Suspension spring point cloud data reconstruction method and device and computing equipment

Abstract

The invention discloses a suspension spring point cloud data reconstruction method and device and computing equipment. The method comprises the steps that first point cloud data, collected by a three-dimensional sensor, of partial missing of a suspension spring is acquired; determining a long side direction vector of a minimum enclosing rectangle of the first point cloud data; correcting the distribution direction of the first point cloud data based on a first rotation transformation matrix between the long side direction vector and a Z-axis positive direction vector of a world coordinate system to obtain corrected point cloud data; acquiring end circle point cloud data in the corrected point cloud data; fitting the end circle point cloud data by using a spatial circle fitting algorithm to obtain end circle feature data; obtaining a starting endpoint on the contour center line of the correction point cloud data based on the end ring feature data by using an endpoint search algorithm; and performing contour reconstruction on the corrected point cloud data based on the end ring feature data and the starting end point by using a contour reconstruction algorithm to obtain contour reconstruction data. According to the invention, contour reconstruction can be carried out on partially missing suspension spring point cloud data.

Description

Technical Field

[0001] This invention relates to the field of three-dimensional measurement technology, and in particular to a method, device and computing equipment for reconstructing suspension spring point cloud data. Background Technology

[0002] As a key component of the automotive suspension system, the quality defects of suspension springs seriously affect the safety of vehicle operation. With the development of the automotive industry and the gradual increase in quality control requirements, traditional automotive component testing methods can no longer meet the higher requirements for testing capabilities and efficiency.

[0003] Currently, 3D sensors such as laser rangefinders are widely used in the field of 3D measurement due to their reliable measurement accuracy, high efficiency, and non-contact operation. However, in practical engineering applications, the point cloud data acquired by 3D sensors is often incomplete due to factors such as workspace limitations and the special structure of the measured object, which seriously interferes with subsequent measurement processes and measurement accuracy.

[0004] Suspension spring point cloud data reconstruction is a crucial step in achieving 3D measurement of suspension springs. The point cloud reconstruction capability directly affects the complexity of subsequent measurement methods and the accuracy of the measurement results. However, the reconstruction methods widely used in existing technologies fill in missing data by modeling local parts of the point cloud data. This method has high requirements for the integrity of the point cloud data and cannot meet the reconstruction needs of partially missing suspension spring point cloud data under severe occlusion conditions.

[0005] Therefore, a suspension spring point cloud data reconstruction method is needed to solve the problems existing in the above technical solutions. Summary of the Invention

[0006] Therefore, the present invention provides a method and apparatus for reconstructing suspension spring point cloud data to solve or at least alleviate the problems mentioned above.

[0007] According to one aspect of the present invention, a method for reconstructing suspension spring point cloud data is provided, executed in a computing device, comprising: acquiring first point cloud data of a suspension spring collected by a three-dimensional sensor, wherein the first point cloud data is suspension spring point cloud data with partially missing data; determining the minimum bounding rectangle of the first point cloud data, and determining the long side direction vector of the minimum bounding rectangle, wherein the long side direction vector is consistent with the spiral direction of the first point cloud data; and correcting the distribution direction of the first point cloud data based on a first rotation transformation matrix between the long side direction vector and the positive Z-axis direction vector of the world coordinate system, so as to correct the spiral direction of the first point cloud data. The direction is adjusted to be consistent with the positive Z-axis to obtain corrected point cloud data; end-loop point cloud data is obtained from the corrected point cloud data; the end-loop point cloud data is fitted using a spatial circle fitting algorithm to obtain end-loop feature data, which includes the end-loop axial direction vector, the center coordinates and radius of the fitted spatial circle; the starting endpoint is obtained on the contour centerline of the corrected point cloud data using an endpoint search algorithm based on the end-loop feature data, which includes a first endpoint and a second endpoint; the contour is reconstructed using a contour reconstruction algorithm based on the end-loop feature data and the starting endpoint to obtain contour reconstruction data.

[0008] Optionally, in the suspension spring point cloud data reconstruction method according to the present invention, the end ring includes a first end ring and a second end ring, and the end ring feature data includes first end ring feature data and second end ring feature data; fitting the end ring point cloud data using a spatial circle fitting algorithm to obtain end ring feature data includes: acquiring the first end ring point cloud data and the second end ring point cloud data in the corrected point cloud data, and fitting the first end ring point cloud data and the second end ring point cloud data respectively using a spatial circle fitting algorithm to obtain corresponding first end ring feature data and second end ring feature data; wherein, the first end ring feature data includes a first end ring axial direction vector, the first center coordinates and the first radius of the first end ring fitted spatial circle, and the second end ring feature data includes a second end ring axial direction vector, the second center coordinates and the second radius of the second end ring fitted spatial circle.

[0009] Optionally, in the suspension spring point cloud data reconstruction method according to the present invention, fitting the end ring point cloud data using a spatial circle fitting algorithm to obtain end ring feature data includes: obtaining the center point coordinates of the end ring point cloud data; performing a decentering operation on the end ring point cloud data based on the center point coordinates; performing plane fitting on the end ring point cloud data to obtain the principal direction vector of the end ring point cloud data, which is used as the end ring axial direction vector in the end ring feature data, wherein the principal direction vector of the end ring point cloud data represents the normal vector of the optimal fitting plane of the end ring point cloud data; and rotating the end ring point cloud data with the center point coordinates as the rotation center, based on the principal direction vector and the Z-axis of the world coordinate system. A second rotation transformation matrix between positive direction vectors is used to transform the end-loop point cloud data to obtain end-loop transformed data; the end-loop transformed data is projected onto the XY plane of the world coordinate system to obtain projection data; a planar circle is fitted based on the projection data, and the center and radius of the planar circle are obtained, with the radius of the planar circle as the radius of the end-loop fitted spatial circle; with the center of the planar circle as the rotation center, an inverse projection transformation is performed on the center of the planar circle based on the inverse transformation matrix of the second rotation transformation matrix to obtain the center coordinates of the spatial circle; a decentering inverse operation is performed on the center coordinates of the spatial circle to obtain the center coordinates of the end-loop fitted spatial circle.

