4 and one end of the second connecting rod 5 are both disposed on the prism rod 1, and the other end of the first connecting rod 4 and the other end of the second connecting rod 5 are both connected to the nine-axis MEMS sensor 3.

[0047] It is worth noting that this embodiment utilizes the first connecting rod 4 and the second connecting rod 5 to form a double-layer fixing structure, thereby improving the stability of the nine-axis MEMS sensor 3 mounted on the prism rod 1 and reducing the vibration of the nine-axis MEMS sensor 3 itself.

[0048] As one optional embodiment, both the first connecting rod 4 and the second connecting rod 5 are detachably connected to the prism rod 1 and the nine-axis MEMS sensor 3. Preferably, the first connecting rod 4 and the second connecting rod 5 are made of lightweight and strong aluminum alloy.

[0049] As a preferred embodiment, the prism rod device further includes a power supply module; the power supply terminal of the power supply module is connected to the power receiving terminal of the nine-axis MEMS sensor 3.

[0050] The prism rod device provided in this embodiment of the invention integrates the inertial-based nine-axis MEMS sensor with the total station's photoelectric ranging technology by setting a nine-axis MEMS sensor on the prism rod. This makes the measurement of ground feature details unaffected by the measurement environment and can achieve the measurement of ground feature details without requiring the prism and total station to have a line of sight. This helps to reduce the number of total stations deployed and significantly improves the measurement efficiency and accuracy of ground feature details.

[0051] See Figure 2 and Figure 3 The second aspect of the present invention provides a method for measuring ground feature detail points, using a prism rod device as described in any embodiment of the first aspect, including the following steps S1 to S6:

[0052] Step S1: Use the six-position method to calibrate the inertial error of the nine-axis MEMS sensor of the prism rod device and determine several calibration inertial errors.

[0053] Step S2: Set the prism rod device at a preset known control point. Based on the position information of the known control point, determine the attitude angle of the prism rod device at the known control point using the nine-axis MEMS sensor, and convert the attitude angle into an initial attitude matrix.

[0054] Step S3: Control the prism rod device to move from the known control point. Detect the specific force, angular velocity, and magnetic flux density at each moment during the movement using the nine-axis MEMS sensor. Based on the initial attitude matrix and the closed-loop corrected specific force, angular velocity, and magnetic flux density values ​​at each moment, obtain the navigation parameters of the prism rod device at each moment using a mechanical arrangement algorithm. Specifically, the specific force, angular velocity, and magnetic flux density values ​​at the initial moment are corrected by eliminating the calibrated inertial error to achieve closed-loop correction; the specific force, angular velocity, and magnetic flux density values ​​at non-initial moments are corrected by eliminating the inertial error estimated by the filtering at the previous moment to achieve closed-loop correction.

[0055] Step S4: Based on the distance between the center of the nine-axis MEMS sensor and the prism center of the prism rod device, the navigation parameters at each moment are corrected to obtain the corrected navigation parameters at each moment.

[0056] Step S5: The correction navigation parameter error and inertial error at each moment are used as state variables. The zero-velocity pseudo-observation value obtained by the nine-axis MEMS sensor when the known control point or the part to be measured is stationary, the attitude angle calculated by the three-axis magnetometer of the nine-axis MEMS sensor, and the position information of the known control point are used as observation values. The extended Kalman filter algorithm is used to obtain the state matrix, state covariance matrix and state transition matrix at each moment.

[0057] Step S6: Based on the state matrix, state covariance matrix and state transition matrix at each time step, the location information of the fragment point to be measured is obtained using the RTS smoothing filtering algorithm.

[0058] Specifically, the prism rod device is set at a preset known control point and left stationary for a period of time, such as 4 seconds. The length of the stationary period is used to distinguish whether the prism rod is located at a fragment point or a control point. The location information of the known control point is obtained through RTK measurement or total station measurement. The observation data at each moment are collected by a nine-axis MEMS sensor: specific force value, angular velocity value, and magnetic induction intensity value. In this embodiment, the calibration of the measured ground object is achieved by using the stationary state of the nine-axis MEMS sensor. At the same time, during the stationary period, the measured specific force value is theoretically approximately equal to zero, and the angular velocity value is theoretically equal to the Earth's rotation angular velocity, which increases the number of pseudo-observation values, thereby effectively suppressing the error accumulation of the nine-axis MEMS sensor.

