Laser radar station planning method for aircraft profile automatic scanning

Abstract

The invention relates to the technical field of aircraft profile automatic scanning station planning, and solves the problems that in the laser radar deployment process during aircraft profile scanning, laser radar station deployment depending on experience is low in efficiency, excessive scanning and missing scanning are caused, and redundant stations are increased. The laser radar station planning method comprises the steps that the relation between the scanning angle and distance of a laser radar and the scanning error is analyzed; based on an improved region growing algorithm, the scanning points are classified, and initial stations are obtained; circularly optimizing the scanning station and the scanning area according to the scanning error and the scanning shielding rate; and re-dividing the unreasonable scanning points and areas to form a final laser radar station. According to the invention, through scanning error analysis, the scanning station and the scanning area of the laser radar are optimized. While the scanning precision and the scanning integrity are ensured, the number and the redundancy of scanning stations are reduced, and the automation of airplane profile scanning is conveniently realized.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic scanning station planning for aircraft shapes, and particularly to a lidar station planning method for automatic scanning of aircraft shapes. Background Art

[0002] Aircraft shape scanning is an important means for aircraft inspection. By scanning the point cloud, the assembly errors, surface defects, skin gaps and steps of the aircraft are detected, so as to find out the surface defects of the aircraft and ensure the aerodynamic performance, stealth performance and safety performance during flight.

[0003] As a three-dimensional measurement device, lidar has the advantages of non-contact measurement, high precision, wide scanning range, etc., and is widely used in the aircraft shape scanning operation. However, relying on manual deployment of lidar stations not only has low efficiency, but also the station allocation based only on experience is unreasonable, resulting in low scanning accuracy, problems such as over-scanning and missed-scanning; at the same time, increasing the number of lidar deployments leads to a large amount of data, which is not easy to process. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a lidar station planning method for automatic scanning of aircraft shapes, which solves the problems of low efficiency, over-scanning and missed-scanning, and adding redundant stations in the process of lidar deployment during aircraft shape scanning, greatly improving the efficiency of aircraft shape scanning to achieve automatic scanning of aircraft shapes.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: A lidar station planning method for automatic scanning of aircraft shapes, the method includes the following steps: S1. Measure the scanning errors of the lidar at different scanning distances and scanning angles, and obtain the scanning error relationships between the scanning errors and the scanning distances and scanning angles respectively; S2. Adaptively adjust the sampling density of the scanning points according to the curvature to form a set of scanning points , classify the scanning points using an improved region growing algorithm to obtain multiple scanning regions, and generate an initial scanning station for each scanning region to form a set of scanning stations S ; S3. Calculate the total error of each scanning point under all initial scanning stations according to the scanning error relationship, re-divide the scanning points to obtain new scanning regions and update them to the set of scanning stations S ; optimize and re-divide the scanning stations according to the new scanning regions and update them to the set of scanning points ; S4. Put the scanning points that cannot be scanned by each scanning station into a temporary group and in the temporary group perform quadratic programming to obtain the final programming result, which is used to control the automatic scanning equipment to reach the specified position for the external shape scanning of the aircraft.

[0006] Furthermore, in step S1, the specific process includes the following steps: S11. Use lidar to scan a standard part with a scanning distance of A meters and a scanning angle of 0°, and use the obtained scanning data as a benchmark; S12. Place the standard part at a distance of A + 2k meters from the lidar, and use the scanning data with a scanning angle of 0° as the first group of experimental data, where ; S13. Then place the standard part at a distance of A meters from the lidar, and use the scanning data with a laser incident angle of (0 + 5 f )° as the second group of experimental data, where ; S14. Analyze the first group of experimental data to obtain the relationship between the scanning distance error and the scanning distance as: ; where, and are both known parameters obtained from actual experiments; S15. Analyze the second group of experimental data to obtain the relationship between the scanning angle error and the scanning angle as: ; where, is the scanning error of the standard part at a distance of A meters from the lidar with a scanning angle of 0°.

