A method, device and medium for uncontrolled self-calibration of a mobile measurement system for round trip measurement

Abstract

This invention discloses an uncontrolled self-calibration method, device, and medium for round-trip measurements of a mobile measurement system. Based on the round-trip measurement data acquired by the mobile measurement system during reciprocating travel, the method first uses three-dimensional feature points in the data for matching, extracts reliable corresponding feature points under prior pose constraints, then constructs a self-calibration model for the mobile measurement system's placement error, establishes virtual observation equations for corresponding points affected by system placement errors, and determines a weighted optimization model, ultimately achieving reliable adjustment and correction of placement parameters. This invention only requires data from the reciprocating travel of the mobile measurement system for self-calibration, and the entire calibration process possesses the characteristics of high efficiency, flexibility, and convenience of self-calibration technology, showing broad application prospects.

Description

Technical Field

[0001] This invention relates to the field of lidar measurement technology, specifically to a method, device, and medium for uncontrolled self-calibration of a mobile measurement system for round-trip measurements. Background Technology

[0002] Mobile measurement systems integrate multiple sensors on a single platform, including LiDAR, GNSS, and INS. This allows for the rapid acquisition of spatial location and attribute data of roads and adjacent features from a street view perspective, making it a crucial tool for obtaining large-scale, detailed 3D urban models. The LiDAR system efficiently and quickly acquires local 3D point clouds in scanner coordinates; the panoramic imaging system obtains rich texture and color information of target object surfaces; GNSS can determine the precise position, velocity, and time of a moving vehicle, but it suffers from lock-off issues due to multipath propagation and urban canyon obstruction; INS is an autonomous navigation system unaffected by external environmental interference. Its key component is the Inertial Measurement Unit (IMU), which provides the pose and velocity of the moving vehicle. However, it suffers from the drawback of sensor errors accumulating over time, requiring correction and compensation from other positioning signals to ensure the accuracy and reliability of the inertial navigation system.

[0003] GNSS / INS combined operations address the issue of error accumulation and divergence in INS by compensating for GNSS global positioning information. Furthermore, they leverage the short-term, high-frequency pose information from INS to compensate for errors caused by interference such as GNSS signal loss, achieving complementary advantages and acquiring high-precision, high-temporal-resolution position and attitude information of the mobile platform. Through mechanisms such as PPS (Pulse Per Second) signals, time synchronization with the laser scanner is achieved. After calibration and setup parameters, the 3D coordinates of the lidar point cloud in an external reference frame are obtained. Significant layering inconsistencies often exist in overlapping areas across multiple measurements due to setup errors in the mobile measurement system, hindering the reliability of intelligent applications such as 3D point cloud information extraction.

[0004] In recent years, with the widespread application of integrated navigation, how to calibrate the placement error of the integrated system and improve the system accuracy has always been a cutting-edge research topic in international academia. Paul Furgale used the characteristic differences of a black and white checkerboard pattern to calibrate the spatiotemporal offset of the IMU and camera. Luis Alberto Rosero proposed calibrating the relationship between lidar and camera sensors using triangular reflectors in urban environments. Magdy Elbahnasawy performed calibration of lidar and camera systems based on point-pair image pixel adjustment techniques. In summary, existing methods for calibrating multi-source sensors in motion measurement integrated systems do not take into account the interrelationships between multiple sensors, and require the use of high-intensity reflectors for calibration. Due to the large interval between laser scan lines in motion measurement, it is usually impossible to accurately extract the center of the reflector, resulting in poor practicality of existing techniques. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention provides a method, device, and medium for uncontrolled self-calibration of a mobile measurement system for round trip measurements, which effectively alleviates data inconsistency and improves point cloud position accuracy, thus solving the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for uncontrolled self-calibration of a mobile measurement system for round-trip measurements, comprising the following steps:

[0007] S1. Using the mobile measurement system to acquire the point cloud data collected from the opposite direction and the trajectory data POS obtained by coupling and solving the original data of the combined navigation, the laser point cloud coordinate reference system is transformed to obtain the three-dimensional spatial coordinates of the laser point cloud in the world coordinate system.

