Airborne bathymetric LiDAR installation angle deviation calibration method and system

Abstract

The present invention belongs to the technical field of marine surveying and mapping, and discloses a method and system for calibrating the installation angle deviation of an airborne bathymetric LiDAR. The method constructs an airborne bathymetric LiDAR point position reduction model that takes into account the correction of optical zero position errors, laying a foundation for the calibration of the installation angle deviation of the airborne bathymetric LiDAR; constructs a pitch angle self-calibration model for a single-strip uncontrolled plane to perform self-calibration of the pitch angle of the airborne bathymetric LiDAR; constructs a roll angle self-calibration model for minimizing the normal vector of a two-way uncontrolled plane to perform self-calibration of the roll angle of the airborne bathymetric LiDAR. The present invention solves the problem of insufficient accuracy of the point cloud measured by the airborne bathymetric LiDAR, and can not only provide technical support for the refined processing of airborne LiDAR bathymetric data, but also provide high-precision data for underwater topographic measurements in coastal areas and other areas, promoting research and application development in the fields of marine science and marine surveying and mapping.

Description

Technical Field

[0001] The invention belongs to the technical field of marine surveying and mapping, and in particular relates to a method and system for calibrating an airborne depth sounding LiDAR placement angle deviation. Background Art

[0002] Airborne LiDAR Bathymetry (ALB) is an emerging underwater topographic measurement technology that integrates global navigation satellite system, inertial navigation system, laser ranging, etc. It has the characteristics of high measurement accuracy, high efficiency, strong mobility, and amphibious use. It can effectively fill the gap in water depth measurement in shallow coastal areas. However, when the airborne LiDAR bathymetry system is integrated and installed, the installation angle deviation between the laser scanner and the inertial navigation system and the arm error between the phase center of the global navigation satellite system and the inertial navigation system will seriously affect the positioning accuracy of the laser foot point. Among them, the arm error can be accurately obtained by high-precision observation and other methods, but the installation angle deviation cannot be accurately obtained by direct observation. Therefore, an effective method must be used to correct the installation angle error. The installation angle deviation calibration of airborne bathymetry LiDAR can solve the problem of insufficient accuracy of airborne bathymetry LiDAR measurement point cloud. It can not only provide technical support for the fine processing of airborne LiDAR bathymetry data, but also provide high-precision data for underwater topographic measurement in coastal areas and other regions, and promote research and application development in the fields of marine science and marine surveying and mapping.

[0003] The existing methods mainly include: step-by-step geometry method, which is simple and convenient to operate, but prone to manual interpretation errors; conjugate homonymous linear feature algorithm, which uses the line features between flight strips to calibrate the installation angle deviation. It is simple to operate and easy to extract, but the calibration result is unstable; the minimum distance method of homonymous points, which can effectively reduce the installation angle deviation, but it is difficult to determine the homonymous points and has high requirements on point density; the reference plane feature constraint method, which is easy to extract features and has high efficiency, but requires known high-precision calibration field data.

[0004] Through the above analysis, the problems and defects of the existing technology are as follows: in the existing technology, it is difficult to obtain on-site calibration control points and it is difficult to determine the sparse features of the same name in the measurement point cloud, which makes it difficult to correct the installation angle, resulting in low accuracy of the airborne bathymetric LiDAR measurement point cloud. Summary of the invention

[0005] In order to overcome the problems existing in the related art, the embodiments disclosed in the present invention provide a method and system for calibrating the installation angle deviation of an airborne bathymetric LiDAR, and specifically relate to a method for calibrating the installation angle deviation of an airborne bathymetric LiDAR without on-site control for sparse point clouds.

[0006] The technical solution is as follows: A method for calibrating an airborne depth-measuring LiDAR installation angle deviation comprises:

[0007] S1: Construct an airborne bathymetric LiDAR point position reduction model that takes into account the correction of optical zero position error, which is used for the calibration of the installation angle deviation of the airborne bathymetric LiDAR.

[0008] S2: Construct a pitch angle self-calibration model for a single flight strip without a control plane, and perform pitch angle self-calibration for airborne bathymetric LiDAR;

[0009] S3: Construct a roll angle self-calibration model with bidirectional uncontrolled normal vector minimization to perform self-calibration of the roll angle of the airborne bathymetric LiDAR.

