A rigid body rotational inertia product measurement system and method based on point cloud registration

Abstract

The application provides a rigid body rotational inertia product measurement system and method based on point cloud registration, and relates to the field of inertia product measurement.The system comprises a torsion pendulum measurement platform, a structured light 3D model scanning device and a computer.The torsion pendulum measurement platform is used for loading a measured object and performing torsional vibration on the measured object.The torsion pendulum measurement platform is arranged in the scanning range of the structured light 3D model scanning device.The structured light 3D model scanning device is used for acquiring point cloud data and a CAD model of the measured object.The structured light 3D model scanning device is in communication connection with the computer, and the acquired point cloud data and CAD model of the measured object are transmitted to the computer.The computer is used for calculating the rotational inertia product of the measured object according to the point cloud data and the CAD model of the measured object.The application can accurately calculate the rotational inertia and the rotational inertia product of the measured object.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of inertial product measurement, and particularly relates to a rigid body rotational inertia product measurement system and method based on point cloud registration. BACKGROUND

[0002] Inertia product is a physical quantity for describing the rotational inertia of a rigid body, is used for analyzing the rotational motion of the rigid body in three-dimensional space, and is an important engineering parameter for studying, designing and controlling the motion law of a rotating body. The inertia product of the body is related to the shape of the body, the position of the rotating shaft and the mass distribution, and directly affects the initial disturbance, motion stability and motion orbit characteristics of the body.

[0003] All equipment related to rotational behavior needs to be tested for rotational inertia, especially for intercontinental missiles, hypersonic aircraft, spacecraft and kinetic energy interceptors, which require precise attitude and orbit control. Precise measurement of rotational inertia product is of great significance, and precise measurement of rotational inertia product provides design input for motion analysis and attitude control system of such spacecraft, and can also be used to verify the rationality of the layout of the spacecraft load structure.

[0004] However, in the measurement of rotational inertia by the traditional torsion pendulum method, the influence of damping is usually ignored, the amplitude of the torsional vibration of the measured object is attenuated, the period of the motion is changed, the measurement error is large, the physical process and characteristics of the entire torsional vibration cannot be completely displayed, and the traditional method needs to repeatedly install and position the object during the measurement of multi-attitude rotational inertia, which increases the complexity and time consumption of the measurement, and cannot meet the industrial demand, resulting in low accuracy of the measurement results. SUMMARY

[0005] In order to solve the technical problem that the traditional torsion pendulum method usually ignores the influence of damping when measuring rotational inertia, the amplitude of the torsional vibration of the measured object is attenuated, the period of the motion is changed, the measurement error is large, the physical process and characteristics of the entire torsional vibration cannot be completely displayed, the traditional method needs to repeatedly install and position the object during the measurement of multi-attitude rotational inertia, which increases the complexity and time consumption of the measurement, and cannot meet the industrial demand, resulting in low accuracy of the measurement results, the present application provides a rigid body rotational inertia product measurement system and method based on point cloud registration.

[0006] The technical scheme provided by the embodiments of the present application is as follows:

[0007] First aspect:

[0008] The rigid body rotational inertia product measurement system based on point cloud registration provided by the embodiments of the present application comprises a torsion pendulum measurement table, a structured light 3D model scanning device and a computer.

[0009] The torsional pendulum measuring table is used for loading the measured object and performing torsional vibration on the measured object.

[0010] The torsional pendulum measuring table is arranged in the scanning range of the structured light 3D model scanning device.

[0011] The structured light 3D model scanning device is used for acquiring point cloud data and a CAD model of the measured object.

[0012] The structured light 3D model scanning device is in communication connection with the computer, and the acquired point cloud data and the CAD model of the measured object are transmitted to the computer.

[0013] The computer is used for calculating the moment of inertia of the measured object according to the point cloud data and the CAD model of the measured object.

[0014] The second aspect:

[0015] The embodiment of the application provides a rigid body moment of inertia measurement method based on point cloud registration, which comprises the following steps:

[0016] S1: acquiring point cloud data of a measured object;

[0017] S2: preprocessing the point cloud data;

[0018] S3: scanning the measured object to generate a CAD model of the measured object;

[0019] S4: performing point cloud registration on the preprocessed point cloud data and the CAD model, acquiring a transformation matrix of a coordinate system of the measured object relative to a camera coordinate system, and calculating information matrices of each coordinate axis of the coordinate system of the measured object in the camera coordinate system;

[0020] S5: calculating geometric parameters of the object table based on the information matrices, wherein the geometric parameters comprise an included angle between each coordinate axis of the measured object and a central axis of the object table and a distance between a centroid of the measured object and the central axis of the object table;

[0021] S6: measuring the moment of inertia of the measured object according to the geometric parameters;

[0022] S7: changing the load attitude, repeating steps S1 to S6, measuring the moments of inertia of the measured object under multiple load attitudes, and calculating the moment of inertia of the measured object.