[0008] S2. Extract reliable corresponding feature points from the opposite acquisition data, and combine them with the corresponding POS trajectory data to construct a self-checking model for placement error;

[0009] S3. Optimize the self-calibration model based on the different weights of rotation and translation;

[0010] S4. The optimal correction value of the installation error is calculated iteratively using the least squares method, and finally the high-precision system installation parameters are obtained.

[0011] Preferably, the coordinate reference system transformation in step S1 specifically includes: firstly, through... and The process involves transforming the coordinates from lidar to the carrier coordinates, and then interpolating the POS using the lidar point cloud acquisition time to obtain the carrier's position in the Earth-centered Earth-fixed system at the time of acquisition. and posture Further, the carrier coordinates are transformed to geocentric and geofixed coordinates, and finally transformed to the projected coordinate system to obtain the three-dimensional spatial coordinates of the laser point cloud in the world coordinate system;

[0012] The formula for transforming the reference coordinate system is expressed as follows:

[0013]

[0014] In the formula, P s The local coordinates of the lidar representing the laser point cloud; This indicates the attitude of the lidar in the carrier coordinate system; This indicates the position of the lidar in the carrier coordinate system; Indicates the attitude of the carrier within the Earth's core and Earth-solid system; Indicates the location of the carrier within the Earth's core and solid system; P e It represents the geocentric and geofixed coordinates of the laser point cloud and can be transformed to other projected coordinate systems.

[0015] Preferably, the reliable corresponding feature points in step S2 are obtained by matching three-dimensional feature points in the data under prior pose constraints, and then a self-calibration error model for the moving measurement point cloud is constructed, specifically including the following:

[0016] Assuming that coordinate reference system transformation is affected by placement errors, the coordinate transformation relationship of corresponding points in the collected data is expressed as follows:

[0017]

[0018] Where: This represents the world coordinates of pairs of points with the same name affected by the placement error; ΔT represents the local coordinates of the lidar for a pair of points of the same name; ΔT represents the placement offset error between the carrier coordinate system and the lidar scanner coordinate system; ΔR describes the placement attitude error.

[0019] Where ΔR(Δheading,Δpitch,Δroll) and ΔT(Δx,Δy,Δz) are unknowns that need to be checked, ΔR is a rotation matrix composed of rotations based on the Z, X, and Y axes by a certain angle, and (Δheading,Δpitch,Δroll) are Euler angles;

[0020] During the coordinate transformation process of corresponding points obtained from opposite directions, a self-checking error model is constructed to check the placement error. Based on the optimized objective function, the placement errors ΔR and ΔT are found to minimize the objective function and make the coordinates of the corresponding connection points approximate.

[0021] Preferably, the objective function is expressed as follows:

[0022]

[0023] The self-calibration error model is expressed as follows:

[0024]

[0025] Preferably, in step S3, the optimized self-calibration model is expressed as follows:

[0026]

[0027] Preferably, step S4 specifically includes: using the least squares iterative algorithm to check and optimize the self-calibration model, calculating the optimal correction value for the placement error, correcting the geometric relationship between the carrier coordinate system and the lidar coordinate system using the calibration result, applying it to the laser point cloud coordinate transformation, obtaining high-precision laser point cloud data, and obtaining high-precision system placement parameters.

[0028] In addition, to achieve the above objectives, the present invention also provides the following technical solution: a mobile measurement system reciprocating measurement uncontrolled self-calibration device, the device comprising:

[0029] Coordinate reference system transformation module: The trajectory data POS obtained by coupling and solving the point cloud data acquired by the mobile measurement system and the original data of the integrated navigation is used to transform the laser point cloud coordinate reference system and obtain the three-dimensional spatial coordinates of the laser point cloud in the world coordinate system.

[0030] The self-calibration model construction module for placement error extracts reliable corresponding feature points from the opposite acquisition data and combines them with the corresponding POS trajectory data to construct a self-calibration model for placement error.

[0031] Self-calibration model optimization module: Optimizes the self-calibration model based on different weights for rotation and translation;

[0032] Least squares method iterative calculation module: Iteratively calculates the optimal correction value of the placement error using the least squares method, and finally obtains high-precision system placement parameters.

