CN109917356B - Airborne laser scanning system error calibration method - Google Patents
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Abstract
The invention belongs to the technical field of airborne laser scanning geometric positioning, and particularly discloses an airborne laser scanning system error calibration method, which comprises an error calibration step of detecting and repairing cycle slip of GNSS data, wherein the GNSS data comprises airborne data and ground reference station data, so that the integrity of the data is ensured; and performing cycle slip detection and repair by adopting an ambiguity assisted bridging method, and assuming that the ambiguity of the previous epoch enables the positioning to reach a smaller variance, the ambiguity of the group also meets the later epoch, so that the cycle slip can be judged according to whether the positioning variance of the later epoch approaches zero, differential processing is performed on GNSS data, the ambiguity of the whole cycle of a GNSS double-difference observation equation is determined, and a calibration mathematical model is established. The invention does not need to arrange a control field independently, is simple and easy to implement, can improve the automation of system error calibration, reduces the operation time and improves the operation efficiency.
Description
Technical Field
The invention relates to the technical field of airborne laser scanning geometric positioning, in particular to an error calibration method for an airborne laser scanning system.
Background
The laser scanning measurement technology is an important technical means for acquiring spatial data, is also a research hotspot in recent years, can quickly acquire high-precision three-dimensional geographic coordinate data by being carried on a mobile platform, and is one of the most advanced three-dimensional geographic data acquisition means. The airborne laser scanning system integrates a laser scanner, a GNSS positioning system and an inertial measurement unit IMU, the three-dimensional position and three attitude (pitch angle, roll angle and navigation angle) information of the mobile platform can be determined through the GNSS/IMU combination, and the scanned ground point unified geographic coordinates can be calculated by combining the three-dimensional position and the three attitude information with body coordinates acquired by the laser scanner. However, when the geographic coordinates are calculated, the coordinate origin points referred by the laser body coordinate system and the GNSS/IMU combined result are different, and the directions of the coordinate axes of the reference coordinate system are not parallel to each other, so that the obtained geographic coordinates have a system error. It shows that the geographic coordinate values obtained by scanning the same target twice are inconsistent. And solving the translation and rotation between the laser scanning coordinate system and the GNSS/IMU positioning result so as to eliminate the system error of the laser scanning system for acquiring the geographic coordinate, wherein the process is called airborne laser scanning system error calibration.
The error calibration of the current airborne laser scanning system comprises a control field layout method and a repeated scanning method. The method for laying the control field is to design specific mark points which are reasonably distributed, accurately measure the three-dimensional coordinates of each mark point by using a high-precision total station, then scan the control field by using an airborne laser scanning system, extract the coordinates of the mark points from the scanned point cloud, and establish a mathematical relationship with the actual coordinates observed by the total station so as to solve the system error parameters. The control field needs to be laid by a large amount of manpower and material resources, the control field needs to be scanned before or after data acquisition, the calibration process is lack of flexibility, and the control field also needs to be maintained regularly. The repeated scanning method is characterized in that the same scene is scanned for multiple times, and the constraint condition that the geographic coordinates of the same point are equal is utilized for calibration, so that a control field does not need to be arranged. However, due to the uncertainty of the laser scanning system, even if the same scene is scanned twice, the obtained scanning points do not correspond to each other, which results in inconsistency of the size of the actually scanned area, the number of points, and the like, and thus it is very difficult to find the same scanning point in the results of the two scans.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides an error calibration method for an airborne laser scanning system.