[0010] Optionally, in the suspension spring point cloud data reconstruction method according to the present invention, the starting endpoint on the contour centerline of the correction point cloud data is obtained based on the end ring feature data using an endpoint search algorithm, including: extracting contour centerline data from the correction point cloud data based on the end ring feature data using a skeleton extraction algorithm; traversing to obtain multiple nearest neighbor points in the contour centerline data; determining the direction vector of each nearest neighbor point relative to the contour centerline data; determining whether the direction vectors of each nearest neighbor point relative to the contour centerline data are all in the same direction; if they are all in the same direction, then selecting the data point with the smallest Z value and the data point with the largest Z value from the contour centerline data, respectively as the first endpoint and the second endpoint of the starting endpoint.

[0011] Optionally, in the suspension spring point cloud data reconstruction method according to the present invention, the contour centerline data includes multiple contour center points; using a skeleton extraction algorithm, based on the end ring feature data, the contour centerline data is extracted from the corrected point cloud data, including: taking the first circle center coordinates in the first end ring feature data as a first reference point, and the second circle center coordinates in the second end ring feature data as a second reference point; taking the positive X-axis direction of the world coordinate system as the starting angle, and based on a first predetermined angle step size, collecting each data point on the fitted space circle of the first end ring as each third reference point and forming a third reference point set; traversing and obtaining... Take each third reference point in the third reference set, and determine a first reference plane based on the first reference point, the second reference point, and the third reference point. The first reference plane is used to segment the corrected point cloud data. Obtain each data point in the corrected point cloud data whose distance value from the first reference plane is less than a first threshold to obtain second point cloud data. Perform density clustering on the second point cloud data to obtain multi-cluster third point cloud data. Use a spatial circle fitting algorithm to fit the third point cloud data to obtain multiple contour center points of the third point cloud data, and form the contour centerline data based on the multiple contour center points.

[0012] Optionally, in the suspension spring point cloud data reconstruction method according to the present invention, a contour reconstruction algorithm is used to reconstruct the contour of the correction point cloud data based on the end ring feature data and the starting endpoint, including: taking the first circle center coordinates in the first end ring feature data as a first reference point, and the second circle center coordinates in the second end ring feature data as a second reference point; taking the direction determined by the first endpoint and the first reference point as a starting angle, and collecting each data point on the fitted space circle of the first end ring based on a second predetermined angle step size, as each fourth reference point and forming a fourth reference point set; traversing to obtain each fourth reference point in the fourth reference set, and determining a second reference plane based on the first reference point, the second reference point, and the fourth reference point, wherein the second reference plane is used to reconstruct the correction point. The cloud data is segmented; data points whose distance to the second reference plane is less than a second threshold are obtained from the corrected point cloud data to obtain fourth point cloud data; density clustering is performed on the fourth point cloud data to obtain multi-cluster fifth point cloud data; the fifth point cloud data is fitted using a spatial circle fitting algorithm to obtain the fifth axial direction vector of the fifth point cloud data, the fifth center coordinates and the fifth radius of the fitted spatial circle of the fifth point cloud data; a complete spatial circle data is generated with the fifth center coordinates as the reconstruction circle center, the fifth radius as the reconstruction radius, and the fifth axial direction vector as the rotation axis direction, wherein the complete spatial circle data includes multiple data points distributed circumferentially; data points located at the first endpoint and the second endpoint in the complete spatial circle data are removed to obtain contour reconstruction data.

[0013] Optionally, in the suspension spring point cloud data reconstruction method according to the present invention, the end ring point cloud data is spatially spirally distributed.

[0014] Optionally, in the suspension spring point cloud data reconstruction method according to the present invention, the three-dimensional sensor includes one or more of a laser rangefinder, a laser profilometer, and a structured light sensor.

[0015] According to one aspect of the present invention, a suspension spring point cloud data reconstruction device is provided, deployed in a computing device, comprising: a first acquisition unit for acquiring first point cloud data of a suspension spring collected by a three-dimensional sensor, wherein the first point cloud data is suspension spring point cloud data with partially missing data; a determination unit for determining the minimum bounding rectangle of the first point cloud data and determining the long side direction vector of the minimum bounding rectangle, wherein the long side direction vector is consistent with the spiral direction of the first point cloud data; and a correction unit for correcting the distribution direction of the first point cloud data based on a first rotation transformation matrix between the long side direction vector and the positive Z-axis direction vector of the world coordinate system, so as to adjust the spiral direction of the first point cloud data. The system is first aligned with the positive Z-axis to obtain corrected point cloud data; then, a second acquisition unit acquires end-loop point cloud data from the corrected point cloud data; a fitting unit uses a spatial circle fitting algorithm to fit the end-loop point cloud data to obtain end-loop feature data, which includes the end-loop axial direction vector, the center coordinates of the fitted spatial circle, and the radius; an endpoint search unit uses an endpoint search algorithm to acquire the starting endpoint on the contour centerline of the corrected point cloud data based on the end-loop feature data, which includes a first endpoint and a second endpoint; and a reconstruction unit uses a contour reconstruction algorithm to reconstruct the contour of the corrected point cloud data based on the end-loop feature data and the starting endpoint to obtain contour reconstruction data.

[0016] According to one aspect of the present invention, a computing device is provided, comprising: at least one processor; and a memory storing program instructions, wherein the program instructions are configured to be executed by the at least one processor, the program instructions including instructions for performing the suspension spring point cloud data reconstruction method as described above.

[0017] According to one aspect of the present invention, a computer program product is provided, comprising a computer program / instructions, wherein the computer program / instructions, when executed by a processor, implement the method as described above.

[0018] According to one aspect of the present invention, a readable storage medium storing program instructions is provided, which, when read and executed by a computing device, causes the computing device to perform the suspension spring point cloud data reconstruction method as described above.

[0019] According to the technical solution of the present invention, a method for reconstructing suspension spring point cloud data is provided. After correcting the distribution direction of the suspension spring point cloud data, end ring point cloud data is obtained from it. A spatial circle fitting algorithm is used to fit the end ring point cloud data to obtain end ring feature data (including the end ring axial direction vector, the center coordinates and radius of the fitted spatial circle). Then, an endpoint search algorithm is used based on the end ring feature data to obtain the starting endpoint on the contour centerline of the point cloud data. Finally, a contour reconstruction algorithm is used based on the end ring feature data and the starting endpoint to reconstruct the contour of the point cloud data, obtaining contour reconstruction data. Based on this, even when the suspension spring is severely obscured, contour reconstruction can be performed on partially missing suspension spring point cloud data acquired by a 3D sensor, filling in the missing data. This simplifies subsequent measurement and data processing procedures and improves the accuracy of 3D measurement of suspension springs.

