IMU-GNSS System Installation Deviation Angle Calibration Method, Surveying Method and System

Abstract

This invention relates to the field of surveying and mapping, specifically to a method, surveying method, and system for calibrating the installation deviation angle of an IMU-GNSS unit. It involves calibrating the IMU-GNSS unit in two scenarios: a single-axis turntable and a horizontal platform, effectively avoiding the influence of environmental and human factors, and achieving high accuracy and reliability. Furthermore, it corrects the pose data generated by the IMU-GNSS unit based on the installation deviation angle of the IMU-GNSS unit on the mobile carrier. Based on the attitude angle sub-data contained in the pose data, it performs operational correction on the laser detection data generated during the movement of the mobile carrier, generating several laser point cloud frames. Based on these laser point cloud frames, it constructs a local map set. Based on the position sub-data contained in the pose data and the local map set, it constructs a global map, thereby updating the global map library. Correcting the pose data through the installation deviation angle ensures the accuracy of the local and global map construction, improving the reliability of surveying and mapping.

Description

Technical Field

[0001] This invention relates to the field of surveying and mapping, and in particular to a method for calibrating the installation deviation angle of an IMU-GNSS unit, a surveying and mapping method, and a system. Background Technology

[0002] The introduction of an Inertial Measurement Unit (IMU) improves the efficiency of surveying operations, especially in vehicle-mounted mobile surveying scenarios. The IMU, combined with a Global Navigation Satellite System (GNSS), enables large-scale point cloud data acquisition, providing a real-time and accurate data foundation for space surveying. However, a significant challenge in this IMU-GNSS combined surveying scenario is that the IMU's Z-axis is not perfectly parallel to the centering rod; an angle exists between them. This angle, when decomposed into the IMU's X and Y axes, corresponds to the roll and pitch installation angles. These two installation angles are also required during the projection of the GNSS antenna phase center onto the bottom of the centering rod. Therefore, it is necessary to calibrate these two installation angles.

[0003] Current calibration techniques involve calibrating in open environments, fixing the bottom of the centering rod, and shaking it several times from the initial azimuth, rotating it 90° several times each time, for a total of three rotations, and then shaking it in four directions. This calibration method requires operation in a well-maintained open environment, and its accuracy is affected by the environment and the operator's skill level, failing to meet the requirements for improved calibration precision and reproducibility. If an IMU-GNSS unit is directly applied to a surveying scene without accurate calibration, it will result in significant surveying errors. Therefore, developing a calibration method for the installation angle deviation of the IMU-GNSS unit that is unaffected by external environmental factors is of great significance for improving the reliability of the acquired data and enhancing surveying accuracy. Summary of the Invention

[0004] Considering the good manufacturing process of the IMU-GNSS unit and the centering rod, the stable fit between the bottom of the unit and the top of the centering rod, and the good verticality of the centering rod, it can be assumed that the installation deviation angle of the IMU within the unit and the installation deviation angle between the IMU and the vertical plane of the centering rod are approximately equal. Therefore, calibrating the installation deviation angle of the IMU within the unit can complete the calibration between the IMU and the centering rod, and can also achieve automatic calibration of the installation deviation angle, reducing the influence of environmental and human factors, and improving the reliability and accuracy of surveying data. This invention provides a method for calibrating the installation deviation angle of an IMU-GNSS unit, the method comprising the following steps:

[0005] S1: The IMU-GNSS unit is mounted on a single-axis turntable and the single-axis turntable is tilted. The single-axis turntable is rotated by a preset angle and then stopped, thereby collecting several first-table data of the IMU-GNSS unit.

[0006] S2: Place the IMU-GNSS unit on a horizontal platform and let it stand still, thereby collecting the second table data of the IMU-GNSS unit;

[0007] S3: The first table data and the second table data are calibrated to obtain the installation deviation angle of the IMU-GNSS unit; wherein, the installation deviation angle includes the roll installation deviation angle and the pitch installation deviation angle between the phase center of the GNSS antenna and the IMU arm inside the IMU-GNSS unit.

[0008] Preferably, in S1, the single-axis turntable is rotated a preset angle and then brought to a stop, thereby collecting several first-table data from the IMU-GNSS unit, specifically:

[0009] The single-axis turntable is rotated 60° and then stationary for a preset time period, thereby collecting six first table data corresponding to the six rotation angle positions of the IMU-GNSS whole machine;

[0010] In step S2, the IMU-GNSS unit is placed on a horizontal platform and brought to a standstill. The second data from the IMU-GNSS unit is then collected, specifically as follows:

[0011] The IMU-GNSS unit is placed on a water platform and left stationary for a preset time, with the bottom of the IMU-GNSS unit in contact with the water platform, thereby collecting a second set of data from the IMU-GNSS unit.

[0012] Preferably, in S3, the plurality of first table data and second table data are calibrated to obtain the installation deviation angle of the IMU-GNSS unit, specifically as follows:

[0013] Based on the data from the second addition table, estimate the zero bias of the addition table's Z-axis;

[0014] Based on the six first table data, estimate the zero bias of the table X-axis, the zero bias of the table Y-axis, the scale coefficient of the table X-axis, the scale coefficient of the table Y-axis, the error coefficient of the table Y-axis relative to the X-axis caused by non-orthogonality, and the error coefficient of the table X-axis relative to the Y-axis caused by non-orthogonality.