[0059] After collecting observation data, MEMS data processing is required. The first step is to perform MEMS error calibration and attitude initialization.

[0060] For MEMS error calibration, preferably, the inertial error calibration of the nine-axis MEMS sensor of the prism rod device using the six-position method to determine several calibration inertial errors specifically includes the following steps:

[0061] With the six planes of the nine-axis MEMS sensor facing upwards and stationary, a set of output values ​​from a three-axis accelerometer, a set of output values ​​from a three-axis gyroscope, and a set of output values ​​from a three-axis magnetometer are obtained.

[0062] Based on the obtained output values ​​of the triaxial accelerometer, the following expression is solved using the Gauss-Newton iteration method to obtain the accelerometer inertial error:

[0063]

[0064] Where, θ a Indicates the accelerometer inertial error; k represents time; g represents the local gravitational acceleration; h(a s ,θ a The expression represents the output value of the triaxial accelerometer after error correction; the accelerometer inertial error includes accelerometer zero bias error, accelerometer mounting angle error, and accelerometer scaling factor error.

[0065] Based on the obtained output values ​​of the triaxial accelerometer and the triaxial gyroscope, the following expression is solved using the Gauss-Newton iteration method to obtain the gyroscope inertial error:

[0066]

[0067] Where, θ gry Indicates the gyroscope's inertial error; a k s μ represents the output value of the triaxial accelerometer at time k after error correction. k This represents the acceleration value after attitude transformation; the gyroscope inertial error includes gyroscope zero bias error, gyroscope mounting angle error, and gyroscope scaling factor error.

[0068] Based on the obtained output value of the triaxial magnetometer, the following expression is solved using the Gauss-Newton iteration method to obtain the magnetometer's inertial error:

[0069]

[0070] Where, θ m The magnetometer's inertial error is represented by m; the local magnetic field strength is represented by h(m). s ,θ m The expression represents the output value of the triaxial magnetometer after error correction; the magnetometer inertial error includes the magnetometer zero bias error, the magnetometer mounting angle error, and the magnetometer scaling factor error.

[0071] Specifically, the nine-axis MEMS sensor is a closed, autonomous pose transfer sensor that is less susceptible to external interference and can achieve high-precision pose transfer of a carrier in a short time. Pose transfer based on the nine-axis MEMS sensor first requires initialization, which includes two parts: MEMS inertial error calibration and attitude initialization.

[0072] The inertial errors of a nine-axis MEMS sensor include zero-bias error, scaling factor error, and mounting angle error. Inertial error calibration is typically performed indoors. Considering cost and practicality, MEMS inertial error calibration does not rely on any external tools. The calibration of the three-axis accelerometer is constrained by local gravitational acceleration, the three-axis gyroscope is constrained by accelerometer readings and attitude angles, and the three-axis magnetometer is calibrated using the Earth's magnetic field. The sensor noise model is determined using power spectral density, and the noise power spectral density is calibrated using Allan variance.

[0073] MEMS inertial error is specifically calibrated using a six-position method: First, the six faces of the nine-axis MEMS sensor are placed statically for a period of time, each facing upwards, to obtain a continuous set of accelerometer, gyroscope, and magnetometer output data. The initial calibration accuracy depends on the detection of the MEMS's static and dynamic states, which can be achieved by detecting the MEMS's motion state using the covariance of the three-axis accelerometer observation data. Then, based on the detected static time period of the MEMS, the accelerometer is calibrated first. The accelerometer inertial error is defined as θ. a This includes the accelerometer zero bias error. Accelerometer installation angle error [α] yz α zy α zx ] T Accelerometer proportional factor error This can be expressed as:

[0074]

[0075] The output value of the triaxial accelerometer is corrected by the expression h(a) for error correction. s ,θ a ), which is represented as follows:

[0076]

[0077] When the nine-axis MEMS sensor is stationary, the output value of the three-axis accelerometer is equal to the local gravitational acceleration, which can be calculated using a gravity model, based on the following criteria:

[0078]

[0079] The accelerometer inertial error can be obtained by using the Gauss-Newton iteration method.