[0020] Furthermore, according to the contour reconstruction algorithm of the present invention, by removing data points at the endpoints, the incorrect contour reconstruction of the suspension spring point cloud data due to insufficient data points can be effectively avoided, thereby improving the quality of the reconstructed contour.

[0021] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] To achieve the foregoing and related objectives, certain illustrative aspects are described herein in conjunction with the following description and accompanying drawings. These aspects indicate various ways in which the principles disclosed herein may be practiced, and all aspects and their equivalents are intended to fall within the scope of the claimed subject matter. The foregoing and other objectives, features, and advantages of the invention will become more apparent from the following detailed description, taken in conjunction with the accompanying drawings. Throughout the invention, the same reference numerals generally refer to the same parts or elements.

[0023] Figure 1 A schematic diagram of a computing device 100 provided according to an embodiment of the present invention is shown;

[0024] Figure 2 A schematic flowchart of a suspension spring point cloud data reconstruction method 200 provided according to an embodiment of the present invention is shown;

[0025] Figure 3 A flowchart illustrating step 250 of a suspension spring point cloud data reconstruction method according to an embodiment of the present invention is shown.

[0026] Figure 4 A schematic diagram illustrating the principle of the spatial circle fitting algorithm according to an embodiment of the present invention is shown;

[0027] Figure 5 A flowchart illustrating step 260 of a suspension spring point cloud data reconstruction method provided according to an embodiment of the present invention is shown.

[0028] Figure 6 A flowchart illustrating step 261 according to an embodiment of the present invention is shown;

[0029] Figure 7 A schematic diagram illustrating the principle of the skeleton extraction algorithm according to an embodiment of the present invention is shown;

[0030] Figure 8 A flowchart illustrating step 270 of a suspension spring point cloud data reconstruction method provided according to an embodiment of the present invention is shown.

[0031] Figure 9 A schematic diagram of a suspension spring point cloud data reconstruction device 900 provided according to an embodiment of the present invention is shown. Detailed Implementation

[0032] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0033] To address the problem that existing methods for reconstructing suspension spring point cloud data have high requirements for the integrity of the point cloud data and cannot meet the reconstruction needs of partially missing suspension spring point cloud data under severe occlusion, this invention proposes a suspension spring point cloud data reconstruction method that can complete and reconstruct partially missing original suspension spring point cloud data collected by 3D sensors. This simplifies subsequent measurement and data processing procedures and improves the accuracy of 3D measurement of suspension springs.

[0034] The suspension spring point cloud data reconstruction method in this embodiment of the invention can be executed in a computing device.

[0035] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0036] Figure 1 A schematic diagram of a computing device 100 according to an embodiment of the present invention is shown. Figure 1As shown, in a basic configuration, computing device 100 includes at least one processing unit 102 and system memory 104. According to one aspect, depending on the configuration and type of the computing device, the processing unit 102 may be implemented as a processor. System memory 104 includes, but is not limited to, volatile memory (e.g., random access memory), non-volatile memory (e.g., read-only memory), flash memory, or any combination of such memories. According to one aspect, system memory 104 includes an operating system 105.

[0037] According to one aspect, operating system 105 is, for example, suitable for controlling the operation of computing device 100. Furthermore, examples are practiced in conjunction with graphics libraries, other operating systems, or any other applications, and are not limited to any particular application or system. Figure 1 The basic configuration is illustrated by the components within the dashed lines. According to one aspect, the computing device 100 has additional features or functions. For example, according to one aspect, the computing device 100 includes additional data storage devices (removable and / or non-removable), such as disks, optical discs, or magnetic tapes. This additional storage... Figure 1 The middle part is shown by removable storage device 109 and non-removable storage device 110.

[0038] As stated above, according to one aspect, program module 103 is stored in system memory 104. According to one aspect, program module 103 may include one or more applications. The present invention does not limit the type of application; for example, applications may include: email and contact applications, word processing applications, spreadsheet applications, database applications, slideshow applications, drawing or computer-aided applications, web browser applications, etc.

[0039] According to one aspect, program module 103 may include a plurality of program instructions adapted to perform the suspension spring point cloud data reconstruction method 200 of the present invention, such that computing device 100 is configured to perform the suspension spring point cloud data reconstruction method 200 of the present invention.

[0040] According to one aspect, program module 103 may include a suspension spring point cloud data reconstruction device 900, which may be configured to perform the suspension spring point cloud data reconstruction method 200 of the present invention.

[0041] According to one aspect, examples can be practiced on circuits including discrete electronic components, packaged or integrated electronic chips containing logic gates, circuits utilizing microprocessors, or on a single chip containing electronic components or a microprocessor. For example, it can be practiced via wherein... Figure 1Each or many of the components shown can be implemented as an example by integrating a System-on-a-Chip (SOC) on a single integrated circuit. According to one aspect, such an SOC device may include one or more processing units, graphics units, communication units, system virtualization units, and various application functions, all integrated (or “burned in”) as a single integrated circuit onto a chip substrate. When operating via the SOC, the functions described herein can be operated via dedicated logic integrated on a single integrated circuit (chip) with other components of the computing device 100. Embodiments of the invention can also be implemented using other techniques capable of performing logical operations (e.g., AND, OR, and NOT), including but not limited to mechanical, optical, fluid, and quantum technologies. Additionally, embodiments of the invention can be implemented within a general-purpose computer or in any other circuit or system.

[0042] According to one aspect, computing device 100 may also have one or more input devices 112, such as a keyboard, mouse, pen, voice input device, touch input device, etc. It may also include output devices 114, such as a display, speaker, printer, etc. The foregoing devices are examples and other devices may also be used. Computing device 100 may include one or more communication connections 116 that allow communication with other computing devices 118. Examples of suitable communication connections 116 include, but are not limited to: RF transmitter, receiver and / or transceiver circuitry; Universal Serial Bus (USB), parallel and / or serial ports.