[0015] A correction model is constructed based on the zero bias of the Z-axis, the zero bias of the X-axis, the zero bias of the Y-axis, the scale coefficient of the X-axis, the scale coefficient of the Y-axis, the error coefficient of the Y-axis relative to the X-axis caused by non-orthogonality, and the error coefficient of the X-axis relative to the Y-axis caused by non-orthogonality.

[0016] The parameters to be estimated in the calibration model are iteratively optimized to obtain the optimal result of the parameters to be estimated;

[0017] Based on the optimization results, the second added table data is corrected to obtain the corrected second added table data.

[0018] Based on the correction results of the second table data, the installation deviation angle of the entire IMU-GNSS unit is obtained.

[0019] Preferably, iteratively optimizing the parameters to be estimated in the calibration model to obtain the optimal result of the parameters to be estimated includes:

[0020] S31: Initialize the parameters to be estimated and the damping coefficient of the correction model;

[0021] S32: Calculate the residuals and Jacobian matrix based on the correction model;

[0022] S33: Calculate the sum of squared residuals based on the residuals; calculate the increment based on the damping coefficient and the Jacobian matrix;

[0023] S34: Calculate the new residual sum of squares based on the increment;

[0024] S35: Compare the sum of squared residuals with the new sum of squared residuals to adjust the damping coefficient; adjust the parameter to be estimated according to the increment;

[0025] S36: Determine whether the increment is greater than or equal to a preset threshold or whether a preset number of iterations has been reached; if yes, use the adjusted parameter to be estimated as the optimal result of the parameter to be estimated; if no, repeat S31-S35 above until the increment is greater than or equal to the preset threshold or the preset number of iterations has been reached.

[0026] On the other hand, the present invention provides a surveying method, the method comprising the following steps:

[0027] S100: Correct the pose data generated by the IMU-GNSS unit based on the installation deviation angle of the IMU-GNSS unit on the mobile carrier; wherein, the installation deviation angle of the IMU-GNSS unit is obtained by the above-mentioned calibration method based on the installation deviation angle of the IMU-GNSS unit;

[0028] S200: Based on the attitude corner data included in the pose data, perform runtime correction on the laser detection data generated during the movement of the mobile carrier to generate several laser point cloud frames; construct a local map set based on the several laser point cloud frames;

[0029] S300: Construct a global map based on the position sub-data contained in the pose data and the local map set, thereby updating the global map library.

[0030] Preferably, in S100, the pose data generated by the IMU-GNSS unit is corrected based on the installation deviation angle of the IMU-GNSS unit on the mobile carrier, specifically as follows:

[0031] Acquire attitude and position sub-data generated by the IMU and GNSS receivers of the mobile carrier during its movement;

[0032] Based on the installation deviation angle of the IMU-GNSS unit, determine the projection position deviation of the GNSS antenna phase center of the GNSS receiver onto the IMU arm; based on the projection position deviation, correct the position sub-data; combine the attitude angle sub-data and the corrected position sub-data to form the attitude data of the IMU-GNSS unit.

[0033] Preferably, in S200, based on the attitude corner data included in the pose data, the laser detection data generated during the movement of the mobile carrier is corrected to generate several laser point cloud frames; based on the several laser point cloud frames, a local map set is constructed, specifically as follows:

[0034] The pose data includes pose sub-data which are sampled and subjected to temporal transformation processing to obtain a pose feature sequence; wherein the pose feature sequence includes several pose feature sub-sequences and the several pose feature sub-sequences correspond one-to-one with several adjacent time intervals;

[0035] Based on the posture feature sequence, motion posture distortion correction is performed on several sub-data of the laser detection data generated during the movement of the mobile carrier to obtain several laser point cloud datasets corresponding to the several time intervals, thereby generating several laser point cloud frames.

[0036] For all feature points and pose features within each laser point cloud frame, a local map is generated, thereby forming a local map set from the local maps of all laser point cloud frames.

[0037] And in S300, a global map is constructed based on the position sub-data contained in the pose data and the local map set, thereby updating the global map library, specifically as follows:

[0038] Based on the position sub-data contained in the pose data, determine the position difference relationship of feature points of the same attribute in all local maps within the local map set;

[0039] Based on the location difference relationship, determine the location change constraints between every two adjacent local maps in the spatial context of the local map set, and then overlay all the local maps to form a global map; then, based on the error distribution of the global map, determine whether to update the global map to the global map library.

[0040] On the other hand, the present invention provides a surveying system, the system comprising the following modules:

[0041] The calibration module is used to correct the pose data generated by the IMU-GNSS unit based on the installation deviation angle of the IMU-GNSS unit on the mobile carrier; wherein the installation deviation angle of the IMU-GNSS unit is obtained by the above-mentioned calibration method based on the installation deviation angle of the IMU-GNSS unit.

[0042] The point cloud frame generation module is used to perform operational correction on the laser detection data generated during the movement of the mobile carrier based on the attitude corner data contained in the pose data, and generate several laser point cloud frames.

[0043] A local map generation module is used to construct a local map set based on the plurality of laser point cloud frames;

[0044] A global map construction module is used to construct a global map based on the location sub-data contained in the pose data and the local map set;

[0045] The map library update module is used to update the global map library.

