Target pose measurement system and method based on curved surface features

Abstract

The invention belongs to the field of target pose measurement, and particularly relates to a target pose measurement system and method based on curved surface features. The system comprises a laser emission module, a feature identification module and a processing module. The laser emission module and the feature recognition module are arranged towards a to-be-measured curved surface target, and M feature points are arranged on the to-be-measured curved surface target; the laser emission module is used for emitting N non-parallel laser beams to a to-be-measured curved surface target, N laser spots are formed on the to-be-measured curved surface target, and the mass centers of the N laser spots are not collinear; the view field of the feature recognition module covers all different pose envelope ranges of the to-be-detected curved surface target; the receiving end of the processing module is electrically connected with the sending end of the feature recognition module. According to the invention, the measurement range can be expanded through the expansion of the number of laser beams and the field of view of the detector, so that the pose measurement of an ultra-large array target can be realized, and the cost is low.

Description

Technical Field

[0001] This invention belongs to the field of target pose measurement, specifically relating to a target pose measurement system and method based on curved surface features. Background Technology

[0002] Large antennas have an overall aperture of tens or even hundreds of meters. Their reflective surfaces are usually made up of hundreds or thousands of rigid curved reflective panels with a size of about one meter. Therefore, the reflective surfaces are easily deformed by the external environment, which leads to a reduction in the accuracy of the reflective surfaces and greatly affects working efficiency and performance.

[0003] Currently, the following equipment and methods are mainly used for pose measurement of large antennas: Laser trackers generally use mature optoelectronic equipment, but the required measurement and usage conditions are quite demanding, and cooperative targets need to be set up, making the measurement system quite complex. This often requires human intervention and operation, which is not conducive to long-term stable measurement. When using photogrammetry, a large number of cooperative targets need to be set up on the reflective surface, and a large number of images are needed to complete accurate pose calculation. This greatly increases the computation cycle and makes real-time measurement difficult. Furthermore, for spatial dimensions on the order of hundreds of meters, photogrammetry is extremely difficult to operate.

[0004] Chinese patents CN119044991B, "An Environment-Adaptive Array Target Pose Measurement System and Method," and CN116952129A, "An Attitude Measurement System and Method Based on Target Plane Edge Characteristics," primarily focus on achieving high-precision pose measurement for planar targets. Chinese patent CN110455181B, "A Rapid Pose Measurement System and Method," utilizes a detector to directly capture laser spot information, but it is limited by the size of the target being measured, thus having certain limitations for pose measurement of ultra-large array targets, and the cost is relatively high.

[0005] Therefore, to meet the high-precision pose measurement requirements of curved reflective surfaces in ultra-large array targets, a new pose measurement system and method need to be designed to achieve faster and more accurate pose measurement of curved reflective surfaces, thereby enabling real-time feedback and control of the pose adjustment system. Summary of the Invention

[0006] The purpose of this invention is to solve the technical problems of existing measurement methods being limited by the size of the target being measured and having high measurement costs, and to provide a target pose measurement system and method based on curved surface features.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A target pose measurement system based on curved surface features, which is special in that: It includes a laser emission module, a feature recognition module, and a processing module; The laser emission module and the feature recognition module are respectively positioned facing the target surface to be measured, and the target surface has M feature points; M≥3; The laser emitting module is used to emit N non-parallel laser beams to the target surface to be tested, forming N laser spots on the target surface, and the centroids of the N laser spots are not collinear; N≥3; The field of view of the feature recognition module covers all different pose envelopes of the target surface under test, and is used to acquire two-dimensional images of the target surface under test containing laser spots and feature points; The receiving end of the processing module is electrically connected to the transmitting end of the feature recognition module. It is used to receive the two-dimensional image sent by the feature recognition module and obtain the actual pose change parameters of the target surface under test based on the received two-dimensional image, thereby realizing the target pose measurement.

[0008] Furthermore, it also includes a support platform; the laser emission module and the feature recognition module are respectively installed on the support platform.

[0009] Furthermore, the feature recognition module includes an optical lens and a detector; The object plane of the optical lens is located at the target surface to be tested, and the image plane is located at the target surface of the detector. The field of view of the optical lens covers the entire different pose envelope range of the target surface to be tested, and is used to image the image information of the target surface to be tested onto the target surface of the detector. The detector is used to acquire a two-dimensional image containing laser spots and feature points based on the image information of the surface target to be tested.