[0080] MEMS gyroscope calibration differs from accelerometer calibration. Because MEMS gyroscopes have lower precision and cannot sense the Earth's rotational angular velocity, gyroscope calibration must be based on accelerometer calibration results. Firstly, the gyroscope's zero-bias error can be obtained by averaging the gyroscope's output data over a stationary period. Subtracting the zero-bias error from the original gyroscope output angular velocity yields observation data containing only installation angle error and scaling factor error. Since the MEMS involves attitude changes as it faces different planes upwards, we have:

[0081]

[0082] Let μ represent the error-corrected output values ​​of the triaxial accelerometer at times k and k-1, respectively. Let q represent the attitude quaternion. To ensure attitude accuracy, a fourth-order Runge-Kutta integral algorithm is used. k Let the acceleration value after attitude transformation be equal to the output value of the triaxial accelerometer after error correction. Then, the following criterion applies:

[0083]

[0084] The inertial error of the gyroscope can be obtained by using the Gauss-Newton iteration method.

[0085] The error calibration method for magnetometers is the same as that for accelerometers. First, the zero bias error of the magnetometer is defined. Installation angle error and scaling factor error This can be expressed as:

[0086]

[0087] The output value of the triaxial magnetometer, after error correction, is expressed as h(m) s ,θ m ), which is represented as follows:

[0088]

[0089] The output value of the triaxial magnetometer is equal to the local magnetic field strength. The inertial error of the magnetometer can be obtained using the Gauss-Newton iteration method based on the following criteria:

[0090]

[0091] Based on the raw output of MEMS, the Allan variance of the accelerometer, gyroscope and magnetometer are calculated respectively. Then, the double logarithmic curve of the Allan variance is calculated, and the noise power spectral density of different sensors is obtained from the double logarithmic curve.

[0092] For attitude initialization, which involves determining the attitude angles (pitch, roll, and yaw) of the carrier relative to the geographic coordinate system at its initial position, in this embodiment, preferably, the determination of the attitude angles of the prism rod device at the known control point based on the position information of the known control point using the nine-axis MEMS sensor specifically includes the following steps:

[0093] Based on the position information of the known control point, the pitch angle and roll angle of the prism rod device at the known control point are determined by the triaxial accelerometer of the nine-axis MEMS sensor.

[0094] The magnetic declination of the prism rod device at the known control point is determined by the three-axis magnetometer of the nine-axis MEMS sensor, and the heading angle in the geographic coordinate system is obtained based on the magnetic declination.

[0095] The attitude angle of the prism rod device at the known control point is determined based on the pitch angle, the roll angle, and the heading angle.

[0096] It is worth noting that when initializing the heading angle, due to the poor accuracy of the gyroscope, a magnetometer is needed. The magnetometer calculates the magnetic declination, and then the heading angle in the geographic coordinate system is obtained from the magnetic declination. The attitude angle is then converted into the initial attitude direction cosine matrix, which is used as the initial attitude matrix.

[0097] Furthermore, based on the initial attitude matrix, the closed-loop corrected specific force value, angular velocity value, and magnetic induction intensity value at each moment, the navigation parameters of the prism rod device at each moment are obtained using a mechanical arrangement algorithm. Preferably, this specifically includes the following steps:

[0098] The attitude matrix at time k is obtained by multiplying the attitude matrix at time k-1, the navigation coordinate system transformation matrix from time k-1 to time k, and the vehicle coordinate system transformation matrix; where k is an integer greater than 0; when k = 1, the attitude matrix at time k-1 is the initial attitude matrix.

[0099] Based on the attitude matrix at time k-1, the specific force and angular velocity values ​​after closed-loop correction at time k-1, and the specific force and angular velocity values ​​after closed-loop correction at time k, determine the specific force-velocity increment and Coriolis velocity increment at time k. Then, based on the velocity value at time k-1, the sum of the specific force-velocity increment and the Coriolis velocity increment at time k, obtain the velocity value at time k.

[0100] Based on the velocity value at time k and the velocity value at time k-1, obtain the position information of the prism rod device at time k.