[0046] Preferably, the correction module is used to correct the pose data generated by the IMU-GNSS unit based on the installation deviation angle of the IMU-GNSS unit on the mobile carrier, specifically as follows:

[0047] Acquire attitude and position sub-data generated by the IMU and GNSS receivers of the mobile carrier during its movement;

[0048] Based on the installation deviation angle of the IMU-GNSS unit, determine the projection position deviation of the GNSS antenna phase center of the GNSS receiver onto the IMU arm; based on the projection position deviation, correct the position sub-data; combine the attitude angle sub-data and the corrected position sub-data to form the attitude data of the IMU-GNSS unit.

[0049] Preferably, the point cloud frame generation module is used to perform operational correction on the laser detection data generated during the movement of the mobile carrier based on the attitude corner data included in the pose data, and generate a number of laser point cloud frames, specifically:

[0050] The pose data includes pose sub-data which are sampled and subjected to temporal transformation processing to obtain a pose feature sequence; wherein the pose feature sequence includes several pose feature sub-sequences and the several pose feature sub-sequences correspond one-to-one with several adjacent time intervals;

[0051] Based on the posture feature sequence, motion posture distortion correction is performed on several sub-data of the laser detection data generated during the movement of the mobile carrier to obtain several laser point cloud datasets corresponding to the several time intervals, thereby generating several laser point cloud frames.

[0052] The local map generation module is used to construct a local map set based on the plurality of laser point cloud frames, specifically:

[0053] For all feature points and pose features within each laser point cloud frame, a local map is generated, thereby forming a local map set from the local maps of all laser point cloud frames.

[0054] The global map construction module is used to construct a global map based on the location sub-data contained in the pose data and the local map set, specifically:

[0055] Based on the position sub-data contained in the pose data, determine the position difference relationship of feature points of the same attribute in all local maps within the local map set;

[0056] Based on the positional difference relationship, determine the positional change constraints between every two adjacent local maps in the spatial context of the local map set, and then overlay all the local maps to form a global map.

[0057] The map database update module is used to update the global map database, specifically:

[0058] Based on the error distribution of the global map, determine whether to update the global map to the global map library.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] This invention presents a calibration method for IMU-GNSS unit installation deviation angles. The method performs measurement calibration on both a single-axis turntable and a horizontal platform, effectively avoiding the influence of environmental and human factors, and achieving high accuracy and reliability. Furthermore, the mapping method and system within the invention correct the pose data generated by the IMU-GNSS unit based on the installation deviation angle of the IMU-GNSS unit on the mobile carrier. Based on the attitude angle sub-data contained in the pose data, the system performs operational correction on the laser detection data generated during the movement of the mobile carrier, generating several laser point cloud frames. Based on these laser point cloud frames, a local map set is constructed. Finally, based on the position sub-data contained in the pose data and the local map set, a global map is constructed, thereby updating the global map library. Correcting the pose data through installation deviation angles ensures the accuracy of local and global map construction, improving the reliability of the mapping. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0062] Figure 1 This is a flowchart of the IMU-GNSS whole-machine installation deviation angle calibration method provided by the present invention.

[0063] Figure 2 This is a schematic diagram of the inspection of the entire machine using a single-axis rotary table.

[0064] Figure 3 This is a top view of a single-axis turntable.

[0065] Figure 4 This is a flowchart of the surveying method provided by the present invention.

[0066] Figure 5 It is a laser point cloud frame.

[0067] Figure 6 It is the process of constructing a global map from a local map.

[0068] Figure 7 This is a structural diagram of the surveying system provided by the present invention. Detailed Implementation

[0069] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for explaining the present invention and not for limiting the present invention. Furthermore, it should be noted that, for ease of description, only the parts related to the present invention are shown in the accompanying drawings, not all structures. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of the present invention.

[0070] The terms "comprising" and "having," and any variations thereof, used in this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0071] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0072] Please see Figure 1 As shown, this invention provides a method for calibrating the installation deviation angle of an IMU-GNSS system, which includes the following steps:

[0073] S1: Install the IMU-GNSS unit on a single-axis turntable and tilt the turntable. Rotate the turntable by a preset angle and then stop. Collect several first-level data from the IMU-GNSS unit in this way.

[0074] S2: Place the IMU-GNSS unit on a horizontal platform and keep it stationary to collect the second table data of the IMU-GNSS unit;

[0075] S3: Perform calibration processing on several first and second table data to obtain the installation deviation angle of the IMU-GNSS unit; wherein, the installation deviation angle includes the roll installation deviation angle and the pitch installation deviation angle between the phase center of the GNSS antenna and the IMU arm inside the IMU-GNSS unit.

[0076] Furthermore, in S1, the single-axis turntable is rotated a preset angle and then brought to a stop, thereby collecting several first-table data points from the entire IMU-GNSS unit, specifically:

[0077] After rotating the single-axis turntable 60°, it is allowed to remain stationary for a preset time period. In this way, the first table data of the IMU-GNSS system is collected at the six rotation angle positions.

[0078] Furthermore, in S2, the IMU-GNSS unit is placed on a horizontal platform and brought to a standstill to collect the second set of data from the IMU-GNSS unit, specifically as follows:

[0079] After placing the IMU-GNSS unit on a water platform and keeping it stationary for a preset time, with the bottom of the IMU-GNSS unit in contact with the water platform, a second set of data from the IMU-GNSS unit is collected.

[0080] Please see Figure 3The single-axis turntable can be, but is not limited to, a turntable with a disk shape, which can rotate about an axis that passes perpendicularly through the center of the disk; the area near the circumference of the turntable has several grooves evenly distributed, and the IMU-GNSS unit can be placed in one of the grooves, so that it rotates with the rotation of the single-axis turntable.