[0023] The technical scheme provided by the embodiment of the application has at least the following beneficial effects:

[0024] In the application, the motion characteristics are accurately analyzed through point cloud registration, the measurement error caused by ignoring the damping effect is avoided, the amplitude attenuation and period change are avoided, the physical process and characteristics of the torsional vibration motion are fully displayed, the measurement accuracy is greatly improved, the torsional vibration excitation is accurately controlled through the torsional pendulum measuring platform, the controllability and stability of the torsional vibration motion are ensured, a reliable foundation is provided for the measurement of the rotational inertia, in the point cloud data preprocessing, the noise removal, hole filling and model fitting are adopted, the quality of the point cloud data is effectively improved, the measurement error is reduced, and the stability of the final calculation result is ensured, through the structured light 3D model scanning device and the point cloud registration technology, the three-dimensional point cloud data and the CAD model of the object to be measured can be accurately obtained, combined with the high-precision coordinate system conversion and registration calculation, the accuracy of the rotational inertia measurement result is ensured, the load attitude is automatically adjusted, and multi-angle data acquisition and calculation are performed, the measurement process of the multi-attitude rotational inertia is simplified, the operation complexity and time cost are reduced, and the measurement efficiency of the rigid body rotational inertia is improved. BRIEF DESCRIPTION OF DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0026] Figure 1 A structural schematic diagram of a rigid body rotational inertia measurement system based on point cloud registration provided by the embodiment of the present application is shown in the figure.

[0027] Figure 2 A flowchart of a rigid body rotational inertia measurement method based on point cloud registration provided by the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0028] The technical solutions in the present application will be described below with reference to the drawings.

[0029] In the embodiments of the present application, the words such as "example", "for example" are used to represent as an example, illustration or description. Any embodiment or design scheme described as "example" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present application, the meaning expressed by "and / or" can be both, or can be one of the two.

[0030] In the embodiments of the present application, the terms 'image' and 'picture' can be used interchangeably, and it should be noted that the meanings expressed by the two terms are consistent when the distinction between them is not emphasized.

[0031] In the embodiments of the present application, the subscript such as W1 can be written in the form of non-subscript such as W1, and the meanings expressed by the two forms are consistent when the distinction between them is not emphasized.

[0032] To make the technical problems, technical solutions and advantages of the present application clearer, the following will be described in detail with reference to the drawings and specific embodiments.

[0033] Reference is made to the accompanying drawings and specific embodiments of the present application are described in detail. Figure 1 Fig. 1 shows a structural schematic diagram of a rigid body rotational inertia product measurement system based on point cloud registration according to an embodiment of the present application.

[0034] The present application provides a rigid body rotational inertia product measurement system based on point cloud registration, which comprises a torsion measurement platform, a structured light 3D model scanning device and a computer.

[0035] The torsion measurement platform is used to load the measured object and perform torsional vibration on the measured object.

[0036] The torsion measurement platform is arranged within the scanning range of the structured light 3D model scanning device.

[0037] The structured light 3D model scanning device is used to obtain the point cloud data and CAD model of the measured object.

[0038] The structured light 3D model scanning device is in communication connection with the computer, and the obtained point cloud data and CAD model of the measured object are transmitted to the computer.

[0039] The computer is used to calculate the rotational inertia product of the measured object according to the point cloud data and CAD model of the measured object.

[0040] Optionally, the torsion measurement platform comprises a torsional vibration excitation device, a loading platform, an elastic element, a torsion measurement platform base and a torsion measurement platform shell; the torsion measurement platform base and the torsion measurement platform shell are connected and form a containing space inside; one end of the elastic element is arranged inside the containing space, and the other end extends outside the containing space; the elastic element is connected with the loading platform; the measured object is loaded on the loading platform; the torsional vibration excitation device is connected with the loading platform; under the excitation of the torsional vibration excitation device, the loading platform drives the measured object to make reciprocating torsional vibration around the central axis of the elastic element.

[0041] Optionally, the elastic element is connected with the object table through a bearing.

[0042] In the present application, the elastic element is connected with the object table through a bearing, which can reduce mechanical friction and damping, improve measurement accuracy and equipment life, and ensure the stability and regularity of movement, which is an important measure to optimize the design of the torsional pendulum measuring table.

[0043] Optionally, the elastic element is specifically a torsion bar or a spiral spring.

[0044] Optionally, the torsional vibration excitation device comprises an STM32 control board, a large torque steering engine and a cam mechanism. The STM32 control board is connected with the large torque steering engine; the large torque steering engine is connected with the cam mechanism; and the cam mechanism is connected with the object table.

[0045] In the present application, through the cooperative work of the STM32 control board, the large torque steering engine and the cam mechanism, accurate control, efficient transmission and reliable output of the torsional vibration excitation process are realized, which provides a stable, adjustable and low-interference excitation source for the measuring table, and also has good adaptability and expansibility. This structure not only improves the measurement accuracy and system stability, but also reduces the use and maintenance cost, which is a highly efficient and reliable design scheme.

[0046] Optionally, the structured light 3D model scanning device comprises a camera support, a binocular vision camera and a light projector. The binocular vision camera is arranged on the camera support; the light projector projects a structured light pattern onto the surface of the object to be measured; the surface geometry of the object to be measured causes the light pattern to deform; the binocular vision camera captures the deformed light pattern from different angles, extracts the phase information of the object surface through the captured pattern, and obtains the point cloud data and CAD model of the object to be measured.

