Automatic positioning method for airborne equipment test points based on fuselage point cloud features

Abstract

The present invention innovatively proposes an automatic positioning method for test points of airborne equipment based on the body point cloud features, belonging to the field of intelligent testing. The method includes: First, obtain the positions of each test point in the body coordinate system, and then use the laser point cloud drawing and mapping clipping technology to realize the extraction of the landing gear point cloud of the aircraft; Then, combine the calculation of the center of gravity coordinates of the landing gear to calculate the rotation and translation vectors, and complete the process of converting the positions of each test point from the body coordinate system to the lidar coordinate system to achieve precise positioning. The present invention takes the aircraft landing gear as an important reference basis. Only by obtaining the positional relationship between the test point and the nose landing gear once, can the automatic positioning of the test point be quickly and accurately completed at any position, thereby promoting the transformation of the test activity subject from humans to test equipment and injecting new vitality into the development of the equipment support field.

Description

Technical Field

[0001] The present invention relates to the field of intelligent testing, and in particular to an automatic positioning method for test points of airborne equipment based on the body point cloud features. Background Art

[0002] With the rapid development of technologies such as unmanned driving and the Internet of Things, people's lives have been greatly enriched and brought many conveniences.

[0003] Currently, in unmanned equipment, to achieve in-situ testing in the field, a common method is to set up a test antenna at the test point. By radiating test signals and receiving interference signals, the warning function and interference effect of the equipment can be evaluated.

[0004] In the above process, the selection of the test point is particularly important because: when using the radiation method for in-situ testing, due to the directivity of the antenna and the attenuation characteristics of electromagnetic waves in space, both the distance and azimuth angle between the test antenna and the equipment antenna need to be determined in order to obtain core performance indicators such as the sensitivity and radiation power of the equipment through corresponding conversions. In an ideal situation, the aircraft can accurately park at a fixed position according to the ground markings, and the test personnel can quickly complete the numerical conversion of the signal link loss based on the pre-calibrated test points. However, in actual operation, affected by multiple factors such as the field environment conditions and task urgency, there are often certain deviations in the parking position of the aircraft. At this time, the test personnel need to re-use measurement tools to determine the test points, which is a time-consuming process and is prone to errors due to human factors; even if the measurement is accurate, its effectiveness is limited to the current test run and cannot provide a reference for subsequent measurement processes. Summary of the Invention

[0005] The present invention innovatively proposes an automatic positioning method for test points based on the characteristics of the aircraft body. This method uses the aircraft landing gear as an important reference basis and combines the point cloud data provided by the lidar to automatically and accurately complete the precise positioning of test points at any position, thereby providing strong technical support for the intelligent transformation of field maintenance and support work.

[0006] To achieve the above object, the technical solution of the present invention is as follows:

[0007] An automatic positioning method for test points of airborne equipment based on the body point cloud features, which includes the following three parts:

[0008] S1. Test point selection and recording;

[0009] S2. Point cloud drawing and mapping clipping;

[0010] S3. Test point position calculation.

[0011] Further, the test point selection and recording in step S1 specifically include:

[0012] S11. According to the position and orientation of the airborne antenna, the tester selects test points one by one to ensure that a clear and stable positional relationship can be established between the test antenna and the airborne antenna;

[0013] S12. Take the nose landing gear of the aircraft as the origin , and construct a plane rectangular coordinate system ; Preferably, the backward direction of the central axis of the fuselage is defined as the positive direction of the U axis;

[0014] S13. With the help of measuring tools, accurately measure the coordinates of each test point in the coordinate system , denoted as .

[0015] Further, the point cloud drawing and mapping clipping in step S2 refer to after drawing the point cloud in the software, using the mapping clipping technology to realize the mapping relationship between the selected point coordinates of the screen window and the actual point cloud coordinates, so that the tester can conveniently select the region of interest (ROI) by using computer peripherals such as a mouse;

[0016] Preferably, the ROI is the three landing gears of the aircraft;

[0017] Further, step S2 specifically includes:

[0018] S21. Point cloud drawing;

[0019] S22. Step-by-step dimensionality reduction and mapping clipping.