In order to achieve the purpose, the invention adopts the following technical scheme:
an error calibration method of an airborne laser scanning system comprises an error calibration step,
s1, detecting and repairing cycle slip of GNSS data, wherein the GNSS data comprise airborne data and ground reference station data, and before positioning is completed by using the data, the cycle slip is detected and repaired on a carrier phase observation value, so that the integrity of the data is ensured; adopting ambiguity auxiliary bridging method to detect and repair cycle slip, assuming the ambiguity of the previous epoch to make the positioning reach smaller variance, then the ambiguity group also satisfies the next epoch, so that the cycle slip can be judged according to whether the positioning variance of the next epoch approaches zero or not;
s2, performing differential processing on the GNSS data, converting the airborne GNSS data and the ground reference station GNSS data into a standard data exchange format, calculating the difference of the observation values of the synchronously observed satellites, selecting the satellite with the largest altitude angle as a reference satellite on the basis, and calculating the difference of the observation values of other satellites and the observation values of the reference satellite to obtain a double-difference observation value;
s3, determining the integer ambiguity of the GNSS double-difference observation equation, wherein the carrier phase observation value is required to be adopted for realizing high-precision positioning, the determination of the integer ambiguity is a key problem, the double-difference observation value is formed on the basis of cycle slip detection, the integer unknown number is determined by adopting a recursive filtering mode, and a high-precision positioning result is realized;
s4, combining the differential result with the IMU data to realize that the IMU data sampling interval has very high frequency, the error accumulation is fast when the IMU data is simply used for positioning and attitude determination, and the IMU data is combined with the differential positioning result of the GNSS data to eliminate the error of IMU integral calculation so as to obtain high-precision position and attitude data;
s5, interpolating the combined result according to the scanning time, so that the following effects are achieved, when a laser system scans surrounding objects, the corresponding fixed frequency is provided, each scanning point records the scanning time, the time keeps time synchronization with the GNSS/IMU combined result, but the time points cannot correspond to one another, the GNSS/IMU combined result needs to be interpolated according to the time of the scanning points, and the three-dimensional position and attitude information of the airborne platform at the scanning moment is obtained;
s6, calculating the three-dimensional geographic coordinates of the scanning points, and reserving corresponding original data, wherein after the positions and the postures of the scanning points at the corresponding moments are obtained, the geographic coordinates of the scanning points can be calculated according to the laser scanning body coordinates, the three-dimensional coordinates of the flying platform at the scanning moments and a rotation matrix formed by the postures of the flying platform at the scanning moments on the assumption that no system error exists;
s7, point cloud registration of the overlapped area is realized, wherein the point cloud registration is actually a process of uniformly integrating point cloud data under different visual angles to a specified coordinate system through translation and rotation rigid body transformation by coordinate transformation calculation; firstly, extracting local geometric features from a point cloud, and quickly calculating the corresponding transformation relation of the point cloud through geometric feature matching; then obtaining an accurate conversion relation through an iterative closest point algorithm on the basis; finally, evaluating a registration result by calculating the mean square error of the transformed point cloud;
s8, after registration, point cloud grid division and grid center coordinate calculation are realized, after point cloud registration of overlapped scanning areas, two sets of point clouds are guaranteed to be in a unified geographic coordinate system, and then due to uncertainty of the scanned point clouds, the point clouds cannot correspond to one another in position and quantity even under the registration condition; in order to calibrate errors of an airborne laser scanning system, a corresponding relation between two sets of point clouds is established, grid division is carried out on the registered point clouds, and the mean value of point cloud coordinates in the grid is used as the coordinates of a grid center;
and S9, establishing a calibration mathematical model, solving parameters, and realizing the following steps, after grid division, considering that grid point centers are in one-to-one correspondence, establishing an error equation for each grid point pair, solving a correction number by adopting a least square indirect adjustment method, correcting an initial value, then reestablishing error equation iterative calculation, and judging the correction number calculated each time until the requirement of tolerance is met.
Preferably, in S1, cycle slip detection and recovery of GNSS data, establishing an observation equation for observation data of two adjacent epochs as follows:
order to
Then equation (1) has the minimum variance, while equation (2) transforms to:
if there is no cycle slip,due to B 1 ，B 2 The medium element is similar, equation (3) has smaller variance, and if the variance is not close to zero, cycle slip is indicated; the cycle slip can be repaired by firstly positioning and resolving 4 continuous cycle slip-free satellite observation values and then obtaining the cycle slip size through positioning and back-calculation.
Preferably, in S2, the GNSS data difference processing respectively establishes an observation equation for the carrier phase observation values of the airborne data and the reference station data:
and subtracting corresponding terms on the left side and the right side in the formula to obtain a single-difference observation equation:
when a plurality of satellites are observed, one satellite is selected as a reference, and the homodyne observation equation of the other satellites and the homodyne observation equation of the reference satellite are subtracted:
in the above formula
Is the carrier phase observed value, lambda is the carrier wavelength, N is the whole cycle unknown number, C is the speed of light, dt is the clock error parameter, V ion Is ionospheric error, V trop The troposphere error is shown, i, j are an airborne station and a base station, p, q are numbers of observation satellites; equation (6) is the difference processing result.