[0033] In addition, to achieve the above objectives, the present invention also provides the following technical solution: a device, the device comprising: a processor; and a memory for storing one or more programs;

[0034] When the one or more programs are executed by the processor, the processor performs the self-checking method.

[0035] In addition, to achieve the above objectives, the present invention also provides the following technical solution: a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the self-checking method.

[0036] The beneficial effects of this invention are: This invention utilizes the method of extracting corresponding feature points from opposing data for self-calibration, which improves the accuracy of the positioning parameters of the mobile measurement system, thereby obtaining more accurate laser point cloud position information and improving the calibration efficiency of the positioning error of the mobile measurement system. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the method steps of the present invention;

[0038] Figure 2 This is a schematic diagram of the moving measuring device according to an embodiment of the present invention;

[0039] Figure 3 This is a schematic diagram of the self-calibration device module of the present invention;

[0040] Figure 4 This is a schematic diagram of the device structure of the present invention;

[0041] In the diagram, 110 is the coordinate reference system transformation module; 120 is the self-calibration model construction module for installation error; 130 is the self-calibration model optimization module; 140 is the least squares method iterative calculation module; 210 is the processor; and 220 is the storage device. Detailed Implementation

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0043] Please see Figures 1-4 This invention provides a technical solution: a method for uncontrolled self-calibration of a mobile measurement system for round-trip measurements, such as... Figure 1 As shown, it includes the following steps:

[0044] A. Calculation of point cloud coordinates using mobile measurement lidar

[0045] The trajectory data POS obtained by coupling and solving the point cloud data acquired by the mobile measurement system and the raw data of the integrated navigation is used to transform the laser point cloud coordinate reference system, thereby obtaining the three-dimensional spatial coordinates of the laser point cloud in the world coordinate system. First, the coordinates are transformed from the local coordinate system of the lidar to the carrier coordinate system, then from the carrier coordinate system to the geocentric coordinate system, and finally from the geocentric coordinate system to the projected coordinate system.

[0046] By performing coordinate system transformation on the raw laser point cloud data collected in the field and the raw data from the integrated navigation system, the spatial three-dimensional coordinates of the laser point cloud are obtained. The moving measurement device, such as... Figure 2As shown in the figure, the coordinate acquisition of mobile measurement laser point clouds mainly involves the transformation between three reference coordinate systems: the lidar coordinate system, the carrier coordinate system, and the Earth-Centered Earth Fixed (ECEF) coordinate system, as shown in the following equation.

[0047]

[0048] Where:

[0049] P s (X s ,Y s Z s () represents the local coordinates of the laser point cloud from the lidar sensor.

[0050] This indicates the attitude of the lidar in the carrier coordinate system;

[0051] This indicates the position of the lidar in the carrier coordinate system;

[0052] Indicates the attitude of the carrier within the Earth's core and Earth-solid system;

[0053] Indicates the location of the carrier within the Earth's core and Earth-solid system;

[0054] P e (X e ,Y e Z e ) represents the geocentric and geofixed coordinates of the laser point cloud, and can be transformed to other projected coordinate systems.

[0055] Specifically, the laser point cloud coordinate reference system transformation of the mobile measurement system is first achieved through the relative placement relationship between the LiDAR and IMU sensors, as calibrated by the manufacturer. and The process involves converting lidar coordinates to vehicle coordinates. Then, in the positioning and orientation system (POS) data obtained during vehicle movement from the coupled solution of the original navigation data, the POS is interpolated using the lidar point cloud acquisition time to obtain the vehicle's position in the Earth-centered Earth-fixed system at the time of acquisition. and posture Further, the carrier coordinates are transformed to geocentric and geofixed coordinates, and then transformed to the projected coordinate system to obtain a three-dimensional point cloud.

[0056] B. Extract corresponding feature points and establish a self-calibration error model.

[0057] Reliable corresponding feature points are extracted from the acquired data and combined with the corresponding POS trajectory data to construct a self-calibration model for placement error. Reliable corresponding feature points are obtained by matching 3D feature points in the data under prior pose constraints, and then a self-calibration error model for the moving measurement point cloud is constructed.