[0020] Further, the point cloud drawing in step S21 refers to using OPENGL to complete the rendering and drawing of the point cloud information collected by the lidar. The specific process includes:

[0021] S211. Take the lidar as the coordinate origin , and establish the original point cloud coordinate system , where is the horizontal plane, and the Z axis is perpendicular to upward. Thus, all the point clouds in the lidar field of view in this coordinate system can be obtained. Denote the coordinates of a certain point N as ; Use the pcl::compute3DCentroid function in the point cloud PCL library to calculate the coordinates at its centroid position, denoted as ;

[0022] S212. Translate the point cloud origin to ; For the translated point cloud data, it is necessary to select the maximum absolute value in the X, Y, and Z directions , and then perform normalization processing, which realizes the conversion of the original point cloud into a format suitable for display in the OPENGL interval ( );

[0023] S213. Use the perspective projection matrix provided by OPENGL , and project the normalized point cloud data onto the screen interface for display; Therefore, the coordinates of point U in OPENGL can be expressed by Equation (1) as:

[0024] (1)

[0025] Further, the step-by-step dimensionality reduction and mapping clipping described in step S22 means that since the window and the point cloud dimensions are inconsistent, the point cloud obtained through step S21 processing needs to be temporarily reduced in dimension in two steps to cooperate with window point selection to obtain the landing gear point cloud; Preferably, it can be implemented according to the following process:

[0026] S221. In , let the coordinate z value of each point be 0, then the two-dimensional point cloud distribution on the horizontal plane is obtained; If the coordinate of any point after dimensionality reduction is , then after perspective projection according to Equation (1), its Z coordinate component is a fixed value, denoted as ;

[0027] S222. Use the mouse to select a point A in the point cloud in the window, and record its coordinates in the window as ;

[0028] Preferably, the origin of the window coordinate system is located at the upper left corner of the window, while the origin of the OPENGL coordinate system is set at the center of the window. Let the length and width of the window be , respectively, then the first 2 dimensions of the coordinates of point A in OPENGL are:

[0029] (2)

[0030] S223. Since point A is selected from a point in the interface after perspective projection, there is ; According to the perspective projection matrix , the actual normalized coordinates of point A before perspective projection can be restored:

[0031] (3)

[0032] Combining Equations (1) to (3), the coordinates of point A mapped to are:

[0033] (4)

[0034] Take The first two dimensions, which are the coordinates of the window selection point A after mapping to the original point cloud x and y coordinates;

[0035] S224. Restore the coordinates of each point value, and then set the coordinates of each point x value to 0, that is, the point cloud distribution in the vertical plane ( ) is obtained. After perspective projection by Equation (1), a mapping relationship can be established with the window selection point again, that is, cropping in the third dimension is achieved, thus completing the three-dimensional extraction of the landing gear point cloud in the coordinate system .

[0036] Furthermore, the calculation of the test point positions described in step S3 refers to the process of converting the test point coordinates obtained in step S13 from the airframe coordinate system to the lidar coordinate system , which specifically includes:

[0037] S31. Calculate the translation vector ;

[0038] S32. Calculate the rotation vector ;

[0039] S33. Realize the positioning of the test point: According to the translation vector and the rotation vector , the transformation matrix can be calculated; according to the coordinates of each test point in in step S13 , then through the matrix inverse transformation , the position of the test point in the lidar coordinate system can be obtained , realizing the positioning.

[0040] The calculation of the translation vector described in step S31, the process of which is:

[0041] S311. Regarding the point clouds of the three landing gears as mass points, an isosceles triangle can be formed, where the vertex corresponds to the position of the nose landing gear ;

[0042] S312. In the coordinate system , calculate the difference between the lengths of any two sides of the isosceles triangle. When the difference is the smallest, the common point of the corresponding two sides is the vertex , and in coordinates , this coordinate is the translation vector ;

[0043] S313. Translate to . The coordinate system after translation is denoted as . This coordinate system and the fuselage coordinate system share the origin .

