Lightweight binocular vision structure three-dimensional displacement and motion trail measurement method and system

Abstract

The invention discloses a lightweight binocular vision structure three-dimensional displacement and motion trail measurement method and system. The method comprises the following steps: performing binocular calibration on binocular equipment consisting of a master camera and a slave camera to obtain camera parameters; collecting a motion image of the to-be-detected structure with the single square composite target; processing the image to identify a positioning point locking target area and extract angular point coordinates; judging whether the target exceeds the field of view of the main camera according to the angular point coordinates, if so, switching the data of the slave camera, and fusing the data of the double cameras in the overlapping region; and calculating six-degree-of-freedom displacement based on camera parameters, angular point coordinates and switched data to obtain three-dimensional displacement and a motion track. The system comprises a binocular camera, a calibration module, an image processing module, a view field switching module, a calculation module and a display storage module. According to the method, the anti-interference performance and the arrangement efficiency are improved through the single-target design, the measurement range is expanded through switching of the field of view of the master and slave cameras, the efficiency is improved through lightweight calculation, the high-precision, large-range and dynamic measurement requirements can be met, and the method is suitable for engineering structure monitoring and precise manufacturing scenes.

Description

Technical Field

[0001] This invention relates to the field of visual measurement technology, specifically to a lightweight binocular vision structure three-dimensional displacement and motion trajectory measurement method and system. Background Technology

[0002] In fields such as modern engineering structural health monitoring, precision manufacturing assembly, and robot motion control, the demand for high-precision, lightweight measurement of structural three-dimensional displacement and motion trajectory is increasingly urgent. With the rapid development of industrial intelligence, traditional contact measurement methods are gradually failing to meet practical application needs due to problems such as inconvenient installation, susceptibility to environmental interference, and difficulty in adapting to dynamic measurement scenarios. For example, in the health monitoring of large structures such as bridges and buildings, contact sensors need to be directly connected to the structure being measured, which not only affects the structure itself but also makes it difficult to achieve simultaneous measurement over a large area and at multiple points. In precision machining processes, contact measurement can damage the workpiece surface and has low measurement efficiency. Therefore, developing a non-contact, lightweight, and high-precision three-dimensional displacement and motion trajectory measurement technology is key to solving current industry pain points, providing reliable data support for quality control and safety assessment in various fields, and has significant engineering application value.

[0003] Traditional three-dimensional displacement and motion trajectory measurement technologies mainly fall into two categories: contact measurement and non-contact measurement. Contact measurement technologies, represented by dial indicators, micrometers, and displacement sensors, offer advantages such as high measurement accuracy and wide application in static small displacement measurement scenarios. For example, in the dimensional calibration of mechanical parts, dial indicators can achieve micron-level precision measurements. However, this type of technology has significant limitations: firstly, it requires direct contact with the object being measured, which can easily cause scratches or damage to the surface, making it particularly unsuitable for measuring special workpieces such as precision optical components and soft materials; secondly, the measurement range is limited, making it difficult to achieve dynamic, large-scale displacement tracking, and the stability and lifespan of sensors decrease significantly under complex working conditions (such as high temperature, high pressure, and strong vibration environments). Early non-contact measurement technologies primarily relied on laser rangefinders and total stations. Laser rangefinders can achieve long-distance single-point displacement measurement but cannot acquire three-dimensional trajectory information; while total stations can achieve three-dimensional coordinate measurement, they are bulky, expensive, and slow, making it difficult to meet the needs of real-time dynamic monitoring.

[0004] With the development of computer vision technology, monocular vision-based measurement technology has gradually become a research hotspot and has been applied in some scenarios. This technology acquires images through a single camera and combines them with image processing algorithms to achieve displacement measurement. It has advantages such as simple equipment, low cost, and convenient operation, and has been applied to some extent in the planar displacement measurement of small components. However, monocular vision measurement technology has inherent limitations: due to the lack of depth information, it cannot directly achieve accurate measurement of three-dimensional displacement. It usually requires indirect calculation through a reference object of known size or by establishing a complex mathematical model. The measurement accuracy is easily affected by factors such as the installation accuracy of the reference object and changes in ambient lighting. In dynamic measurement scenarios, monocular vision has difficulty accurately capturing changes in the spatial posture of objects, especially when the object undergoes significant rotation or three-dimensional translation, the measurement error will increase significantly. In addition, the field of view of monocular vision is limited. When the measured object goes beyond the camera's field of view, the camera position needs to be readjusted or additional measuring equipment needs to be added. It cannot achieve continuous large-scale measurement, which limits its application in fields such as large structure monitoring and robot motion trajectory tracking.

[0005] Chinese patent (CN112033286A) discloses a method for measuring structural displacement based on binocular vision. This method acquires images of the structure under test using a binocular camera, calculates three-dimensional coordinates using feature point matching and triangulation principles, and achieves displacement measurement. This technical solution addresses the lack of depth information in monocular vision to some extent, enabling three-dimensional displacement measurement. However, this solution has serious drawbacks: First, it uses multiple discrete feature points as targets, which can easily lead to mismatches during image processing, especially in complex backgrounds or environments with significant lighting changes, resulting in a sharp decrease in feature point recognition accuracy and increased displacement calculation errors. Second, the method lacks a field-of-view switching and data fusion mechanism. When the measured structure moves beyond the field of view of a single camera, continuous trajectory measurement is impossible, limiting the measurement range. Furthermore, in its six-degree-of-freedom displacement calculation, it only calculates translational displacement through changes in feature point coordinates, without considering the measurement of structural attitude angles (such as yaw and pitch angles), thus failing to comprehensively reflect the motion state of the measured structure. These shortcomings result in insufficient reliability and applicability in practical applications, especially in high-precision, large-scale, and dynamically complex measurement scenarios, making it difficult to meet the stringent requirements of engineering monitoring. Summary of the Invention

[0006] Based on the aforementioned technical problems, this application discloses a method and system for measuring the three-dimensional displacement and motion trajectory of a lightweight binocular vision structure. Specifically, the method for measuring the three-dimensional displacement and motion trajectory of a lightweight binocular vision structure includes:

[0007] Perform binocular camera positioning on a binocular camera device consisting of a main camera and a slave camera, and acquire camera parameters;

[0008] Images of the structure under test during its motion are acquired using the binocular camera device. A single square composite target is provided on the surface of the structure under test, and the single square composite target is provided with positioning points.