[0007] Furthermore, in step S2, the specific process includes the following steps: S21. Based on the surface curvature K of the aircraft digital model, determine the row spacing parameter of the sampling path of the row cutting method, and obtain the sampling density for regulating the sampling scan points on the aircraft surface. The calculation formula is: ; where, represents the sampling density at the sampling point , is the th sampling point; and are the minimum and maximum sampling densities respectively; and are the minimum and maximum curvatures of the aircraft digital mock-up surface, respectively; is the curvature influence coefficient; is the natural constant; is the curvature of the aircraft digital mock-up surface at the sampling point ; S22. Obtain the three-dimensional coordinates and unit normal information of all scanned points and put them into the scanned point set , and number each scanned point; S23. Use the improved region growing algorithm to classify the scanned points, and divide the scanned regions according to the normal vectors and curvatures of the scanned points; S24. Calculate the position center and the normal center of each scanned region, and the calculation formulas are: ; ; where, are the scanned point coordinates, , is the number of scanned points in the current scanned region; is the normal unit vector of the th scanned point; S25. Generate the laser tracker station space according to the reachable positions of the laser tracker in the workspace , and offset along the position center of the scanned region towards the normal center until the initial scan stations are generated within the range of the laser tracker station space ; S26. Obtain the three-dimensional coordinates of all initial scan stations and number them, and store the data in the scan station set S .

[0008] Furthermore, in step S23, the specific process includes the following steps: S231. Select the scanned point with the lowest curvature as the initial seed point, and judge whether the included angle between the normal vectors of the adjacent points around the initial seed point and the normal vector of the initial seed point is less than the threshold ; If it is less than, merge the adjacent point into the current region, otherwise do not merge; S232. Judge the curvature of the adjacent points in the current region. If the curvature is less than the threshold , then take this point as the new seed point; S233. Repeat the above growth process, and name the scanned points merged into the current region as , is the number of the current area, named in the order of area growth, is the current area the th point added to the current area; S234. Real-time judge the number of adjacent points and the threshold size; If the number of adjacent points is always less than the threshold during the growth process, continue the current growth process until no new seed points are generated; If the number of adjacent points exceeds the threshold during the growth process, project the scanned points of the current area onto a plane perpendicular to the normal direction of the area average, and generate an OBB bounding rectangle; S235. Judge the area of the OBB bounding rectangle; If the rectangle area exceeds the maximum allowable area , stop growing; If not, make the growth direction grow towards the shorter side of the rectangle until the rectangle area reaches the maximum allowable area and then stop growing.

[0009] Furthermore, in step S3, the specific process includes the following steps: S31. Calculate the distance between the scanned point and the current scanning position , and the calculation formula is: ; where, is the coordinate of the scanned point, , is the number of scanned points; is the coordinate of the initial scanning position, , is the number of the initial scanning position; S32. Calculate the scanning incident angle between the scanned point and the initial scanning position , and the calculation formula is: ; ; where, is the normal unit vector of the th scanned point; S33. According to the distance and the scanning incident angle , obtain the total error of the scanned point under the current initial scanning position through the scanning error relationship, that is: ; In the formula, is the scanning angle error; is the scanning distance error; S34. According to the total error of the scanning points at each initial scanning position , re-divide the scanning points to the initial scanning position that minimizes the total error of this point , and obtain a new scanning area; S35. Calculate a new scanning position that makes the scanning position within the working space of the lidar and minimizes the comprehensive measurement of the sum of the total errors of the scanning points and the occlusion ratio at this scanning position, and then re-divide the scanning points according to the new scanning position; S36. Repeat steps S34 - S35 until the scanning area and the scanning position no longer change, then complete the optimization of the scanning position; S37. Update the new scanning points to the scanning point set , and add the serial number of the scanning position to which the scanning points belong; update the new scanning position to the scanning position set S , and add all the serial numbers of the scanning points at the scanning position.

[0010] Furthermore, in step S35, the optimization function can be expressed as follows: ; Among them, is the sum of the total errors of the scanning points at the new scanning position; and are the weights of the sum of the total errors and the weight of the occlusion ratio respectively; is the number of scanning points that cannot be scanned at the new scanning position.