[0044] Further, the process of calculating the rotation vector described in step S32 is as follows:

[0045] S321. For an isosceles triangle, its centroid G is located on the perpendicular line from the vertex to the base. Therefore, the coordinates of point G in are characterized by ; If the coordinates of point G in do not satisfy , it indicates that the same point G has different coordinates in two coordinate systems with a common origin . Therefore, there is a rotational deviation between the two coordinate systems and it needs to be rotationally corrected;

[0046] S322. Set the angle variable , increment it in steps of 0.5°, and rotate clockwise around point. Each time it rotates, record the current and into the array PL until it rotates one full circle;

[0047] S323. Traverse the array PL to find the angle corresponding to the coordinates of point G satisfying and being closest to 0, which is the angle difference between the two coordinate systems; the rotation vector corresponding to this angle is ;

[0048] The present invention proposes an automatic positioning method for test points of airborne equipment based on fuselage point cloud features. This method takes the aircraft landing gear as an important reference basis. Only by obtaining the positional relationship between the test points and the nose landing gear once, it can combine the point cloud data provided by the lidar, and use the stepwise dimensionality reduction mapping and clipping technology and coordinate transformation technology to realize the transformation of the positioning work of the test points of airborne equipment from manual to automatic and from low efficiency to high efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the embodiments. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0050] Figure 1 is the overall flowchart of the present invention;

[0051] Figure 2 is the point cloud map of the actual test scenario in the embodiment of the present invention;

[0052] Figure 3 is the lidar coordinate system in the embodiment of the present invention , test point selection, and airframe coordinate system distribution schematic diagram;

[0053] Figure 4 is the point cloud mapping and cropping result in the embodiment of the present invention;

[0054] Figure 5 is the display diagram of the positioning results of 4 test points in the embodiment of the present invention;

[0055] Figure 6 is the software architecture design diagram involved in the present invention. Specific embodiments

[0056] The following will further elaborate on the present invention in conjunction with the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the relevant invention and not to limit the invention. Additionally, it should be noted that for the sake of description, only the parts related to the relevant invention are shown in the accompanying drawings. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The following will detail the present application with reference to the accompanying drawings and embodiments.

[0057] Appendix Figure 1 is the overall flowchart of an airborne equipment test point automatic positioning method based on airframe point cloud features provided by the embodiment of the present invention. In combination with this drawing, the specific steps of the present invention include:

[0058] S1. Test point selection and recording.

[0059] In this embodiment, first, take the front landing gear of the aircraft as the origin to construct a plane rectangular coordinate system. Then measure the positions of each test point and record the position information. The plane rectangular coordinate system is like a map that can accurately locate the positions of the test points on the aircraft airframe.

[0060] It should be noted that the nose landing gear of the aircraft is a fixed and easily recognizable position. By constructing a coordinate system with this as the origin, a unified and stable reference standard for the positions of the test points can be obtained, providing a clear position reference for subsequent test work and ensuring the accuracy and consistency of the test points.

[0061] S2. Point cloud drawing and mapping clipping.

[0062] This embodiment relates to point cloud drawing. The specific process starts from the data collected by the lidar and converts it into a format that can be displayed on the screen. A three-dimensional coordinate system is established with the lidar as the origin, and the centroid positions of all points in the entire scene are calculated. The origin of the point cloud is translated to the centroid, and the point cloud is normalized to suit the display range of the screen. Finally, the normalized point cloud data is projected onto the screen using the perspective projection matrix. It can be seen that the centroid calculation and translation operations can make the point cloud data more concentrated. Normalization and perspective projection can make the point cloud data meet the requirements of screen display and facilitate user interaction.

[0063] This embodiment also realizes the purpose of step-by-step dimensionality reduction and mapping clipping, which is to reverse the corresponding positions in the actual point cloud from the selected points on the screen. Specifically, first, the coordinate values of the point cloud in a certain direction are set to zero, so as to reduce the three-dimensional point cloud to a two-dimensional plane. Then, a point is selected in the screen window by the mouse, and using the inverse transformation of the perspective projection matrix, the selected point on the screen is restored to the position in the actual point cloud. Finally, this process is repeated to complete the clipping in the third dimension, so as to extract the point cloud of the landing gear.

[0064] As an implementation manner of this embodiment, the point cloud drawing and mapping clipping described in step S2, which completes the extraction of the point cloud of the aircraft landing gear, specifically includes the following steps:

[0065] S21. Point cloud drawing.

[0066] The point cloud drawing described in step S21 refers to using OPENGL to complete the rendering and drawing of the point cloud information collected by the lidar. Its specific process includes:

[0067] S211. Taking the lidar as the coordinate origin , establish a point cloud coordinate system , and record the coordinates of a certain point N as ; use the pcl::compute3DCentroid function in the point cloud PCL library function to calculate the centroid position in the entire scene, denoted as ;

[0068] S212. Translate the origin of the point cloud to ; select the maximum absolute value of the point cloud in the X, Y, and Z directions , then perform normalization to convert the original point cloud into the format for display in the OPENGL interval;

[0069] S213. Use the perspective projection matrix provided by OPENGL , and project the normalized point cloud data onto the screen interface for display; among them, the coordinates of point N in OPENGL are:

[0070] (1).