[0010] In step S1, an airborne bathymetric LiDAR point position reduction model taking into account optical zero position error correction is constructed, including:

[0011] Step 1.1: Before the installation angle deviation calibration, according to the mechanical structure of the airborne bathymetric LiDAR system, the airborne bathymetric LiDAR point reduction model is established, and the reflected light direction vector is fused with the aerial slant distance information to obtain the laser foot point coordinates. The fusion posture data is used to convert the coordinate information into the WGS-84 coordinate system;

[0012] Step 1.2: Extract the corner points of the building with the same name as the control points and calculate the distance difference of the points with the same name ΔY1, ΔY2…ΔY i , define the distance difference objective function S(n);

[0013] Step 1.3: The zero scale of the code disk corresponds to the optical zero position. Correct the installation deviation of the optical zero position, process the ALB three-dimensional point cloud and the point pairs with the same name, and judge if S(n)=0, then it is in the optical zero position error, and end the optical zero position error correction. If S(n)=0 is not true, adjust the code disk reading, repeat the processing of the ALB three-dimensional point cloud and the point pairs with the same name, and continue to judge whether S(n) is equal to zero, until the output S(n)=0, and end the optical zero position error correction.

[0014] In step 1.1, the fusion pose data converts the coordinate information to the WGS-84 coordinate system, and the expression is:

[0015]

[0016] In the formula, is the WGS-84 coordinate of the fused pose data, R N The rotation matrix for transforming the local horizontal coordinate system to the WGS-84 coordinate system, R LLS The rotation matrix for transforming the carrier coordinate system to the local horizontal coordinate system, R BFS The scanner coordinate system is transformed into the carrier coordinate system rotation matrix, R AZJ The rotation matrix for the angle error correction is set up, S is the distance from the laser to the target point, RY To drive the motor coordinate system to transform to the scanner coordinate system rotation matrix, V1 is the direction vector of the reflected light, the expression is:

[0017]

[0018] In the formula, is the angle between the reflective mirror and the vertical plane of the drive motor shaft, θ is the angle between the laser incident direction and the drive motor shaft, and λ is the rotation angle of the code disk. The expression is:

[0019]

[0020] Where λ is the rotation angle of the code disk, ε is the code disk reading, and E is the code disk resolution;

[0021] In step 1.2, the distance difference objective function S(n) is expressed as:

[0022]

[0023] In the formula, S(n) is the distance difference objective function, i is the distance difference index, n is the number of pairs of points with the same name, ΔY i is the distance difference between the i-th pair of homonymous points, and D is the distance difference threshold of homonymous points.

[0024] In step 1.3: Correct the installation deviation of the optical zero position. The correction formula is:

[0025]

[0026] Where ε' is the deviation between the corrected code disk reading and the initial value of the optical zero position. is the initial value of the optical zero position.

[0027] In step S2, a pitch angle self-calibration model of a single flight strip without a control plane is constructed to perform airborne depth measurement LiDAR pitch angle self-calibration, including:

[0028] Step 2.1: For the two layers of parallel point clouds caused by the pitch angle error, use the RANSAC algorithm to fit the plane. Randomly select three non-collinear points to build a plane model and calculate the plane equations corresponding to the three points. For each point P in the point cloud i (x i ,y i ,z i ), calculate its perpendicular distance d to the plane to determine the inner point, and set the number of iterations With the distance threshold T, determine the two plane parameters (A, B, C, D1) and (A, B, C, D2), and calculate the distance f (ΔP) between the two planes;

[0029] Step 2.2: Take ΔP as the unknown, perform linearization, perform Taylor expansion and retain the first-order term;

[0030] Step 2.3: Calculate the distance error between the two planes;

[0031] Step 2.4: When the least squares adjustment model constraint V of the expression T When PV is minimum, the normal equation is obtained;

[0032] Step 2.5: Obtain the pitch angle error parameter from the normal equation.

[0033] In step 2.1, the distance f(ΔP) between the two planes is calculated, and the expression is:

[0034]

[0035] Where ΔP is the pitch angle error, D1(ΔP) and D2(ΔP) are two plane parameters determined by fitting based on re-solving the point cloud after adding the pitch angle error, and A, B, and c are the coefficients of the x, y, and z terms of the plane equation respectively;

[0036] In step 2.2, the above equation (6) is linearized and Taylor expanded, and the first-order term is retained. The expression is:

[0037]

[0038] In the formula, f ry is the function value after correction, f0 is the function value in the initial state, is the partial derivative of function f with respect to the pitch angle error ΔP.

[0039] In step 2.3, the distance error equation between the two planes is calculated, and the expression is:

[0040] v = bx-l(8);

[0041] Where V is the distance error between the two planes, b is the coefficient matrix of unknown numbers when Taylor's formula is expanded, l is the distance between the two planes, and x is the pitch angle error ΔP to be solved;

[0042] In step 2.4, the normal equation is:

[0043] b T Pbx-b T Pl = 0 (9);

[0044] Where b T is the transposed matrix of b, and P is the weight matrix;

[0045] In step 2.5, the pitch angle error parameter calculation formula is:

[0046] x=-(b T Pb) -1 b T Pl(10).