[0011] Furthermore, in step S4, the specific process includes the following steps: S41. Put the scanning points that will be occluded at each scanning position into the temporary group , calculate the occlusion ratio of the scanning points at each scanning position point, and find the area ε where the occlusion ratio is greater than the threshold ; Cancel the scanning position points in the area , and put the scanning points in the corresponding area into the temporary group ; S42. Perform scanning cone analysis on the scanning points in the temporary group , that is, calculate the standing range of the lidar that can scan the current scanning point; S43. Through the temporary group For the effective scanning cone range of all scanning points, determine the overlapping area between the effective scanning cone range and the working space of the lidar. From this, screen out the optimal intersection space that can cover the most points to be scanned, and select a position within the optimal intersection space that minimizes the sum of total errors as the scanning position for these scanning points; And remove these scanning points from the temporary group ; Put this scanning position into the scanning position set S , and the corresponding scanning points are removed from the temporary group and put into the scanning point set ; S44. Repeat steps S42 - S43 for the remaining scanning points in the temporary group , until the number of scanning points in the temporary group is lower than the threshold , then stop repeating the steps, and subsequently, depending on the importance and quantity of these scanning points, choose to ignore them or perform manual supplementary measurements; S45. Using the final scanning point set and the scanning position set S as the planning results of the scanning area and scanning positions, control the automatic scanning equipment to reach the specified position to perform the external shape scanning of the aircraft.

[0012] Further, in step S42, according to the scanning range of the lidar, the constraint conditions of the scanning cone can be obtained: ; Among them, is the pitch angle of the lidar; is the horizontal angle; is the maximum allowable scanning distance.

[0013] By means of the above technical solution, the present invention provides a lidar position planning method for aircraft external shape automatic scanning, which at least has the following beneficial effects: 1. The present invention solves the problems of low efficiency, multiple scanning and missed scanning, and redundant positions in the process of aircraft external shape scanning caused by the placement of the lidar relying on the experience of workers, and effectively improves the efficiency and automation ability of aircraft external shape scanning.

[0014] 2. The present invention uses an improved region growing algorithm, which can ensure more reasonable division of scanning points, prevent the occurrence of irregular regions or overly large regions, resulting in redundant generated positions or inability to cover the current region. At the same time, a reasonable initial scanning position is provided, thereby reducing the number of loop times in the optimization process and the number of invalid scanning positions.

[0015] 3. The present invention optimizes the scanning area and scanning station by establishing an optimization function. According to the optimization function, it cycles and optimizes to find the optimal scanning station and the corresponding scanning area. Then, through quadratic programming, it ensures the scanning coverage rate and reduces the occlusion rate of the scanning stations, facilitating the realization of the automation of aircraft shape scanning. Description of the Drawings

[0016] The drawings described herein are used to provide a further understanding of the present application and form a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation to the present application. In the drawings: Figure 1 is a flowchart of the method for planning the lidar stations in the present invention; Figure 2 is a division result diagram using the conventional region growing algorithm in the present invention; Figure 3 is a flowchart of the optimization of the scanning area and scanning stations in the present invention. Detailed Embodiment

[0017] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the drawings and specific embodiments. Thus, the implementation process of how the present application uses technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly.

[0018] This embodiment proposes a method for planning lidar stations for aircraft shape automatic scanning. Based on the analysis of the relationship between the lidar scanning angle, scanning distance, and scanning error, first, an adaptive point sampling is performed on the aircraft digital model, and an improved region growing algorithm is used to classify and generate initial scanning stations. Subsequently, considering the scanning error and occlusion rate indexes comprehensively, an iterative optimization algorithm is used to dynamically adjust the station layout. Finally, a secondary planning is performed on the abnormal scanning areas, and the optimal lidar scanning station plan is output, effectively improving the efficiency and automation ability of aircraft shape scanning. As Figures 1 - 3 shown, the method includes the following steps: S1. Measure the scanning error of the lidar at different scanning distances and scanning angles through experiments, and obtain the scanning error relationships between the scanning error and the scanning distance and the scanning angle respectively. The specific process includes the following steps: S11. Use the lidar to scan a standard part with a scanning distance of A meters and a scanning angle of 0°, and use the obtained scanning data as a reference; Place the standard part at a distance of A + 2k meters from the lidar, and use the scanning data with a scanning angle of 0° as the first group of experimental data, where ; S13. Then place the standard part at a distance of A meters from the lidar, and use the scan data with a laser incident angle of (0 + 5 f )° as the second set of experimental data, where ; S14. Analyze the first set of experimental data to obtain the relationship between the scan distance error and the scan distance as: ; where and are both known parameters obtained from actual experiments; S15. Analyze the second set of experimental data to obtain the relationship between the scan angle error and the scan angle as: ; where is the scan error when the standard part is at a distance of A meters from the lidar and the scan angle is 0°.