[0071] S22. Step - by - step dimensionality reduction and mapping clipping.

[0072] The step - by - step dimensionality reduction and mapping clipping described in step S22 realizes the mapping relationship between the selected point coordinates in the screen window and the actual point cloud coordinates to obtain the landing gear point cloud. The specific process is as follows:

[0073] S221. In , let the coordinate z value of each point be 0, then the point cloud can be dimensionally reduced to be distributed on the horizontal coordinate system ; if the coordinate of any point after dimensionality reduction is , then after perspective projection according to formula (1), its Z - coordinate component is a fixed value, denoted as ;

[0074] S222. Use the mouse to select a point A in the point cloud in the interface window, and record its coordinates in the window as ; assume that the length and width of the window are respectively, and the OPENGL coordinate system is located in the center of the window, then the first 2 dimensions of the coordinates of point A in OPENGL are:

[0075] (2)

[0076] S223. Since point A is selected from a point in the interface after perspective projection, there is ; according to the perspective projection matrix , the actual normalized coordinates of point A before perspective projection can be restored:

[0077] (3)

[0078] Combining formulas (1) - (3), the coordinates of point A mapped to are:

[0079] (4)

[0080] Take The first two dimensions are the coordinates after the window selection point A is mapped ;

[0081] S224. Restore the z values of each point in the point cloud, and then set the x value of each point coordinate to 0, that is, the distribution of the point cloud reduced to the vertical plane is obtained. After perspective projection by OPENGL, a mapping relationship is established with the window selection point again, that is, clipping in the third dimension is realized, so as to complete the three-dimensional extraction of the landing gear point cloud in the coordinate system .

[0082] The step-by-step dimensionality reduction in this embodiment can simplify the calculation and reduce the complexity of data processing. The mapping relationship between the screen selection point and the actual point cloud coordinates enables the user to directly complete the point cloud extraction through interface operations. The three-dimensional point cloud is gradually reduced to a two-dimensional plane by using mathematical methods, and then the position information in the original point cloud is restored through inverse transformation, so as to realize accurate point cloud extraction.

[0083] S3. Calculate the test point positions.

[0084] In this embodiment, in order to convert the test points from the airframe coordinate system to the lidar coordinate system, the translation vector between the two coordinate systems is calculated first. The specific method is to regard the three landing gear point clouds as mass points, form an isosceles triangle, and find the position of the front landing gear corresponding to the vertex. By calculating the case where the difference in the side lengths of the isosceles triangle is the smallest, the position of the front landing gear can be determined, and then the translation vector can be obtained.

[0085] Since there may be a rotational deviation between the airframe coordinate system and the lidar coordinate system, a rotation vector needs to be calculated for correction. The specific method is to find the centroid of the isosceles triangle and adjust the coordinate system through rotation operations so that the positions of the centroid in the two coordinate systems are the same. The last step is to construct a transformation matrix according to the translation vector and the rotation vector to convert the test points from the airframe coordinate system to the lidar coordinate system. Through matrix inverse transformation, the position of the test points in the lidar coordinate system can be obtained.

[0086] The calculation of the test point positions described in step S3 of this embodiment refers to the process of converting the test point coordinates obtained in step S1 from the airframe coordinate system to the lidar coordinate system , which specifically includes:

[0087] S31. Calculate the translation vector .

[0088] The calculation of the translation vector described in step S31, the process of which is:

[0089] S311. Treat the point clouds of the three landing gears as mass points to form an isosceles triangle, where the vertex corresponds to the position of the nose landing gear. ;

[0090] S312. In the coordinate system , calculate the difference between the lengths of any two sides of the isosceles triangle. When the difference is the smallest, the common point of the corresponding two sides is , and the coordinate of in can be obtained. This coordinate is the translation vector ; ;

[0091] S313. Denote the coordinate system after translating to as . This coordinate system shares the origin with the fuselage coordinate system . . ;

[0092] S32. Calculate the rotation vector .