Preferably, in S3, the integer ambiguity of the GNSS double-difference observation equation is determined, and after double-difference processing, many observation errors, including receiver clock difference, satellite orbit error, and ionospheric and tropospheric errors, are eliminated, so that the integer ambiguity can be conveniently solved by using a search or filtering method.
Preferably, in S4, the combination of the difference result and the IMU data first measures a rotation angular velocity of the carrier with respect to the inertial space by using the IMU gyroscope, and calculates a coordinate transformation matrix of the carrier coordinate system with respect to the navigation computation coordinate system; then, the specific force observation measured by the accelerometer is converted into a navigation calculation coordinate system, and the navigation calculation coordinate system is correspondingly corrected; and finally, obtaining a speed parameter through primary integration, obtaining a position parameter through secondary integration, and extracting an attitude angle of the carrier relative to a calculation coordinate system through a coordinate transformation matrix.
Preferably, in S7, the point cloud registration of the overlapping area, and the registration process is performed according to a rough registration first and a precise registration later, and the specific steps are as follows: a) Extracting geometric characteristics such as straight lines, planes, curved surfaces and the like from the two sets of point cloud data in the overlapped area, and completing coarse registration of the point cloud data by using the characteristics; b) On the basis of rough registration, one set of point cloud is taken as a reference, the closest point to the point cloud is searched in the other set of reference point cloud, and all corresponding point pairs are found; c) Calculating registration transformation parameter rotation R and translation T according to a rigid body transformation criterion; d) Carrying out coordinate transformation on the reference point cloud by using the conversion parameters to obtain a new reference point cloud; e) Iterating the new reference point cloud from the step b, and repeating the steps b-d; f) And judging the difference value of the corresponding points of the reference point cloud and the reference point cloud, and finishing the calculation and finishing the registration when the difference value is smaller than a threshold value.
Preferably, in S8, the registered point clouds are only the closest overall, but due to the number and distribution of the points, the points cannot meet the one-to-one correspondence, and a grid is divided in the registered point cloud overlapping region for system error calibration; the method comprises the steps of firstly obtaining the total size of point cloud data in X, Y and Z directions, then evenly dividing the three directions into three equal parts to obtain a 3X 3 grid, selecting the point cloud data most adjacent to the grid points, and calibrating, so that the number of points used for calibration is guaranteed, and the points are distributed uniformly.
Preferably, in S9, a calibration mathematical model is established, and when the system error of the laser scanner is considered, the geographic coordinates of the two sets of point clouds are calculated as:
when scanning the same target, equations (7) and (8) should be equal, i.e.:
the system error parameters (Δ x, Δ y, Δ z) and R required for solution are included in the formula (9) x Three rotation angles of; according to the process S8, after point cloud registration, the corresponding relation of two sets of point clouds can be obtained, and an error equation, namely a calibrated mathematical model, is established by using coordinate data on 27 grid points and using the formula (9);
preferably, in S9, the solution of the system error parameter cannot be directly solved since equation (9) is a non-linear equation, but R is x The three rotation angles in (1) are generally small angles, so that R can be removed x The approximate expression of (c) is:
the formula (10) is taken into the formula (9), and a linear equation about the unknown number can be obtained after the formula (10) is expanded; in addition, because the number of unknowns to be solved is only 6, and the number of equations to be established is 27, the least square method is adopted to obtain the optimal solution of the parameters during solving.