[0009] The acquired images are processed to identify the positioning points to lock the target area, and the corner coordinates of the single square composite target are extracted.

[0010] Based on the corner coordinates, it is determined whether the single square composite target exceeds the field of view of the main camera. If it does, the data is switched to the slave camera. In the overlapping area of ​​the fields of view of the main camera and the slave camera, the data of the main camera and the slave camera are fused together.

[0011] Based on the camera parameters, corner coordinates, and image data after field of view switching, the six-degree-of-freedom displacement of the structure under test is calculated, thereby obtaining the three-dimensional displacement and motion trajectory of the structure under test.

[0012] Preferably, the step of performing binocular positioning on a binocular camera device consisting of a main camera and a slave camera to obtain camera parameters includes:

[0013] Calibration was performed using a checkerboard calibration plate.

[0014] The checkerboard calibration board is placed at different positions and orientations near the structure under test, and the main camera and the slave camera are controlled to acquire multiple calibration images respectively.

[0015] Corner point identification is performed on the acquired calibration images, and calibration images with errors exceeding a preset threshold are removed;

[0016] The remaining calibration images are used to perform calibration calculations to obtain the intrinsic and extrinsic parameters of the main camera and the slave camera, and to establish a world coordinate system that coincides with the coordinate system of the main camera. The camera parameters include the intrinsic and extrinsic parameters and the world coordinate system parameters.

[0017] Preferably, the step of processing the acquired image, identifying the positioning points to lock the target area, and extracting the corner coordinates of the single-square composite target includes:

[0018] The acquired image is converted to grayscale to obtain a grayscale image;

[0019] The grayscale image is subjected to adaptive threshold binarization to enhance the contrast between the single square composite target and the background.

[0020] The template matching algorithm is used to identify the positioning points in the grayscale image, and the target area of ​​the single square composite target is locked based on the position of the positioning points.

[0021] A fixed-window sub-pixel corner detection algorithm is used to extract the corner coordinates of the single square composite target within the target area. The extracted corner coordinates are iteratively optimized until the coordinate correction amount is less than a preset pixel threshold.

[0022] Preferably, the step of determining whether the single-square composite target exceeds the field of view of the main camera based on the corner coordinates, and if it does, switching to slave camera data, and processing the overlapping area of ​​the fields of view of the main camera and the slave camera using a method of fusing the main camera data and slave camera data, includes:

[0023] The distance between the edge of the single-square composite target and the edge of the image captured by the main camera is calculated in real time.

[0024] If the distance is less than a preset ratio threshold of the width of the image captured by the main camera, it is determined that the single square composite target is beyond the field of view of the main camera, and the system switches to image data captured by the camera.

[0025] If the single-square composite target is located in the overlapping area of ​​the fields of view of the main camera and the slave camera, a weighted fusion method with preset weight allocation is used to process the data from the main camera and the slave camera. The fused data ,in Main camera data, To obtain data from the camera, Weighting of main camera data. Weights are derived from camera data.

[0026] Preferably, the calculation of the six-degree-of-freedom displacement includes: taking the midpoint of the diagonal of the single-square composite target as a representative point, converting the image point coordinates into world coordinates in combination with the camera parameters, and comparing them with the initial frame coordinates to obtain... The displacement components are oriented, and the formula for calculating the displacement components is as follows: ,in The world coordinates of the representative point in the current frame are... The initial frame world coordinates of the representative point are used; the yaw and pitch angles are calculated using the long side vector of the single square composite target.

[0027] Preferably, the calculation of the yaw angle and pitch angle using the long side vector of the single-square composite target includes:

[0028] Determine the two corner points corresponding to the long side of the single-square composite target, denoted as corner point 1 and corner point 2, and obtain the world coordinates of corner point 1. and the world coordinates of corner point 2 ;

[0029] The projection vector of the longer side vector onto the horizontal plane is calculated based on the world coordinates. The projection vector is... ;

[0030] Calculate the yaw angle using the arctangent function. The calculation formula is: ;

[0031] Calculate the magnitude of the long side vector The calculation formula is:

[0032]

[0033] Calculate the angle between the longer side vector and the horizontal plane, and use this angle as the pitch angle. The calculation formula is: .

[0034] Preferably, after calculating the six-degree-of-freedom displacement of the structure under test, the method further includes:

[0035] Calculate the resultant displacement of the structure under test. The calculation formula is: ,in They are respectively Directional displacement components;

[0036] Real-time display of the structure under test Displacement curves in direction, resultant displacement curves, and curves showing changes in yaw and pitch angles;

[0037] The six-degree-of-freedom displacement data and the resultant displacement data are stored in a preset format file according to the timestamp. The preset format file includes the corner coordinates of the single square composite target, displacement components in each direction, resultant displacement, and yaw and pitch angle information.

[0038] Preferably, the step of using the remaining calibration images to perform calibration calculations and obtain the intrinsic and extrinsic parameters of the main camera and the slave camera includes:

[0039] Based on Zhang Zhengyou's calibration algorithm, the pixel coordinates of the corner points in the remaining calibration images are calculated. with world coordinates The corresponding match;

[0040] Based on the matching results, a system of calibration equations is constructed to solve for the intrinsic parameters of the master camera and the slave camera, including the focal length. , Principal point coordinates and distortion coefficients, wherein the distortion coefficients include radial distortion coefficients and tangential distortion coefficients, wherein the pixel coordinates are related to the camera coordinate system coordinates. The transformation relationship satisfies ;

[0041] The relative pose parameters between the master camera and the slave camera are calculated using epipolar constraints and triangulation principles to obtain extrinsic parameters, including a rotation matrix. Translation vector The transformation relationship between world coordinate system coordinates and camera coordinate system coordinates satisfies .

[0042] Preferably, acquiring images of the structure under test during motion using the binocular camera device includes:

[0043] By controlling the main camera and the slave camera to trigger image acquisition simultaneously, the image frames are synchronized, and the frame synchronization error is less than a preset time threshold.