[0093] The process of calculating the rotation vector described in step S32 is as follows:

[0094] S321. For the isosceles triangle, its centroid G is located on the perpendicular line from the vertex to the base. Then the coordinate of point G in the fuselage coordinate system has the characteristic of ; if the coordinate of point G in does not satisfy , it indicates that the same point G has different coordinates in two coordinate systems with a common origin . There is a rotation deviation between the two coordinate systems, and rotation correction is required; ;

[0095] S322. Rotate step by step clockwise around point. Each time it rotates, record the current rotation angle and the coordinate of point G , and store them in the array PL until it rotates one full circle;

[0096] S323. Traverse the array PL to find the angle corresponding to the situation where the coordinate of point G satisfies and is closest to 0. This is the angle between the two coordinate systems; the rotation vector corresponding to this angle is ; .

[0097] S33. Realize the positioning of the test point: According to the translation vector and the rotation vector , the transformation matrix can be calculated ; For each test point coordinate recorded in step S1 , through the matrix inverse transformation , the position of the test point in i.e., the position in the lidar coordinate system can be obtained, realizing positioning.

[0098] It can be seen that in this embodiment, the translation vector is determined by an isosceles triangle, avoiding the complex coordinate matching process. Ensuring the accurate relative position relationship between the two coordinate systems. Using geometric relationships and distance calculations to find the translation deviation between the two coordinate systems, thereby determining the translation vector. Through rotation correction, the angular deviation between the two coordinate systems is eliminated. Ensuring that the test points will not be displaced due to rotation errors during the conversion process. Moreover, through rotation operations and the matching of the centroid positions, the angular deviation between the two coordinate systems is adjusted, thereby achieving precise alignment. The use of the transformation matrix makes the conversion between coordinate systems efficient and accurate.

[0099] In this embodiment, an Advantech EPC-301 embedded industrial computer and a LIVOX AVIA lidar are used to form a test platform, which is placed about 15 m away from the aircraft's center of gravity; after the lidar irradiates the aircraft fuselage to obtain point cloud data, the attached Figure 2 shown actual test scene point cloud map is drawn, including objects such as the aircraft, the ground, and buildings. Combining this scene, the automatic positioning of test points is gradually realized according to steps S1~S3. The specific process is as follows:

[0100] (1) For the test point selection described in step S1, in this embodiment, a total of 4 test points are selected based on the local equipment of the aircraft, denoted as , and their distribution schematic diagram in the fuselage coordinate system is as shown in Figure 3 . The specific position and angle information of each point are recorded in Table 1. Table 1 is the position and angle information of each test point in the fuselage coordinate system .

[0101] Table 1

[0102]

[0103] (2) When the fuselage point cloud data is obtained, the software will execute the point cloud drawing and mapping clipping described in step S2 to obtain the point cloud of the landing gear area; the specific constraint interval is:

[0104] (5)

[0105] That is, the interval of [12, 45, 14.25] is removed in the X direction, the interval of [1.72, 3.75] is removed in the Y direction, and the interval of [-0.18, 0.73] is reserved in the Z direction. The unit of the interval is m. The point cloud of the landing gear obtained by cropping is as Figure 4 shown; regarding the three landing gears as mass points, an isosceles triangle O’BC can be formed; point O’ represents the front landing gear, and points B and C represent the two rear landing gears respectively.

[0106] (3) Taking the coordinates of each point listed in Table 1 as known quantities and executing the test point position calculation method described in step S3, the exact coordinates of the 4 test points in this embodiment can be obtained in only a very short time, that is, the point positioning work is completed. The results are shown in the column of "Measured Coordinates / m" in Table 2. Table 2 gives the test point positioning and error comparison results. That is, the point positioning work is completed, and the results are shown in the column of "Measured Coordinates / m" in Table 2. Table 2 gives the test point positioning and error comparison results.

[0107] Table 2

[0108]

[0109] Figure 5 This is a result display diagram of the automatic positioning of test points of an airborne equipment based on the point cloud features of the fuselage provided by the embodiment of the present invention. It can be clearly observed in the figure that the distribution of the four test points around the landing gear, and at the same time, the coordinates of each point in are also listed in the Figure 5 table;

[0110] In addition, Figure 5 the table also provides angle information, and its meaning is specifically illustrated by taking point as an example: at the beginning, the test platform is located at , and its positive direction, that is, the test platform direction, is ; after calculating the position (15.45, 9.73) of in the coordinate system through step S3, the platform first rotates counterclockwise by 32°, so that the positive direction points to , and then advances to . After that, in order to make the platform face the equipment antenna, it is necessary to initially make the test platform face the front landing gear . Therefore, it is necessary to rotate counterclockwise by 239° again with itself as the axis. In this way, the sum of the two rotation amounts is 271°, which explains the origin of 32°, 239°, and 271° in the table.