[0081] Please see Figure 2 The single-axis turntable is tilted at an angle θ, where θ can be, but is not limited to, 30° or 45°. When the turntable is tilted, the turntable is rotated 60° around its axis of rotation. After each 60° rotation, it remains stationary for a preset time (e.g., 5s or 10s). After each stationary period, the corresponding first metering data of the IMU-GNSS unit is collected. The data collection time for the first metering data can be, but is not limited to, 5s. Using this method, six first metering data points of the IMU-GNSS unit are collected at six rotation angles during the single-axis turntable's rotation. The corresponding information is as follows:

[0082]

[0083] In the above formula, , , These represent the detection data of the IMU-GNSS system at the i-th rotation angle position corresponding to the X-axis, Y-axis, and Z-axis, respectively, where i = 1, 2, 3, 4, 5, 6.

[0084] After the IMU-GNSS unit is placed on a water platform and remains stationary for a preset time (e.g., 5s or 10s), the corresponding second-level data from the IMU-GNSS unit is collected after it has come to a standstill. The aforementioned second-level data... The corresponding information is as follows:

[0085]

[0086] In the above formula, , , These represent the detection data corresponding to the X-axis, Y-axis, and Z-axis when the MU-GNSS unit is placed on a water platform.

[0087] Furthermore, in S3, several sets of first and second table data are calibrated to obtain the installation deviation angle of the entire IMU-GNSS unit, specifically:

[0088] Based on the data from the second table, estimate the zero bias of the Z-axis of the table;

[0089] Based on the data from the six first tables, estimate the zero bias of the X-axis, the zero bias of the Y-axis, the scale factor of the X-axis, the scale factor of the Y-axis, the error factor of the Y-axis relative to the X-axis caused by non-orthogonality, and the error factor of the X-axis relative to the Y-axis caused by non-orthogonality.

[0090] A correction model is constructed based on the zero bias of the Z-axis, the zero bias of the X-axis, the zero bias of the Y-axis, the scale coefficient of the X-axis, the scale coefficient of the Y-axis, the error coefficient of the Y-axis relative to the X-axis caused by non-orthogonality, and the error coefficient of the X-axis relative to the Y-axis caused by non-orthogonality.

[0091] The parameters to be estimated in the calibration model are iteratively optimized to obtain the optimal result of the parameters to be estimated;

[0092] Based on the optimization results, the data in the second added table is corrected to obtain the corrected data in the second added table.

[0093] Based on the correction results of the second table, the installation deviation angle of the entire IMU-GNSS unit is obtained.

[0094] Furthermore, the parameters to be estimated in the calibration model are iteratively optimized to obtain the optimal results of the parameters to be estimated, including:

[0095] S31: Initialize the parameters to be estimated and the damping coefficients of the calibration model;

[0096] S32: Calculate the residuals and Jacobian matrix based on the correction model;

[0097] S33: Calculate the sum of squared residuals based on the residuals; calculate the increment based on the damping coefficient and the Jacobian matrix;

[0098] S34: Calculate the new residual sum of squares based on the increment;

[0099] S35: Compare the residual sum of squares with the new residual sum of squares to adjust the damping coefficient; adjust the parameter to be estimated based on the increment.

[0100] S36: Determine whether the increment is greater than or equal to the preset threshold or whether the preset number of iterations has been reached; if yes, use the adjusted parameter to be estimated as the optimal result of the parameter to be estimated; if no, repeat S31-S35 above until the increment is greater than or equal to the preset threshold or the preset number of iterations has been reached.

[0101] In practice, the data from the second added table is used. Estimate the zero offset of the Z-axis using the following formula:

[0102]

[0103] In the above formula, This indicates that the Z-axis offset is zero. This represents the gravity value, where the unit of gravity value and the unit of the data in the table are both gravitational acceleration g.

[0104] Using six first-table data points, the Levenberg-Marquardt (LM) algorithm was used to estimate the zero bias of the table's X-axis. Add Y-axis zero bias Add X-axis scale coefficient to the table Add Y-axis scale coefficient to the table The error coefficient of the Y-axis relative to the X-axis caused by non-orthogonality. The error coefficient of the X-axis relative to the Y-axis caused by non-orthogonality. Thus, the parameters to be estimated are as follows:

[0105] .

[0106] Construct a correction model based on the following formula:

[0107]

[0108] In the above formula, , , These represent the corrected X-axis data, Y-axis data, and Z-axis data of the i-th group, respectively. , , These represent the original X-axis data, Y-axis data, and Z-axis data of the i-th group, respectively; i = 1, 2, 3, 4, 5, 6.

[0109] The parameters to be estimated and the damping coefficient λ of the correction model are initialized according to the following formula:

[0110]

[0111]

[0112]

[0113]

[0114] Using the following formula, calculate the residuals and Jacobian matrix based on the correction model.

[0115]

[0116]

[0117] In the above formula, This represents the residuals of the corrected X-axis, Y-axis, and Z-axis data in the table for the i-th group. Let dx represent the Jacobian matrix of the corrected X-axis, Y-axis, and Z-axis data of the i-th group, and let dx represent the perturbation error of the parameter to be estimated.

[0118] Calculate the sum of squared residuals using the following formula. ,

[0119]

[0120] Using the following formula, based on the damping coefficient and the Jacobian matrix, the increment δ of the parameter x to be estimated is calculated.