[0019] S2. Adjust the sampling density of the scan points adaptively according to the curvature to form a scan point set , use the improved region growing algorithm to classify the scan points to obtain multiple scan regions, and generate an initial scan position for each scan region to form a scan position set S . The specific process includes the following steps: S21. Based on the surface curvature K of the aircraft digital model, determine the row spacing parameter of the sampling path of the row cutting method, and obtain the sampling density for regulating the sampling scan points on the aircraft surface. The calculation formula is: ; where represents the sampling density at the sampling point , is the th sampling point; and are the minimum and maximum sampling densities respectively; and are the minimum and maximum curvatures of the aircraft digital model surface respectively; is the curvature influence coefficient; is the natural constant; is the curvature of the aircraft digital model surface at the sampling point .

[0020] S22. Obtain the three-dimensional coordinates and unit normal information of all scan points and put them into the scan point set , and number each scan point.

[0021] S23. Classify the scanned points using an improved region growing algorithm, and divide the scanning area according to the normal line and curvature at that point. When using the traditional region growing algorithm to divide the surface of an aircraft into regions, problems such as those in Figure 2 will occur, that is: due to random growth, the shape of some regions is irregular; due to the small change in curvature of the upper surface, the area of some regions is too large.

[0022] To solve this problem, in this embodiment, the region growing algorithm is improved. The scanned points are classified using the improved region growing algorithm, and the scanning area is divided according to the normal line and curvature at that point. The specific process includes the following steps: S231. Select the scanned point with the lowest curvature as the initial seed point, and determine whether the included angle between the normal line of the adjacent points around the initial seed point and the normal line of the initial seed point is less than the threshold ; If it is less than, merge the adjacent point into the current region, otherwise do not merge; S232. Determine the curvature of the adjacent points in the current region. If the curvature is less than the threshold , then use this point as a new seed point; S233. Repeat the above growth process. During the growth process, name the scanned points merged into the current region as , is the number of the current region, named in the order of region growth, is the current region the th point added to the current region; S234. Continuously determine the number of adjacent points and the threshold ; If the number of adjacent points is always less than the threshold during the growth process, continue the current growth process until no new seed points are generated; If the number of adjacent points exceeds the threshold during the growth process, project the scanned points of the current region onto a plane perpendicular to the average normal of the region and generate an OBB bounding rectangle.

[0023] S235. Determine the area of the OBB bounding rectangle; If the area of the rectangle exceeds the maximum allowable area , stop growing; If it does not exceed, make the growth direction grow towards the shorter side of the rectangle until the area of the rectangle reaches the maximum allowable area Stop growing when

[0024] The present invention uses an improved region growing algorithm, which can ensure a more reasonable division of scanning points, prevent the occurrence of irregular regions or overly large regions, resulting in redundant generated standing positions or failure to cover the current region. At the same time, a reasonable initial scanning standing position is provided, thereby reducing the number of cycles in the optimization process and the number of invalid scanning standing positions.

[0025] S24. Calculate the position center of each scanning region and the normal center , and the calculation formula is: ; ; wherein, is the scanning point coordinate, , is the number of scanning points in the current scanning region; is the th normal unit vector of the scanning point; S25. Generate a laser tracker standing position space according to the positions reachable by the laser tracker in the working space , and offset along the position center of the scanning region towards the normal center until an initial scanning standing position is generated within the range of the laser tracker standing position space ; S26. Obtain the three-dimensional coordinates of all initial scanning standing positions and number them, and store the data in the scanning standing position set S .

[0026] S3. Calculate the total error of each scanning point under all initial scanning standing positions according to the scanning error relationship, re-divide the scanning points to obtain new scanning regions and update them to the scanning standing position set S in, optimize and re-divide the scanning points according to the new scanning regions and update them to the scanning point set in.

[0027] In this embodiment, the total error of each scanning point under all initial scanning standing positions is calculated according to the scanning error relationship, and the scanning points are re-divided to the scanning standing position with the minimum total error to obtain a new scanning region. Then, according to the new scanning point region, a new scanning standing position that minimizes the comprehensive measure of the sum of the total errors and the occlusion ratio of the current type of scanning points is calculated, and the scanning points are re-divided according to the new scanning standing position, and this step is repeated until the scanning point region and the scanning standing position no longer change. The specific process includes the following steps: S31. Calculate the distance between the scanning point and the current scanning standing position, and the calculation formula is: ; Among them, is the coordinate of the scanning point, , is the number of scanning points; is the coordinate of the initial scanning position, , is the number of initial scanning positions.