[0111] To ensure the accuracy and reliability of the positioning results, error analysis is also carried out in this embodiment. For this purpose, after completing the test point selection in step S1, 4 marks are set for each test point on the ground. Subsequently, using measuring tools, the positions of these 4 marks in The coordinates in the coordinate system were used as standard values and compared with the measured values obtained in this embodiment. The results are listed in Table 2. It can be seen from Table 2 that the positioning results of the 4 test points obtained in this embodiment are relatively accurate. The absolute distance error between them and the standard value is controlled within the order of 10 cm, and the relative error does not exceed 1%, fully verifying the accuracy and reliability of the method involved in the present invention.

[0112] To implement the above steps, this application has also developed a set of control software with rich functions. Refer to Figure 6 As shown, it includes an interface layer (user interface: QT framework, OPENGL rendering: VAO, VBO), an application layer (data acquisition - point cloud processing - point selection mapping - cutting and cropping - coordinate transformation - test point calibration), a bottom - layer dependency layer (Ubuntu18.04, ROS Melodic), and a hardware layer (LIDAR, GPU, CPU). This software runs under the UBUNTU18.04 operating system and is built and compiled based on the Qt5.9 development environment. The interface design of the software is clear and has strong interactivity. Its core functions include the drawing of three - dimensional point clouds, mapping and cropping, and the execution of point position analysis and measurement algorithms. These functions together form the basis for realizing the automatic positioning of test points involved in the present invention. In terms of software architecture design, in order to obtain and process lidar data in real - time, the software integrates the ROS (Robot Operating System) distributed communication architecture. By establishing nodes and subscribing to lidar - related topics, the software can receive and process point cloud data in real - time. At the same time, with the help of GPU acceleration technology in OPENGL, the software realizes the smooth rendering of 240,000 point cloud data per second in a three - dimensional space, providing important support for subsequent position calculation. In addition, in order to ensure the efficient operation of each algorithm, the software allocates specific threads for the operations at each stage. By calling independent CPU resources, it ensures that the main interface thread can continuously respond to input and output.

[0113] The beneficial effects of the above embodiments include:

[0114] 1. Utilizing the high - precision point cloud data provided by the lidar, through mapping and cropping and coordinate system transformation, the automatic positioning of test points is realized;

[0115] 2. Simplifying the operation process, while significantly reducing the repeatability and error of manual measurement, and remarkably improving the efficiency and accuracy of in - situ field tests;

[0116] 3. Providing key technical support for the intelligent transformation of maintenance and support work, and promoting the technological progress of the industry;

[0117] 4. Providing a feasible solution for the test platform with an antenna to complete preliminary alignment, and expanding the application scope.

[0118] In summary, the embodiments of the present invention have shown significant beneficial effects in aspects such as accurately positioning test points, improving measurement efficiency, and application expansion.

[0119] The above has specifically described the preferred embodiments of the present invention. However, the present invention is not limited to the described embodiments. Those skilled in the art can make equivalent deformations or substitutions without departing from the spirit of the present invention. These equivalent deformations and substitutions are all included within the scope defined by the claims of this application.