[0047] In the present application, the structured light 3D model scanning device can efficiently and accurately obtain the three-dimensional geometric information of the object to be measured in a non-contact manner. The generation of point cloud data and CAD model provides key support for inertial measurement and other engineering analysis, and improves the automation, flexibility and applicability of the measurement process. This scheme has significant advantages in precision, efficiency and economy.

[0048] Optionally, the structured light pattern is specifically a sinusoidal stripe or a grid.

[0049] It should be noted that the excitation device of the torsional pendulum measuring platform can ensure that the measured object performs stable torsional vibration, high-resolution continuous motion images are collected by the industrial imaging device, point cloud data and CAD models of the measured object are obtained by combining the structured light 3D model scanning device, the geometric characteristics and vibration behavior of the object are accurately restored, the system eliminates installation errors by using point cloud registration technology, improves the measurement accuracy, and realizes flexible adaptation to complex shape and asymmetric structure objects, and efficiently improves the accuracy and efficiency of multi-pose moment of inertia measurement.

[0050] Referring to the accompanying drawings Figure 2 , a flowchart of a rigid body moment of inertia measurement method based on point cloud registration is shown.

[0051] The embodiment of the application provides a rigid body moment of inertia measurement method based on point cloud registration, comprising:

[0052] S1: obtaining point cloud data of the measured object.

[0053] The point cloud data is a set composed of a large number of three-dimensional coordinate points, used to represent the spatial structure of the object surface, and is usually collected by 3D scanning technology (such as laser scanning or structured light scanning).

[0054] In one possible implementation, S1 specifically comprises:

[0055] S101: obtaining a sinusoidal fringe structured light of a known pattern:

[0056] I(x,y) = I0 + I1 cos(P2πx + φ')

[0057] Wherein, I(x,y) represents the sinusoidal fringe structured light of the known pattern, I0 represents the average intensity of the sinusoidal fringe structured light, I1 represents the fringe amplitude of the sinusoidal fringe structured light, P represents the fringe period of the sinusoidal fringe structured light, and φ' represents the initial phase of the sinusoidal fringe structured light.

[0058] S102: projecting the sinusoidal fringe structured light to the object to be measured by using a light projector.

[0059] The sinusoidal fringe structured light is a technology that projects light with periodic fringes onto the surface of an object by a projector, and a sinusoidal fringe is usually used to capture the shape deformation of the object surface.

[0060] It should be noted that the sinusoidal fringe structured light is projected onto the surface of the object to be measured by using the light projector, and the three-dimensional shape of the object surface can be accurately obtained by fringe deformation analysis, providing high-resolution and high-precision surface data, and avoiding damage or contact error of the object surface caused by traditional measurement methods.

[0061] S103: Capture the projected sinusoidal fringe pattern with the binocular vision camera to obtain the projection pattern:

[0062] I'(x, y) = R(x, y) [A(x, y) + B(x, y) cos φ(x, y)]

[0063] Where I'(x, y) represents the projection pattern, R(x, y) represents the reflectivity of the object surface at point (x, y), φ((x, y) represents the phase value of the projection pattern at point (x, y), A(x, y) represents the background light intensity of the projection pattern, and B(x, y) represents the modulation light intensity.

[0064] S104: Calculate the phase difference of the projection pattern by N-step phase shift method:

[0065]

[0066] Δφ(x, y) = φ(x, y) - φ0(x, y)

[0067] Where Δφ(x, y) represents the phase difference of the projection pattern, φ(x, y) represents the phase value of the projection pattern at point (x, y), N represents the total number of frames of the projection pattern phase shift, φk(x, y) represents the light intensity value of the kth frame of the projection pattern at point (x, y), and φ0(x, y) represents the reference phase.

[0068] S105: Determine the surface height of the object to be measured according to the phase difference:

[0069]

[0070] Where h represents the surface height of the object to be measured, L0 represents the distance between the light projector and the binocular vision camera, T represents the period of the projection pattern, and d represents the optical axis distance between the light projector and the binocular vision camera.

[0071] S106: Use feature point detection algorithm to extract features from the projection head image to obtain feature points.

[0072] Where the feature point detection algorithm is a computer vision technology used to identify and extract key points or features with unique and stable characteristics from images. These feature points are usually the prominent parts of corners, edges or textures in the image, which can help identify objects or accurately match and analyze images.

[0073] It should be noted that the feature point detection algorithm can effectively capture important information in the image and provide stable recognition results in complex background or variable surface conditions, improving the accuracy of subsequent three-dimensional reconstruction.

[0074] S107: determining a plurality of three-dimensional coordinates of the surface of the object to be measured according to the surface height and the feature points of the object to be measured through coordinate conversion and triangulation:

[0075]

[0076] wherein (x, y, z) represents the three-dimensional coordinates of the surface of the object to be measured, represents the pixel coordinates of the feature points in the projection pattern, K represents the intrinsic matrix of the binocular vision camera, and R and Q represent the extrinsic matrix of the binocular vision camera, represents the normalized coordinates of the feature points in the binocular vision camera coordinate system, A c represents the baseline length between the binocular vision cameras.

[0077] S108: performing three-dimensional reconstruction on the three-dimensional coordinates to form point cloud data.