[0010] Meanwhile, the present invention also provides a target pose measurement method based on curved surface features, which employs the aforementioned target pose measurement system based on curved surface features, and is characterized by including the following steps: Step 1: Calibrate the N laser beams emitted from the laser emitting module and obtain the equivalent spatial linear equations of the N laser beams; Step 2: Calibrate the M feature points set on the surface target to be tested, and obtain the relative positional relationship and distribution data between each pair of feature points; at the same time, calibrate the geometric parameters of the surface target to be tested, including the shape, size and initial pose of the surface target to be tested; Step 3: The laser beam emitted from the laser emitting module is incident on the surface target to be tested. A two-dimensional image containing the laser spot and feature points is acquired through the feature recognition module. Then, through image correction, the feature plane formed by the feature points on the two-dimensional image is obtained based on the relative positional relationship and distribution data between each pair of feature points, and the two-dimensional projection image formed by the projection points formed by the laser spot projected onto the feature plane. A two-dimensional coordinate system is constructed with any feature point on the two-dimensional projection image as the origin, and the projection coordinates of the projection point and the other feature points on the two-dimensional coordinate system are obtained. Step 4: Based on the equivalent spatial straight line equation of each laser beam and the projected coordinates of each feature point and projection point in the two-dimensional coordinate system, the three-dimensional coordinates of each projection point are calculated using the principle that the length of the same spatial line segment is equal. Step 5: After the pose of the target surface changes, use the same method as in Step 3 and Step 4 to obtain the three-dimensional coordinates of each projection point after the pose change. Step 6: Using the equivalent rigid body transformation method, the change in pose of the target surface under test is equivalently converted into the adjustment of the laser beam direction and the position of the feature recognition module; on this basis, combined with the calibrated geometric parameters of the target surface under test and the three-dimensional coordinates of the projection point after the pose change, an equivalent pose measurement model is established. Step 7: Based on the equivalent pose measurement model, obtain the adjustment amount of the laser beam direction and the position of the feature recognition module, and then use the inverse equivalent rigid body transformation method to restore the adjustment amount of the laser beam direction and the position of the feature recognition module to the actual pose change parameters of the surface target to be measured, thus completing the target pose measurement.

[0011] Furthermore, in step 6, the expression for the equivalent pose measurement model is: =0; ; In the formula: It is a nonlinear residual function; The standard space equation of the surface target to be measured. ; L represents the initial emission point coordinates of the laser beam; T is the translation amount; R is the amount of rotation; The scaling factor for the laser beam; The direction vectors of the N laser beams are pre-calibrated; E is the initial origin of the feature recognition module; The three-dimensional coordinates of the projection point after the pose change; The projection scale factor; Regarding Euler angles , and A function, j = 1, 2, 3, ..., N; In step 7, based on the equivalent pose measurement model, the adjustment amount of the laser beam direction and the position of the feature recognition module is obtained. The adjustment amount includes translation T and rotation R.

[0012] Furthermore, let M feature points be defined as T1, T2, ..., T... M ; In step 3: with feature point T1 as the origin of the two-dimensional coordinate system, the line connecting feature point T1 and feature point T2 is selected as the y-axis of the two-dimensional coordinate system, and a perpendicular line from feature point T1 to the y-axis is drawn as the x-axis of the two-dimensional coordinate system, thus realizing the establishment of the two-dimensional coordinate system.

[0013] Further, In step 1, the equivalent spatial linear equation for the N laser beams is expressed as: ; In the formula: , and Let be the coordinates of any point on the laser beam; , , , , and is a constant in the equation of the equivalent spatial line; The parameters are the variable parameters of the equivalent spatial linear equation.

[0014] The beneficial effects of this invention are: 1. The present invention has a very large measurement range, which facilitates the expansion of measurement applications. The measurement range can be expanded by increasing the number of laser beams and the detector field of view. The expansion of the number of laser beams can be accomplished simply by increasing the number of laser emission modules and through precise calibration. The expansion of the detector field of view can be accomplished by selecting an optical system with a larger field of view and a detector with a larger target surface, or by using multiple detectors. This enables the pose measurement of ultra-large array targets at a low cost.