[0028] S32. Calculate the scanning incident angle between the scanning point and the initial scanning position , and the calculation formula is: ; ; Among them, is the normal unit vector of the th scanning point.

[0029] S33. According to the distance and the scanning incident angle , obtain the total error of the scanning point under the current initial scanning position through the scanning error relationship, that is: ; In the formula, is the scanning angle error; is the scanning distance error.

[0030] S34. According to the total error of the scanning point under each initial scanning position, re-divide the scanning point under the initial scanning position that minimizes the total error of this point to obtain a new scanning area.

[0031] S35. Calculate a new scanning position that makes the scanning position within the working space of the lidar and minimizes the comprehensive measure of the sum of the total errors of the scanning points under this scanning position and the occlusion ratio. Then, re-divide the scanning points according to the new scanning position. The optimization function can be expressed as follows: ; Among them, is the sum of the total errors of the scanning points under the new scanning position; and are the weights of the sum of the total errors and the weight of the occlusion ratio respectively; is the number of scanning points that cannot be scanned under the new scanning position.

[0032] S36. Repeat steps S34 - S35 until the scanning area and the scanning position no longer change, then the optimization of the scanning position is completed.

[0033] S37. Update the new scanning points to the set of scanning points, and add the serial number of the scanning station to which the scanning points belong; update the new scanning station to the set of scanning stations and add all the serial numbers of the scanning points under the scanning station to it. S S4. Put the scanning points that cannot be scanned by each scanning station into a temporary group

[0034] and perform quadratic programming on the temporary group to obtain the final planning result for controlling the automatic scanning equipment to reach the specified position for the external shape scanning of the aircraft. In this embodiment, by putting the scanning points that cannot be scanned by each scanning station into a temporary group, calculating the occlusion ratio under each scanning station, canceling the scanning stations with the occlusion ratio exceeding the threshold, and putting the scanning points under the scanning station into the temporary group; then performing quadratic programming on the temporary group to obtain the final scanning station. The specific process includes the following steps:

[0035] S41. Put the scanning points that will be occluded under each scanning station into the temporary group calculate the occlusion ratio of the scanning points under each scanning station point, and find out the area where the occlusion ratio is greater than the threshold ε . , is the number of areas where the occlusion ratio is greater than the threshold ε . Cancel the scanning station points in the area and put the scanning points in the corresponding area into the temporary group .

[0036] S42. Perform scanning cone analysis on the scanning points in the temporary group , that is, calculate the range of the standing positions of the lidar that can scan the current scanning point. According to the scanning range of the lidar, the constraint conditions of the scanning cone can be obtained: ; where is the pitch angle of the lidar; is the horizontal angle; is the maximum allowable scanning distance.

[0037] S43. Determine the overlapping area between the effective scanning cone range and the working space of the lidar through the effective scanning cone ranges of all the scanning points in the temporary group , select the optimal intersection space that can cover the most points to be scanned from it, and select a point with the minimum sum of total errors in the optimal intersection space as the scanning station for these scanning points; and remove these scanning points from the temporary group Remove from; put this scanning position into the set of scanning positions S In, the corresponding scanning points are removed from the temporary group Remove and put into the set of scanning points In it.

[0038] S44. For the remaining scanning points in the temporary group Repeat steps S42 - S43 until the number of scanning points in the temporary group Is lower than the threshold , then stop repeating the steps, and then select to ignore or manually re - measure according to the importance and quantity of these scanning points.

[0039] S45. Using the final set of scanning points And the set of scanning positions S As the planning result of the scanning area and scanning positions, control the automatic scanning equipment to reach the specified position for the external shape scanning of the aircraft.

[0040] By establishing an optimization function for the scanning area and scanning positions, this invention circularly optimizes according to the optimization function to find the optimal scanning positions and the corresponding scanning areas. Then, through quadratic programming, it ensures the scanning coverage rate and reduces the occlusion rate of the scanning positions, effectively improving the efficiency and automation ability of the external shape scanning of the aircraft.