[0043] As used herein, the term computer-readable medium includes computer storage medium. Computer storage medium can include volatile and non-volatile, removable and non-removable media implemented using any method or technology for storing information (e.g., computer-readable instructions, data structures, or program module 103). System memory 104, removable storage device 109, and non-removable storage device 110 are examples of computer storage media (i.e., memory storage). Computer storage media can include random access memory (RAM), read-only memory (ROM), electrically erasable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical storage, magnetic tape, magnetic tape, disk storage or other magnetic storage devices, or any other article of manufacture that can be used to store information and is accessible by computing device 100. According to one aspect, any such computer storage medium can be part of computing device 100. Computer storage media does not include carrier waves or other transmitted data signals.

[0044] According to one aspect, the communication medium is implemented by computer-readable instructions, data structures, program modules 103, or other data in a modulated data signal (e.g., a carrier wave or other transmission mechanism), and includes any information transmission medium. According to one aspect, the term "modulated data signal" describes a signal having one or more sets of characteristics or altered in a manner that encodes information in the signal. By way of example and not limitation, the communication medium includes wired media such as wired networks or direct wired connections, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.

[0045] In an embodiment of the present invention, a computing device 100 is configured to perform the suspension spring point cloud data reconstruction method 200 of the present invention. The computing device 100 includes one or more processors and one or more readable storage media storing program instructions that, when configured to be executed by the one or more processors, cause the computing device to perform the suspension spring point cloud data reconstruction method 200 of the present invention.

[0046] Figure 2 A schematic flowchart of a suspension spring point cloud data reconstruction method 200 according to an embodiment of the present invention is shown. The suspension spring point cloud data reconstruction method 200 can be executed in a computing device (e.g., the aforementioned computing device 100).

[0047] like Figure 2 As shown, the suspension spring point cloud data reconstruction method 200 includes the following steps 210 to 270.

[0048] Step 210: The computing device 100 can acquire the first point cloud data of the suspension spring collected by the three-dimensional sensor.

[0049] It should be noted here that the first point cloud data is suspension spring point cloud data with some missing data. For example, when the suspension springs are severely obstructed, the point cloud data of the suspension springs collected by the 3D sensor may have some missing data. In some embodiments, the first point cloud data may be clean point cloud data that has undergone preprocessing such as denoising.

[0050] In embodiments of the present invention, the first point cloud data is spatially spirally distributed. The first point cloud data can be a collection of multiple data points of the suspension spring acquired by various three-dimensional sensors. The three-dimensional sensors used to acquire the first point cloud data of the suspension spring may include, for example, a laser rangefinder, a laser profilometer, or a structured light sensor, but are not limited thereto. In some embodiments, the three-dimensional sensor may include one or more of a laser rangefinder, a laser profilometer, and a structured light sensor.

[0051] Step 220: The computing device 100 can determine the minimum bounding rectangle of the first point cloud data and determine the direction vector of the long side of the minimum bounding rectangle.

[0052] It should be noted that, in this embodiment of the invention, the direction vector of the long side of the minimum bounding rectangle is consistent with the spiral direction of the first point cloud data. The direction of the minimum bounding rectangle is not aligned with the world coordinate system. The world coordinate system coincides with the three-dimensional sensor coordinate system and remains fixed during point cloud data processing.

[0053] Next, in order to adjust the spiral direction of the first point cloud data to be consistent with the positive Z-axis direction of the world coordinate system, a first rotation transformation matrix can be constructed between the long side direction vector and the positive Z-axis direction vector of the world coordinate system. This first rotation transformation matrix is ​​then used to correct the distribution direction of the first point cloud data, as detailed in step 230. It can be understood that the first rotation transformation matrix represents the rotation transformation matrix required to adjust the long side direction vector to be consistent with the positive Z-axis direction.

[0054] Step 230: The computing device 100 can correct the distribution direction of the first point cloud data based on the first rotation transformation matrix between the long side direction vector and the positive Z-axis direction vector of the world coordinate system, so as to adjust the spiral direction of the first point cloud data to be consistent with the positive Z-axis direction of the world coordinate system. After correction, the corrected point cloud data corresponding to the first point cloud data can be obtained.

[0055] It should be noted that the corrected point cloud data is the point cloud data obtained after correcting the distribution direction of the first point cloud data. To distinguish it from the initial first point cloud data, it will be referred to as the corrected point cloud data below.

[0056] It should be understood that, in this embodiment of the invention, since the direction vector of the long side of the smallest bounding rectangle is consistent with the spiral direction of the first point cloud data, this long side direction vector can be selected as the reference for adjusting the distribution direction of the first point cloud data. In other embodiments, the direction vector can be selected according to the actual style of the suspension spring.

[0057] Step 240: The computing device 100 can acquire the end circle point cloud data (i.e., end circle local data) in the calibration point cloud data.

[0058] It should be noted that an end ring is part of a suspension spring, and a suspension spring can include multiple end rings. The end ring point cloud data is distributed in a spatial spiral pattern.

[0059] Step 250: The computing device 100 can use a spatial circle fitting algorithm to fit the end-loop point cloud data to obtain end-loop feature data. The end-loop feature data includes the end-loop axial direction vector (i.e., the principal direction vector of the end-loop point cloud data), the coordinates of the center of the fitted spatial circle, and the radius of the fitted spatial circle. The specific process of the spatial circle fitting algorithm will be detailed below.

[0060] In an embodiment of the present invention, the end rings include a first end ring (lower end ring) and a second end ring (upper end ring) of the suspension spring. In other words, the end ring point cloud data obtained in step 240 includes both first and second end ring point cloud data. Correspondingly, the end ring feature data in step 250 includes both first and second end ring feature data. The first end ring feature data includes the axial direction vector of the first end ring, the coordinates of the first center of the fitted spatial circle of the first end ring (lower end ring), and the first radius. The second end ring feature data includes the axial direction vector of the second end ring, the coordinates of the second center of the fitted spatial circle of the second end ring (upper end ring), and the second radius.

[0061] In other words, in step 240, the computing device 100 can acquire the first end-loop point cloud data and the second end-loop data from the corrected point cloud data. Subsequently, in step 250, the computing device 100 can use a spatial circle fitting algorithm to fit the first end-loop point cloud data and the second end-loop data respectively, to obtain the first end-loop feature data corresponding to the first end-loop point cloud data and the second end-loop feature data corresponding to the second end-loop data. That is, by fitting the first end-loop point cloud data using the spatial circle fitting algorithm, the first end-loop feature data can be obtained; by fitting the second end-loop point cloud data using the spatial circle fitting algorithm, the second end-loop feature data can be obtained. The first end-loop feature data includes the first end-loop axial direction vector, the first center coordinates and the first radius of the fitted spatial circle; the second end-loop feature data includes the second end-loop axial direction vector, the second center coordinates and the second radius of the fitted spatial circle.