[0015] 2. The laser emission module and feature recognition module of the present invention are respectively installed on the same support platform, and the emission reference and recognition reference are stable and reliable, which is beneficial to improving measurement accuracy and stability.

[0016] 3. This invention uses non-contact measurement, which has good environmental adaptability and operability, and can realize real-time measurement.

[0017] 4. This invention utilizes the equivalent rigid body transformation method, which simplifies the solution process and improves the measurement speed.

[0018] 5. This invention utilizes characteristic planes to transform the solution of the spatial three-dimensional coordinates of curved laser spots into a more mature solution of the spatial three-dimensional coordinates of planar laser spots, ensuring the feasibility of the measurement. Furthermore, the characteristic planes are fitted using pre-calibrated feature points; using multiple feature points to fit multiple characteristic planes can significantly improve measurement redundancy and solution accuracy.

[0019] 6. This invention incorporates the geometric parameters of the surface target under test into the pose measurement. This method is also compatible with the pose measurement of planar targets, further expanding the application scenarios. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of an embodiment of a target pose measurement system based on curved surface features according to the present invention (processing module is not shown in the figure). Figure 2 This is a schematic diagram of image acquisition and two-dimensional coordinate system definition in an embodiment of a target pose measurement method based on curved surface features according to the present invention; Figure 3 This is a schematic diagram of the target pose measurement system when the target surface pose changes, according to an embodiment of the target pose measurement system based on surface features of the present invention; Figure 4 This is a schematic diagram of an equivalent pose measurement model in an embodiment of a target pose measurement method based on curved surface features according to the present invention; Figure 5 A feature plane projection diagram in an embodiment of a target pose measurement method based on curved surface features according to the present invention.

[0021] The attached figures are labeled as follows: 01 - Target surface to be tested; 1-Laser emission module, 2-Feature recognition module, 3-Support platform. Detailed Implementation

[0022] To make the objectives, advantages, and features of the present invention clearer, the target pose measurement system and method based on curved surface features proposed in this invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The advantages and features of the present invention will become clearer from the following detailed embodiments.

[0023] See Figure 1 This embodiment is a target pose measurement system based on curved surface features, which mainly includes a laser emission module 1, a feature recognition module 2, a support platform 3, and a processing module.

[0024] The laser emitting module 1 includes at least one laser emitting component, capable of emitting N non-parallel laser beams to the target surface under test. The laser beams emitted by the laser emitting module 1 are stable and can be calibrated and fitted to an equivalent spatial linear equation based on the coordinate system of the laser emitting module 1. The N laser beams form N laser spots on the target surface 01 under test, and the centroids of the N laser spots are not collinear; N ≥ 3. In this embodiment, N = 3 is used as an example.

[0025] The feature recognition module 2 includes an optical lens and a detector. The object plane of the optical lens is located at the target surface 01 to be tested, and the image plane is located on the target surface of the detector. The field of view of the optical lens covers the entire different pose envelope of the target surface 01 to be tested, and is used to image the image information of the target surface 01 to be tested onto the target surface of the detector. The detector is used to acquire a two-dimensional image containing laser spot and feature points based on the image information of the target surface 01 to be tested.

[0026] The laser emission module 1 and the feature recognition module 2 are respectively positioned facing the target surface 01 to be tested, and are respectively installed on the same side of the support platform 3.

[0027] The surface target 01 to be measured can be described by a definite mathematical expression—the standard space equation—and has good rigidity, so it will not deform. M feature points with pre-calibrated positions are set on its surface, where M ≥ 3. In this embodiment, M = 4 is used as an example. These feature points should form a feature plane and can be located at the edge or inside of the surface target 01. The feature points must possess characteristics such as clarity, accuracy, easy identification, and stability. They can be inherent features or additional markers on the surface of the surface target 01 to facilitate accurate mapping of their relative positional relationships and distribution data.

[0028] The receiving end of the processing module is electrically connected to the transmitting end of the feature recognition module 2. It can receive the two-dimensional image sent by the feature recognition module 2 and obtain the actual pose change parameters of the target surface 01 under test based on the received two-dimensional image, thereby realizing the target pose measurement.