[0058] After the laser point cloud coordinates are calculated, ISS 3D feature points are extracted from the round-trip measurement data for data matching. Reliable corresponding feature points are extracted using the prior pose constraints calibrated by the manufacturer. In an ideal situation, the coordinate transformation of the moving measurement laser point cloud is as shown in equation (1). However, in most cases, it is affected by the placement error, and the mathematical relationship of the coordinate transformation of corresponding points in the round-trip acquisition data is as shown in equation (2):

[0059]

[0060] In the formula: This represents the world coordinates of pairs of points with the same name affected by the placement error; The coordinates of the corresponding point pairs are represented by the local coordinates of the LiDAR; ΔT represents the placement offset error between the carrier coordinate system and the LiDAR scanner coordinate system; ΔR describes the placement attitude error; where ΔR(Δheading,Δpitch,Δroll) and ΔT(Δx,Δy,Δz) are the unknowns to be checked, ΔR is a rotation matrix composed of rotations based on the Z, X, and Y axes by a certain angle, and (Δheading,Δpitch,Δroll) are Euler angles; during the coordinate transformation process of the corresponding point pairs acquired from opposite directions, a self-checking error model is constructed to check the placement error. Based on the optimized objective function, the attitude and offset placement errors ΔR and ΔT are found to minimize the objective function and approximate the coordinates of the corresponding connection points.

[0061] The objective function that needs to be optimized is as follows:

[0062]

[0063] In the objective function, n represents the number of pairs of corresponding points, where i represents the i-th pair of corresponding point data; find the corrections ΔR(Δheading,Δpitch,Δroll) and ΔT(Δx,Δy,Δz) for the placement error, so that the objective function is minimized and the coordinates of the corresponding connection points are approximated. The error model is as shown in equation (4):

[0064]

[0065] C. Optimization of the self-calibration error model

[0066] The self-calibration model is optimized based on different weights for rotation and translation. When constructing the least squares model, a loss function is built to ensure the accuracy of the results and reduce the impact of outliers on the calibration results. Considering the correlation between parameters, the different weights for rotation and translation in the self-calibration model are optimized to obtain a more reliable and stable mathematical model. Taking into account the influence of outliers, different weights are assigned to different errors. The optimized self-calibration model for placement error is shown in the following equation:

[0067]

[0068] In the formula: n represents the number of pairs of corresponding points; λ is a custom weight for the loss, defined according to the importance of the corresponding points and the weights of angle and offset. It is assumed that the corresponding feature points have the greatest impact on the calibration results of the placement error, and their weight λ is defined as 0.9; σ v σ is the standard deviation of point sampling. r Let σ be the standard deviation of the angle. t The offset standard deviation is used. The optimized self-calibration model introduces weighted observations with placement angle information and weighted observations with offset information, treating them as observations with certain weights.

[0069] D. The least squares method iteratively calculates the optimal correction for the placement error.

[0070] The optimal correction value for the placement error is calculated iteratively using the least squares method, ultimately yielding high-precision system placement parameters. A self-calibration model for the placement error is constructed by combining matched feature points. By minimizing the spatial differences between corresponding points, a virtual observation equation is established, and a self-calibration error model is built. The placement error is then calibrated using a least squares iterative algorithm. The calibration results are used to correct the geometric relationship between the carrier coordinate system and the laser scanner coordinate system, and applied to laser point cloud coordinate transformation to obtain high-precision laser point cloud data.

[0071] This invention addresses the problem of mutually coupled error sources in mobile measurement systems and the unclear impact on point cloud measurement accuracy. It proposes a self-calibration method for placement parameters that considers the correlation between different errors. This method eliminates the need for a dedicated calibration site, and the entire calibration process possesses the high efficiency, flexibility, and convenience of self-calibration technology. Based on the self-calibration error model, it corrects placement errors in the integration of mobile measurement equipment, alleviates inconsistencies in point cloud acquisition from opposite directions, improves the overall accuracy of the point cloud, provides support for the calibration of placement errors in mobile measurement systems, and lays the foundation for subsequent multi-time-series point cloud stitching and high-precision 3D modeling.

[0072] Furthermore, a mobile measurement system with uncontrolled self-calibration for round trip measurements, such as... Figure 3 As shown, the device includes:

[0073] Coordinate reference system transformation module 110: The trajectory data POS obtained by coupling and solving the point cloud data acquired by the mobile measurement system and the original data of the combined navigation is used to transform the laser point cloud coordinate reference system and obtain the three-dimensional spatial coordinates of the laser point cloud in the world coordinate system.