Claims

1. An automatic positioning method for test points of airborne electronic countermeasure equipment based on the characteristics of fuselage point cloud, characterized in that It includes the following three steps: S1. Test point selection and recording: Use the nose landing gear of the aircraft as the origin , and construct a plane rectangular coordinate system ; Measure the coordinates of each test point , and record them as ; S2. Point cloud drawing and mapping clipping to complete the extraction of the point cloud of the aircraft landing gear, which specifically includes the following steps: S21. Point cloud drawing; S22. Step-by-step dimensionality reduction and mapping clipping; The step-by-step dimensionality reduction and mapping clipping realizes the mapping relationship between the selected point coordinates in the screen window and the actual point cloud coordinates to obtain the landing gear point cloud. The specific process is as follows: S221. In , let the coordinate values of each point z be 0, then the point cloud can be reduced in dimension to be distributed on the horizontal coordinate system ; if the coordinate of any point after dimension reduction is , then after perspective projection according to Equation (1), its Z coordinate component is a fixed value, denoted as ; S222. Select a point A in the point cloud using the mouse in the interface window, and record its coordinates in the window as ; Assume the length and width of the window are respectively , and the OPENGL coordinate system is located in the center of the window. Then, the first two dimensions of the coordinates of point A in OPENGL are: （2） S223. Since point A is selected from a point on the interface after perspective projection, there is ; According to the perspective projection matrix , the actual normalized coordinates of point A before perspective projection can be restored : （3） Combined with equations (1) to (3), point A is mapped to coordinates of as follows: （4） Take The first two dimensions of are the coordinates after the window selection point A is mapped S224. Restore the z values of each point in the point cloud, and then set the x coordinate values of each point to 0, that is, the distribution of the point cloud reduced to the vertical plane is obtained. After perspective projection by OPENGL, a mapping relationship is established with the selected points in the window again, that is, the clipping in the third dimension is realized, so as to complete the three-dimensional extraction of the landing gear point cloud in the coordinate system; S3. Test point position calculation.

2. The automatic positioning method for test points of airborne electronic countermeasure equipment based on fuselage point cloud features according to claim 1, wherein The point cloud drawing described in step S21 refers to using OPENGL to complete the rendering and drawing of the point cloud information collected by the lidar. The specific process includes: S211. Take the lidar as the coordinate origin , and establish a point cloud coordinate system . Denote the coordinates of a certain point N in it as ; Use the pcl::compute3DCentroid function in the point cloud PCL library function to calculate the centroid position in the entire scene, denoted as ; S212. Translate the origin of the point cloud to and select the maximum absolute value of the point cloud in the X, Y, and Z directions . Then perform normalization processing to convert the original point cloud into the format for display in the OPENGL interval; S213. Use the perspective projection matrix provided by OPENGL , and project the normalized point cloud data onto the screen interface for display; among them, the coordinates of point N in OPENGL are as follows: （1）。 3. The automatic positioning method of test points for airborne electronic countermeasure equipment based on fuselage point cloud features according to claim 1, characterized in that The test point calculation described in step S3 refers to the process of converting the test point coordinates obtained in step S1 from the airframe coordinate system to the lidar coordinate system . Specifically, it includes: S31. Calculate the translation vector ; S32. Calculate the rotation vector ; S33. Implement test point positioning: According to the translation vector and the rotation vector , the transformation matrix can be calculated; the coordinates of each test point recorded in step S1 , through the matrix inverse transformation , can obtain the position of the test point in , that is, the position in the lidar coordinate system , to achieve positioning.

4. The automatic positioning method for test points of airborne electronic countermeasure equipment based on fuselage point cloud features as claimed in claim 3, wherein The calculation of the translation vector described in step S31 , and the process is as follows: S311. Treat the point clouds of the three landing gear as mass points to form an isosceles triangle, where the vertex corresponds to the position of the nose landing gear ; S312. In the coordinate system , calculate the difference between any two side lengths of an isosceles triangle. When the difference is the smallest, the common point of the corresponding two sides is . It can be obtained that in the coordinates . This coordinate is the translation vector ; S313. Translate to . Denote the coordinate system after translation as . This coordinate system shares the origin with the airframe coordinate system .​​​​​​​​​​ 5. The automatic positioning method for test points of airborne electronic countermeasure equipment based on the fuselage point cloud features according to claim 4, wherein, The calculation of the rotation vector described in step S32 , and the process is as follows: S321. For an isosceles triangle, its centroid G is located on the perpendicular line from the vertex to the bottom edge. Then point G is in the fuselage coordinate system. Coordinates in Features ; If point G is Coordinates Dissatisfied , it means that the same point G is at two common origins The coordinates in the coordinate system are different, and there is a rotation deviation between the two coordinate systems, which needs to be rotated and corrected; S322. Rotate step by step clockwise around point. Each time a rotation is made, record the current rotation angle and the coordinates of point G , and store them in the array PL until a full rotation is completed; S323. Traverse the array PL to find the angle corresponding to the coordinates of point G that satisfy and is closest to 0 , which is the angle difference between the two coordinate systems ; the rotation vector corresponding to this angle is .

Images