[0078] It should be noted that obtaining the point cloud data can accurately capture the three-dimensional shape of the object, is suitable for complex and irregular geometric objects, can obtain high-precision object surface information, provides a reliable basis for subsequent point cloud registration and model reconstruction, avoids errors that may be introduced by traditional methods, and can greatly reduce the difficulty of measuring complex geometric objects.

[0079] S2: preprocessing the point cloud data.

[0080] In one possible implementation, S2 specifically includes:

[0081] The preprocessing of the point cloud data includes denoising, hole filling and model fitting.

[0082] Denoising refers to removing noise points in the point cloud data, which are usually caused by sensor errors or environmental interference and can affect the accuracy of subsequent processing. Hole filling refers to filling missing data regions in the point cloud due to scanning angle, occlusion or other reasons. Model fitting refers to matching the point cloud data with a specific geometric model (such as a plane, a curved surface or other shapes) using mathematical methods (such as least squares method, surface fitting, etc.) to obtain a more accurate object shape.

[0083] It should be noted that the preprocessing of the point cloud data, including denoising, hole filling and model fitting, can significantly improve the quality of the point cloud data, reduce errors caused by noise and missing points, help obtain a smoother and continuous three-dimensional model, and ensure the accuracy of subsequent calculations and analyses.

[0084] S3: scanning the object to be measured to generate a CAD model of the object to be measured.

[0085] In one possible implementation, S3 specifically includes:

[0086] The object to be measured is scanned by the structured light 3D model scanning device to generate a CAD model of the object to be measured.

[0087] The CAD (Computer-Aided (Design) model is a three-dimensional model generated based on computer-aided design, which is used to accurately describe the geometric shape and structure of the object.

[0088] It should be noted that the CAD model generated by the structured light 3D model scanning device can quickly and accurately restore the geometric shape of the object to be measured, which is convenient for subsequent analysis and calculation, and can reduce the error caused by human intervention.

[0089] S4: Point cloud registration is performed on the pre-processed point cloud data and the CAD model to obtain a transformation matrix of the coordinate system of the object to be measured relative to the camera coordinate system, and to calculate the information matrix of each coordinate axis of the object to be measured coordinate system in the camera coordinate system.

[0090] The point cloud registration is to align two sets of point cloud data (such as the scanned point cloud and the CAD model point cloud), and to make them coincide in space by calculating the best transformation relationship between them. The transformation matrix describes the geometric relationship between the two coordinate systems, including rotation and translation, and is used to map the points of one coordinate system to another coordinate system. The information matrix is a mathematical representation of the distribution characteristics (such as position, direction, and weight) of each axis of the coordinate system, which is used to quantify the spatial distribution information of the object. The camera coordinate system refers to a three-dimensional coordinate system defined in the camera system, usually with the camera optical axis as the Z axis, and the X and Y axes parallel to the camera imaging plane, which is used to describe the relative position of the object in the image.

[0091] In one possible implementation, S4 specifically includes:

[0092] S401: Calculate the covariance matrix of the pre-processed point cloud data:

[0093]

[0094] Wherein, r i represents the i-th data point in the point cloud data, i = 1, 2,..., m, m represents the total number of data points in the point cloud data, J represents the covariance matrix of the point cloud data, and T represents the transpose.

[0095] S402: Perform PCA decomposition on the covariance matrix to obtain eigenvalues and eigenvectors;

[0096]

[0097] Wherein, N represents the covariance matrix after PCA decomposition, N1, N2 and N3 all represent eigenvectors, and N ijdenotes the element of the covariance matrix after PCA decomposition, i = j = 1, 2, 3.

[0098] S403: Constructing the coordinate system of the object to be measured according to the eigenvalue and the eigenvector:

[0099]

[0100] Wherein R represents the coordinate system of the object to be measured.

[0101] S404: Based on the coordinate system of the object to be measured, the point cloud data after preprocessing and the CAD model are registered to obtain the transformation matrix of the coordinate system of the object to be measured relative to the camera coordinate system:

[0102]

[0103] Wherein, t represents the translation matrix of the coordinate system of the object to be measured relative to the camera coordinate system, T represents the transformation matrix of the coordinate system of the object to be measured relative to the camera coordinate system, p x , p y and p z respectively represent the translation amount of the centroid of the coordinate system of the object to be measured in the camera coordinate system.

[0104] S405: According to the conversion matrix, the camera coordinate system is obtained and the information matrix of each coordinate axis of the object to be measured in the camera coordinate system is calculated:

[0105]

[0106] Wherein, A C represents the information matrix, and A0 represents the unit vector of the three coordinate axes of the coordinate system of the object to be measured.

[0107] In the present application, the phase unwrapping uses the double-frequency heterodyne method in the multi-frequency phase shift method to synthesize the global phase from the normalized phase difference:

[0108]

[0109] Wherein, is the normalized phase, for any point on the object, λ1n1= λ2n2 is satisfied, that is, the camera coordinate values in different frequency maps are the same, and Φ1 and Φ2 represent the absolute phase.