[0047] In step S3, a roll angle self-calibration model for minimizing the uncontrolled normal vector in a two-way flight strip is constructed, including:

[0048] Step 3.1: Use formula (11) to obtain the normal vectors N1 = (A1, B1, C1) and N2 = (A2, B2, C2) of the two planes offset in the same area of ​​the two-way flight strip caused by the roll angle error, and calculate the modulus. The expression is:

[0049]

[0050] Step 3.2: Calculate the sine of the angle between the normal vectors of the two planes using formula (12), which is expressed as:

[0051]

[0052] Where Δr is the angle between the two plane normal vectors;

[0053] Step 3.3: Calculate the roll angle error. The expression is:

[0054]

[0055] Where ΔR is the roll angle error.

[0056] Another object of the present invention is to provide an airborne bathymetric LiDAR installation angle deviation calibration system, which implements the airborne bathymetric LiDAR installation angle deviation calibration method, and the system comprises:

[0057] The point reduction model construction module is used to construct an airborne depth measurement LiDAR point reduction model that takes into account the optical zero error correction, and is used for the airborne depth measurement LiDAR installation angle deviation calibration;

[0058] The pitch angle self-calibration model construction module is used to build a pitch angle self-calibration model for a single flight band without a control plane, and perform airborne depth measurement LiDAR pitch angle self-calibration;

[0059] The roll angle self-calibration model construction module is used to construct a roll angle self-calibration model with minimized uncontrolled normal vector in a two-way flight zone to perform self-calibration of the roll angle of airborne bathymetric LiDAR.

[0060] Combined with all the above technical solutions, the beneficial effects of the present invention are as follows: the airborne bathymetric LiDAR installation angle deviation calibration method and system provided by the present invention, by constructing an optical zero position error correction model that takes into account the distance difference of the same-name corner points, performs optical zero position deviation correction of the internal code disk device of the airborne bathymetric LiDAR system, laying the foundation for the calibration of the installation angle; then, by constructing an installation angle deviation calibration model based on plane feature constraints, combined with random sampling consistency and least squares adjustment method, the pitch angle deviation is corrected with the minimum distance between the two planes of the single flight strip layer as the constraint; the roll angle deviation is corrected with the minimum angle between the normal vectors of the two planes of the two-way flight strip as the constraint. The airborne bathymetric LiDAR installation angle deviation calibration of the present invention solves the problem of insufficient accuracy of the airborne bathymetric LiDAR measurement point cloud, and can not only provide technical support for the refined processing of airborne LiDAR bathymetric data, but also provide high-precision data for underwater topographic measurements in coastal areas and other areas, promoting research and application development in the fields of marine science and marine surveying and mapping.

[0061] The present invention can reduce the installation angle error caused by the failure of the coordinate systems of the laser scanning system and the inertial measurement unit system in the airborne laser radar to be strictly aligned or parallel due to the limitation of the manufacturing process, and improve the positioning accuracy of the laser foot point; to meet the depth measurement accuracy standard of the "Marine Engineering Topographic Survey Specification", thereby obtaining an integrated high-precision airborne LiDAR depth measurement three-dimensional point cloud above and below the water, and then a high-precision seabed terrain model can be established, providing strong support for marine resource development, marine environmental protection, etc.

[0062] Compared with most installation angle deviation calibration methods that require known control points or high-precision three-dimensional data in the calibration field, the present invention has no specific requirements for the calibration field and can complete the installation angle deviation calibration without the known data of the calibration field. Compared with terrestrial laser radar, the ALB system has a lower point frequency, resulting in a sparse point density. The present invention proposes a installation angle deviation calibration scheme for sparse point clouds. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The accompanying drawings herein are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description, serve to explain the principles of the present disclosure;

[0064] Figure 1 This is a flow chart of a method for calibrating an airborne bathymetric LiDAR installation angle deviation provided by an embodiment of the present invention;

[0065] Figure 2 Schematic diagram of the installation angle deviation calibration principle provided by an embodiment of the present invention;

[0066] Figure 3 It is a structural diagram of the airborne depth-measuring LiDAR system in the present invention;

[0067] Figure 4 It is a road layering diagram caused by the pitch angle error in the present invention;

[0068] Figure 5 It is the position offset diagram caused by the roll angle error;

[0069] Figure 6 It is the road point cloud profile before and after the pitch angle calibration;

[0070] Figure 7 It is the road point cloud profile before and after the roll angle calibration;

[0071] Figure 8 This is the cloud profile at the top of the house before and after the heading angle calibration;

[0072] Fig. 9 It is the relative distribution map of Mapper 20KU, RTK and SBES points;

[0073] Fig.10 This is the elevation difference map of the same-name points between Mapper 20KU and RTK in the elevation difference distribution of the same-name points between Mapper 20KU and verification data;

[0074] Fig.11 This is the elevation difference map of the same-name points between Mapper 20KU and SBES in the elevation difference distribution of the same-name points between Mapper 20KU and the verification data. DETAILED DESCRIPTION

[0075] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific implementation disclosed below.