[0101] Based on the attitude matrix, velocity value, and position information of the prism rod device at each moment, the navigation parameters of the prism rod device at each moment are determined.

[0102] Specifically, the mechanical orchestration of MEMS refers to obtaining the attitude, velocity and position information of the carrier at the current moment relative to the previous moment based on the output values ​​of the accelerometer and gyroscope. The direction cosine matrix of the initial attitude has been obtained through attitude initialization.

[0103] First, attitude update is performed based on the gyroscope output value, that is, the direction cosine matrix at time k is obtained in the carrier coordinate system b relative to the navigation coordinate system n (northeast coordinate system). The direction cosine matrix is ​​equal to the change of the n-sequence from time k-1 to k. The direction cosine matrix at time k-1 and changes in the carrier coordinate system The product of, i.e.:

[0104]

[0105]

[0106] φ k Let φ be the equivalent rotation vector in the b-system. k × is its antisymmetric matrix, which can be expressed as follows based on the bisample hypothesis:

[0107]

[0108] Δθ k θ k-1 These are the integrals of the angular velocity at different times;

[0109]

[0110] ζ k Let ζ be the equivalent rotation vector in the n-system. k × is its antisymmetric matrix, which can be expressed as:

[0111]

[0112] Let n be the projection of the Earth's rotational angular velocity in terms of n. Let Δt be the entrainment angular velocity in the n-system, where Δt = t(k) - t(k-1).

[0113] Velocity updates are based on attitude updates and accelerometer output values ​​to estimate the vehicle's velocity. The velocity at the current time k can be expressed as the velocity at k-1. Acceleration (specific force) velocity increment and Coriolis velocity increment The sum of

[0114]

[0115] The Coriolis velocity increment can be expressed as:

[0116]

[0117] In the formula g n It is the acceleration due to gravity in the navigation system. This is the Earth's rotational angular velocity. The entrainment angular velocity;

[0118] The specific force velocity increment is expressed as:

[0119]

[0120] I is the identity matrix, ζ n(k-1),n(k) The value represents the change in angular velocity in the n-system; × indicates antisymmetric operation. Let k be the direction cosine matrix at time k-1. This is the specific force-velocity term, where the change in angular velocity ζ n(k-1),n(k) It can be represented as:

[0121]

[0122] The specific force velocity increment under the twin-sample hypothesis is equal to:

[0123]

[0124] In the formula, Δv is the specific force integral term, and Δθ is the angular velocity integral term, which are expressed as follows:

[0125]

[0126]

[0127] Position update is the result of integrating the velocity of the carrier, based on the geodetic coordinate ellipsoid height h and latitude. The formula for updating the position of longitude λ can be expressed as follows. k The height h of the ellipsoid at time k It can be represented as:

[0128]

[0129] In the formula h k-1 For t k-1 Elevation at any moment, v D,k-1 v D,k t k-1 Time, t k The ground velocity at any given moment.

[0130] For latitude updates, ignoring changes in the meridian and elevation within the integration interval, t k Dimension of Time Represented as;

[0131]

[0132] In the formula, It is t k-1 The dimension of time, v N,k v N,k-1 They are t k t k-1 The northbound speed, R M,k-1 It is t k-1 The radius of the meridian at any given time. For t k t k-1 The average elevation, i.e.

[0133] Similarly, the longitude update algorithm can be obtained, then t k Longitude λ at time k :

[0134]

[0135] λ k-1 For t k-1 Longitude of the moment, v E,k v E,k-1 They are t k t k-1 The eastward velocity, R N,k-1 / 2 It is the radius of the circle between the hour and the hour at the midpoint. It is t k With t k-1 The average latitude at any given time, i.e.

[0136] Before mechanical arrangement, it is necessary to eliminate the inertial errors of calibration or filtering estimation from the raw observation data of accelerometers and gyroscopes to achieve closed-loop correction, thereby reducing error accumulation.

[0137] Furthermore, since the nine-axis MEMS sensor is fixed on the prism rod and does not coincide with the center of the prism, it is necessary to perform rod arm calibration between the nine-axis MEMS sensor and the prism. The rod arm length is the distance between the center of the nine-axis MEMS sensor and the center of the prism, which can be measured with a steel ruler. Then, the rod arm is projected onto the carrier coordinate system of the MEMS, and the navigation parameters relative to the center of the MEMS are calibrated to the center of the prism to obtain the calibrated navigation parameters at each moment.