[0110] And for the same point, the integer fringe order measured by different period gratings needs to satisfy the condition:

[0111]

[0112] It can be deduced that:

[0113]

[0114] After obtaining the absolute phase, the height h of the object surface can be calculated through the relationship between the phase and the height:

[0115]

[0116] It should be noted that by point cloud registration on the pre-processed point cloud data and the CAD model, the position and pose of the object coordinate system in the camera coordinate system can be accurately obtained, ensuring the consistency of the measurement data and the model, improving the accuracy of the geometric parameter calculation, providing high-precision input data for the subsequent calculation of the inertial parameters, greatly improving the measurement efficiency and reliability, and being especially suitable for dynamic measurement of complex objects.

[0117] S5: calculating the geometric parameters of the object table based on the information matrix, wherein the geometric parameters include the included angle between each coordinate axis of the object to be measured and the center axis of the object table and the distance between the centroid of the object to be measured and the center axis of the object table.

[0118] The information matrix is used to represent and calculate the distribution of different coordinate axes of the object in space, which can provide detailed information such as the direction, position and mass distribution of each axis of the object, the geometric parameters describe the mathematical quantities of the spatial relationship between the object and the object table, usually including the included angle and distance between the object and the center axis of the table, which are used to define the relative position and motion trajectory of the object, and the centroid refers to the center of mass of the object, which is the balance point of the mass distribution of each part of the object.

[0119] In one possible implementation, S5 specifically includes:

[0120] S501: unifying each coordinate axis of the object coordinate system to be measured and the center axis of the object table in the camera coordinate system through a cylindrical fitting algorithm.

[0121] The cylindrical fitting algorithm is a mathematical method that fits a set of data points to a cylindrical shape model, and optimizes the model parameters (such as radius and center position) to reduce the error between the data points and the model, which is commonly used to process objects with symmetrical structure, and the center axis of the object table refers to the center line or axis of the object table, which is usually the reference axis for object fixation and rotation, and all measurements and calculations are based on this axis.

[0122] In the present application, unifying each coordinate axis of the object coordinate system to be measured and the center axis of the object table in the camera coordinate system through a cylindrical fitting algorithm specifically includes:

[0123] In three-dimensional space, a cylinder is a set of points whose distance to the central axis is a constant r:

[0124]

[0125] In the least square algorithm fitting process, the fitting error of the point is defined as the difference between the distance from the point to the central axis of the cylindrical stage and the radius of the wobble stage:

[0126]

[0127] The fitting algorithm based on geometric analysis linearizes the error equation of the formula, discards the high-order terms, and then iteratively calculates according to the Gauss-Newton method to obtain the final wobble stage axis estimation value in the camera coordinate system.

[0128] It should be noted that by using the cylindrical fitting algorithm, the coordinate axes of the object coordinate system and the center axis of the stage are unified into the camera coordinate system, which helps to accurately align the object and the measuring equipment, and can eliminate the errors caused by the posture or position deviation of the object, making the subsequent measurement and analysis more accurate.

[0129] S502: In the camera coordinate system, obtain the information matrix, the centroid coordinates of the object to be measured, and the stage information, including the reference point coordinates on the center axis of the stage and the vector of the center axis of the stage:

[0130]

[0131] p0(x0,y0,z0)

[0132] wherein, represents the vector of the center axis of the stage, p0(x0,y0,z0) represents the reference point coordinates on the center axis of the stage, and respectively represent the coordinate axes of the object coordinate system in the camera coordinate system.

[0133] S503: Calculate the geometric parameters of the stage according to the information matrix, the centroid coordinates of the object to be measured, and the stage information:

[0134]

[0135] wherein, α, β and γ respectively represent the included angles between the coordinate axes of the object to be measured and the center axis of the stage, d represents the distance between the centroid of the object to be measured and the center axis of the stage, and t represents the proportion factor of the centroid of the object to be measured projected onto the center axis of the stage.

[0136] It should be noted that the geometric parameters are calculated based on the information matrix, which can accurately describe the spatial relationship between the object to be measured and the stage, providing necessary geometric information for the calculation of the moment of inertia, and improving the accuracy of the measurement results, ensuring high-precision measurement of complex objects, especially in multi-attitude or dynamic measurement process.

[0137] S6: Measure the moment of inertia of the object to be measured according to the geometric parameters.

[0138] wherein the moment of inertia is a measure of an object's resistance to changes in its rotational motion, depending on the mass distribution of the object and the location of the axis of rotation, the greater the moment of inertia of the object, the more difficult it is to change its rotational state.

[0139] In a possible implementation, S6 specifically is:

[0140] measuring the moment of inertia of the object to be measured by using a torsion pendulum measuring platform:

[0141]

[0142] wherein J represents the moment of inertia of the object to be measured, k represents the stiffness coefficient of the object platform, T represents the torsional vibration period of the object to be measured, J0 represents the unloaded moment of inertia of the object to be measured, and π represents the circular constant.

[0143] It should be noted that measuring the moment of inertia of the object to be measured according to the geometric parameters can efficiently and accurately evaluate the rotational characteristics of the object, and through the understanding of the geometric relationship between the object coordinate system and the object platform, errors caused by installation deviation or measurement angle change can be effectively avoided.

[0144] S7: changing the load attitude, repeating steps S1 to S6, measuring the moment of inertia of the object to be measured under multiple load attitudes, and calculating the moment of inertia of the object to be measured.