[0121]

[0122] In the above formula, Representing the Jacobian matrix The transpose of , where I represents the identity matrix; the above equation can be solved using Gaussian elimination or LU decomposition.

[0123] Calculate the new sum of squared residuals according to the following formula. ,

[0124]

[0125] when If the condition is not met, then the update will be refused and the damping parameter λ will be increased; when... If so, the update is accepted and the damping parameter λ is reduced.

[0126] The parameter x to be estimated is updated as follows:

[0127]

[0128] In the above formula, This indicates the updated parameters to be estimated.

[0129] Finally, determine whether the increment is greater than or equal to the preset threshold or whether the preset number of iterations has been reached; if so, adjust the parameters to be estimated. If the result is the optimal result of the parameter to be estimated, then repeat the above steps until the increment is greater than or equal to the preset threshold or the preset number of iterations is reached.

[0130] Using the following formula, based on the optimization results of the parameters to be estimated, the data in the second supplementary table is corrected to obtain the corrected data in the second supplementary table.

[0131]

[0132] In the above formula, , , This represents the X-axis data, Y-axis data, and Z-axis data of the second added data table after correction.

[0133] This represents the optimal result of the parameter to be estimated.

[0134] Finally, the roll installation deviation angle Δφ and pitch installation deviation angle Δθ are obtained using the following formulas.

[0135] .

[0136] The above-mentioned installation deviation angle calibration method can be completed using both single-axis turntables and water platforms. It is less affected by environmental and human factors, can accurately calibrate the installation deviation angle, and ensures the reliability of the installation deviation angle calibration.

[0137] Please see Figure 4 As shown, the present invention provides a surveying method, which includes the following steps:

[0138] S100: Correct the pose data generated by the IMU-GNSS unit based on the installation deviation angle of the IMU-GNSS unit on the mobile carrier; wherein, the installation deviation angle of the IMU-GNSS unit is obtained by the above-mentioned calibration method based on the installation deviation angle of the IMU-GNSS unit.

[0139] Furthermore, in S100, the pose data generated by the IMU-GNSS unit is corrected based on the installation deviation angle of the IMU-GNSS unit on the mobile carrier, specifically as follows:

[0140] Acquire attitude and position sub-data generated by the IMU and GNSS receivers of the mobile carrier during its movement;

[0141] Based on the installation deviation angle of the IMU-GNSS unit, determine the projection position deviation of the GNSS antenna phase center of the GNSS receiver onto the IMU arm; based on the projection position deviation, correct the position sub-data; combine the attitude angle sub-data and the corrected position sub-data to form the attitude data of the IMU-GNSS unit.

[0142] To map large, open areas, IMU-GNSS units and laser equipment are typically mounted together on mobile platforms such as vehicles. These platforms move along designated paths within the area, and the IMU-GNSS unit and laser equipment work synchronously during this movement. Each time the platform reaches a new location, the IMU-GNSS unit generates pose data (including attitude angle sub-data and position sub-data), while the laser equipment generates laser detection data. As can be seen, the installation deviation angle of the IMU-GNSS unit affects the accuracy of the pose data. Therefore, pose data correction is necessary. Based on the roll and pitch installation deviation angles included in the installation deviation angle, the projected position deviation of the GNSS antenna phase center on the IMU boom is determined. This means determining the projected position deviation of the GNSS antenna phase center in the roll and pitch directions. The corrected position data is then obtained by combining the original position sub-data with these projected position deviations (e.g., by performing arithmetic and calculations between the original position sub-data and the projected position deviations). This ensures that the corrected position data accurately represents the actual position of the mobile platform. Finally, the original attitude sub-data and the corrected position sub-data are combined to form the pose data of the entire IMU-GNSS system, providing a reliable and accurate data foundation for subsequent large-area mapping.

[0143] S200: Based on the attitude corner data contained in the pose data, perform operational correction on the laser detection data generated during the movement of the mobile carrier to generate several laser point cloud frames; based on the several laser point cloud frames, construct a local map set.

[0144] Furthermore, in S200, based on the attitude corner data included in the pose data, the laser detection data generated during the movement of the mobile carrier is corrected to generate several laser point cloud frames; based on these laser point cloud frames, a local map set is constructed, specifically as follows:

[0145] The pose data includes pose sub-data, which are sampled and processed in the temporal domain to obtain a pose feature sequence. The pose feature sequence includes several pose feature sub-sequences, and each of the several pose feature sub-sequences corresponds one-to-one with several adjacent time intervals.

[0146] Based on the attitude feature sequence, motion attitude distortion correction is performed on several sub-data of the laser detection data generated during the movement of the mobile carrier to obtain several laser point cloud datasets corresponding to several time intervals, thereby generating several laser point cloud frames.

[0147] For all feature points and pose features within each laser point cloud frame, a local map is generated, thereby forming a local map set from the local maps of all laser point cloud frames.