[0076] The innovation of the present invention is that it is difficult to obtain on-site calibration control points in coastal areas and other areas, and it is difficult to determine the same-name features due to the sparse point cloud of the bathymetric radar. An airborne bathymetric LiDAR placement angle deviation calibration algorithm for sparse point clouds / no on-site control is proposed. First, by constructing an optical zero-position error correction model that takes into account the distance difference of the same-name corner points, the optical zero-position deviation of the internal code disk device of the ALB system is corrected, laying the foundation for the calibration of the placement angle; then, by constructing a placement angle deviation calibration model based on plane feature constraints, combined with random sampling consistency (RANSAC) and the least squares adjustment method, the pitch angle deviation is corrected with the minimum distance between the two planes of the single-strip stratification as the constraint; the roll angle deviation is corrected with the minimum angle between the normal vectors of the two planes of the two-way flight strip as the constraint.

[0077] Embodiment 1, as Figure 1 As shown, the airborne bathymetric LiDAR installation angle deviation calibration method provided by the embodiment of the present invention includes:

[0078] S1: Construct an airborne bathymetric LiDAR point position reduction model that takes into account the correction of optical zero position error, which is used for the calibration of the installation angle deviation of the airborne bathymetric LiDAR.

[0079] S2: Construct a pitch angle self-calibration model for a single flight strip without a control plane, and perform pitch angle self-calibration for airborne bathymetric LiDAR;

[0080] S3: Construct a roll angle self-calibration model with bidirectional uncontrolled normal vector minimization to perform self-calibration of the roll angle of the airborne bathymetric LiDAR.

[0081] In step S1 of the embodiment of the present invention, the airborne LiDAR Bathymetry (ALB) system is mainly composed of a laser, a reflective mirror, a drive motor, an encoding disc, etc. Figure 3 As shown. To construct the point reduction model of the ALB system, the center of the reflective mirror is taken as the coordinate origin, the aircraft flight direction is the Y axis, the vertical direction of the Y axis is the X axis, and the Z axis is vertically upward to establish the scanner right-hand coordinate system; on this basis, the Y axis of the scanner coordinate system is taken as the rotation axis, and the scanner coordinate system is rotated counterclockwise by an angle of α to establish the drive motor coordinate system. At this time, the Z axis becomes the drive motor shaft, and the Y axis is still the aircraft flight direction. Due to its high-resolution characteristics, the internal code disk of the ALB system can have a significant impact on the accuracy of the point cloud with a small deviation, which further affects the calibration of the installation angle deviation. Therefore, before the calibration of the installation angle deviation, it is necessary to correct the optical zero position error of the code disk. Under ideal conditions, the scanning trajectory of the ALB system should be elliptical, and the coordinate points in the scanner coordinate system should be symmetrically distributed relative to the Y axis.

[0082] In step S1 of the embodiment of the present invention, constructing an airborne depth measurement LiDAR point position reduction model taking into account optical zero position error correction includes:

[0083] Step 1.1: Before the installation angle deviation is calibrated, an airborne LiDAR point reduction model is established according to the mechanical structure of the airborne LiDAR (ALB) system. The reflected light direction vector is fused with the aerial slant distance information to obtain the laser foot point coordinates. Then, the position data is fused to convert the coordinate information into the WGS-84 coordinate system, as shown in the innovative formula (1) proposed by the present invention:

[0084]

[0085] In the formula, is the WGS-84 coordinate of the fused pose data, R N The rotation matrix for transforming the local horizontal coordinate system to the WGS-84 coordinate system, R LLS The rotation matrix for transforming the carrier coordinate system to the local horizontal coordinate system, R BFS The scanner coordinate system is transformed into the carrier coordinate system rotation matrix, R AZJ The rotation matrix for the angle error correction is set up, S is the distance from the laser to the target point, R Y To drive the motor coordinate system to transform to the scanner coordinate system rotation matrix, V1 is the direction vector of the reflected light, the expression is:

[0086]

[0087] In the formula, is the angle between the reflective mirror and the vertical plane of the drive motor shaft, θ is the angle between the laser incident direction and the drive motor shaft, and λ is the rotation angle of the code disk. The expression is:

[0088]

[0089] Where λ is the rotation angle of the code disk, ε is the code disk reading, and E is the code disk resolution;

[0090] Step 1.2: Extract the corner points of the building with the same name as the control points and calculate the distance difference of the points with the same name ΔY1, ΔY2…ΔY i , define the distance difference objective function S(n); as the innovative formula (4) proposed by the present invention:

[0091]

[0092] In the formula, S(n) is the distance difference objective function, i is the distance difference index, n is the number of pairs of points with the same name, ΔY i is the distance difference between the i-th pair of homonymous points, and D is the distance difference threshold of homonymous points.