Compared with the prior art, the invention has the beneficial effects that: according to the method, overlapping data between adjacent flight zones are utilized to perform point cloud registration, then grid division is performed on the registered point cloud data, and airborne laser scanning system errors are calibrated by using grid center adjacent coordinates obtained by two flights. The method does not need to independently arrange a control field, is simple and easy to implement, can improve the automation of system error calibration, reduces the operation time and improves the operation efficiency.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
An error calibration method for an airborne laser scanning system comprises an error calibration step,
s1, detecting and repairing cycle slip of GNSS data, wherein the GNSS data comprise airborne data and ground reference station data, and before positioning is completed by using the data, the cycle slip is detected and repaired on a carrier phase observation value to ensure the integrity of the data; adopting ambiguity auxiliary bridging method to carry out cycle slip detection and repair, assuming that the ambiguity of the previous epoch enables the positioning to reach smaller variance, then the ambiguity of the group also meets the next epoch, so the cycle slip can be judged according to whether the positioning variance of the next epoch approaches zero or not;
s2, performing differential processing on the GNSS data, converting the airborne GNSS data and the ground reference station GNSS data into a standard data exchange format, calculating the difference of the observation values of synchronously observed satellites, selecting the satellite with the largest altitude angle as a reference satellite on the basis, and calculating the difference of the observation values of other satellites and the observation values of the reference satellite to obtain a double-difference observation value;
s3, determining the integer ambiguity of the GNSS double-difference observation equation, wherein the carrier phase observation value is required to be adopted for realizing high-precision positioning, the determination of the integer ambiguity is a key problem, the double-difference observation value is formed on the basis of cycle slip detection, the integer unknown number is determined by adopting a recursive filtering mode, and a high-precision positioning result is realized;
s4, combining the differential result with the IMU data to realize that the IMU data sampling interval has very high frequency, the error accumulation is fast when the IMU data is simply used for positioning and attitude determination, and the IMU data is combined with the differential positioning result of the GNSS data to eliminate the error of IMU integral calculation so as to obtain high-precision position and attitude data;
s5, interpolating the combined result according to the scanning time, so that the following effects are achieved, when a laser system scans surrounding objects, the corresponding fixed frequency is provided, each scanning point records the scanning time, the time keeps time synchronization with the GNSS/IMU combined result, but the time points cannot correspond to one another, the GNSS/IMU combined result needs to be interpolated according to the time of the scanning points, and the three-dimensional position and attitude information of the airborne platform at the scanning moment is obtained;
s6, calculating the three-dimensional geographic coordinates of the scanning points, and reserving corresponding original data, wherein the geographic coordinates of the scanning points can be calculated according to the laser scanning body coordinates, the three-dimensional coordinates of the flying platform at the scanning moment and a rotation matrix formed by the postures of the flying platform at the scanning moment on the assumption that no system error exists after the positions and the postures of the scanning points at the corresponding moments are obtained;
s7, point cloud registration of the overlapped area is realized, wherein the point cloud registration is actually a process of uniformly integrating point cloud data under different visual angles to a specified coordinate system through translation and rotation rigid body transformation by coordinate transformation calculation; firstly, extracting local geometric features from point cloud, and quickly calculating the corresponding conversion relation of the point cloud through geometric feature matching; then obtaining an accurate conversion relation through an iterative closest point algorithm on the basis; finally, evaluating a registration result by calculating the mean square error of the transformed point cloud;
s8, after registration, point cloud grid division and grid center coordinate calculation are realized, after point cloud registration of overlapped scanning areas, two sets of point clouds are guaranteed to be in a unified geographic coordinate system, and then due to uncertainty of the scanned point clouds, the point clouds cannot correspond to one another in position and quantity even under the registration condition; in order to calibrate errors of the airborne laser scanning system, a corresponding relation between two sets of point clouds is established, the registration point clouds are subjected to grid division, and the mean value of point cloud coordinates in a grid is used as the coordinates of a grid center;
and S9, establishing a calibration mathematical model, solving parameters, and realizing the following steps, after grid division, considering that grid point centers are in one-to-one correspondence, establishing an error equation for each grid point pair, solving a correction number by adopting a least square indirect adjustment method, correcting an initial value, then reestablishing error equation iterative calculation, and judging the correction number calculated each time until the requirement of tolerance is met.
In this embodiment, in S1, cycle slip detection and recovery of GNSS data, establishing an observation equation for observation data of two adjacent epochs is as follows:
order to
Then equation (1) has the minimum variance while equation (2) transforms to:
if there is no cycle slip, because B 1 ，B 2 The element is similar to the element, the equation (3) has smaller variance, and if the variance is not close to zero, cycle slip is indicated; the cycle slip restoration method can firstly carry out positioning calculation by utilizing 4 continuous non-cycle slip satellite observation values, and then obtains the cycle slip size through positioning back calculation.
In this embodiment, in S2, the GNSS data difference processing is performed to respectively establish an observation equation for the carrier phase observation values of the airborne data and the reference station data:
and subtracting corresponding terms on the left side and the right side in the formula to obtain a single-difference observation equation:
when a plurality of satellites are observed, one satellite is selected as a reference, and the homodyne observation equation of the other satellites and the homodyne observation equation of the reference satellite are subtracted:
in the above formula
Is the observed value of carrier phase, lambda is the carrier wavelength, N is the whole-cycle unknown number, C is the speed of light, dt is the clock error parameter, V ion Is ionospheric error, V trop The troposphere error is represented by i, j is an airborne station and a base station, and p, q are numbers of observation satellites; equation (6) is the difference processing result.