[0044] Images of the structure under test during its motion are acquired at a preset acquisition frequency. The acquisition frequency meets the requirements for medium-frequency motion monitoring, and the processing time for a single frame image does not exceed a preset time to ensure the real-time performance of acquisition and processing.

[0045] The lightweight binocular vision structure three-dimensional displacement and motion trajectory measurement system described herein specifically includes:

[0046] The binocular camera module consists of a main camera and a slave camera, used to acquire images of the structure under test during its motion. The surface of the structure under test is provided with a single square composite target.

[0047] The calibration module is used to perform binocular camera calibration on the binocular camera module and obtain camera parameters, including intrinsic parameters, extrinsic parameters and world coordinate system parameters.

[0048] The image processing module is used to perform grayscale and binarization processing on the images acquired by the binocular camera module, identify the positioning points on the single square composite target to lock the target area, and use a sub-pixel corner detection algorithm to extract the corner coordinates of the single square composite target.

[0049] The field-of-view switching module is used to calculate the distance between the single square composite target and the edge of the main camera image based on the corner coordinates, determine whether it exceeds the field of view of the main camera, and if it does, switch to the data of the slave camera. In the overlapping area of ​​the field of view, the main and slave camera data are fused based on a preset weight formula.

[0050] The calculation module is used to calculate the directional displacement components, resultant displacement, yaw angle, and pitch angle of the structure under test based on the camera parameters and corner coordinates, to obtain the six-degree-of-freedom displacement, and to generate a three-dimensional motion trajectory based on multiple frames of six-degree-of-freedom displacement data.

[0051] The display and storage module is used to display displacement change curves, resultant displacement change curves, and attitude angle change curves in real time, and to store the measured data as a preset format file according to the timestamp.

[0052] Compared with the prior art, the technical solution of this application has the following technical effects:

[0053] The single-square composite target design adopted in this invention can quickly lock the target area and extract corner coordinates through positioning points, reducing the risk of mismatch in complex backgrounds. By applying sub-pixel corner detection and iterative optimization algorithms, the accuracy of corner coordinate extraction is improved. The non-contact binocular vision measurement method avoids damage to the measured structure caused by contact measurement and is not directly affected by complex working conditions such as high temperature and high pressure. It can maintain stable measurement performance in various engineering scenarios and meet the core requirements of high-precision monitoring.

[0054] This invention enhances the applicability and practicality of the system through a master-slave camera field-of-view switching and data fusion mechanism. When a single square composite target exceeds the master camera's field of view, the system automatically switches to slave camera data to ensure uninterrupted motion trajectory measurement. In the overlapping field of view area, a weighted fusion formula is used to process the dual-camera data, effectively eliminating the measurement breakpoint problem caused by field-of-view switching. This field-of-view splicing and expansion method enables the measurable displacement range of the binocular camera device to cover the typical monitoring needs of small and medium-sized structures.

[0055] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the preferred embodiments of this application are described in detail below with reference to the accompanying drawings.

[0056] The above and other objects, advantages and features of this application will become more apparent to those skilled in the art from the following detailed description of specific embodiments in conjunction with the accompanying drawings. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In all drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0058] Based on the description of the figures and their corresponding technical content in the document, the titles of the figures are as follows:

[0059] Figure 1 A schematic diagram of the overall process for a lightweight binocular vision structure 3D displacement measurement method;

[0060] Figure 2 A schematic diagram of corner detection and reprojection during binocular camera localization;

[0061] Figure 3 A schematic diagram showing the correspondence between the world coordinate system and camera coordinates after the stereo camera has been calibrated.

[0062] Figure 4 A diagram illustrating the data interaction architecture of each module in a lightweight binocular vision measurement system.

[0063] Figure 5 A schematic diagram of the experimental setup for verifying the accuracy of linear displacement measurement of a single-tube model.

[0064] Figure 6 This is a schematic diagram of the experimental scenarios for a single-tube model under different placement angles of the slide rail;

[0065] Figure 7 A graph comparing data from a linear displacement measurement system and a wire gauge under different motion directions;

[0066] Figure 8 A graph showing the measurement results of the attitude angles (yaw angle, roll angle, pitch angle) of the suspended tunnel model;

[0067] Figure 9 A schematic diagram of the experimental setup for verifying the circular motion trajectory of a model with a square target.

[0068] Figure 10 The image shows the measurement results of the three-dimensional circular motion trajectory of the model under different placement angles of the slide rail. Detailed Implementation

[0069] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. In the following description, specific details such as specific configurations and components are provided merely to help fully understand the embodiments of this application. Therefore, those skilled in the art should understand that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. In addition, for clarity and brevity, descriptions of known functions and structures are omitted in the embodiments.

[0070] It should be understood that the phrase "an embodiment" or "this embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "an embodiment" or "this embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments.

[0071] Furthermore, reference numerals and / or letters may be repeated in different examples within this application. Such repetition is for the purpose of simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or settings discussed.

[0072] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist simultaneously. The term " / and" in this article describes another type of relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " in this article generally indicates that the related objects before and after it are in an "or" relationship.

[0073] In this article, the term "at least one" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, "at least one of A and B" can mean: A exists alone, A and B exist simultaneously, or B exists alone.

[0074] It should also be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion.

[0075] Example 1

[0076] This embodiment mainly describes a lightweight binocular vision structure for measuring three-dimensional displacement and motion trajectory, such as... Figure 1 As shown, it specifically includes:

[0077] Perform binocular camera positioning on a binocular camera device consisting of a main camera and a slave camera, and acquire camera parameters;

[0078] Images of the structure under test during its motion are acquired using the binocular camera device. A single square composite target is provided on the surface of the structure under test, and the single square composite target is provided with positioning points.

[0079] The acquired images are processed to identify the positioning points to lock the target area, and the corner coordinates of the single square composite target are extracted.

[0080] Based on the corner coordinates, it is determined whether the single square composite target exceeds the field of view of the main camera. If it does, the data is switched to the slave camera. In the overlapping area of ​​the fields of view of the main camera and the slave camera, the data of the main camera and the slave camera are fused together.