[0148] The attitude corner data within the pose data is generated by continuous IMU detection during the movement of the mobile vehicle. It includes a series of three-dimensional attitude angle values, characterizing the spatial attitude changes of the mobile vehicle during the mapping process. Considering that the attitude corner data contains a large amount of attitude angle information and its detection time label information (the detection and generation time information of the attitude angle information), directly using the attitude corner data containing each attitude angle information and detection time label information to correct laser detection data with the same time detection label information would not only require a large amount of computing power but also consume a lot of time, reducing the efficiency of laser detection data correction. Therefore, the attitude corner data is sampled and processed in the time domain to obtain the changes in the attitude corner data. The changes may include, but are not limited to, the rate of change of angles in the pitch, roll, and yaw directions. Based on the changes, an attitude feature sequence is obtained. The attitude feature sequence contains several attitude feature subsequences. Each attitude feature subsequence includes several pitch angle features, several roll angle features, and several yaw angle features. All attitude feature subsequences are sequentially connected in time, and angle features of the same type within the same attitude feature subsequence are sequentially connected in time. That is, several pitch angle features are sequentially connected in time, several roll angle features are sequentially connected in time, and several yaw angle features are sequentially connected in time. In addition, the difference between the maximum and minimum values ​​of the same type of angle features within the same attitude feature subsequence is less than a preset threshold. This means that each attitude feature subsequence corresponds to only one time interval, and the attitude angle change value of each attitude feature subsequence within the corresponding time interval is small. This makes it easier to correct motion attitude distortion by matching the average pitch angle, average roll angle, and average yaw angle within each attitude feature subsequence with the laser detection sub-data generated by matching it with its corresponding time interval.

[0149] By utilizing all attitude feature sub-sequences contained in the attitude feature sequence, motion attitude distortion correction is applied to several detection sub-data points under the laser detection data generated during the movement of the mobile carrier. This results in several laser point cloud datasets corresponding to several time intervals, which are then used to generate several laser point cloud frames, ensuring the correctness of each laser point cloud frame. Please refer to [link / reference]. Figure 5 Laser point cloud frames can comprehensively and accurately reflect the spatial structure distribution. Furthermore, the system performs fusion processing on all feature points and attitude features (i.e., the average pitch angle, average roll angle, and average yaw angle of the attitude feature subsequence corresponding to each laser point cloud frame) within each laser point cloud frame to generate a local map. This allows the local maps of all laser point cloud frames to be combined into a local map set, ensuring the reliability of the local maps.

[0150] S300: Construct a global map based on the position sub-data and local map set contained in the pose data, thereby updating the global map library.

[0151] Furthermore, in S300, a global map is constructed based on the position sub-data and local map set contained in the pose data, thereby updating the global map library, specifically as follows:

[0152] Based on the positional sub-data contained in the pose data, determine the positional difference relationship of feature points of the same attribute in all local maps within the local map set;

[0153] Based on the positional difference relationship, determine the positional change constraints between every two adjacent local maps in the spatial context of the local map set, and then overlay all local maps to form a global map; then, based on the error distribution of the global map, determine whether to update the global map to the global map library.

[0154] Each local map corresponds to map information about a specific area during motion mapping of a mobile carrier. It is understandable that different local maps have different spatial locations, especially between adjacent local maps. These location differences can be determined through differential calculations using the location sub-data contained in the pose data. Furthermore, each local map contains various objects with different structures. To effectively classify the objects within a local map, feature points of corresponding objects can be divided into rod-shaped feature points and area feature points based on their shapes. The positional difference relationships (i.e., the positional differences in the X, Y, and Z axes of three-dimensional space) of feature points with the same attribute (such as rod-shaped or area feature points) across all local maps can be determined from the location sub-data contained in the pose data. This provides a positional basis for the subsequent accurate integration of all local maps in the three-dimensional spatial domain.

[0155] Please see Figure 6Once the positional differences of feature points of the same attribute in all local maps are determined, the relative positional relationship between any two local maps in the three-dimensional spatial domain can be accurately represented. However, considering that excessively large or small positional spacing between two adjacent local maps during the integration process can lead to visual discrepancies, it is necessary to set positional change constraints between two adjacent local maps. These constraints can be, but are not limited to, the range of positional changes along the X, Y, and Z axes in three-dimensional space during the stitching and integration process. Combining the aforementioned positional differences and positional change constraints, every two adjacent local maps in all local maps are stitched and integrated, thereby overlaying all local maps into a global map. The system also obtains the planar error distribution and elevation error distribution of the global map. The planar error distribution refers to the first average error value per unit area in the planar dimension of the global map, and the elevation error distribution refers to the second average error value per unit area in the elevation dimension of the global map. If both the first average error value and the second average error value are less than the preset error threshold, the global map is updated and replaced in the global map library. Otherwise, the global map library is not updated to ensure that the global map library is always updated with highly accurate surveying and mapping maps.

[0156] Please see Figure 7 As shown, the present invention provides a surveying system, which includes the following modules:

[0157] The calibration module is used to correct the pose data generated by the IMU-GNSS unit based on the installation deviation angle of the IMU-GNSS unit on the mobile carrier; wherein, the installation deviation angle of the IMU-GNSS unit is obtained through the above-mentioned calibration method based on the installation deviation angle of the IMU-GNSS unit.

[0158] The point cloud frame generation module is used to perform operational correction on the laser detection data generated during the movement of the mobile carrier based on the attitude corner data contained in the pose data, and generate several laser point cloud frames.

[0159] The local map generation module is used to construct a local map set based on several laser point cloud frames;

[0160] The global map construction module is used to construct a global map based on the location sub-data and local map set contained in the pose data;

[0161] The map library update module is used to update the global map library.

[0162] Furthermore, the correction module is used to correct the pose data generated by the IMU-GNSS unit based on the installation deviation angle of the IMU-GNSS unit on the mobile carrier, specifically as follows:

[0163] Acquire attitude and position sub-data generated by the IMU and GNSS receivers of the mobile carrier during its movement;

[0164] Based on the installation deviation angle of the IMU-GNSS unit, determine the projection position deviation of the GNSS antenna phase center of the GNSS receiver onto the IMU arm; based on the projection position deviation, correct the position sub-data; combine the attitude angle sub-data and the corrected position sub-data to form the attitude data of the IMU-GNSS unit.