[0029] The specific implementation steps are as follows: Step 1: Calibrate the three laser beams emitted from laser emission module 1 and obtain the equivalent spatial linear equations of the three laser beams.

[0030] Define L1, L2, and L3 as the equivalent spatial linear equations for the three laser beams; L1: , , ; L2: , , ; L3: , , ; In the formula: ( , , Let L1 be the coordinates of any point in space along the laser beam's spatial straight line L1. ( , , Let L2 be the coordinates of any point in space along the laser beam's spatial straight line L2. ( , , Let L3 be the coordinates of any point in space along the laser beam's spatial straight line L3. , , , , , , , , , , , , , , , , , is a constant in the equation of the equivalent spatial line; , , The parameters are the variable parameters of the equivalent spatial linear equation.

[0031] Step 2: Calibrate the four feature points set on the surface target 01 to be tested, and obtain the relative positional relationship and distribution data between each pair of the four feature points; at the same time, calibrate the geometric parameters of the surface target 01 to be tested, which are specifically the shape, size and initial pose of the surface target 01 to be tested.

[0032] Step 3: The laser beam emitted from the laser emitting module 1 is incident on the surface target 01 to be tested. The feature recognition module 2 acquires a two-dimensional image containing the laser spot and feature points. Then, through image correction, the feature plane formed by the feature points on the two-dimensional image is obtained based on the relative positional relationship and distribution data between the feature points. The two-dimensional projection image is formed by the projection points formed by the laser spot projected onto the feature plane. A two-dimensional coordinate system is constructed with any feature point on the two-dimensional projection image as the origin, and the projection coordinates of the projection point and the other feature points on the two-dimensional coordinate system are obtained.

[0033] like Figure 2 As shown, the feature plane is formed by feature points T1, T2, T3 and T4 on the surface target 01 to be measured, and C1, C2 and C3 are projection points formed by the laser spots P1, P2 and P3 projected onto the feature plane.

[0034] Feature points T1, T2, T3, and T4 in the 2D projection image are pre-calibrated. Based on the rigid body characteristics of the target surface 01, their relative positions remain unchanged during the measurement process. Therefore, a 2D coordinate system is defined in the 2D projection image. The line connecting feature points T1 and T2 is selected as the y-axis. A perpendicular line is drawn from point T1 to the line connecting T1 and T2, and this perpendicular line is used as the x-axis. The intersection of the x-axis and y-axis (i.e., point T1) is used as the origin to establish the 2D coordinate system. The 2D coordinates of the remaining feature points and the projection points C1, C2, and C3 are defined using this 2D coordinate system, and the definition of the 2D coordinate system remains unchanged in subsequent measurements.

[0035] Step 4: Based on the equivalent spatial straight line equation of each laser beam and the two-dimensional coordinates of each feature point and projection point, the three-dimensional coordinates of each projection point are calculated using the principle that the lengths of the same spatial line segments are equal. For details, please refer to the specific calculation method disclosed in Chinese Patent CN116952129A, "An Attitude Measurement System and Method Based on Target Plane Edge Characteristics".

[0036] Step 5: After the pose of the target surface 01 changes, the position and spatial pointing baseline relationship between the laser emission module 1 and the feature recognition module 2 remain fixed. However, when the pose of the target surface 01 changes, the position information of the feature points also changes, such as... Figure 3 As shown, the laser beam forms a new laser spot on the surface of the target 01 under test. , , At this point, the two-dimensional image acquired by feature recognition module 2 changes, the position of the feature plane changes, and a new laser spot projection point is obtained through image correction. , , Two-dimensional coordinates.

[0037] The target surface 01 under test has rigid body characteristics. Since the positions and spatial orientation of the laser emitting module 1 and the feature recognition module 2 remain fixed relative to the baseline, the emitting end (laser emitting module 1) and the receiving end (feature recognition module 2) can be considered as a single unit. Therefore, the pose transformation of the target surface 01 under test can be converted into a pose transformation of the laser emitting module 1 and the feature recognition module 2 as a single unit using an equivalent rigid body transformation method, such as... Figure 4 As shown, in the equivalent rigid body transformation method, the target surface 01 to be measured can be considered fixed, and its pose information has been determined; at the same time, the feature plane formed by feature points can also be considered fixed. Using the pre-calibrated two-dimensional coordinates of the feature points and the projection points... , , The projection point can be calculated from the two-dimensional coordinates. , , The three-dimensional coordinates.