[0093] Step 1.3: The zero scale of the code disk will directly correspond to the optical zero position, but due to the installation deviation, the optical zero position must be corrected using the innovative formula (5) proposed by the present invention:

[0094]

[0095] Where ε' is the deviation between the corrected code disk reading and the initial value of the optical zero position. is the initial value of the optical zero position.

[0096] In step S2 of the embodiment of the present invention, in the airborne LiDAR bathymetric system, the pitch angle has the most significant effect on the scanning measurement, and its error causes the ground objects to be layered along the flight direction. This problem is particularly obvious in the measurement of one-way flight strips. Therefore, the pitch angle error is first corrected, and based on its characteristics, only one-way flight strips are used for correction to enhance the stability of the calibration process. Compared with point features and line features, plane features have geometric stability and are easy to detect and identify. In the ALB system, the pitch angle will cause the straight road to be layered, forming two layers of parallel point clouds caused by the pitch angle error, such as Figure 4 As shown in the figure, the two layers of parallel point clouds caused by the pitch angle error are fitted with planes based on the RANSAC algorithm, and based on this, a least square adjustment model with the distance between planes as a constraint is constructed to iteratively solve the pitch angle error.

[0097] Among them, the pitch angle self-calibration model of a single flight strip without a control plane is constructed, and the pitch angle self-calibration of the airborne bathymetric LiDAR includes:

[0098] Step 2.1: Use the RANSAC algorithm to fit the plane of the two layers of parallel point clouds caused by the pitch angle error. Randomly select three non-collinear points to build a plane model and calculate the plane equations corresponding to the three points. For each point P in the point cloud i (x i ,y i ,z i ), calculate its vertical distance d to the plane to determine the inner point, set the number of iterations N and the distance threshold T, determine the two plane parameters (A, B, C, D1) and (A, B, C, D2), and use the innovative formula (6) proposed by the present invention to calculate the distance f (ΔP) between the planes:

[0099]

[0100] Where ΔP is the pitch angle error, D1(ΔP) and D2(ΔP) are two plane parameters determined by fitting based on re-solving the point cloud after adding the pitch angle error, and A, B, and C are the coefficients of the x, y, and z terms of the plane equation respectively;

[0101] Step 2.2: Take ΔP as an unknown variable, perform Taylor expansion on the above equation in a linear manner and retain the first-order term, and obtain the innovative formula (7) proposed by the present invention:

[0102]

[0103] In the formula, f ry is the function value after correction, f0 is the function value in the initial state, is the partial derivative of function f with respect to the pitch angle error ΔP.

[0104] Step 2.3: Theoretically, the distance between the two planes is 0, but due to the existence of the pitch angle, it is not actually 0, so the error equation formula (8) innovatively proposed by the present invention is obtained:

[0105] V = bx-1 (8);

[0106] Where V is the distance error between the two planes, b is the coefficient matrix of unknown numbers when Taylor's formula is expanded, l is the distance between the two planes, and x is the pitch angle error ΔP to be solved;

[0107] Step 2.4: When the expression V T When PV is minimum, the normal equation is obtained, as shown in the innovative formula (9) proposed by the present invention:

[0108] b T Pbx-b T Pl = 0 (9);

[0109] Where b T is the transposed matrix of b, and P is the weight matrix;

[0110] In step 2.5, the pitch angle error parameter calculation formula is:

[0111] x=-(b T Pb) -1 b T Pl(10).

[0112] In step S3 of the embodiment of the present invention, the roll angle will cause the scanning plane to tilt and cause the ground object to shift in the vertical plane of the flight direction. Manual calibration usually visually estimates the distance between the straight road profile point clouds on the round-trip overlapping flight strip, but it is easily affected by subjective visual bias. The roll angle error is iteratively solved with the minimum normal vector angle between the two planes as the constraint. By extracting the point cloud of the overlapping area of ​​the straight road in the round-trip flight strip, the point cloud plane is fitted with the RANSAC algorithm to obtain the normal vector angle between the two planes, such as Figure 5 As shown in , half of the angle is the required roll angle, and this process is iterated until the roll angle reaches the best estimated value. In order to improve the robustness of the calculation, the sine function is used to calculate the angle between the two planes.