[0165] Furthermore, the point cloud frame generation module is used to perform operational corrections on the laser detection data generated during the movement of the mobile carrier based on the attitude corner data contained in the pose data, and to generate several laser point cloud frames, specifically:

[0166] The pose data includes pose sub-data, which are sampled and processed in the temporal domain to obtain a pose feature sequence. The pose feature sequence includes several pose feature sub-sequences, and each of the several pose feature sub-sequences corresponds one-to-one with several adjacent time intervals.

[0167] Based on the attitude feature sequence, motion attitude distortion correction is performed on several sub-data of the laser detection data generated during the movement of the mobile carrier to obtain several laser point cloud datasets corresponding to several time intervals, thereby generating several laser point cloud frames.

[0168] The local map generation module is used to construct a local map set based on several laser point cloud frames, specifically:

[0169] For all feature points and pose features within each laser point cloud frame, a local map is generated, thereby forming a local map set from the local maps of all laser point cloud frames.

[0170] Furthermore, the global map construction module is used to construct a global map based on the location sub-data contained in the pose data and the local map set, specifically:

[0171] Based on the positional sub-data contained in the pose data, determine the positional difference relationship of feature points of the same attribute in all local maps within the local map set;

[0172] Based on the positional difference relationship, determine the positional change constraints between every two adjacent local maps in the spatial context of the local map set, and then overlay all the local maps to form a global map.

[0173] The map library update module is used to update the global map library, specifically:

[0174] Based on the error distribution of the global map, determine whether to update the global map to the global map library.

[0175] The operation and effects of the surveying system of the present invention are consistent with those of the surveying method described above, and the surveying system will not be described again here.

[0176] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of a necessary general-purpose hardware platform, or by a combination of hardware and software. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a computer product. The present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0177] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Other embodiments may also be used. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An IMU-GNSS-based overall installation bias angle calibration method, characterized in that, The method comprises the following steps: S1: install the IMU-GNSS integrated machine on a single-axis turntable and set the single-axis turntable to be inclined, and make the single-axis turntable stationary after rotating by a preset angle, so as to collect a plurality of first accelerometer data of the IMU-GNSS integrated machine, specifically: make the single-axis turntable stationary after rotating by 60° for a preset length of time, so as to collect six first accelerometer data of the IMU-GNSS integrated machine corresponding to six rotating angle positions; S2: place the IMU-GNSS integrated machine on a water platform and make it stationary, so as to collect second accelerometer data of the IMU-GNSS integrated machine, specifically: place the IMU-GNSS integrated machine on a water platform and make it stationary for a preset length of time, wherein the bottom end of the IMU-GNSS integrated machine is attached to the water platform, so as to collect one second accelerometer data of the IMU-GNSS integrated machine; S3: calibrate the plurality of first accelerometer data and the second accelerometer data to obtain an installation deviation angle of the IMU-GNSS integrated machine; wherein the installation deviation angle comprises a roll installation deviation angle and a pitch installation deviation angle of a GNSS antenna phase center in the IMU-GNSS integrated machine and an IMU rod arm, specifically: estimate accelerometer Z-axis zero offset according to the second accelerometer data; estimate accelerometer X-axis zero offset, accelerometer Y-axis zero offset, accelerometer X-axis scale factor, accelerometer Y-axis scale factor, error coefficient of accelerometer Y-axis to X-axis caused by non-orthogonality, and error coefficient of accelerometer X-axis to Y-axis caused by non-orthogonality according to the six first accelerometer data; construct a correction model according to the accelerometer Z-axis zero offset, the accelerometer X-axis zero offset, the accelerometer Y-axis zero offset, the accelerometer X-axis scale factor, the accelerometer Y-axis scale factor, the error coefficient of accelerometer Y-axis to X-axis caused by non-orthogonality, and the error coefficient of accelerometer X-axis to Y-axis caused by non-orthogonality; iteratively optimize to-be-estimated parameters of the correction model to obtain an optimized result of the to-be-estimated parameters, comprising: S31: initialize the to-be-estimated parameters and damping coefficients of the correction model; S32: calculate residual error and a Jacobian matrix according to the correction model; S33: calculate residual error square sum according to the residual error, and calculate an increment according to the damping coefficients and the Jacobian matrix; S34: calculate a new residual error square sum according to the increment; S35: compare the residual error square sum with the new residual error square sum to adjust the damping coefficients, and adjust the to-be-estimated parameters according to the increment; S36: determine whether the increment is greater than or equal to a preset threshold value or whether a preset iteration number is reached; if yes, the adjusted to-be-estimated parameters are taken as the optimized result of the to-be-estimated parameters; if not, repeat the above S31-S35 until the increment is greater than or equal to the preset threshold value or the preset iteration number is reached; correct the second accelerometer data according to the optimized result to obtain a second accelerometer data correction result; obtain the installation deviation angle of the IMU-GNSS integrated machine according to the second accelerometer data correction result.