[0081] Based on the camera parameters, corner coordinates, and image data after field of view switching, the six-degree-of-freedom displacement of the structure under test is calculated, thereby obtaining the three-dimensional displacement and motion trajectory of the structure under test.

[0082] Furthermore, such as Figure 2 As shown, binocular camera calibration is performed on a binocular camera device consisting of a main camera and a slave camera to obtain camera parameters, including:

[0083] Calibration was performed using a checkerboard calibration plate.

[0084] The checkerboard calibration board is placed at different positions and orientations near the structure under test, and the main camera and the slave camera are controlled to acquire multiple calibration images respectively.

[0085] Corner point identification is performed on the acquired calibration images, and calibration images with errors exceeding a preset threshold are removed;

[0086] like Figure 3 As shown, calibration calculations are performed using the remaining calibration images to obtain the intrinsic and extrinsic parameters of the main camera and the slave camera, and a world coordinate system that coincides with the coordinate system of the main camera is established. The camera parameters include the intrinsic and extrinsic parameters and the world coordinate system parameters.

[0087] Further, the processing of the acquired image, identifying the positioning points to lock the target area, and extracting the corner coordinates of the single-square composite target includes:

[0088] The acquired image is converted to grayscale to obtain a grayscale image;

[0089] The grayscale image is subjected to adaptive threshold binarization to enhance the contrast between the single square composite target and the background.

[0090] The template matching algorithm is used to identify the positioning points in the grayscale image, and the target area of ​​the single square composite target is locked based on the position of the positioning points.

[0091] A fixed-window sub-pixel corner detection algorithm is used to extract the corner coordinates of the single square composite target within the target area. The extracted corner coordinates are iteratively optimized until the coordinate correction amount is less than a preset pixel threshold.

[0092] Furthermore, the step of determining whether the single-square composite target exceeds the field of view of the main camera based on the corner coordinates, and if it does, switching to slave camera data, and processing the overlapping area of ​​the fields of view of the main camera and slave camera using a method of fusing main camera data and slave camera data, includes:

[0093] The distance between the edge of the single-square composite target and the edge of the image captured by the main camera is calculated in real time.

[0094] If the distance is less than a preset ratio threshold of the width of the image captured by the main camera, it is determined that the single square composite target is beyond the field of view of the main camera, and the system switches to image data captured by the camera.

[0095] If the single-square composite target is located in the overlapping area of ​​the fields of view of the main camera and the slave camera, a weighted fusion method with preset weight allocation is used to process the data from the main camera and the slave camera. The fused data ,in Main camera data, To obtain data from the camera, Weighting of main camera data. Weights are derived from camera data.

[0096] Furthermore, the calculation of the six-degree-of-freedom displacement includes: taking the midpoint of the diagonal of the single-square composite target as a representative point, converting the image point coordinates into world coordinates in combination with the camera parameters, and comparing them with the initial frame coordinates to obtain... The displacement components are oriented, and the formula for calculating the displacement components is as follows: , ,in The world coordinates of the representative point in the current frame are... The initial frame world coordinates of the representative point are used; the yaw and pitch angles are calculated using the long side vector of the single square composite target.

[0097] Furthermore, the yaw and pitch angles are calculated using the long side vector of the single-square composite target, including:

[0098] Determine the two corner points corresponding to the long side of the single-square composite target, denoted as corner point 1 and corner point 2, and obtain the world coordinates of corner point 1. and the world coordinates of corner point 2 ;

[0099] The projection vector of the longer side vector onto the horizontal plane is calculated based on the world coordinates. The projection vector is... ;

[0100] Calculate the yaw angle using the arctangent function. The calculation formula is: ;

[0101] Calculate the magnitude of the long side vector The calculation formula is:

[0102]

[0103] Calculate the angle between the longer side vector and the horizontal plane, and use this angle as the pitch angle. The calculation formula is: .

[0104] Furthermore, after calculating the six degrees of freedom displacement of the structure under test, the method further includes:

[0105] Calculate the resultant displacement of the structure under test. The calculation formula is: ,in They are respectively Directional displacement components;

[0106] Real-time display of the structure under test Displacement curves in direction, resultant displacement curves, and curves showing changes in yaw and pitch angles;

[0107] The six-degree-of-freedom displacement data and the resultant displacement data are stored in a preset format file according to the timestamp. The preset format file includes the corner coordinates of the single square composite target, displacement components in each direction, resultant displacement, and yaw and pitch angle information.

[0108] Furthermore, calibration calculations are performed using the remaining calibration images to obtain the intrinsic and extrinsic parameters of the main camera and the slave camera, including:

[0109] Based on Zhang Zhengyou's calibration algorithm, the pixel coordinates of the corner points in the remaining calibration images are calculated. with world coordinates The corresponding match;

[0110] Based on the matching results, a system of calibration equations is constructed to solve for the intrinsic parameters of the master camera and the slave camera, including the focal length. , Principal point coordinates and distortion coefficients, wherein the distortion coefficients include radial distortion coefficients and tangential distortion coefficients, wherein the pixel coordinates are related to the camera coordinate system coordinates. The transformation relationship satisfies ;

[0111] The relative pose parameters between the master camera and the slave camera are calculated using epipolar constraints and triangulation principles to obtain extrinsic parameters, including a rotation matrix. Translation vector The transformation relationship between world coordinate system coordinates and camera coordinate system coordinates satisfies .

[0112] Furthermore, the binocular camera device is used to acquire images of the structure under test during its motion, including:

[0113] By controlling the main camera and the slave camera to trigger image acquisition simultaneously, the image frames are synchronized, and the frame synchronization error is less than a preset time threshold.

[0114] Images of the structure under test during its motion are acquired at a preset acquisition frequency. The acquisition frequency meets the requirements for medium-frequency motion monitoring, and the processing time for a single frame image does not exceed a preset time to ensure the real-time performance of acquisition and processing.

[0115] The single-square composite target is a black square structure of a preset size, and the positioning point is white. The color contrast between the positioning point and the single-square composite target meets the template matching and recognition accuracy requirements. The main camera and the slave camera are arranged at a fixed distance. The fields of view of the main camera and the slave camera overlap. The measurable displacement range of the binocular camera device is expanded by splicing the fields of view. The expanded measurable range covers the typical displacement monitoring needs of small and medium-sized structures.