[0145] wherein the load attitude refers to the spatial position and direction of the object to be measured relative to the measuring device during rotation, and the change of the load attitude means that the rotation axis and the center of mass of the object change relative to the position of the measuring platform, and the moment of inertia is the product sum of the moment of inertia of the object in each direction and the corresponding distance (the distance from the center of mass to the rotation axis), which is a comprehensive indicator of the moment of inertia of the object, and is usually used to describe the moment of inertia of the object in multiple directions.

[0146] In a possible implementation, S7 specifically is:

[0147] measuring the moment of inertia of the object to be measured under each load attitude to obtain the moment of inertia of the object to be measured:

[0148]

[0149] wherein J1-md1 2 , J2-md2 2 , J3-md3 2 , J4-md4 2 , J5-md5 2 and J6-md6 2 respectively represent the moment of inertia of the object to be measured under each load attitude, and J x, J y and J z respectively represent the rotational inertia of the measured object around each coordinate axis of the measured object coordinate system, and α1, α2, α3, α4, α5 and α6 respectively represent the included angle between the coordinate axis X of the measured object and the center axis of the object table under each load attitude, β1, β2, β3, β4, β5 and β6 respectively represent the included angle between the coordinate axis Y of the measured object and the center axis of the object table under each load attitude, and γ1, γ2, γ3, γ4, γ5 and γ6 respectively represent the included angle between the coordinate axis Z of the measured object and the center axis of the object table under each load attitude,J yz , J xz and J xy respectively represent the inertia product of the measured object around the yz plane, the xz plane and the xy plane.

[0150] It should be noted that by repeatedly measuring at multiple load attitudes, the rotational inertia of the measured object in different directions can be comprehensively understood, and more accurate rotational inertia products can be obtained, which can eliminate errors that may be caused by a single attitude, improve the reliability and comprehensiveness of the measurement results, and provide more data support through repeated measurement, which helps to obtain more stable and accurate rotational inertia values through comprehensive analysis, and for objects with complex geometric shapes, changing the load attitude can more comprehensively capture the anisotropic characteristics of the object, further improving the accuracy and applicability of the rotational inertia measurement.

[0151] The technical scheme provided by the embodiment of the present application has at least the following beneficial effects:

[0152] In the present application, the motion characteristics are accurately analyzed through point cloud registration, the measurement error caused by ignoring the damping effect is avoided, the physical process and characteristics of the torsional vibration motion are fully displayed, the measurement accuracy is greatly improved, the torsional vibration excitation is accurately controlled through the torsional pendulum measurement platform, the controllability and stability of the torsional vibration motion are ensured, a reliable foundation is provided for the rotational inertia measurement, in the point cloud data preprocessing, denoising, hole filling and model fitting are adopted, the quality of the point cloud data is effectively improved, the measurement error is reduced, and the stability of the final calculation result is ensured, through the structured light 3D model scanning device and the point cloud registration technology, the three-dimensional point cloud data and the CAD model of the measured object can be accurately obtained, combined with high-precision coordinate system conversion and registration calculation, the accuracy of the rotational inertia product measurement result is ensured, the load attitude is automatically adjusted and multi-angle data acquisition and calculation are performed, the multi-attitude rotational inertia measurement process is simplified, the operation complexity and time cost are reduced, and the rigid body rotational inertia product measurement efficiency is improved.

[0153] It should be appreciated that a processor in the embodiments of the present application can be a central processing unit (CPU), and can also be other general purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic, discrete hardware components, etc. The general purpose processor can be a microprocessor, or the processor can be any conventional processor.

[0154] It should also be appreciated that the memory in the embodiments of the present application can be a volatile memory or a nonvolatile memory, or can include both volatile and nonvolatile memory. The nonvolatile memory can be a read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be a random access memory (RAM) used as external cache. By way of example, and not limitation, many forms of random access memory (RAM) can be used, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchlink dynamic random access memory (SLDRAM), and direct rambus random access memory (DR RAM).

[0155] The above-described embodiments can be implemented in part or in whole through software, hardware (e.g., circuitry), firmware, or any combination thereof. When implemented in software, the above-described embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When loaded and executed by a computer, the computer instructions or computer programs can generate the flow or function according to the embodiments of the present application in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable apparatus. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, such as from one website site, computer, server, or data center to another website site, computer, server, or data center through a wired (e.g., infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server, data center, etc. containing a set of one or more available media. The available media can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0156] It should be understood that the term "and / or" used herein is merely an association relationship between the associated objects, which means that there can be three relationships, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone, where A and B can be singular or plural. In addition, the character " / " herein generally represents an "or" relationship between the associated objects before and after it, but it can also represent an "and / or" relationship, which can be understood in the context before and after it.

[0157] In the present application, "at least one" means one or more, and "multiple" means two or more. "At least one of the following" or the like means any combination of the items, including any combination of single or multiple items. For example, at least one of a, b, or c can mean a, (b, (c, (a-b, (a-c, (b-c, or a-b-c, where a, b, and c can be single or multiple.

[0158] It should be understood that in various embodiments of the present application, the size of the sequence number of the above-described processes does not mean the order of execution, and the execution order of the processes should be determined by their functions and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0159] Those skilled in the art can clearly understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0160] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the devices, apparatuses and units described above can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0161] In several embodiments provided by the present application, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely schematic, for example, the division of units is only a logical function division, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0162] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0163] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically independently, or two or more units can be integrated into one unit.