[0038] Step 6: Using the equivalent rigid body transformation method described above, the pose change of the target surface 01 is equivalently converted into the adjustment of the laser beam direction and the detector position. Based on this, the three-dimensional coordinates of the projection point after the pose change and other pre-calibrated geometric parameters (such as the shape and size of the target surface) can be used to establish an equivalent pose measurement model.

[0039] The steps for establishing an equivalent pose measurement model are as follows: 1) Characterization of laser beam pose changes Before the pose of the target surface 01 changes in the initial state, the spatial vector equation of each laser beam can be expressed as: (Formula 1) in, The initial intersection points of the three laser beams and the target surface 01 are shown. The spatial direction vectors of the three pre-calibrated laser beams, This is the scaling factor for the three laser beams in the initial state. The coordinates are the initial emission points of the three laser beams.

[0040] Taking the ellipsoidal surface (in the form of a sub-reflector of a Glyphic double-reflector antenna) as an example, the standard space equation of the target surface 01 to be tested satisfies: (Formula 2) After the equivalent rigid body transformation, the emission point and spatial orientation of the laser beam change, which can be described as rotation. (Transformation of the initial emission point coordinates L of the laser beam) and the translation amount T.

[0041] Among them, rotation amount Parameterized using Euler angles (e.g., zyx order), let the Euler angles be... , These are the azimuth, pitch, and roll angles, which can be used to construct rotation. for: (Formula 3) in: (Formula 4) (Formula 5) (Formula 6) Rotation amount The system has three degrees of freedom, describing its rotation about a reference point, and the translation T describes its translation in space, also having three degrees of freedom. After the equivalent rigid body transformation, the spatial vector equation of the laser beam can be expressed as: (Formula 7) To obtain the intersection point between the converted laser beam and the target surface 01. , , It is necessary to solve for the scaling factor of the laser beam. , so that: (Formula 8) The three laser beams constitute three nonlinear scalar constraint equations, which reflect the physical constraints of the intersection between the laser beams and the target surface 01 to be measured.

[0042] 2) Three-dimensional projection characterization of the laser spot Initially, the detector's origin is located at point E. After a translation of amount T, its origin becomes... The intersection point of the laser beam and the characteristic plane after the equivalent rigid body transformation is... , The projection point of the image onto the feature plane after image correction is: , The three-dimensional coordinates can be obtained using the method described in step 4 above. Based on the detector's imaging model, This can be represented as starting from the origin of the detector. Start along The intersection of the direction and the characteristic plane then yields: (Formula 9) in, is the projection scale factor, which is an intermediate variable to be determined in this embodiment.

[0043] (Formula 10) We can obtain: (Formula 11) Therefore, the detector observation conditions corresponding to each laser spot can be written as a spatial vector equation: (Formula 12) The detector's observation conditions provide a total of nine scalar constraint equations.

[0044] 3) Solving the pose of the target surface 01 Combining the above nonlinear scalar constraint equations and scalar constraint equations, for each laser spot It has two constraints, namely (Equation 8) and (Equation 12), the former representing the physical constraints and the latter representing the projection constraints, totaling 12 constraint equations and 12 unknowns.

[0045] Therefore, the following unknown vector can be constructed. : (Formula 13) And nonlinear residual functions: (Formula 14) Among them, the last three equations of (Equation 14) are expanded as follows: (Formula 15) For about The function, i=1, 2, 3, j=1, 2, 3, that is: (Formula 16) In step 7, by solving... The unknown vector can then be obtained. The unknowns in the equation are used to obtain the rotation amount R and the translation amount T. Then, the adjustment amount of the laser beam direction and the detector position is restored to the actual pose change parameters of the target surface 01 under test by the inverse equivalent rigid body transformation method, thus completing the target pose measurement.

[0046] To verify the feasibility of the proposed target pose measurement method based on curved surface features, this embodiment conducts a pose measurement simulation example for the sub-reflector of a 110mQTT dual-reflector antenna to evaluate the feasibility of the measurement principle and the measurement accuracy.