[0041] By analyzing the scanning error, this invention optimizes the scanning positions and scanning areas of the lidar. While ensuring the scanning accuracy and scanning integrity, it reduces the number and redundancy of the scanning positions, facilitating the realization of the automation of the external shape scanning of the aircraft.

[0042] Those of ordinary skill in the art can understand that all or part of the steps in implementing the methods of the above - mentioned embodiments can be completed by instructing relevant hardware through a program. Therefore, this application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can adopt the form of a computer program product implemented on one or more computer - usable storage media (including but not limited to disk memories, CD - ROMs, optical memories, etc.) containing computer - usable program codes.

[0043] The above - mentioned embodiments have introduced the present invention in detail. Specific examples are used in this article to elaborate on the principle and implementation manner of the present invention. The description of the above - mentioned embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A lidar station layout planning method for aircraft external shape automatic scanning, characterized in that The method includes the following steps: S1. Measure the scanning errors of the lidar at different scanning distances and scanning angles, and obtain the relationships between the scanning errors and the scanning distances and scanning angles respectively; S2. Adaptively adjust the sampling density of the scanning points according to the curvature to form a set of scanning points , classify the scanning points using an improved region growing algorithm to obtain multiple scanning regions, and generate an initial scanning position for each scanning region to form a set of scanning positions S ; S3. Calculate the total error of each scanning point under all initial scanning positions according to the scanning error relationship, re-divide the scanning points to obtain a new scanning area and update it to the scanning position set S In the scanning position set, optimize and re-divide the scanning points according to the new scanning area and update them to the scanning point set In; S4. Put the scan points that cannot be scanned by each scan station into a temporary group and perform secondary planning on the temporary group to obtain the final planning result, which is used to control the automatic scanning equipment to reach the specified position for the external shape scanning of the aircraft.

2. The lidar station planning method according to claim 1, wherein In step S1, the specific process includes the following steps: S11. Use a lidar to scan a standard part with a scanning distance of A meters and a scanning angle of 0°, and use the obtained scanning data as a reference; S12. Place the standard part at a distance of A + 2k meters from the lidar, and use the scan data with a scan angle of 0° as the first set of experimental data, where ; S13. Then place the standard part at a distance of A meters from the lidar, and use the scan data with a laser incident angle of (0 + 5 f )° as the second set of experimental data, where ; S14. Analyze the first set of experimental data to obtain the scanning distance error and the scanning distance The relational expression between them is as follows: ； Among them, and are all known parameters obtained from actual experiments; S15. Analyze the second set of experimental data to obtain the scanning angle error and the scanning angle The relationship between them is as follows: ； Among them, is the scanning error of the standard part at a distance of A meters from the lidar when the scanning angle is 0°.

3. The lidar station planning method according to claim 1, wherein In step S2, the specific process includes the following steps: S21. Determine the row spacing parameter of the row cutting method sampling path based on the surface curvature K of the aircraft digital model, and obtain the sampling density for regulating the sampling scan points on the aircraft surface. The calculation formula is: ； Among them, represents the sampling density at the sampling point ; is the th sampling point; and are the minimum and maximum sampling densities respectively; and are the minimum and maximum curvatures of the aircraft digital mock-up surface respectively; is the curvature influence coefficient; is the natural constant; is the curvature of the aircraft digital mock-up surface at the sampling point ; S22. Obtain the three-dimensional coordinates and unit normal information of all scan points, put them into the scan point set , and number each scan point; S23. Classify the scan points using an improved region growing algorithm, and divide the scan region according to the normal vectors and curvatures of the scan points; S24. Calculate the position center of each scanning area and the normal center , and the calculation formula is as follows: ； ； Among them, is the scanning point coordinate, , is the number of scanning points in the current scanning area; is the th normal unit vector of the scanning point; S25. Generate the laser tracker station space based on the positions reachable by the laser tracker in the working space , and along the position center of the scanning area offset towards the normal center until reaching within the range of the laser tracker station space to generate the initial scanning stations; S26. Obtain the three-dimensional coordinates of all initial scanning stations, number them, and store the data in the scanning station set S .