[0138] Furthermore, the Extended Kalman Filter (EKF) algorithm is a nonlinear filtering estimation algorithm that balances the observation matrix and the state matrix through Kalman gain to obtain the optimal state estimate. State estimation based on EKF first requires determining the state equation and the observation equation.

[0139] The state variables of the extended Kalman filter algorithm include: position error δr, velocity error δv, attitude angle error φ, and gyroscope bias error b. g accelerometer zero bias error b a gyroscope scaling factor error s g Accelerometer proportional factor error s a Among them, the gyroscope zero bias error b g accelerometer zero bias error b a gyroscope scaling factor error s g Accelerometer proportional factor error s a Furthermore, its noise power spectral density has been obtained through error calibration. It is worth noting that the inertial error estimated by the filter at the current moment is used in the mechanical arrangement process at the next moment. Therefore, the state matrix is ​​represented as follows:

[0140]

[0141] The state equation can be expressed as:

[0142] δx k =Φ k / k-1 δx k-1 +ω k-1

[0143] Φ k / k-1 =I+F(t) k-1 )Δt

[0144] In the formula Φ k / k-1 Let ω be the state transition matrix. k-1 For the noise of the state matrix, F(t) k-1 ) for t k-1 The coefficient matrix of the state variables at time step.

[0145] The observations for the extended Kalman filter algorithm include: the attitude angle detected by the triaxial magnetometer of the nine-axis MEMS sensor, and the velocity of the zero-velocity pseudo-observation. and angular velocity Control point coordinates r z The zero-velocity pseudo-observation value is obtained by the nine-axis MEMS sensor when the known control point or the measured part point is stationary. In this embodiment, the known control point and the measured part point are calibrated based on the length of the stationary time. In this embodiment, the original output triaxial acceleration threshold is less than 0.5 m / s². 2 To achieve zero-speed detection of the prism rod device.

[0146] The attitude angle observation equation is as follows:

[0147]

[0148] The equation for observing the zero-velocity pseudo-velocity value is as follows:

[0149]

[0150] The equation for observing the zero-velocity pseudo-angular velocity is as follows:

[0151]

[0152] Control point coordinates r z The coordinates r of the MEMS n Add lever arm l b The correction is then based on the observation equations of the control point coordinates;

[0153]

[0154] In the formula Let G be the direction cosine matrix. -1 This involves transforming the location from the Northeast coordinate system to the geographic coordinate system, which can be represented as follows:

[0155]

[0156] In the formula, R M R N h These are the meridian radius, the tropospheric radius, the ellipsoidal height, and the latitude value.

[0157] The overall observation equation is:

[0158]

[0159] Z=Hδx k +R z

[0160] The observation matrix is:

[0161]

[0162] The noise matrix is:

[0163]

[0164] The noise q of the observation m For the noise term of the magnetometer, q vv For the velocity noise term, q va For angular velocity noise, q rThe noise term represents the control point. Using the observation equation and the state equation, the carrier state is estimated based on the extended Kalman filter algorithm. The entire filtering update algorithm can be expressed as a process of time update and measurement update. First, there is the time update, which involves predicting the state and state covariance. The time update can be expressed as:

[0165] δx k / k-1 =Φ k / k-1 δx k-1

[0166]

[0167] In the formula Q p This represents the noise term of the state covariance matrix; the meanings of the other symbols are as described above.

[0168] Measurement updates can be represented as:

[0169]

[0170] δx k =δx k / k-1 +K k (z k -H k δx k / k-1 )

[0171]

[0172] In the above formula, K k Here, P is the gain matrix, H is the covariance of the state variables, R is the coefficient matrix of the observation equation, and z is the noise of the observations. k For the observations, the meanings of other symbols are as described above. During filtering, the state matrix, state covariance matrix, and state transition matrix need to be stored to prepare for RTS (Rauch-Tung-Striebel, fixed-point volumetric smoothing filter) smoothing.