[0047] The sub-reflector of this dual-reflector antenna is a standard ellipsoid, and its surface shape conforms to the standard spatial equation of an ellipsoid: (Formula 17) in, All length units in this simulation example are in mm, and all angle units are in degrees.

[0048] The three-dimensional coordinates of the four feature points on the sub-reflector surface of the dual-reflector antenna are T1(0, 1600, 18479.27); T2(1600, 0, 18479.27); T3(0, -1600, 18479.267); T4(-1600, 0, 18479.27).

[0049] The initial emission point L of the laser beam has three-dimensional coordinates of (0, 0, 0). Three laser beams with known spatial directions are emitted from this point. Initially, the spatial direction vectors of the three laser beams are: d1(0, 0.0645, 0.9979); d2(0.0538, -0.0538, 0.9972); d3(-0.0538, -0.0538, 0.9970). Initially, the detector's position E has three-dimensional coordinates of (100, 100, 100).

[0050] 1) Simulation Example 1 of Rotation and Translation Working Conditions A translation T with a direction of (2, -2, 2) and a rotation with Euler angles of (0.5°, -0.5°, 0.5°) are applied to the sub-reflector of the dual-reflector antenna. The rotation center is the center of the sub-reflector of the dual-reflector antenna. Through equivalent rigid body transformation, this is equivalent to applying a displacement of (-2, 2, -2) and a rotation of (-0.5°, 0.5°, -0.5°) to the laser emission module 1 and the detector, resulting in a two-dimensional projection image as shown below. Figure 5 As shown.

[0051] The results are shown in the table below: Table 1. Comparison of absolute errors between the solutions and actual values ​​in Simulation Example 1

[0052] As can be seen from Table 1, when the target surface 01 is translated and rotated, the solved values ​​of the pose variables are relatively close to the true values. Among them, the absolute error between the solved value of the translation T and the true value is the largest, which is 0.0150 mm. The maximum absolute error between the solved value and the true value is 0.002233°, which meets the error requirements for pose measurement.

[0053] 2) Simulation Example 2 of Rotation and Translation Working Conditions A translation T with a direction of (20, -20, 20) and a rotation with Euler angles of (0.1°, -0.1°, 0.1°) are applied to the sub-reflector of the dual-reflector antenna. Through equivalent rigid body transformation, it is equivalent to applying a displacement with a direction of (-20, 20, -20) and a rotation with an Euler angle of (-0.1°, 0.1°, -0.1°) to the laser emitting module 1 and the detector.

[0054] The results are shown in the table below: Table 2 Comparison of absolute errors between the solution and the actual values ​​in simulation example 2

[0055] As can be seen from Table 2, under this condition, the absolute error between the solved values ​​of the pose variables and the true values ​​is also small. Specifically, the absolute error between the solved value of the translation T and the true value is on the order of e-05 to e-04 (mm), and the absolute error between the solved value of the rotation R and the true value is on the order of e-07 to e-05 (°), which can well meet the requirements of high-precision pose measurement of small magnitude.

Claims

1. A target pose measurement system based on curved surface features, characterized in that: It includes a laser emission module (1), a feature recognition module (2), and a processing module; The laser emission module (1) and the feature recognition module (2) are respectively positioned facing the surface target (01) to be tested, and M feature points are set on the surface target (01); M≥3； The laser emitting module (1) is used to emit N non-parallel laser beams to the surface target (01) to be tested, forming N laser spots on the surface target (01), and the centroids of the N laser spots are not collinear; N≥3; The field of view of the feature recognition module (2) covers all different pose envelopes of the surface target (01) under test, and is used to obtain a two-dimensional image of the surface target (01) under test containing laser spot and feature points; The receiving end of the processing module is electrically connected to the sending end of the feature recognition module (2) to receive the two-dimensional image sent by the feature recognition module (2) and obtain the actual pose change parameters of the surface target (01) to be measured based on the received two-dimensional image, thereby realizing the target pose measurement.

2. The target pose measurement system based on curved surface features according to claim 1, characterized in that: It also includes the support platform (3); The laser emission module (1) and the feature recognition module (2) are respectively installed on the support platform (3).