In this embodiment, in S3, the integer ambiguity of the GNSS double-difference observation equation is determined, and after double-difference processing, many observation errors, including receiver clock difference, satellite orbit error, and ionosphere and troposphere error, are eliminated, so that the integer ambiguity can be conveniently solved by using a search or filtering method.
In this embodiment, in S4, the combination of the difference result and the IMU data first measures the rotation angular velocity of the carrier with respect to the inertial space using the IMU gyroscope, and calculates the coordinate transformation matrix of the carrier coordinate system with respect to the navigation computation coordinate system; then, the specific force observation measured by the accelerometer is converted into a navigation calculation coordinate system, and the navigation calculation coordinate system is correspondingly corrected; and finally, obtaining a speed parameter through primary integration, obtaining a position parameter through secondary integration, and extracting an attitude angle of the carrier relative to a calculation coordinate system through a coordinate transformation matrix.
In this embodiment, in S7, the point cloud registration of the overlapping area is performed according to a coarse registration process and a precise registration process, and the specific steps are as follows: a) Extracting geometric characteristics such as straight lines, planes, curved surfaces and the like from the two sets of point cloud data in the overlapped area, and completing coarse registration of the point cloud data by using the characteristics; b) On the basis of rough registration, one set of point cloud is taken as a reference, the closest point to the point cloud is searched in the other set of reference point cloud, and all corresponding point pairs are found; c) Calculating registration transformation parameter rotation R and translation T according to rigid body transformation criterion; d) Carrying out coordinate transformation on the reference point cloud by using the conversion parameters to obtain a new reference point cloud; e) Iterating the new reference point cloud from the step b, and repeating the steps b-d; f) And judging the difference value of the corresponding points of the reference point cloud and the reference point cloud, and finishing the calculation and finishing the registration when the difference value is smaller than a threshold value.
In this embodiment, in S8, the point cloud after registration is only the closest overall, but due to the number and distribution of the points, the points cannot meet the one-to-one correspondence, and for system error calibration, a grid is divided in the point cloud overlapping area after registration; the method comprises the steps of firstly obtaining the total size of point cloud data in X, Y and Z directions, then evenly dividing the three directions into three equal parts to obtain a 3X 3 grid, selecting the point cloud data most adjacent to the grid points, and calibrating, so that the number of points used for calibration is guaranteed, and the points are uniformly distributed.
In this embodiment, in S9, a calibration mathematical model is established, and when the system error of the laser scanner is considered, the geographic coordinates of the two sets of point clouds are calculated as:
when scanning the same target, equations (7) and (8) should be equal, i.e.:
the system error parameters (Δ x, Δ y, Δ z) and R required for solution are included in the formula (9) x Three rotation angles of; according to the process S8, after point cloud registration, the corresponding relation of two sets of point clouds can be obtained, and an error equation, namely a calibrated mathematical model, is established by using coordinate data on 27 grid points and using the formula (9);
in the present embodiment, in S9, the solution of the system error parameter cannot be directly solved since equation (9) is a nonlinear equation, but R is x The three rotation angles in (1) are generally small angles, so that R can be removed x The approximate expression of (c) is:
the formula (10) is taken into the formula (9), and a linear equation about the unknown number can be obtained after the formula (10) is expanded; in addition, since the number of unknowns to be solved is only 6, and the number of equations to be established is 27, the least square method is adopted to obtain the optimal solution of the parameters during solving.