[0113] Among them, the roll angle self-calibration model for minimizing the uncontrolled normal vector of the two-way flight strip includes:

[0114] Step 3.1: Use formula (11) to obtain the normal vectors N1 = (A1, B1, C1) and N2 = (A2, B2, C2) of the two planes offset in the same area of ​​the two-way flight strip caused by the roll angle error, and calculate their modulus:

[0115]

[0116] Step 3.2: Calculate the sine of the angle between the normal vectors of the two planes using formula (12):

[0117]

[0118] Where Δr is the angle between the two plane normal vectors;

[0119] Step 3.3: Calculate the roll angle error, as shown in formula (13):

[0120]

[0121] Where ΔR is the roll angle error.

[0122] The heading angle is corrected using the conventional calibration method of a gabled roof house in which the ridge line is perpendicular to the flight direction in the calibration route.

[0123] It can be seen from the above embodiments that the method of the present invention can complete the calibration of the installation angle deviation without taking a known high-precision three-dimensional calibration field as a reference, and is easier to detect and identify than the plane features used in the point feature and line feature methods. It can solve the problems of difficulty in obtaining on-site calibration control points and difficulty in determining sparse features of the same name in the measurement point cloud, which lead to difficulties in correcting the installation angle. It can not only provide technical support for the refined processing of airborne LiDAR bathymetric data, but also provide high-precision data for underwater topographic measurements in coastal areas and other regions, and promote research and application development in the fields of marine science and marine surveying and mapping.

[0124] Embodiment 2, the airborne depth sounding LiDAR installation angle deviation calibration system provided by the embodiment of the present invention comprises:

[0125] The point reduction model construction module is used to construct an airborne depth measurement LiDAR point reduction model that takes into account the optical zero error correction, and is used for the airborne depth measurement LiDAR installation angle deviation calibration;

[0126] The pitch angle self-calibration model construction module is used to build a pitch angle self-calibration model for a single flight band without a control plane, and perform airborne depth measurement LiDAR pitch angle self-calibration;

[0127] The roll angle self-calibration model construction module is used to construct a roll angle self-calibration model with minimized uncontrolled normal vector in a two-way flight zone to perform self-calibration of the roll angle of airborne bathymetric LiDAR.

[0128] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0129] In order to verify the performance of the proposed method for sparse point cloud / non-site control airborne bathymetric LiDAR placement angle deviation calibration, the airborne LiDAR bathymetric data of a university and a coastal area were used for verification. The ALB equipment used in the experiment is Mapper 20KU developed by a certain optical precision instrument research institute. The laser center wavelength is 532nm, the laser repetition rate is 20kHz, the scanning angle is ±20°, the laser divergence angle is 1mrad, and the weight is 6kg. The equipment integrates NovAtel's EG370N high-precision inertial navigation, and the flight carrier is the vertical take-off and landing fixed-wing UAV RY-V50.

[0130] In September 2023, a calibration field was designated at a university and data collection for the calibration of the installation angle deviation was carried out. The experimental flight altitude was 120m, the flight speed was 22m / s, and the point cloud density was about 10 / m 2 In order to verify the measurement accuracy of the ALB system after the angular deviation of the system was calibrated, verification data were collected along the coast of a certain area. The altitude and speed were the same as above, the overlap of the navigation belt was 30%, the transparency of the saddle plate was 3.7m, and the maximum detection depth was about 7.55m. At the same time, the real-time kinematic (RTK) control points were collected as the land point external coincidence accuracy verification data, and the data collected by the ship-borne single beam echo sounder (SBES) system were used as the seabed point external coincidence accuracy verification data.

[0131] Qualitative analysis of calibration results: Using the calibration field data of a university, the optical zero position error correction model of Mapper 20KU was constructed according to the proposed method, in which the distance threshold D was set to 0.002m, the initial value of the optical zero position was 130901, and it was 130100 after calibration; on this basis, the installation angle deviation calibration model was constructed, and the calculated pitch angle deviation was 0.85°, the roll angle deviation was 0.19°, and the heading angle deviation was 0.78°. The feature area point clouds before and after the three installation angle error calibration are as follows Figure 6-Figure 8 As shown. Figure 6 It can be seen that before the pitch angle calibration, the straight road is divided into two layers of parallel point clouds caused by the pitch angle error. After the calibration, the two layers of point clouds almost overlap into one layer. Figure 7 It can be seen that before the roll angle calibration, the round-trip road point cloud profile forms an angle, and after the calibration, it is restored to be straight and overlapped as one; Figure 8 It can be seen that the position of the tip of the gable house in the round-trip flight strip was significantly offset before the heading angle calibration, and it can be well overlapped after the calibration. In summary, this method can effectively reduce the installation angle deviation of the airborne LiDAR system.

[0132] Quantitative analysis of calibration results:

[0133] (1) Internal compliance accuracy assessment.

[0134] In order to analyze the elevation deviation between adjacent flight strips and verify the stability of Mapper 20KU in detecting the same area, the elevation errors of the same-name points in the overlapping areas of the four flight strips were selected and calculated, and the internal coincidence accuracy was calculated. The statistical results are shown in Table 1.