4. The lidar station layout planning method according to claim 3, wherein In step S23, the specific process includes the following steps: S231. Select the scanning point with the lowest curvature as the initial seed point, and determine whether the angle between the normal of the neighboring points around the initial seed point and the normal of the initial seed point is less than the threshold ; If it is less than, then merge the adjacent point into the current region, otherwise do not merge; S232. Determine the curvature of adjacent points in the current area. If the curvature is less than the threshold , then use this point as a new seed point; S233. Repeat the above growth process, and during the growth process, name the scan points merged into the current region as , is the number of the current region, named in the order of region growth, is the current region The th point added to the current region; S234. Real-time determination of the number of adjacent points and the threshold in terms of size; If the number of neighboring points during the growth process is always less than the threshold , then continue the current growth process until no new seed points are generated; If the number of neighboring points during growth exceeds the threshold , project the scan points in the current area onto a plane perpendicular to the normal of the area average, and generate an OBB bounding rectangle; S235. Judge the area of the OBB bounding rectangle; If the area of the rectangle exceeds the maximum allowable area , growth is stopped; If it does not exceed, the growth direction is set to grow along the shorter side of the rectangle until the area of the rectangle reaches the maximum allowable area and then the growth stops.

5. The lidar station planning method according to claim 1, wherein In step S3, the specific process includes the following steps: S31. Calculate the distance between the scanning point and the current scanning position , and the calculation formula is as follows: ； Among them, are the coordinates of the scanning points, , is the number of scanning points; are the coordinates of the initial scanning positions, , is the number of initial scanning positions; S32. Calculate the scanning incident angle between the scanning point and the initial scanning position , and the calculation formula is as follows: ； ； Among them, is the unit normal vector of the th scanning point; S33. According to the distance and the scanning incident angle , obtain the total error of the scanning point at the current initial scanning station position through the scanning error relationship , that is: ； In the formula, is the scanning angle error; is the scanning distance error; S34. According to the total error of the scanning points at each initial scanning station , re-divide the scanning points to the initial scanning station that minimizes the total error of the point to obtain a new scanning area; S35. Calculate a new scanning position that enables the scanning position to be within the working space of the lidar while minimizing the comprehensive measure of the sum of the total scanning point errors and the occlusion ratio at this scanning position, and then re-divide the scan points according to the new scanning position; S36. Repeat steps S34 - S35 until the scan region and the scanning position no longer change, then the optimization of the scanning position is completed; S37. Update the new scan points to the set of scan points and add the serial number of the scan station to which the scan points belong; update the new scan station to the set of scan stations S and add all the serial numbers of the scan points under the scan station to it.

6. The lidar station layout planning method according to claim 5, characterized in that In step S35, the optimization function can be expressed as follows: ； Among them, is the sum of the total errors of the scanning points at the new scanning position; and are the weights of the sum of the total errors and the weight of the occlusion ratio respectively; is the number of scanning points that cannot be scanned at the new scanning position.

7. The lidar station layout planning method according to claim 1, wherein In step S4, the specific process includes the following steps: S41. Put the scanning points that will be blocked at each scanning position into a temporary group, calculate the occlusion ratio of the scanning points at each scanning position point, and find the area where the occlusion ratio is greater than the threshold value ; ε ;​ Cancel the scanning position points in the area and put the scanning points in the corresponding area into the temporary group ; S42. Perform a scanning cone analysis on the scanning points in the temporary group , that is, calculate the standing position range of the lidar that can scan the current scanning point; S43. Through the temporary group Determine the overlapping area between the effective scanning cone range of all scanning points in the effective scanning cone range and the working space of the lidar, screen out the optimal intersection space that can cover the most points to be scanned, and select a point position with the smallest sum of total errors in the optimal intersection space as the scanning position for these scanning points; And remove these scanned points from the temporary group ; Put this scanned station into the scanned station set S ; Remove the corresponding scanned points from the temporary group and put them into the scanned point set ; S44. For the temporary group repeat steps S42 - S43 for the remaining scan points until the number of scan points in the temporary group is lower than the threshold , then stop repeating the steps, and subsequently select to ignore or manually re - measure according to the importance and quantity of these scan points; S45. Using the final set of scanned points and the set of scanning positions S as the planning results of the scanning area and scanning positions, control the automatic scanning equipment to reach the specified position for the external shape scanning of the aircraft.

8. The lidar station layout planning method according to claim 7, wherein In step S42, the constraint conditions of the scanning cone can be obtained according to the scanning range of the lidar: ； Among them, is the pitch angle of the lidar; is the horizontal angle; is the maximum allowable scanning distance.

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