[0116] Based on the preferred implementation method, on the basis of the original black base + white positioning points, a fluorescent marker layer and a QR code information layer can be added. The fluorescent marker layer can emit light autonomously in complex environments such as low light and backlight, solving the problem of failure in extracting natural features under weak light. The QR code information layer stores basic information such as target ID and size parameters, enabling rapid target differentiation in multi-point scenarios and compensating for the defect of easy confusion of multi-circle targets. Furthermore, through the target recognition algorithm, template matching and deep learning (such as the lightweight YOLO model) can be integrated. Through the pre-trained target feature library, even if the target is occluded by 30%, the corner points can still be accurately located, breaking through the limitation that measurement is interrupted when a single target is occluded and matching fails due to the local loss of natural features.

[0117] Based on the preferred implementation method, this method adds auxiliary verification of the target's short side vector and diagonal vector to the original method of calculating the attitude angle using the long side vector of a single target. By comparing the consistency of the calculation results of the three side vectors, abnormal data is automatically eliminated, improving the accuracy of the attitude angle calculation. At the same time, it introduces inertial measurement unit (IMU) data fusion. When the target temporarily leaves the field of view, the angular velocity and acceleration data collected in real time by the IMU are used to perform short-term prediction compensation for displacement and attitude angle, solving the problem of measurement interruption when the field of view is lost. Furthermore, by establishing a mapping model between parameters and camera intrinsic parameters (such as focal length and principal point coordinates), it dynamically corrects the measurement deviation caused by changes in temperature and humidity, making up for the defects of not considering environmental interference and easy drift of long-term measurement accuracy.

[0118] This implementation is described in detail.

[0119] Example 2

[0120] This embodiment details a lightweight binocular vision structure 3D displacement and motion trajectory measurement system, used to implement a lightweight binocular vision structure 3D displacement and motion trajectory measurement method. The system includes a binocular camera module, a calibration module, an image processing module, a field-of-view switching module, a displacement and trajectory calculation module, and a display and storage module. The modules interact in real time via a data transmission link. Figure 4 As shown, specifically:

[0121] The binocular camera module consists of a main camera and a slave camera, which are rigidly connected to maintain a fixed distance and avoid relative position shifts during measurement. The main camera serves as the primary image acquisition unit, prioritizing the acquisition of motion images of the structure under test. The slave camera serves as an auxiliary acquisition unit, used to cover the field of view not covered by the main camera. A single-square composite target is pre-attached to the surface of the structure under test. This target is a square structure with color contrast and has positioning points on its surface for rapid positioning. The binocular camera module controls the main camera and slave camera to acquire images simultaneously through a synchronous triggering mechanism, ensuring frame synchronization of the acquired images and avoiding measurement errors caused by acquisition time differences.

[0122] The calibration module is equipped with a calibration image acquisition and control unit and a parameter calculation unit. The calibration image acquisition and control unit guides the user to place the checkerboard calibration board at different positions and in different postures near the structure under test, and controls the main camera and the slave camera to acquire multiple calibration images respectively. The parameter calculation unit first performs corner point recognition on the acquired calibration images, filters and removes invalid calibration images whose corner point recognition errors exceed a preset threshold, and then calculates the intrinsic and extrinsic parameters of the main camera and the slave camera based on the valid calibration images. At the same time, it establishes a world coordinate system that coincides with the coordinate system of the main camera, and finally integrates the intrinsic, extrinsic and world coordinate system parameters into a complete camera parameter set and stores it, providing basic parameter support for subsequent image data processing and displacement calculation.

[0123] The image processing module includes an image preprocessing unit, a target localization unit, and a corner extraction unit. The image preprocessing unit sequentially performs grayscale processing and adaptive threshold binarization processing on the raw images acquired by the binocular camera module. Grayscale processing eliminates color channel interference, while binarization highlights the grayscale difference between the single-square composite target and the background, improving the target recognition clarity. The target localization unit loads a preset localization point template and uses a template matching algorithm to identify localization points on the single-square composite target in the preprocessed image. Based on the pixel coordinates of the localization points, it locks the local area where the target is located, reducing the interference of the background area on subsequent processing. The corner extraction unit uses a fixed-window sub-pixel corner detection algorithm to search for corners in the locked target area, extracts the coordinates of the four corners of the single-square composite target, and iteratively optimizes the extracted corner coordinates until the coordinate correction is less than a preset pixel threshold, ensuring the accuracy of the corner coordinates.

[0124] The field-of-view switching module comprises a distance calculation unit, a field-of-view judgment unit, and a data fusion unit. The distance calculation unit reads the corner coordinates output by the image processing module in real time, determines the edge position of the single-square composite target based on the corner coordinates, and then calculates the shortest distance between the target edge and the edge of the image acquired by the main camera. The field-of-view judgment unit compares the calculated shortest distance with a preset ratio threshold of the width of the image acquired by the main camera. If the shortest distance is less than the threshold, it is determined that the single-square composite target exceeds the field of view of the main camera, triggering a data switching command and controlling the system to switch from the main camera data to the slave camera data. When the single-square composite target is in the overlapping area of ​​the fields of view of the main camera and the slave camera, the data fusion unit performs weighted fusion of the image data output by the main camera and the image data output by the slave camera according to a preset weight allocation rule to ensure the continuity and consistency of the measurement data in the overlapping area and avoid data discontinuity caused by field-of-view switching.

[0125] The calculation module includes a displacement calculation unit and a trajectory generation unit. The displacement calculation unit calls the camera parameters stored in the calibration module and combines them with the corner coordinates output by the image processing module. Using the midpoint of the diagonal of the square composite target as the representative point for displacement calculation, the image coordinates of the representative point are converted into world coordinates. By comparing the world coordinates of the representative point in the current frame with the world coordinates in the initial frame, the displacement components of the structure under test in the x, y, and z directions are obtained. At the same time, based on the coordinates of the two corner points corresponding to the long side of the square composite target, the long side vector is calculated and the yaw and pitch angles are derived to form complete six-degree-of-freedom displacement data. The trajectory generation unit integrates and correlates the six-degree-of-freedom displacement data of multiple frames in chronological order. Based on the displacement data at each time point, the spatial motion path of the structure under test is constructed, generating a continuous three-dimensional motion trajectory.