[0135] Table 1 Comparison of Mapper 20KU accuracy

[0136]

[0137]

[0138] As shown in Table 1 above, the average error and root mean square error of the overlapping areas of the four flight strips used in the experiment are both less than 5.0 cm. The maximum value between most flight strips is 7.1 cm, and the minimum value is only 0.1 cm. This proves that after calibration, Mapper20KU has a high internal accuracy between adjacent flight strips, and the measurement results have good stability.

[0139] (2) External compliance accuracy assessment.

[0140] In order to verify the external accuracy of Mapper 20KU after calibration, the data collected by the more mature land and underwater topographic mapping methods (RTK and SBES) are used as the benchmark data to conduct a comparative analysis of the accuracy of land points and seabed points. The distribution of points in the survey area is as follows: Fig. 9 shown.

[0141] In order to quantitatively evaluate the elevation accuracy of Mapper 20KU after calibration, based on the nearest neighbor principle, the ALB point cloud and the corresponding points of the verification data (RTK, SBES) were searched and the elevation differences were compared. The mean absolute error (MAE) and root mean square error (RMSE) were calculated. Fig.10 This is the elevation difference distribution map of the same-name land points between ALB and RTK. Fig.11 This is the elevation difference distribution map of the same-named sounding points of ALB and SBES. The statistical results are shown in Table 2. It should be noted that the Mapper 20KU sounding data has been denoised and refraction corrected.

[0142] Table 2 Comparison of Mapper 20KU external accuracy

[0143]

[0144] As can be seen from the table above, the maximum height difference between the point cloud collected by Mapper 20KU and the RTK same-name point is 17.9cm, the minimum is 0.1cm, the MAE is 6.5cm, and the RMSE is 8.1cm; the maximum height difference between the same-name point and the SBES is 27.6cm, the minimum is 0.1cm, the MAE is 11.5cm, and the RMSE is 13.4cm, which meets the depth measurement accuracy standard of the "Marine Engineering Survey Specification": In depth measurement, when the water depth is ≤20m, the depth measurement error is ≤0.2m. It can be seen that the installation angle deviation of the Mapper 20KU system has been basically eliminated, and it has good measurement accuracy, which further shows that the proposed method is highly applicable to reducing the optical zero error and installation angle deviation of the ALB system.

[0145] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for calibrating the installation angle deviation of an airborne bathymetric LiDAR, characterized in that: The method includes: S1: Construct an airborne bathymetric LiDAR point position reduction model that takes into account the correction of optical zero position error, which is used for the calibration of the installation angle deviation of the airborne bathymetric LiDAR. S2: Construct a pitch angle self-calibration model for a single flight strip without a control plane, and perform pitch angle self-calibration for airborne bathymetric LiDAR; S3: Construct a roll angle self-calibration model with bidirectional flight zone uncontrolled normal vector minimization to perform airborne bathymetric LiDAR roll angle self-calibration; In step S1, an airborne bathymetric LiDAR point position reduction model taking into account optical zero position error correction is constructed, including: Step 1.1: Before the installation angle deviation calibration, according to the mechanical structure of the airborne bathymetric LiDAR system, the airborne bathymetric LiDAR point reduction model is established, and the reflected light direction vector is fused with the aerial slant distance information to obtain the laser foot point coordinates. The fusion posture data is used to convert the coordinate information into the WGS-84 coordinate system; Step 1.2: Extract the corner points of the building with the same name as the control points and calculate the distance difference of the points with the same name ΔY1, ΔY2…ΔY i , define the distance difference objective function S(n); Step 1.3: The zero scale of the code disk corresponds to the optical zero position. The installation deviation of the optical zero position is corrected. The ALB three-dimensional point cloud and the same-name point pairs are processed. If S(n)=0, it is in the optical zero position error, and the optical zero position error correction is ended. If S(n)=0 is not established, the code disk reading is adjusted, and the ALB three-dimensional point cloud and the same-name point pairs are processed repeatedly. It is continued to determine whether S(n) is equal to zero, until the output S(n)=0 is output, and the optical zero position error correction is ended; In step 1.1, the fusion pose data converts the coordinate information to the WGS-84 coordinate system, and the expression is: In the formula, is the WGS-84 coordinate of the fused pose data, R N The rotation matrix for transforming the local horizontal coordinate system to the WGS-84 coordinate system, R LLS The rotation matrix for transforming the carrier coordinate system to the local horizontal coordinate system, R BFS The scanner coordinate system is transformed into the carrier coordinate system rotation matrix, R AZJ The rotation matrix for the angle error correction is set up, S is the distance from the laser to the target point, R Y To drive the motor coordinate system to transform to the scanner coordinate system rotation matrix, V1 is the direction vector of the reflected light, the expression is: In the formula, is the angle between the reflective mirror and the vertical plane of the drive motor shaft, θ is the angle between the laser incident direction and the drive motor shaft, and λ is the rotation angle of the code disk. The expression is: Where λ is the rotation angle of the code disk, ε is the code disk reading, and E is the code disk resolution; In step 1.2, the distance difference objective function S(n) is expressed as: In the formula, S(n) is the distance difference objective function, i is the distance difference index, n is the number of pairs of points with the same name, ΔY i is the distance difference of the i-th pair of homonymous points, and D is the distance difference threshold of homonymous points; In step 1.3, the installation deviation of the optical zero position is corrected. The correction formula is: Where ε′ is the deviation between the corrected code disk reading and the initial value of the optical zero position. is the initial value of the optical zero position.