2. A mapping method characterized by, The method comprises the following steps: S100: correcting pose data generated by an IMU-GNSS integrated machine according to installation deviation angles of the IMU-GNSS integrated machine on a mobile carrier; wherein the installation deviation angles of the IMU-GNSS integrated machine are obtained by the method for calibrating installation deviation angles of an IMU-GNSS integrated machine according to claim 1; S200: performing running correction on laser detection data generated during movement of the mobile carrier according to attitude angle sub-data contained in the pose data, to generate a plurality of laser point cloud frames; and constructing a local map set according to the plurality of laser point cloud frames; S300: constructing a global map according to position sub-data contained in the pose data and the local map set, to update a global map library.

3. The mapping method according to claim 2, wherein, in S100, the pose data generated by the IMU-GNSS integrated machine is corrected according to the installation deviation angles of the IMU-GNSS integrated machine on the mobile carrier, specifically: attitude angle sub-data and position sub-data generated by an IMU and a GNSS receiver of the IMU-GNSS integrated machine during movement of the mobile carrier are obtained; a projection position deviation of a GNSS antenna phase center of the GNSS receiver on an IMU lever arm is determined according to the installation deviation angles of the IMU-GNSS integrated machine; the position sub-data is corrected according to the projection position deviation; and the attitude angle sub-data and the corrected position sub-data are combined to form the pose data of the IMU-GNSS integrated machine.

4. The mapping method according to claim 2, wherein, in S200, the laser detection data generated during movement of the mobile carrier is corrected according to the attitude angle sub-data contained in the pose data, to generate a plurality of laser point cloud frames; and the local map set is constructed according to the plurality of laser point cloud frames, specifically: the attitude angle sub-data contained in the pose data is sampled and processed in time domain to obtain an attitude feature sequence; wherein the attitude feature sequence includes a plurality of attitude feature subsequences, and the plurality of attitude feature subsequences correspond to a plurality of adjacent time intervals one by one; a plurality of detection sub-data of the laser detection data generated during movement of the mobile carrier are corrected in motion attitude distortion according to the attitude feature sequence, to obtain a plurality of laser point cloud data sets corresponding to the plurality of time intervals, so as to generate a plurality of laser point cloud frames; a local map is generated for all feature points and attitude features in each laser point cloud frame, so as to combine local maps of all laser point cloud frames into the local map set; and in S300, the global map is constructed according to the position sub-data contained in the pose data and the local map set, to update the global map library, specifically: a position difference relationship of feature points with the same attribute in all local maps in the local map set is determined according to the position sub-data contained in the pose data. According to the position difference relationship, a position change constraint condition between each two adjacent local maps in space within the local map set is determined, so that all the local maps are superimposed into a global map; and according to an error distribution of the global map, it is determined whether to update the global map to a global map library.

5. A mapping system characterized by, The system comprises the following modules: A correction module is configured to correct pose data generated by an IMU-GNSS integrated machine according to installation deviation angles of the IMU-GNSS integrated machine on a mobile carrier, wherein the installation deviation angles of the IMU-GNSS integrated machine are obtained by the IMU-GNSS integrated machine installation deviation angle calibration method according to claim 1; A point cloud frame generation module is configured to perform running correction on laser detection data generated during movement of the mobile carrier according to attitude angle sub-data contained in the pose data, and generate a plurality of laser point cloud frames; A local map generation module is configured to construct a local map set according to the plurality of laser point cloud frames; A global map construction module is configured to construct a global map according to position sub-data contained in the pose data and the local map set; A map library update module is configured to update a global map library.

6. The mapping system according to claim 5, wherein The correction module is configured to correct pose data generated by an IMU-GNSS integrated machine according to installation deviation angles of the IMU-GNSS integrated machine on a mobile carrier, and specifically: Obtain attitude angle sub-data and position sub-data generated by an IMU and a GNSS receiver of the IMU-GNSS integrated machine during movement of the mobile carrier; Determine a projection position deviation of a GNSS antenna phase center of the GNSS receiver on an IMU lever arm according to the installation deviation angles of the IMU-GNSS integrated machine; correct the position sub-data according to the projection position deviation; and combine the attitude angle sub-data and the corrected position sub-data to form the pose data of the IMU-GNSS integrated machine.

7. The mapping system according to claim 5, wherein The point cloud frame generation module is configured to perform running correction on laser detection data generated during movement of the mobile carrier according to attitude angle sub-data contained in the pose data, and generate a plurality of laser point cloud frames, and specifically: Sample and time domain change process the attitude angle sub-data contained in the pose data to obtain an attitude feature sequence; wherein the attitude feature sequence comprises a plurality of attitude feature subsequences, and the plurality of attitude feature subsequences correspond to a plurality of adjacent time intervals one by one; According to the attitude feature sequence, perform motion attitude distortion correction on a plurality of detection sub-data belonging to the laser detection data generated during movement of the mobile carrier to obtain a plurality of laser point cloud data sets corresponding to the plurality of time intervals, so as to generate a plurality of laser point cloud frames; The local map generation module is configured to construct a local map set according to the plurality of laser point cloud frames, and specifically: Generate a local map for all feature points and attitude features in each laser point cloud frame, so as to combine the local maps of all laser point cloud frames into a local map set; The global map construction module is configured to construct a global map according to position sub-data contained in the pose data and the local map set, specifically: determine position differential relations of feature points of the same attribute in all local maps in the local map set according to position sub-data contained in the pose data; determine position change constraint conditions between every two adjacent local maps in the local map set in space according to the position differential relations, so that all the local maps are superimposed into the global map; The map library updating module is configured to update a global map library, specifically: determine whether to update the global map to the global map library according to error distribution of the global map.

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