[0126] The display and storage module consists of a real-time display unit and a data storage unit. The real-time display unit receives displacement data and three-dimensional motion trajectory output by the displacement and trajectory calculation module, and displays the displacement change trend, resultant displacement change trend, and attitude change trend of the structure under test in the x, y, and z directions, as well as the yaw angle and pitch angle, in real time in the form of curves. The curve interface supports time range adjustment and data point query, making it easy for users to intuitively grasp the motion state of the structure. The data storage unit classifies and stores the complete data in the measurement process according to the timestamp. The stored data includes the corner coordinates of the single square composite target, displacement components in each direction, resultant displacement, yaw angle, pitch angle, and corresponding time information. The storage format adopts a preset general file format, supports data export and subsequent offline analysis, and also has a data backup function to prevent measurement data loss.

[0127] This embodiment is described in detail.

[0128] Based on Embodiment 1 or 2, this embodiment describes a method and system for measuring the three-dimensional displacement and motion trajectory of a lightweight binocular vision structure, including experiments to verify the accuracy of linear displacement measurement, attitude angle measurement, and motion trajectory. Specifically:

[0129] The measurement system was set up and calibrated using two industrial cameras, designated as the master and slave cameras, fixed with rigid supports to maintain a constant distance and prevent relative displacement. A 12×9 checkerboard calibration board was placed at different positions and orientations near the model under test. The image acquisition and control unit of the calibration module controlled the master and slave cameras to acquire 15 calibration images each. The parameter calculation unit of the calibration module performed corner point identification on the acquired calibration images. After removing two invalid images with excessive corner point identification errors, the remaining 13 valid images were used to calculate the intrinsic parameters (focal length, principal point coordinates, distortion coefficients) and extrinsic parameters (rotation matrix, translation vector) of the master and slave cameras using the Zhang Zhengyou calibration algorithm. A world coordinate system coinciding with the master camera's coordinate system was established, and the complete camera parameter set was stored in the system to provide parameter support for subsequent measurements. After calibration, the system output corner point detection results, error distribution curves, and a world coordinate system diagram to ensure that the calibration accuracy meets the requirements.

[0130] The linear displacement measurement accuracy verification experiment uses a single-tube model as the structure under test. The experimental setup is as follows: Figure 5 As shown, a single square composite target (including white positioning points) is pasted onto the surface of the single-tube model. The model is then fixed onto an adjustable slide rail, which can be positioned in nine directions: -40°, -30°, -20°, -10°, 0°, 10°, 20°, 30°, and 40°. Figure 6As shown; a string gauge is placed next to the model as a reference measurement device to verify the measurement accuracy of the system; the binocular camera module is installed at a suitable position directly in front of the slide rail to ensure that the initial field of view of the main camera can cover the model's movement range, and the master and slave cameras are controlled to acquire images simultaneously through a synchronous triggering mechanism, with the acquisition frequency set to a value that meets the requirements of medium-frequency monitoring.

[0131] During the experiment, the single-tube model was manually pushed along the slide rail in different directions. The binocular camera module acquired images of the model's motion in real time and transmitted them to the image processing module. First, the images were processed by grayscale conversion and adaptive threshold binarization. The white positioning points on the target were identified by the template matching algorithm to lock the target area. Then, the sub-pixel corner detection algorithm with a 3×3 fixed window was used to extract the coordinates of the four corner points of the target. After iterative optimization, high-precision corner coordinates were obtained. The field-of-view switching module calculated the distance between the edge of the target and the edge of the main camera image in real time. When the model moved to both ends of the slide rail and the target exceeded the field of view of the main camera, the system automatically switched to the data from the slave camera. In the overlapping area of ​​the field of view, the data from the main camera and slave camera were fused according to the preset weight to ensure continuous measurement. The displacement and trajectory calculation module calculated the displacement components in the x, y, and z directions based on the calibration parameters and corner coordinates, with the midpoint of the target diagonal as the representative point, to obtain the linear displacement data measured by the system.

[0132] After the experiment, the system measurement data was compared with the reference data of the draw wire meter, and the results are as follows: Figure 7 As shown, the linear displacement measurement in different motion directions, under 9 different motion directions, the linear displacement change curves output by the system are completely consistent with the trend of the wire gauge curve, and the deviation of the measurement values ​​at each time point is extremely small, which verifies the high precision of the system in linear displacement measurement in different directions. Moreover, no measurement interruption occurred during the experiment, proving that the field of view switching and data fusion mechanism is effective and can achieve continuous and accurate measurement of linear displacement over a wide range.

[0133] The attitude angle measurement accuracy verification experiment used a large-scale suspended tunnel model as the structure under test. The experimental setup was as follows: Figure 3 The experimental setup for verifying the accuracy of D-axis attitude angle measurement is shown in the diagram: A single square composite target is pasted on the surface of the suspended tunnel model. The model is placed in a two-dimensional glass water tank, which can simulate a water flow environment to drive the model to produce attitude changes. A gyroscope sensor is installed on the top of the model as an attitude angle reference device to compare and verify the attitude angle measurement accuracy of this system. A binocular camera module is installed on the side of the water tank. The camera angle is adjusted so that the field of view of the main camera covers the attitude change range of the model, ensuring that a clear target image can be acquired.

[0134] During the experiment, an external force was applied to the model through a two-dimensional wave-flow water tank, causing the model to undergo attitude changes such as yaw, pitch, and roll. The binocular camera module simultaneously acquired images of the model's motion. The image processing module completed image preprocessing, target localization, and corner point extraction according to a predetermined process, obtaining the real-time coordinates of the four corner points of the target. The displacement and trajectory calculation module determined the two corner points corresponding to the long side of the target based on the corner point coordinates, calculated the projection vector of the long side vector onto the horizontal plane, obtained the yaw angle through the arctangent function, calculated the angle between the long side vector and the horizontal plane to obtain the pitch angle, and combined with the coordinate relationships of other corner points to obtain the roll angle, forming complete attitude angle measurement data. The display and storage module displayed the attitude angle change curve in real time and stored the data according to the timestamp for easy comparison with gyroscope data later.