[0173] Furthermore, to further improve the accuracy of carrier state estimation, this embodiment employs the RTS fixed-interval smoothing filtering algorithm, which is equivalent to a combination of forward filtering and backward filtering. First, based on the state matrix, state covariance matrix, and state transition matrix obtained by extended Kalman filtering, forward filtering is performed from the second-to-last epoch of the observation data to finally estimate the location information of the ground features where the carrier is situated.

[0174] δx k|n =δx k|k +T k (δx k+1|n -δx k+1|k )

[0175]

[0176]

[0177] In the formula, n represents the total number of observation epochs, k = n-1, n-2, ..., 1 represents the observation time of each epoch, and T... k Let P be the gain matrix of the state variables at time k, P be the covariance matrix of the state variables, and Φ be the coefficient matrix of the state variables.

[0178] Finally, when the prism rod device moves to the fragment point to be measured, the prism rod device is controlled to stand still, such as for 2 seconds, so as to realize the calibration of the fragment point measured by the prism rod device. After data processing by the nine-axis MEMS sensor, the position information of the fragment point to be measured is output.

[0179] The present invention provides a method for measuring ground feature detail points by integrating a nine-axis MEMS sensor based on inertial technology with the photoelectric ranging technology of a total station. This method enables the measurement of ground feature detail points to be unaffected by the measurement environment and can achieve the measurement of ground feature detail points without requiring the prism and total station to have a line of sight. This helps to reduce the number of total stations deployed and significantly improves the measurement efficiency and accuracy of ground feature detail points.

[0180] A third aspect of the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method for measuring ground feature detail points as described in any embodiment of the second aspect.

[0181] The terminal device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and memory. The terminal device may also include input / output devices, network access devices, buses, etc.

[0182] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0183] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0184] A fourth aspect of the present invention provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform a ground feature detail measurement method as described in any embodiment of the second aspect.

[0185] Through the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus necessary hardware platforms, and of course, it can also be implemented entirely by hardware. Based on this understanding, all or part of the technical solution of the present invention that contributes to the background art can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

[0186] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for measuring detail points of ground features, characterized in that, Includes the following steps: The inertial error of the nine-axis MEMS sensor of the prism rod device was calibrated using the six-position method, and several calibration inertial errors were determined. The prism rod device is set at a preset known control point. Based on the position information of the known control point, the attitude angle of the prism rod device at the known control point is determined by the nine-axis MEMS sensor, and the attitude angle is converted into an initial attitude matrix. The prism rod device is controlled to move from the known control point. The specific force, angular velocity, and magnetic flux density values ​​at each moment during the movement are detected by the nine-axis MEMS sensor. Based on the initial attitude matrix and the closed-loop corrected specific force, angular velocity, and magnetic flux density values ​​at each moment, the navigation parameters of the prism rod device at each moment are obtained using a mechanical arrangement algorithm. Specifically, the specific force, angular velocity, and magnetic flux density values ​​at the initial moment are corrected by eliminating the calibrated inertial error to achieve closed-loop correction, while the specific force, angular velocity, and magnetic flux density values ​​at non-initial moments are corrected by eliminating the inertial error estimated by the filtering at the previous moment to achieve closed-loop correction. Based on the distance between the center of the nine-axis MEMS sensor and the prism center of the prism rod device, the navigation parameters at each moment are corrected to obtain the corrected navigation parameters at each moment. The corrected navigation parameter error and inertial error at each moment are used as state variables. The zero-velocity pseudo-observation value obtained by the nine-axis MEMS sensor when the known control point or the part to be measured is stationary, the attitude angle calculated by the three-axis magnetometer of the nine-axis MEMS sensor, and the position information of the known control point are used as observation values. The extended Kalman filter algorithm is used to obtain the state matrix, state covariance matrix and state transition matrix at each moment. Based on the state matrix, state covariance matrix and state transition matrix at each time step, the location information of the fragment point to be measured is obtained using the RTS smoothing filtering algorithm. The prism rod assembly includes a prism rod, a prism, and a nine-axis MEMS sensor. The prism is located at the top of the prism rod, and the nine-axis MEMS sensor is located on the prism rod. The nine-axis MEMS sensor includes a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer. The prism rod assembly also includes a first connecting rod and a second connecting rod. One end of the first connecting rod and one end of the second connecting rod are both located on the prism rod, and the other ends of the first connecting rod and the second connecting rod are both connected to the nine-axis MEMS sensor. The prism rod assembly also includes a power module. The power supply terminal of the power module is connected to the power receiving terminal of the nine-axis MEMS sensor.