[0062] Step 260: The computing device 100 can use an endpoint search algorithm to obtain the starting endpoint on the contour centerline of the corrected point cloud data based on the end circle feature data (first end circle feature data and second end circle feature data).

[0063] In embodiments of the present invention, the starting endpoints on the contour centerline of the acquired corrected point cloud data include a first endpoint (lower starting endpoint) and a second endpoint (upper starting endpoint). The specific process of the endpoint search algorithm will be detailed below.

[0064] Step 270: The computing device 100 can use a contour reconstruction algorithm to reconstruct the contour of the correction point cloud data based on the end ring feature data and the starting endpoint, thereby obtaining contour reconstruction data. In this way, the contour reconstruction data can be used to fill in some missing data in the correction point cloud data (first point cloud data), realizing the reconstruction of partially missing suspension spring point cloud data.

[0065] Figure 3 A flowchart illustrating step 250 of a suspension spring point cloud data reconstruction method provided according to an embodiment of the present invention is shown. Figure 4 A schematic diagram illustrating the principle of the spatial circle fitting algorithm according to an embodiment of the present invention is shown. Below, it will be discussed in conjunction with... Figure 3 and Figure 4 This section provides a detailed explanation of the process in step 250 where the spatial circle fitting algorithm is used to fit the end circle point cloud data to obtain the end circle feature data.

[0066] like Figure 3 As shown, step 240 further includes steps 241 to 247.

[0067] Step 241: The computing device obtains the center point coordinates (i.e., the centroid coordinates of the end-circle point cloud data), and performs a decentralization operation on the end-circle point cloud data based on the center point coordinates. Here, centralization can be achieved by uniformly subtracting the center point coordinate value from the end-circle point cloud data.

[0068] Step 242: The computing device can perform plane fitting on the end-loop point cloud data, thereby obtaining the principal direction vector of the end-loop point cloud data, and using the principal direction vector of the end-loop point cloud data as the end-loop axial direction vector in the aforementioned end-loop feature data. Here, the principal direction vector of the end-loop point cloud data represents the normal vector of the optimal fitting plane of the end-loop point cloud data.

[0069] Step 243: The computing device uses the center point coordinates as the rotation center and the second rotation transformation matrix between the principal direction vector of the end circle point cloud data and the positive Z-axis direction vector of the world coordinate system to transform the end circle point cloud data, thereby obtaining the end circle transformed data.

[0070] Here, it can be understood that the second rotation transformation matrix is ​​used to represent the rotation transformation matrix required to adjust the principal direction vector of the end-loop point cloud data to be consistent with the positive Z-axis direction of the world coordinate system. For example... Figure 4 As shown, by transforming the end-loop point cloud data based on the second rotation transformation matrix, the main direction vector of the end-loop point cloud data can be adjusted to be consistent with the positive Z-axis direction of the world coordinate system, thus obtaining the end-loop transformed data.

[0071] Step 244: The computing device can project the end-circle transformation data onto the XY plane of the world coordinate system to obtain the projected data. Here, the XY plane of the world coordinate system is the target projection plane of the end-circle transformation data in the spatial circle fitting algorithm.

[0072] Here, for reference Figure 4 Projecting the end-circle transformation data onto the XY plane of the world coordinate system can generate multiple projection point data distributed along a circle, which is the projection data.

[0073] Step 245: The computing device can fit a 2D planar circle based on the projection data (i.e., perform 2D planar circle fitting on the projection data), see reference. Figure 4 Subsequently, the center and radius of the 2D planar circle can be obtained, and the radius of the 2D planar circle can be used as the radius of the end ring fitting space circle.

[0074] Step 246, Reference Figure 4 The computing device uses the center of the 2D planar circle as the rotation center and performs an inverse projection transformation on the center of the 2D planar circle based on the inverse transformation matrix of the second rotation transformation matrix to obtain the coordinates of the center of the 3D spatial circle. Here, the rotation center is the coordinate value of the center of the 2D planar circle extended to homogeneous coordinates.

[0075] Step 247: The computing device obtains the center coordinates of the fitted spatial circle by performing a decentering inverse operation on the center coordinates of the 3D spatial circle. Here, the decentering inverse operation is achieved by adding the aforementioned center point coordinates to the center coordinates of the 3D spatial circle.

[0076] In the embodiment of the present invention, Figure 4 As shown, the end-loop point cloud data exhibits a spatial spiral distribution. Therefore, the spatial circle fitting can first be simplified to a planar circle fitting through projection operations, and then the three-dimensional information of the spatial circle can be recovered through inverse projection transformation. It should be understood that in specific implementations, different principal direction analysis methods can be used for end-loop point cloud data with different distributions.

[0077] According to the spatial circle fitting algorithm in the embodiment of the present invention, the principal direction vector of the end ring point cloud data is used as the end ring axial direction vector, the radius of the 2D planar circle is used as the radius of the end ring fitting spatial circle, and the center coordinates of the end ring fitting spatial circle are obtained by performing a decentralization inverse operation on the center coordinates of the 3D spatial circle. Based on this, end ring feature data can be obtained based on the end ring axial direction vector, the radius of the end ring fitting spatial circle, and the center coordinates of the end ring fitting spatial circle.

[0078] Figure 5 A flowchart illustrating step 260 of a suspension spring point cloud data reconstruction method according to an embodiment of the present invention is shown. The following will be combined with... Figure 5This section provides a detailed explanation of the process in step 260 where the starting endpoint on the contour centerline of the corrected point cloud data is obtained based on the end circle feature data using the endpoint search algorithm.

[0079] like Figure 5 As shown, step 260 further includes the following steps 261 to 265.

[0080] Step 261: The computing device 100 can use a skeleton extraction algorithm to extract contour centerline data (sparse contour centerline data) from the corrected point cloud data based on end-loop feature data (first end-loop feature data and second end-loop feature data). The specific process of the skeleton extraction algorithm will be detailed below.