[0074] Module 120 for constructing a self-calibration model for placement error: Extract reliable corresponding feature points from the data collected from the opposite direction, and combine them with the corresponding POS trajectory data to construct a self-calibration model for placement error;

[0075] Self-calibration model optimization module 130: Optimizes the self-calibration model based on different weights for rotation and translation;

[0076] Least squares method iterative calculation module 140: Iteratively calculates the optimal correction value of the placement error using the least squares method, and finally obtains high-precision system placement parameters.

[0077] Furthermore, a device, such as Figure 4 As shown, the device includes: a processor 210; and a memory 220 for storing one or more programs;

[0078] When the one or more programs are executed by the processor 210, the processor executes a self-calibration method, which includes: using the point cloud data acquired by the mobile measurement system and the trajectory data POS obtained by coupling and solving the original data of the combined navigation to perform laser point cloud coordinate reference system transformation, and obtaining the three-dimensional spatial coordinates of the laser point cloud in the world coordinate system.

[0079] Reliable corresponding feature points are extracted from the data collected from the opposite direction, and combined with the corresponding POS trajectory data to construct a self-checking model for placement error;

[0080] Optimize the self-calibration model based on different weights for rotation and translation;

[0081] The optimal correction value for the installation error is calculated iteratively using the least squares method, and finally, high-precision system installation parameters are obtained.

[0082] Furthermore, a computer-readable storage medium stores a computer program thereon, which, when executed by processor 210, implements a self-calibration method, the self-calibration method comprising: using a mobile measurement system to acquire point cloud data collected in the opposite direction and trajectory data POS obtained by coupling and solving the original data of the combined navigation, performing a laser point cloud coordinate reference system transformation to obtain the three-dimensional spatial coordinates of the laser point cloud in the world coordinate system;

[0083] Reliable corresponding feature points are extracted from the data collected from the opposite direction, and combined with the corresponding POS trajectory data to construct a self-checking model for placement error;

[0084] Optimize the self-calibration model based on different weights for rotation and translation;

[0085] The optimal correction value for the installation error is calculated iteratively using the least squares method, and finally, high-precision system installation parameters are obtained.

[0086] This invention only requires data from the opposing movement of the mobile measurement system to perform self-calibration. The entire calibration process possesses the characteristics of high efficiency, flexibility, and convenience of self-calibration technology, and has broad application prospects.

[0087] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for uncontrolled self-calibration of a mobile measurement system for round trip measurements, characterized in that, Includes the following steps: S1. Using the mobile measurement system to acquire the point cloud data collected from the opposite direction and the trajectory data POS obtained by coupling and solving the original data of the combined navigation, the laser point cloud coordinate reference system is transformed to obtain the three-dimensional spatial coordinates of the laser point cloud in the world coordinate system. S2. Extract reliable corresponding feature points from the opposite acquisition data, and combine them with the corresponding POS trajectory data to construct a self-checking model for placement error; S3. Optimize the self-calibration model based on different weights for rotation and translation; the optimized self-calibration model is expressed as follows: In the formula: n represents the number of pairs of corresponding points; λ is a custom weight for the loss, defined according to the importance of the corresponding points and the weights of angle and offset. It is assumed that the corresponding feature points have the greatest impact on the calibration results of the placement error, and their weight λ is defined as 0.9; σ v σ is the standard deviation of point sampling. r Let σ be the standard deviation of the angle. t This represents the standard deviation of the offset. S4. The optimal correction value of the installation error is calculated iteratively using the least squares method, and finally the high-precision system installation parameters are obtained.