[0164] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the prior art that essentially contribute to the present application or the parts of the technical solutions can be embodied in the form of a software product stored in a storage medium, including a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program code storage media.

[0165] The embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to realize the point cloud registration based rigid body rotational inertia product measurement method according to the method embodiment.

[0166] The computer readable storage medium provided by the present application can realize the steps and effects of the point cloud registration based rigid body rotational inertia product measurement method according to the method embodiment, and the present application will not be repeated here to avoid repetition.

[0167] The technical solutions provided by the embodiment of the present application have at least the following beneficial effects:

[0168] In the present application, the motion characteristics are accurately analyzed through point cloud registration, the measurement error caused by ignoring the damping effect is avoided, the physical process and characteristics of the torsional vibration motion are completely displayed, the measurement accuracy is greatly improved, the torsional vibration excitation is accurately controlled through the torsional pendulum measurement platform, the controllability and stability of the torsional vibration motion are ensured, a reliable foundation is provided for the rotational inertia measurement, in the point cloud data preprocessing, the noise removal, hole filling and model fitting are adopted, the quality of the point cloud data is effectively improved, the measurement error is reduced, and the stability of the final calculation result is ensured, the three-dimensional point cloud data and the CAD model of the object to be measured are accurately obtained through the structured light 3D model scanning device and the point cloud registration technology, the accuracy of the rotational inertia product measurement result is ensured through the high-precision coordinate system conversion and registration calculation, the load attitude is automatically adjusted, and multi-angle data acquisition and calculation are performed, the measurement process of the multi-attitude rotational inertia is simplified, the operation complexity and time cost are reduced, and the measurement efficiency of the rigid body rotational inertia product is improved.

[0169] The above merely illustrates the specific embodiments of the present application, and the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0170] The following points need to be explained:

[0171] (1) The attached drawings of the embodiments of the present application only involve the structures involved in the embodiments of the present application, and other structures can refer to the general design.

[0172] (2) In order to be clear, the thickness of the layer or region is enlarged or reduced in the drawings used for describing the embodiments of the present application, that is, the drawings are not drawn according to the actual proportion. It can be understood that when an element such as a layer, a film, a region or a substrate is referred to as being located "on" or "under" another element, the element can be "directly" located on or under another element or there can be an intermediate element.

[0173] (3) In the case of no conflict, the embodiments of the present application and the features in the embodiments can be combined with each other to obtain new embodiments.

[0174] The above merely illustrates the specific embodiments of the present application, and the protection scope of the present application is not limited thereto, and the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for measuring the product of rotational inertia of a rigid body based on point cloud registration, characterized in that, The system is applied to a rigid body rotational inertia product measurement system based on point cloud registration. The system includes: a torsional pendulum measuring stage, a structured light 3D model scanning device, and a computer. The torsion pendulum measuring table is used to load the object to be measured and to subject the object to torsion vibration. The torsion measuring stage is positioned within the scanning range of the structured light 3D model scanning device. The structured light 3D model scanning device is used to acquire point cloud data and CAD model of the object under test; The structured light 3D model scanning device is communicatively connected to the computer and transmits the acquired point cloud data and CAD model of the object to be tested to the computer. The computer is used to calculate the rotational inertia product of the object under test based on the point cloud data and CAD model of the object under test. The methods include: S1: Obtain the point cloud data of the object under test; S2: Preprocess the point cloud data; S3: Scan the object to be tested and generate a CAD model of the object to be tested; S4: Perform point cloud registration on the preprocessed point cloud data and the CAD model to obtain the transformation matrix of the coordinate system of the object under test relative to the camera coordinate system, and calculate the information matrix of each coordinate axis of the coordinate system of the object under test in the camera coordinate system. S5: Based on the information matrix, calculate the geometric parameters of the stage, wherein the geometric parameters include the angles between each coordinate axis of the object under test and the central axis of the stage, and the distance between the centroid of the object under test and the central axis of the stage. S6: Measure the moment of inertia of the object under test according to the geometric parameters; S7: Change the load posture, repeat steps S1 to S6, measure the moment of inertia of the object under test under multiple load postures, and calculate the product of rotational inertia of the object under test. Specifically, S4 includes: S401: Calculate the covariance matrix of the preprocessed point cloud data: in, Represents the first point in the point cloud data. Data points, , This represents the total number of data points in the point cloud data. Represents the covariance matrix of point cloud data. Indicates transpose; S402: Perform PCA decomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors; in, This represents the covariance matrix after PCA decomposition. Both represent eigenvectors. The covariance matrix after PCA decomposition is represented by the first... Line number Column elements, ; S403: Construct the coordinate system of the object to be measured based on the eigenvalues ​​and eigenvectors: in Indicate the coordinate system of the object to be measured; S404: Based on the coordinate system of the object under test, perform point cloud registration on the preprocessed point cloud data and the CAD model to obtain the transformation matrix of the coordinate system of the object under test relative to the camera coordinate system: in, t This represents the translation matrix of the object's coordinate system relative to the camera's coordinate system. This shows the transformation matrix of the coordinate system of the object under test relative to the camera coordinate system. These represent the translation of the centroid of the object's coordinate system in the camera coordinate system; S405: Based on the transformation matrix, obtain the camera coordinate system and calculate the information matrix of each coordinate axis of the object to be measured in the camera coordinate system: in, Representing the information matrix, This represents the unit vectors of the three coordinate axes of the coordinate system of the object under test.