[0081] Step 262: The computing device 100 can traverse and obtain multiple nearest neighbor points in the contour centerline data.

[0082] For example, the contour centerline data can be traversed to obtain k nearest neighbor points. The value of k can be set reasonably according to the distribution of the contour centerline data, and k is a positive integer greater than or equal to 2.

[0083] Step 263: The computing device 100 determines the direction vector of each nearest point relative to the contour centerline data.

[0084] It should be noted that by subtracting the contour center point of the contour centerline data from each nearest neighbor point, the direction vector of each nearest neighbor point relative to the contour centerline data can be obtained.

[0085] Step 264: The computing device 100 determines whether the direction vectors of each nearest point and the contour centerline data are in the same direction.

[0086] In some embodiments, the cosine of the angle between any two direction vectors can be calculated to determine whether the two direction vectors are in the same direction. If the cosine of the angle between the two direction vectors is greater than a cosine threshold, then the two direction vectors are determined to be in the same direction. Here, the cosine threshold can be reasonably set according to the distribution of the contour centerline data.

[0087] If the direction vectors of each nearest neighbor point and the contour centerline data are in the same direction, then the following steps 265 can be performed.

[0088] Step 265: The computing device 100 can use the contour centerline data as candidate endpoints, select the data point with the smallest Z value and the data point with the largest Z value from the contour centerline data, and use the data point with the smallest Z value and the data point with the largest Z value as the first endpoint and the second endpoint of the starting endpoint, respectively.

[0089] In this way, the starting endpoint on the contour centerline of the corrected point cloud data can be obtained based on the end-circle point cloud data.

[0090] Figure 6 A flowchart illustrating step 261 according to an embodiment of the present invention is shown. Figure 7 A schematic diagram illustrating the principle of the skeleton extraction algorithm according to an embodiment of the present invention is shown. Below, it will be combined with... Figure 6 and Figure 7 This section provides a detailed explanation of the process in step 261, where the skeleton extraction algorithm is used to extract contour centerline data from the corrected point cloud data based on end-loop feature data. It should be noted that the contour centerline data includes multiple contour center points.

[0091] like Figure 6 As shown, step 261 further includes the following steps 2611 to 2616.

[0092] Step 2611: The computing device 100 can use the first center coordinates in the first end ring feature data as the first reference point and the second center coordinates in the second end ring feature data as the second reference point.

[0093] Step 2612: The computing device 100 takes the positive X-axis direction of the world coordinate system as the starting angle and collects each data point on the first end-circle fitting space circle based on the first predetermined angle step size, as each third reference point, and can form a third reference point set based on each third reference point.

[0094] Here, the first predetermined angular step size is, for example, angle θ. Angle θ is obtained by dividing 360° into n equal parts. Therefore, the third reference point set contains n third reference points, where n is a positive integer greater than or equal to 2, and the value of n can be set according to the actual situation.

[0095] Step 2613: The computing device 100 can iterate through and obtain each third reference point in the third reference set. For each third reference point obtained through the iterative process, a first reference plane can be determined based on the first reference point, the second reference point, and the third reference point, such as... Figure 7 As shown, the first reference plane can be used to subdivide the corrected point cloud data corresponding to the first point cloud data.

[0096] like Figure 7 As shown, the first reference plane is uniquely determined by the first reference point, the second reference point, and the third reference point. Furthermore, the first reference plane serves as the partitioning surface for the corrected point cloud data corresponding to the first point cloud data and can be used to partition the corrected point cloud data.

[0097] It should be noted that the first reference planes determined by traversing and obtaining each third reference point can form a cluster of planes passing through the first reference point and the second reference point.

[0098] Step 2614: The computing device 100 can obtain the second point cloud data by acquiring each data point whose distance value from the first reference plane is less than the first threshold from the corrected point cloud data.

[0099] It should be understood that in step 2614, the second point cloud data can be obtained based on the distance values ​​of each data point to the first reference plane. The first threshold is used to determine the size and distribution of the second point cloud data. The first threshold can be set according to actual circumstances.

[0100] Step 2615: The computing device 100 can perform density clustering on the second point cloud data to obtain multi-cluster third point cloud data.

[0101] Step 2616: The computing device 100 can use a spatial circle fitting algorithm to fit the third point cloud data to obtain multiple contour center points of the third point cloud data. Based on the multiple contour center points, contour centerline data can be formed. In this way, contour centerline data can be extracted from the correction point cloud data based on the end circle point cloud data.

[0102] In step 2616, the projection target plane in the spatial circle fitting algorithm is the first reference plane (corresponding to the XY plane of the world coordinate system in step 244 above).

[0103] Figure 8 A flowchart illustrating step 270 of a suspension spring point cloud data reconstruction method according to an embodiment of the present invention is shown. The following will be combined with... Figure 8 This section provides a detailed explanation of the process in step 270 where the contour reconstruction algorithm is used to reconstruct the contour of the corrected point cloud data based on the end circle feature data and the starting endpoint.

[0104] like Figure 8 As shown, step 270 further includes steps 271 to 278.

[0105] Step 271: Take the first center coordinates in the first end loop feature data as the first reference point, and take the second center coordinates in the second end loop feature data as the second reference point.

[0106] Step 272: Taking the direction determined by the first endpoint and the first reference point as the starting angle, collect each data point on the fitted space circle of the first endpoint according to the second predetermined angle step size, as each fourth reference point, and form a set of fourth reference points based on each fourth reference point.

[0107] Here, the second predetermined angle step size is, for example, an angle. angle It is obtained by dividing 360° into m equal parts. Therefore, the fourth reference point set contains m fourth reference points, where m is a positive integer greater than or equal to 2, and the value of m can be set according to the actual situation.

[0108] Step 273: Iterate through and obtain each fourth reference point in the fourth reference set. For each fourth reference point obtained through the iteration, the second reference plane can be determined based on the first reference point, the second reference point, and the fourth reference point.

[0109] Here, the second reference plane is uniquely determined by the first reference point, the second reference point, and the fourth reference point. Furthermore, the second reference plane serves as the partitioning surface for the corrected point cloud data corresponding to the first point cloud data and can be used to partition the corrected point cloud data.

[0110] It should be noted that the various second reference planes determined by traversing and obtaining each fourth reference point can form a cluster of planes passing through the first and second reference points.