When the method is used, the point cloud registration is firstly carried out by utilizing the overlapped data between the adjacent flight zones, then the registered point cloud data is subjected to grid division, and the airborne laser scanning system error is calibrated by utilizing the grid center adjacent coordinates obtained by two flights. The method does not need to independently arrange a control field, is simple and easy to implement, can improve the automation of system error calibration, reduces the operation time and improves the operation efficiency.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (9)
1. An error calibration method of an airborne laser scanning system is characterized by comprising an error calibration step,
s1, detecting and repairing cycle slip of GNSS data, wherein the GNSS data comprise airborne data and ground reference station data, and before positioning is completed by using the data, the cycle slip is detected and repaired on a carrier phase observation value, so that the integrity of the data is ensured; adopting ambiguity auxiliary bridging method to carry out cycle slip detection and repair, assuming that the ambiguity of the previous epoch enables the positioning to reach smaller variance, then the ambiguity of the group also meets the next epoch, so the cycle slip can be judged according to whether the positioning variance of the next epoch approaches zero or not;
s2, performing differential processing on the GNSS data, converting the airborne GNSS data and the ground reference station GNSS data into a standard data exchange format, calculating the difference of the observation values of the synchronously observed satellites, selecting the satellite with the largest altitude angle as a reference satellite on the basis, and calculating the difference of the observation values of other satellites and the observation values of the reference satellite to obtain a double-difference observation value;
s3, determining the integer ambiguity of the GNSS double-difference observation equation, wherein the carrier phase observation value is needed to be adopted for realizing high-precision positioning, the determination of the integer ambiguity is a key problem, the double-difference observation value is formed on the basis of cycle slip detection, the integer unknown number is determined by adopting a recursive filtering mode, and the high-precision positioning result is realized;
s4, combining the differential result with the IMU data to realize that the IMU data sampling interval has very high frequency, the error accumulation is fast when the IMU data is used for positioning and attitude determination, and the IMU data is combined with the differential positioning result of the GNSS data to eliminate the error of IMU integral calculation, so that high-precision position and attitude data are obtained;
s5, interpolating a combined result according to the scanning time, so that the following is realized, when a laser system scans surrounding objects, the laser system has corresponding fixed frequency, each scanning point records the scanning time, the time keeps time synchronization with the GNSS/IMU combined result, but the time points cannot correspond to one another, the GNSS/IMU combined result needs to be interpolated according to the time of the scanning points, and the three-dimensional position and attitude information of the airborne platform at the scanning moment is obtained;
s6, calculating the three-dimensional geographic coordinates of the scanning points, and reserving corresponding original data, wherein the geographic coordinates of the scanning points can be calculated according to the laser scanning body coordinates, the three-dimensional coordinates of the flying platform at the scanning moment and a rotation matrix formed by the postures of the flying platform at the scanning moment on the assumption that no system error exists after the positions and the postures of the scanning points at the corresponding moments are obtained;
s7, point cloud registration of the overlapped area is realized, wherein the point cloud registration is actually a process of uniformly integrating point cloud data under different visual angles to a specified coordinate system through translation and rotation rigid body transformation by coordinate transformation calculation; firstly, extracting local geometric features from a point cloud, and quickly calculating the corresponding transformation relation of the point cloud through geometric feature matching; then obtaining an accurate conversion relation through an iterative closest point algorithm on the basis; finally, evaluating a registration result by calculating the mean square error of the transformed point cloud;
s8, after registration, point cloud grid division and grid center coordinate calculation are realized, after point cloud registration of overlapped scanning areas, two sets of point clouds are guaranteed to be in a unified geographic coordinate system, and then due to uncertainty of scanning point clouds, the point clouds cannot correspond to one another in position and quantity even under the registration condition; in order to calibrate errors of an airborne laser scanning system, a corresponding relation between two sets of point clouds is established, grid division is carried out on the registered point clouds, and the mean value of point cloud coordinates in the grid is used as the coordinates of a grid center;
and S9, establishing a calibration mathematical model, solving parameters, and realizing the following steps that after grid division, the centers of grid points are considered to be in one-to-one correspondence, an error equation is established for each grid point pair, a least square indirect adjustment method is adopted to solve the correction number, the initial value is corrected, then the iterative calculation of the error equation is reestablished, and the correction number calculated each time is judged until the tolerance requirement is met.
2. The method for calibrating the errors of the airborne laser scanning system according to claim 1, wherein in the S1, cycle slip detection and recovery of GNSS data, and establishing an observation equation for observation data of two adjacent epochs is:
order to
Then equation (1) has the minimum variance, while equation (2) transforms to:
if there is no cycle slip, because B 1 ，B 2 The medium element is similar, equation (3) has smaller variance, and if the variance is not close to zero, cycle slip is indicated; the cycle slip restoration method can firstly carry out positioning calculation by utilizing 4 continuous non-cycle slip satellite observation values, and then obtains the cycle slip size through positioning back calculation.