3. A target pose measurement system based on curved surface features according to claim 1 or 2, characterized in that: The feature recognition module (2) includes an optical lens and a detector; The object plane of the optical lens is located at the surface target (01) to be tested, and the image plane is located at the target surface of the detector; the field of view of the optical lens covers all different pose envelopes of the surface target (01) to be tested, and is used to image the image information of the surface target (01) to be tested onto the target surface of the detector. The detector is used to acquire a two-dimensional image containing laser spot and feature points based on the image information of the surface target (01) to be tested.

4. A target pose measurement method based on curved surface features, employing the target pose measurement system based on curved surface features as described in any one of claims 1-3, characterized in that, Includes the following steps: Step 1: Calibrate the N laser beams emitted by the laser emitting module (1) and obtain the equivalent spatial linear equations of the N laser beams; Step 2: Calibrate the M feature points set on the surface target (01) to be tested, and obtain the relative positional relationship and distribution data between each pair of feature points; at the same time, calibrate the geometric parameters of the surface target (01) to be tested, including the shape, size and initial pose of the surface target (01) to be tested; Step 3: The laser beam emitted from the laser emitting module (1) is incident onto the surface target (01) to be tested. A two-dimensional image containing the laser spot and feature points is acquired through the feature recognition module (2). Then, through image correction, the feature plane formed by the feature points on the two-dimensional image is obtained based on the relative positional relationship and distribution data between the feature points, and the two-dimensional projection image formed by the projection points formed by the laser spot projected onto the feature plane. A two-dimensional coordinate system is constructed with any feature point on the two-dimensional projection image as the origin, and the projection coordinates of the projection point and the other feature points on the two-dimensional coordinate system are obtained. Step 4: Based on the equivalent spatial straight line equation of each laser beam and the projected coordinates of each feature point and projection point in the two-dimensional coordinate system, the three-dimensional coordinates of each projection point are calculated using the principle that the length of the same spatial line segment is equal. Step 5: After the pose of the target surface (01) is changed, the three-dimensional coordinates of each projection point after the pose change are obtained in the same way as in Step 3 and Step 4. Step 6: By using the equivalent rigid body transformation method, the pose change of the target surface (01) to be measured is equivalently converted into the adjustment of the laser beam pointing and the position of the feature recognition module (2); on this basis, combined with the calibrated geometric parameters of the target surface (01) to be measured and the three-dimensional coordinates of the projection point after the pose change, an equivalent pose measurement model is established. Step 7: Based on the equivalent pose measurement model, obtain the adjustment amount of the laser beam pointing and the position of the feature recognition module (2), and then restore the adjustment amount of the laser beam pointing and the position of the feature recognition module (2) to the actual pose change parameters of the surface target (01) under test by the reverse equivalent rigid body transformation method, and complete the target pose measurement.

5. The target pose measurement method based on curved surface features according to claim 4, characterized in that, In step 6, the expression for the equivalent pose measurement model is: =0； ; In the formula: It is a nonlinear residual function; The standard space equation of the surface target (01) to be measured is: , i = 1, 2, 3, ..., N; L represents the initial emission point coordinates of the laser beam; T is the translation amount; R is the amount of rotation; The scaling factor for the laser beam; The direction vectors of the N laser beams are pre-calibrated; E is the initial origin of the feature recognition module (2); The three-dimensional coordinates of the projection point after the pose change; The projection scale factor; Regarding Euler angles , and A function, j = 1, 2, 3, ..., N; In step 7, based on the equivalent pose measurement model, the adjustment amount between the laser beam direction and the position of the feature recognition module is obtained. The adjustment amount includes translation T and rotation R.

6. A target pose measurement method based on curved surface features according to claim 4 or 5, characterized in that: Define M feature points as T1, T2, ..., T M ; In step 3: with feature point T1 as the origin of the two-dimensional coordinate system, the line connecting feature point T1 and feature point T2 is selected as the y-axis of the two-dimensional coordinate system, and a perpendicular line from feature point T1 to the y-axis is drawn as the x-axis of the two-dimensional coordinate system, thus realizing the establishment of the two-dimensional coordinate system.

7. The target pose measurement method based on curved surface features according to claim 6, characterized in that, In step 1, the equivalent spatial linear equation for the N laser beams is expressed as: ； In the formula: , and Let be the coordinates of any point on the laser beam; , , , , and is a constant in the equation of the equivalent spatial line; The parameters are the variable parameters of the equivalent spatial linear equation.

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