[0111] Step 274: Obtain the data points whose distance to the second reference plane is less than the second threshold from the corrected point cloud data to obtain the fourth point cloud data.

[0112] It should be understood that in step 274, the fourth point cloud data can be obtained based on the distance values ​​of each data point to the second reference plane. The second threshold is used to determine the size and distribution of the fourth point cloud data, and the second threshold can be set according to actual conditions.

[0113] Step 275: Perform density clustering on the fourth point cloud data to obtain multi-cluster fifth point cloud data.

[0114] Step 276: Use the spatial circle fitting algorithm to fit the fifth point cloud data to obtain the fifth axial direction vector (main direction vector) of the fifth point cloud data, the coordinates of the fifth center of the fitted spatial circle of the fifth point cloud data, and the fifth radius.

[0115] In step 276, the projection target plane in the spatial circle fitting algorithm is the second reference plane (corresponding to the XY plane of the world coordinate system in step 244 above).

[0116] Step 277: Using the fifth center coordinate as the reconstruction center, the fifth radius as the reconstruction radius, and the fifth axial direction vector as the rotation axis, generate complete spatial circle data. The complete spatial circle data includes multiple data points distributed around the circumference.

[0117] For example, in some embodiments, complete spatial circle data may include s data points distributed circumferentially. The value of s is related to the point cloud data density of the reconstructed contour, and s is a positive integer. The value of s can be set by those skilled in the art according to the actual situation.

[0118] Step 278: Remove the data points located at the first and second endpoints in the complete spatial circle data to obtain the contour reconstruction data.

[0119] Based on the contour reconstruction data, some missing data in the correction point cloud data (first point cloud data) can be supplemented, thereby enabling contour reconstruction of the partially missing suspension spring point cloud data collected by the three-dimensional sensor.

[0120] It should be noted that removing data points at the endpoints can effectively avoid incorrect contour reconstruction of the suspension spring point cloud data due to insufficient data points, thereby improving the quality of the reconstructed contour.

[0121] Figure 9 A schematic diagram of a suspension spring point cloud data reconstruction apparatus 900 according to an embodiment of the present invention is shown. The suspension spring point cloud data reconstruction apparatus 900 can be deployed in a computing device 100, and the suspension spring point cloud data reconstruction apparatus 900 is configured to perform the suspension spring point cloud data reconstruction method 200 of the present invention.

[0122] like Figure 9 As shown, in an embodiment of the present invention, the suspension spring point cloud data reconstruction device 900 includes a first acquisition unit 910, a determination unit 920, a correction unit 930, a second acquisition unit 940, a fitting unit 950, an endpoint search unit 960, and a reconstruction unit 970 that are connected in sequence.

[0123] The first acquisition unit 910 is used to acquire the first point cloud data of the suspension spring collected by the three-dimensional sensor. The first point cloud data is the suspension spring point cloud data with some missing data.

[0124] The determining unit 920 is used to determine the minimum bounding rectangle of the first point cloud data and to determine the direction vector of the long side of the minimum bounding rectangle, wherein the direction vector of the long side is consistent with the spiral direction of the first point cloud data.

[0125] The correction unit 930 is used to correct the distribution direction of the first point cloud data based on the first rotation transformation matrix between the long side direction vector and the positive Z-axis direction vector of the world coordinate system, so as to adjust the spiral direction of the first point cloud data to be consistent with the positive Z-axis direction, thereby obtaining the corrected point cloud data.

[0126] The second acquisition unit 940 is used to acquire the end-circle point cloud data in the calibration point cloud data.

[0127] The fitting unit 950 is used to fit the end ring point cloud data using a spatial circle fitting algorithm to obtain end ring feature data. The end ring feature data includes the end ring axial direction vector, the center coordinates and radius of the fitted spatial circle of the end ring.

[0128] The endpoint search unit 960 is used to obtain the starting endpoint on the contour centerline of the corrected point cloud data based on the endpoint search algorithm and the end circle feature data. The starting endpoint includes a first endpoint and a second endpoint.

[0129] The reconstruction unit 970 is used to perform contour reconstruction on the corrected point cloud data based on the end circle feature data and the starting endpoint using a contour reconstruction algorithm to obtain contour reconstruction data.

[0130] It should be noted that the first acquisition unit 910, the determination unit 920, the correction unit 930, the second acquisition unit 940, the fitting unit 950, the endpoint search unit 960, and the reconstruction unit 970 are respectively used to execute the aforementioned steps 210 to 270. Here, the specific execution logic of each unit can be found in the description of steps 210 to 270 in the previous method 200, and will not be repeated here.

[0131] According to the suspension spring point cloud data reconstruction method 200 of this embodiment, after correcting the distribution direction of the suspension spring point cloud data, end ring point cloud data is obtained from it. A spatial circle fitting algorithm is used to fit the end ring point cloud data to obtain end ring feature data (including the end ring axial direction vector, the center coordinates and radius of the fitted spatial circle). Then, an endpoint search algorithm is used based on the end ring feature data to obtain the starting endpoint on the contour centerline of the point cloud data. Finally, a contour reconstruction algorithm is used based on the end ring feature data and the starting endpoint to reconstruct the contour of the point cloud data, obtaining contour reconstruction data. Based on this, even when the suspension spring is severely obscured, contour reconstruction can be performed on partially missing suspension spring point cloud data acquired by a three-dimensional sensor, filling in the missing data. This simplifies subsequent measurement and data processing procedures and improves the three-dimensional measurement accuracy of the suspension spring.

[0132] Furthermore, according to the contour reconstruction algorithm of the present invention, by removing data points at the endpoints, the incorrect contour reconstruction of the suspension spring point cloud data due to insufficient data points can be effectively avoided, thereby improving the quality of the reconstructed contour.

[0133] The various techniques described herein can be implemented in combination with hardware or software, or a combination thereof. Thus, the methods and apparatus of the present invention, or certain aspects or portions thereof, can take the form of program code (i.e., instructions) embedded in a tangible medium, such as a removable hard disk, USB flash drive, floppy disk, CD-ROM, or any other machine-readable storage medium, wherein when the program is loaded into and executed by a machine such as a computer, the machine becomes an apparatus for practicing the present invention.

[0134] When the program code is executed on a programmable computer, the mobile terminal generally includes a processor, a processor-readable storage medium (including volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device. The memory is configured to store program code; the processor is configured to execute the suspension spring point cloud data reconstruction method of the present invention according to instructions in the program code stored in the memory.