3. The method for calibrating errors of an airborne laser scanning system according to claim 1, wherein in S2, GNSS data are differentially processed, and an observation equation is respectively established for carrier phase observation values of airborne data and reference station data:
subtracting corresponding terms of the left side and the right side in the above formula to obtain a single difference observation equation:
when a plurality of satellites are observed, one satellite is selected as a reference, and the homodyne observation equation of the other satellites and the homodyne observation equation of the reference satellite are subtracted:
in the above formula
Is the carrier phase observed value, lambda is the carrier wavelength, N is the whole cycle unknown number, C is the speed of light, dt is the clock error parameter, V ion Is an ionospheric error, V trop The troposphere error is shown, i, j are an airborne station and a base station, p, q are numbers of observation satellites; equation (6) is the difference processing result.
4. The method of claim 1, wherein in S3, the ambiguity of the whole cycle of the GNSS double-difference observation equation is determined, and after double-difference processing, many observation errors, including receiver clock difference, satellite orbit error, and ionospheric and tropospheric errors, are eliminated, so that the ambiguity of the whole cycle can be easily solved by searching or filtering.
5. The method for calibrating errors of an airborne laser scanning system according to claim 1, wherein in S4, the combination of the difference result and the IMU data is performed by firstly measuring a rotation angular velocity of the carrier with respect to an inertial space by using an IMU gyroscope, and calculating a coordinate transformation matrix of the carrier coordinate system with respect to the navigation computation coordinate system; then, the specific force observation measured by the accelerometer is converted into a navigation calculation coordinate system, and corresponding correction is carried out on the navigation calculation coordinate system; and finally, obtaining a speed parameter through primary integration, obtaining a position parameter through secondary integration, and extracting an attitude angle of the carrier relative to a calculation coordinate system through a coordinate transformation matrix.
6. The method for calibrating the errors of the airborne laser scanning system according to claim 1, wherein in the step S7, point cloud registration of the overlapping area is performed according to a coarse registration process and a precise registration process, and the method comprises the following specific steps: a) Extracting geometric characteristics such as straight lines, planes, curved surfaces and the like from the two sets of point cloud data in the overlapping area, and completing coarse registration of the point cloud data by using the characteristics; b) On the basis of rough registration, one set of point cloud is taken as a reference, the closest point to the point cloud is searched in the other set of reference point cloud, and all corresponding point pairs are found; c) Calculating registration transformation parameter rotation R and translation T according to a rigid body transformation criterion; d) Carrying out coordinate transformation on the reference point cloud by using the conversion parameters to obtain a new reference point cloud; e) Iterating the new reference point cloud from the step b, and repeating the steps b-d; f) And judging the difference value of the corresponding points of the reference point cloud and the reference point cloud, and finishing the calculation and finishing the registration when the difference value is smaller than a threshold value.
7. The method for calibrating errors of an airborne laser scanning system according to claim 1, wherein in S8, the registered point clouds are only the closest overall, but the points cannot meet a one-to-one correspondence due to the number and distribution of the points, and for system error calibration, grids are divided in the overlapping area of the registered point clouds; the method comprises the steps of firstly obtaining the total size of point cloud data in X, Y and Z directions, then evenly dividing the three directions into three equal parts to obtain a 3X 3 grid, selecting the point cloud data most adjacent to the grid points, and calibrating, so that the number of points used for calibration is guaranteed, and the points are uniformly distributed.
8. The method for calibrating errors of an airborne laser scanning system according to claim 1, wherein in S9, a calibration mathematical model is established, and when system errors of the laser scanner are taken into account, the geographic coordinates of the two sets of point clouds are calculated as:
when scanning the same target, equation (7) and equation (8) should be equal, i.e.:
the system error parameters (Δ x, Δ y, Δ z) and R required for solution are included in the formula (9) x Three rotation angles of; according to the above process S8, after point cloud registration, a correspondence between two sets of point clouds can be obtained, and an error equation, that is, a calibrated mathematical model, is established using coordinate data on 27 grid points and using equation (9).
9. The method for calibrating errors of an airborne laser scanning system according to claim 8, wherein in S9, the solution of the systematic error parameters cannot be directly solved because equation (9) is a non-linear equation, but R is x The three angles of rotation in (A) are generally small angles, so that R can be removed x The approximate expression of (c) is:
the formula (10) is taken into the formula (9), and a linear equation about the unknown number can be obtained after the formula (10) is expanded; in addition, because the number of unknowns to be solved is only 6, and the number of equations to be established is 27, the least square method is adopted to obtain the optimal solution of the parameters during solving.
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