2. The method for measuring detail points of ground features as described in claim 1, characterized in that, The step of determining the attitude angle of the prism rod device at the known control point based on the position information of the known control point using the nine-axis MEMS sensor specifically includes the following steps: Based on the position information of the known control point, the pitch angle and roll angle of the prism rod device at the known control point are determined by the triaxial accelerometer of the nine-axis MEMS sensor. The magnetic declination of the prism rod device at the known control point is determined by the three-axis magnetometer of the nine-axis MEMS sensor, and the heading angle in the geographic coordinate system is obtained based on the magnetic declination. The attitude angle of the prism rod device at the known control point is determined based on the pitch angle, the roll angle, and the heading angle.

3. The method for measuring detail points of ground features as described in claim 1, characterized in that, The step of obtaining the navigation parameters of the prism rod device at each moment using a mechanical arrangement algorithm based on the initial attitude matrix, the closed-loop corrected specific force value, angular velocity value, and magnetic induction intensity value at each moment, specifically includes the following steps: The attitude matrix at time k is obtained by multiplying the attitude matrix at time k-1, the navigation coordinate system transformation matrix from time k-1 to time k, and the vehicle coordinate system transformation matrix; where k is an integer greater than 0; when k=1, the attitude matrix at time k-1 is the initial attitude matrix. Based on the attitude matrix at time k-1, the specific force and angular velocity values ​​after closed-loop correction at time k-1, and the specific force and angular velocity values ​​after closed-loop correction at time k, determine the specific force-velocity increment and Coriolis velocity increment at time k. Then, based on the velocity value at time k-1, the sum of the specific force-velocity increment and the Coriolis velocity increment at time k, obtain the velocity value at time k. Based on the velocity value at time k and the velocity value at time k-1, obtain the position information of the prism rod device at time k. Based on the attitude matrix, velocity value, and position information of the prism rod device at each moment, the navigation parameters of the prism rod device at each moment are determined.

4. The method for measuring feature detail points as described in claim 1, characterized in that, The inertial error calibration of the nine-axis MEMS sensor of the prism rod device using the six-position method, and the determination of several calibration inertial errors, specifically includes the following steps: With the six planes of the nine-axis MEMS sensor facing upwards and stationary, the output values ​​of a set of three-axis accelerometers, a set of three-axis gyroscopes, and a set of three-axis magnetometers are obtained. Based on the obtained output values ​​of the triaxial accelerometer, the following expression is solved using the Gauss-Newton iteration method to obtain the accelerometer inertial error: in, This indicates the accelerometer's inertial error; k Indicates time; Indicates the local gravitational acceleration; The expression represents the output value of a triaxial accelerometer after error correction; the accelerometer inertial error includes accelerometer zero bias error, accelerometer mounting angle error, and accelerometer scaling factor error; Based on the obtained output values ​​of the triaxial accelerometer and the triaxial gyroscope, the following expression is solved using the Gauss-Newton iteration method to obtain the gyroscope inertial error: in, This indicates the inertial error of the gyroscope; Indicates the number of errors corrected. k The output value of the triaxial accelerometer at that moment; This represents the acceleration value after attitude transformation; the gyroscope inertial error includes gyroscope zero bias error, gyroscope mounting angle error, and gyroscope scaling factor error. Based on the obtained output value of the triaxial magnetometer, the following expression is solved using the Gauss-Newton iteration method to obtain the magnetometer's inertial error: in, This indicates the inertial error of the magnetometer; m Indicates the local magnetic field strength; The expression represents the output value of a triaxial magnetometer after error correction; the magnetometer inertial error includes the magnetometer zero bias error, the magnetometer mounting angle error, and the magnetometer scaling factor error.

5. A terminal device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method for measuring feature detail points as described in any one of claims 1 to 4.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the ground feature detail measurement method as described in any one of claims 1 to 4.

Images