[0135] The experimental results are shown in the figure, and the attitude angle measurement results are as follows. Figure 8 As shown, Figure 8 (a) is the yaw angle measurement curve, Figure 8 (b) is the roll angle curve, Figure 8 (c) shows the pitch angle curve. The three attitude angle curves measured by this system are highly consistent with the gyroscope reference curve. The attitude angle deviation at each moment is within the allowable range, which proves that the method of calculating the attitude angle by the long side vector of the single square composite target is accurate and reliable, and can effectively capture the attitude change of the structure under test, thus meeting the attitude angle measurement requirements.

[0136] The motion trajectory verification experiment uses a model with a square target as the test object. The experimental setup is as follows: Figure 9 As shown, the model is fixed on a circular slide rail that can rotate around the center. The slide rail can be placed at three angles: -10°, 0°, and 10° to simulate circular motion at different spatial angles. A single square composite target is pasted on the surface of the model to ensure that the target can be captured by the binocular camera during the movement. The binocular camera module is installed on the outside of the circular slide rail. The positions of the master and slave cameras are adjusted so that the field of view of the master camera covers most of the area of ​​the slide rail, and the slave camera covers the edge area not covered by the master camera. The camera is controlled to acquire images at a preset frequency through a synchronous triggering mechanism.

[0137] After the experiment begins, the model is manually pushed to move in circles along the circular track. The binocular camera module continuously acquires images of the model's movement. The image processing module processes the images in real time and outputs the target corner coordinates. The field-of-view switching module determines the target position based on the corner coordinates. When the model moves to the edge of the track and the target exceeds the field of view of the main camera, it automatically switches to the slave camera data and fuses the dual camera data in the overlapping area to ensure continuous output of corner coordinates. The displacement and trajectory calculation module calculates the real-time world coordinates of the midpoint (representative point) of the target's diagonal based on the corner coordinates of each frame, obtains the x, y, and z coordinate data at different time points, and then associates the coordinate points in chronological order to generate the model's three-dimensional motion trajectory.

[0138] Experimental results are as follows Figure 10 As shown, the circular motion trajectory of the slide rail is measured at different placement angles. Figure 10 (a) shows the trajectory of the slide rail when it is at 0°. Figure 10 (b) shows the trajectory at 10°. Figure 10 (c) is the trajectory at -10°. Figure 10 (d) shows the overall trajectory. All three trajectories are regular circles, which are completely consistent with the actual shape of the circular slide rail. The edges of the trajectories are smooth and without breaks, which proves that the system can accurately capture the continuous motion process of the structure under test. The generated three-dimensional motion trajectory can truly reflect the motion path of the structure, which verifies the effectiveness and accuracy of the system in motion trajectory measurement.

[0139] During the three experiments described above, the display and storage module showed the measurement data in real time: For linear displacement measurement, it simultaneously displayed the displacement curves in the x, y, and z directions, as well as the resultant displacement curve; for attitude angle measurement, it output the yaw, pitch, and roll angle curves in real time; and for motion trajectory measurement, it dynamically displayed the formation process of the three-dimensional motion trajectory. All curves supported time range adjustment and data point querying, facilitating real-time observation of the measurement status by the experimenters. Simultaneously, the system stored all measurement data in a preset format file according to the timestamp. The file contained the target corner coordinates, x / y / z displacement components, resultant displacement, yaw angle, pitch angle, and corresponding time information. After the experiment, the file could be exported for offline analysis. The data storage was complete and without loss, providing data support for subsequent experimental report writing and technical solution optimization.

[0140] This embodiment

[0141] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any changes, modifications, substitutions, integrations, and parameter changes made to these embodiments within the spirit and principles of the present invention, without departing from the principles and spirit of the present invention, through conventional substitutions or to achieve the same function, fall within the scope of protection of the present invention.

Claims

1. A lightweight binocular vision structure for measuring three-dimensional displacement and motion trajectory, characterized in that, include: Perform binocular camera positioning on a binocular camera device consisting of a main camera and a slave camera, and acquire camera parameters; Images of the structure under test during its motion are acquired using the binocular camera device. A single square composite target is provided on the surface of the structure under test, and the single square composite target is provided with positioning points. The acquired images are processed to identify the positioning points to lock the target area, and the corner coordinates of the single square composite target are extracted. Based on the corner coordinates, it is determined whether the single square composite target exceeds the field of view of the main camera. If it does, the data is switched to the slave camera. In the overlapping area of ​​the fields of view of the main camera and the slave camera, the data of the main camera and the slave camera are fused together. Based on the camera parameters, corner coordinates, and image data after field of view switching, the six-degree-of-freedom displacement of the structure under test is calculated, thereby obtaining the three-dimensional displacement and motion trajectory of the structure under test.

2. The lightweight binocular vision structure three-dimensional displacement and motion trajectory measurement method according to claim 1, characterized in that, The step of performing binocular camera calibration on a binocular camera device consisting of a main camera and a slave camera, and obtaining camera parameters, includes: Calibration was performed using a checkerboard calibration plate. The checkerboard calibration board is placed at different positions and orientations near the structure under test, and the main camera and the slave camera are controlled to acquire multiple calibration images respectively. Corner point identification is performed on the acquired calibration images, and calibration images with errors exceeding a preset threshold are removed; The remaining calibration images are used to perform calibration calculations to obtain the intrinsic and extrinsic parameters of the main camera and the slave camera, and to establish a world coordinate system that coincides with the coordinate system of the main camera. The camera parameters include the intrinsic and extrinsic parameters and the world coordinate system parameters.

3. The lightweight binocular vision structure three-dimensional displacement and motion trajectory measurement method according to claim 2, characterized in that, The process of processing the acquired image, identifying the positioning points to lock the target area, and extracting the corner coordinates of the single-square composite target includes: The acquired image is converted to grayscale to obtain a grayscale image; The grayscale image is subjected to adaptive threshold binarization to enhance the contrast between the single square composite target and the background. The template matching algorithm is used to identify the positioning points in the grayscale image, and the target area of ​​the single square composite target is locked based on the position of the positioning points. A fixed-window sub-pixel corner detection algorithm is used to extract the corner coordinates of the single square composite target within the target area. The extracted corner coordinates are iteratively optimized until the coordinate correction amount is less than a preset pixel threshold.