2. The method for measuring the product of rotational inertia of a rigid body based on point cloud registration according to claim 1, characterized in that, S1 specifically includes: S101: Obtain sinusoidal fringe structured light with a known pattern: in, Sine fringe structured light representing a known pattern. This represents the average intensity of the sinusoidal fringe structured light. This represents the fringe amplitude of sinusoidal fringe structured light. This represents the fringe period of a sinusoidal fringe structured light. This represents the initial phase of the sinusoidal fringe structured light; S102: Project the sinusoidal fringe structured light onto the object to be tested using a light projector; S103: Using a binocular vision camera to capture the projected sinusoidal fringe structured light, the projected pattern is obtained: in, Represents the projected pattern. Indicates the point on the surface of the object to be measured. reflectivity, Indicates the projected pattern at the point phase value, This indicates the background light intensity of the projected pattern. Indicates the modulated light intensity; S104: Pass The phase difference of the projected pattern is calculated using the step-shift method: in, This represents the phase difference of the projected pattern. Indicates the projected pattern at the point phase value, This represents the total number of frames in the phase shift of the projected pattern. Indicates the first Frame projection pattern at point The light intensity value, Indicates the reference phase; S105: Determine the surface height of the object under test based on the phase difference: in, Indicates the surface height of the object being measured. This indicates the distance between the light projector and the binocular vision camera. Indicates the fringe period of the projected pattern. This indicates the optical axis distance between the light projector and the binocular vision camera; S106: Use a feature point detection algorithm to extract features from the projected head image to obtain feature points; S107: Based on the surface height of the object to be measured and the feature points, determine multiple three-dimensional coordinates of the surface of the object to be measured through coordinate transformation and triangulation: in, Represents the three-dimensional coordinates of the surface of the object being measured. This represents the pixel coordinates of the feature points in the projected pattern. This represents the intrinsic parameter matrix of a binocular vision camera. and This represents the extrinsic parameter matrix of a binocular vision camera. This represents the normalized coordinates of the feature points in the coordinate system of the binocular vision camera. Indicates the baseline length between the binocular vision cameras; S108: Perform three-dimensional reconstruction on the three-dimensional coordinates to form point cloud data.

3. The method for measuring the product of rotational inertia of a rigid body based on point cloud registration according to claim 1, characterized in that, Specifically, S2 is: The point cloud data undergoes preprocessing including denoising, hole filling, and model fitting.

4. The method for measuring the product of rotational inertia of a rigid body based on point cloud registration according to claim 1, characterized in that, Specifically, S3 is: The object to be tested is scanned using the light-emitting 3D model scanning device to generate a CAD model of the object to be tested.

5. The method for measuring the product of rotational inertia of a rigid body based on point cloud registration according to claim 1, characterized in that, S5 specifically includes: S501: By using a cylindrical fitting algorithm, the coordinate axes of the coordinate system of the object under test and the central axis of the stage are unified in the camera coordinate system. S502: In the camera coordinate system, obtain the information matrix, the centroid coordinates of the object under test, and the stage information. The stage information includes the coordinates of a reference point on the central axis of the stage and the vector of the central axis of the stage. in, The vector representing the central axis of the stage. Indicates the coordinates of the reference point on the central axis of the stage. These represent the coordinate axes of the object being measured in the camera coordinate system; S503: Calculate the geometric parameters of the stage based on the information matrix, the centroid coordinates of the object to be measured, and the stage information. in, These represent the angles between each coordinate axis of the object under test and the central axis of the stage. This represents the distance between the center of mass of the object being measured and the central axis of the stage. t The scaling factor represents the projection of the center of mass of the object under test onto the central axis of the stage.

6. The method for measuring the product of rotational inertia of a rigid body based on point cloud registration according to claim 1, characterized in that, Specifically, S6 is: The moment of inertia of the object under test is measured using the aforementioned torsion pendulum measuring stage. in, This represents the moment of inertia of the object being measured. This represents the stiffness coefficient of the stage. This represents the torsional vibration period of the object under test. This represents the unloaded moment of inertia of the object being measured. It represents pi (π).

7. The method for measuring the product of rotational inertia of a rigid body based on point cloud registration according to claim 1, characterized in that, Specifically, S7 is: The moment of inertia of the object under test is measured under various load postures to obtain the product of rotational inertia of the object under test: in, Let represent the moment of inertia of the object under various load attitudes. These represent the moments of inertia of the object under test about each coordinate axis of the object's coordinate system. These represent the coordinate axes of the object under test in various load orientations. X The angle between the stage and the central axis. These represent the coordinate axes of the object under test in various load orientations. Y The angle between the stage and the central axis. These represent the coordinate axes of the object under test in various load orientations. Z The angle between the stage and the central axis These represent the orbits of the object under test around the target. yz flat, xz plane and xy The product of inertia of a plane.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method for measuring the rigid body rotational inertia product based on point cloud registration as described in any one of claims 1 to 7.

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