2. The method for calibrating the installation angle deviation of the airborne depth sounding LiDAR according to claim 1, characterized in that: In step S2, a pitch angle self-calibration model of a single flight strip without a control plane is constructed to perform airborne depth measurement LiDAR pitch angle self-calibration, including: Step 2.1: For the two layers of parallel point clouds caused by the pitch angle error, use the RANSAC algorithm to fit the plane. Randomly select three non-collinear points to build a plane model and calculate the plane equations corresponding to the three points. For each point P in the point cloud i (x i ,y i , z i ), calculate its perpendicular distance d to the plane to determine the inner point, set the number of iterations N and the distance threshold T, determine the two plane parameters (A, B, C, D1) and (A, B, C, D2), and calculate the distance f(ΔP) between the two planes; Step 2.2: Take ΔP as the unknown, perform linearization, perform Taylor expansion and retain the first-order term; Step 2.3: Calculate the distance error between the two planes; Step 2.4: When the least squares adjustment model constraint V of the expression T When PV is minimum, the normal equation is obtained; Step 2.5: Obtain the pitch angle error parameter from the normal equation.

3. The method for calibrating the installation angle deviation of the airborne depth sounding LiDAR according to claim 2, characterized in that: In step 2.1, the distance f(ΔP) between the two planes is calculated using the expression: Where ΔP is the pitch angle error, D1(ΔP) and D2(ΔP) are two plane parameters determined by fitting based on re-solving the point cloud after adding the pitch angle error, and A, B, and C are the coefficients of the x, y, and z terms of the plane equation respectively; In step 2.2, the above equation (6) is linearized and Taylor expanded, and the first-order term is retained. The expression is: In the formula, f ry is the function value after correction, f0 is the function value in the initial state, is the partial derivative of the function f with respect to the pitch angle error ΔP; In step 2.3, the distance error equation between the two planes is calculated, and the expression is: V = bx - l (8); Where V is the distance error between the two planes, b is the coefficient matrix of unknown numbers when Taylor's formula is expanded, l is the distance between the two planes, and x is the pitch angle error ΔP to be solved; In step 2.4, the normal equation is: b T Pbx-b T Pl=0(9); Where b T is the transposed matrix of b, and P is the weight matrix; In step 2.5, the pitch angle error parameter calculation formula is: x=-(b T Pb)- 1 b T Pl(10)。 4. The method for calibrating the installation angle deviation of the airborne depth sounding LiDAR according to claim 1, characterized in that: In step S3, a roll angle self-calibration model for minimizing the uncontrolled normal vector in a two-way flight strip is constructed, including: Step 3.1: Use formula (11) to obtain the normal vectors N1 = (A1, B1, C1) and N2 = (A2, B2, C2) of the two planes offset in the same area of ​​the two-way flight strip caused by the roll angle error, and calculate the modulus. The expression is: Step 3.2: Calculate the sine of the angle between the normal vectors of the two planes using formula (12), which is expressed as: Where Δr is the angle between the two plane normal vectors; Step 3.3: Calculate the roll angle error. The expression is: Where ΔR is the roll angle error.

5. An airborne depth-sounding LiDAR installation angle deviation calibration system, characterized in that: The system implements the airborne bathymetric LiDAR installation angle deviation calibration method as claimed in any one of claims 1 to 4, and the system comprises: The point reduction model construction module is used to construct an airborne depth measurement LiDAR point reduction model that takes into account the optical zero error correction, and is used for the airborne depth measurement LiDAR installation angle deviation calibration; The pitch angle self-calibration model construction module is used to build a pitch angle self-calibration model for a single flight band without a control plane, and perform airborne depth measurement LiDAR pitch angle self-calibration; The roll angle self-calibration model construction module is used to construct a roll angle self-calibration model with minimized uncontrolled normal vector in a two-way flight zone to perform self-calibration of the roll angle of airborne bathymetric LiDAR.

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