4. The lightweight binocular vision structure three-dimensional displacement and motion trajectory measurement method according to claim 1, characterized in that, The step of determining whether the single-square composite target exceeds the field of view of the main camera based on the corner coordinates, and if it does, switching to slave camera data, and processing the overlapping area of ​​the fields of view of the main camera and slave camera using a method of fusing main camera data and slave camera data, includes: The distance between the edge of the single-square composite target and the edge of the image captured by the main camera is calculated in real time. If the distance is less than a preset ratio threshold of the width of the image captured by the main camera, it is determined that the single square composite target is beyond the field of view of the main camera, and the system switches to image data captured by the camera. If the single-square composite target is located in the overlapping area of ​​the fields of view of the main camera and the slave camera, a weighted fusion method with preset weight allocation is used to process the data from the main camera and the slave camera. The fused data ,in Main camera data, To obtain data from the camera, Weighting of main camera data. Weights are derived from camera data.

5. The lightweight binocular vision structure three-dimensional displacement and motion trajectory measurement method according to claim 1, characterized in that, in, The calculation of the six-degree-of-freedom displacement includes: taking the midpoint of the diagonal of the single-square composite target as the representative point, converting the image point coordinates into world coordinates in combination with the camera parameters, and comparing them with the initial frame coordinates to obtain... The displacement components are oriented, and the formula for calculating the displacement components is as follows: ,in The world coordinates of the representative point in the current frame are... The initial frame world coordinates of the representative point are used; the yaw and pitch angles are calculated using the long side vector of the single square composite target.

6. The lightweight binocular vision structure three-dimensional displacement and motion trajectory measurement method according to claim 5, characterized in that, The calculation of the yaw and pitch angles using the long side vector of the single-square composite target includes: Determine the two corner points corresponding to the long side of the single-square composite target, denoted as corner point 1 and corner point 2, and obtain the world coordinates of corner point 1. and the world coordinates of corner point 2 ; The projection vector of the longer side vector onto the horizontal plane is calculated based on the world coordinates. The projection vector is... ; Calculate the yaw angle using the arctangent function. The calculation formula is: ; Calculate the magnitude of the long side vector The calculation formula is: Calculate the angle between the longer side vector and the horizontal plane, and use this angle as the pitch angle. The calculation formula is: .

7. The lightweight binocular vision structure three-dimensional displacement and motion trajectory measurement method according to claim 5, characterized in that, After calculating the six-degree-of-freedom displacement of the structure under test, the method further includes: Calculate the resultant displacement of the structure under test. The calculation formula is: ,in They are respectively Directional displacement components; Real-time display of the structure under test Displacement curves in direction, resultant displacement curves, and curves showing changes in yaw and pitch angles; The six-degree-of-freedom displacement data and the resultant displacement data are stored in a preset format file according to the timestamp. The preset format file includes the corner coordinates of the single square composite target, displacement components in each direction, resultant displacement, and yaw and pitch angle information.

8. The lightweight binocular vision structure three-dimensional displacement and motion trajectory measurement method according to claim 2, characterized in that, The calibration calculation using the remaining calibration images to obtain the intrinsic and extrinsic parameters of the main camera and the slave camera includes: Based on Zhang Zhengyou's calibration algorithm, the pixel coordinates of the corner points in the remaining calibration images are calculated. with world coordinates The corresponding match; Based on the matching results, a system of calibration equations is constructed to solve for the intrinsic parameters of the master camera and the slave camera, including the focal length. , Principal point coordinates and distortion coefficients, wherein the distortion coefficients include radial distortion coefficients and tangential distortion coefficients, wherein the pixel coordinates are related to the camera coordinate system coordinates. The transformation relationship satisfies ; The relative pose parameters between the master camera and the slave camera are calculated using epipolar constraints and triangulation principles to obtain extrinsic parameters, including a rotation matrix. Translation vector The transformation relationship between world coordinate system coordinates and camera coordinate system coordinates satisfies .

9. The lightweight binocular vision structure three-dimensional displacement and motion trajectory measurement method according to claim 1, characterized in that, The acquisition of images of the structure under test during motion using the binocular camera device includes: By controlling the main camera and the slave camera to trigger image acquisition simultaneously, the image frames are synchronized, and the frame synchronization error is less than a preset time threshold. Images of the structure under test during its motion are acquired at a preset acquisition frequency. The acquisition frequency meets the requirements for medium-frequency motion monitoring, and the processing time for a single frame image does not exceed a preset time to ensure the real-time performance of acquisition and processing.

10. A lightweight binocular vision structure three-dimensional displacement and motion trajectory measurement system, characterized in that, For implementing the lightweight binocular vision structure three-dimensional displacement and motion trajectory measurement method as described in any one of claims 1-9, the system comprises: The binocular camera module consists of a main camera and a slave camera, used to acquire images of the structure under test during its motion. The surface of the structure under test is provided with a single square composite target. The calibration module is used to perform binocular camera calibration on the binocular camera module and obtain camera parameters, including intrinsic parameters, extrinsic parameters and world coordinate system parameters. The image processing module is used to perform grayscale and binarization processing on the images acquired by the binocular camera module, identify the positioning points on the single square composite target to lock the target area, and use a sub-pixel corner detection algorithm to extract the corner coordinates of the single square composite target. The field-of-view switching module is used to calculate the distance between the single square composite target and the edge of the main camera image based on the corner coordinates, determine whether it exceeds the field of view of the main camera, and if it does, switch to the data of the slave camera. In the overlapping area of ​​the field of view, the main and slave camera data are fused based on a preset weight formula. The calculation module is used to calculate the directional displacement components, resultant displacement, yaw angle, and pitch angle of the structure under test based on the camera parameters and corner coordinates, to obtain the six-degree-of-freedom displacement, and to generate a three-dimensional motion trajectory based on multiple frames of six-degree-of-freedom displacement data. The display and storage module is used to display displacement change curves, resultant displacement change curves, and attitude angle change curves in real time, and to store the measured data as a preset format file according to the timestamp.

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