2. The uncontrolled self-calibration method for round-trip measurement of a mobile measurement system according to claim 1, characterized in that: The coordinate reference system transformation in step S1 specifically includes: firstly, through... and The process involves transforming the coordinates from lidar to the carrier coordinates, and then interpolating the POS using the lidar point cloud acquisition time to obtain the carrier's position in the Earth-centered Earth-fixed system at the time of acquisition. and posture Further, the carrier coordinates are transformed to geocentric and geofixed coordinates, and finally transformed to the projected coordinate system to obtain the three-dimensional spatial coordinates of the laser point cloud in the world coordinate system; The coordinate reference system transformation formula is expressed as follows: In the formula, P s The local coordinates of the lidar representing the laser point cloud; This indicates the attitude of the lidar in the carrier coordinate system; This indicates the position of the lidar in the carrier coordinate system; Indicates the attitude of the carrier within the Earth's core and Earth-solid system; Indicates the location of the carrier within the Earth's core and solid system; P e It represents the geocentric and geofixed coordinates of the laser point cloud and can be transformed to other projected coordinate systems.

3. The uncontrolled self-calibration method for round-trip measurement of a mobile measurement system according to claim 1, characterized in that: The reliable corresponding feature points in step S2 are obtained by matching three-dimensional feature points in the data under prior pose constraints. Then, a self-calibration error model for the moving measurement point cloud is constructed, which specifically includes the following: Assuming that coordinate reference system transformation is affected by placement errors, the coordinate transformation relationship of corresponding points in the collected data is expressed as follows: In the formula: This represents the world coordinates of pairs of points with the same name affected by the placement error; ΔT represents the local coordinates of the lidar for a pair of points of the same name; ΔT represents the placement offset error between the carrier coordinate system and the lidar scanner coordinate system; ΔR describes the placement attitude error. Where ΔR(Δheading,Δpitch,Δroll) and ΔT(Δx,Δy,Δz) are unknowns that need to be checked, ΔR is a rotation matrix composed of rotations based on the Z, X, and Y axes by a certain angle, and (Δheading,Δpitch,Δroll) are Euler angles; During the coordinate transformation process of corresponding points obtained from opposite directions, a self-checking error model is constructed to check the placement error. Based on the optimized objective function, the placement errors ΔR and ΔT are found to minimize the objective function and make the coordinates of the corresponding connection points approximate.

4. The uncontrolled self-calibration method for round-trip measurement of a mobile measurement system according to claim 3, characterized in that: The objective function is expressed as follows: The self-calibration error model is expressed as follows: In the formula: This represents the world coordinates of pairs of points with the same name affected by placement errors.

5. The uncontrolled self-calibration method for round-trip measurement of a mobile measurement system according to claim 1, characterized in that: Step S4 specifically includes: using the least squares iterative algorithm to check and optimize the self-calibration model, calculating the optimal correction value for the placement error, correcting the geometric relationship between the carrier coordinate system and the lidar coordinate system using the calibration results, applying it to the laser point cloud coordinate transformation, obtaining high-precision laser point cloud data, and obtaining high-precision system placement parameters.

6. A self-calibrating device for uncontrolled round-trip measurement of a mobile measurement system, characterized in that: The device includes: Coordinate reference system transformation module (110): The trajectory data POS obtained by coupling the point cloud data acquired by the mobile measurement system and the original data of the combined navigation is used to transform the coordinate reference system of the laser point cloud and obtain the three-dimensional spatial coordinates of the laser point cloud in the world coordinate system. The self-calibration model construction module for placement error (120) extracts reliable corresponding feature points from the opposite acquisition data and combines them with the corresponding POS trajectory data to construct a self-calibration model for placement error. Self-calibration model optimization module (130): Optimizes the self-calibration model based on different weights for rotation and translation; the optimized self-calibration model is expressed as follows: In the formula: n represents the number of pairs of corresponding points; λ is a custom weight for the loss, defined according to the importance of the corresponding points and the weights of angle and offset. It is assumed that the corresponding feature points have the greatest impact on the calibration results of the placement error, and their weight λ is defined as 0.9; σ v σ is the standard deviation of point sampling. r Let σ be the standard deviation of the angle. t This represents the standard deviation of the offset. Least squares method iterative calculation module (140): The least squares method is used to iteratively calculate the optimal correction value of the placement error, and finally obtain the high-precision system placement parameters.

7. A device, characterized in that: The device includes: a processor (210); and a memory (220) for storing one or more programs; When the one or more programs are executed by the processor (210), the processor performs the self-checking method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that: It stores a computer program that, when executed by a processor (210), implements the self-calibration method as described in any one of claims 1-5.

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