[0135] By way of example, and not limitation, readable media include readable storage media and communication media. Readable storage media stores information such as computer-readable instructions, data structures, program modules, or other data. Communication media generally embodies computer-readable instructions, data structures, program modules, or other data in the form of modulated data signals such as carrier waves or other transmission mechanisms, and includes any information delivery medium. Any combination of the above is also included within the scope of readable media.

[0136] In the specification provided herein, the algorithms and displays are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used with the examples of this invention. The required structure for constructing such systems is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0137] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0138] Similarly, it should be understood that, in order to streamline this disclosure and aid in understanding one or more of the various aspects of the invention, in the description of exemplary embodiments of the invention above, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof.

[0139] Those skilled in the art will understand that modules, units, or components of the devices disclosed in the examples herein can be arranged in the devices described in this embodiment, or alternatively, can be located in one or more devices different from the devices in this example. The modules in the foregoing examples can be combined into a single module or, in addition, can be divided into multiple sub-modules.

[0140] Unless otherwise specified, the use of ordinal numbers such as “first,” “second,” “third,” etc., to describe ordinary objects merely indicates different instances of similar objects and is not intended to imply that the objects being described must have a given order in time, space, ordering, or any other manner.

Claims

1. A method for reconstructing suspension spring point cloud data, executed in a computing device, comprising: Acquire first point cloud data of the suspension spring collected by the three-dimensional sensor, wherein the first point cloud data is point cloud data of the suspension spring with some missing data; Determine a minimum bounding rectangle of the first point cloud data, and determine a long side direction vector of the minimum bounding rectangle, wherein the long side direction vector is consistent with a spiral direction of the first point cloud data; Based on a first rotation transformation matrix between the long side direction vector and the positive direction vector of the Z axis of the world coordinate system, the distribution direction of the first point cloud data is corrected to adjust the spiral direction of the first point cloud data to be consistent with the positive direction of the Z axis, thereby obtaining corrected point cloud data; Acquire end circle point cloud data in the correction point cloud data; The end circle point cloud data is fitted using a space circle fitting algorithm to obtain end circle feature data, wherein the end circle feature data includes an end circle axial direction vector, a center coordinate and a radius of the end circle fitting space circle; Using an endpoint search algorithm, based on the end circle feature data, obtaining a starting endpoint on the contour centerline of the corrected point cloud data, wherein the starting endpoint includes a first endpoint and a second endpoint; The contour reconstruction algorithm is used to reconstruct the contour of the corrected point cloud data based on the end circle feature data and the starting end point to obtain contour reconstruction data.

2. The method of claim 1, wherein: in, The end ring includes a first end ring and a second end ring, and the end ring feature data includes first end ring feature data and second end ring feature data; The end circle point cloud data is fitted using a space circle fitting algorithm to obtain end circle feature data, including: Acquire first end circle point cloud data and second end circle point cloud data in the corrected point cloud data, and respectively fit the first end circle point cloud data and the second end circle point cloud data using a space circle fitting algorithm to obtain corresponding first end circle feature data and second end circle feature data; Among them, the first end circle feature data includes the first end circle axial direction vector, the first center point coordinates and the first radius of the first end circle fitting space circle, and the second end circle feature data includes the second end circle axial direction vector, the second center point coordinates and the second radius of the second end circle fitting space circle.

3. The method of claim 2, wherein: The end circle point cloud data is fitted using a space circle fitting algorithm to obtain end circle feature data, including: Acquire the center point coordinates of the end circle point cloud data, and perform a decentralization operation on the end circle point cloud data based on the center point coordinates; Performing plane fitting on the end circle point cloud data to obtain a main direction vector of the end circle point cloud data as an end circle axial direction vector in the end circle feature data, wherein the main direction vector of the end circle point cloud data represents a normal vector of the optimal fitting plane of the end circle point cloud data; Taking the center point coordinates as the rotation center, based on a second rotation transformation matrix between the main direction vector and the positive direction vector of the Z axis of the world coordinate system, transform the end circle point cloud data to obtain end circle transformation data; Projecting the end circle transformation data onto the XY plane of the world coordinate system to obtain projection data; Fitting a plane circle based on the projection data, obtaining the center of the plane circle and the radius of the plane circle, and using the radius of the plane circle as the radius of the end circle fitting space circle; Taking the center of the plane circle as the rotation center, performing an inverse projection transformation on the center of the plane circle based on the inverse transformation matrix of the second rotation transformation matrix to obtain the center coordinates of the space circle; A decentralized inverse operation is performed on the center coordinates of the space circle to obtain the center coordinates of the end ring fitting space circle.

4. The method according to claim 2 or 3, wherein: Using an endpoint search algorithm, based on the end circle feature data, obtaining a starting endpoint on the contour centerline of the corrected point cloud data, including: Extracting contour centerline data from the corrected point cloud data based on the end circle feature data using a skeleton extraction algorithm; Traversing and acquiring a plurality of neighboring points in the contour centerline data; Determine the direction vector between each of the neighboring points and the contour centerline data; Determine whether the direction vectors of each of the neighboring points and the contour centerline data are in the same direction; If they are both in the same direction, the data point with the smallest Z value and the data point with the largest Z value are selected from the contour centerline data as the first endpoint and the second endpoint of the starting endpoint respectively.

5. The method of claim 4, wherein: The contour centerline data includes a plurality of contour center points; Extracting contour centerline data from the corrected point cloud data based on the end circle feature data using a skeleton extraction algorithm, including: Using the first circle center coordinates in the first end circle feature data as a first reference point, and using the second circle center coordinates in the second end circle feature data as a second reference point; Taking the positive direction of the X-axis of the world coordinate system as the starting angle, collecting data points on the first end circle fitting space circle based on a first predetermined angle step as third reference points to form a third reference point set; Traversing and acquiring each third reference point in the third reference set, determining a first reference plane based on the first reference point, the second reference point and the third reference point, wherein the first reference plane is used to segment the corrected point cloud data; Acquire, from the corrected point cloud data, each data point whose distance value from the first reference plane is less than a first threshold value, to obtain second point cloud data; performing density clustering on the second point cloud data to obtain multiple clusters of third point cloud data; The third point cloud data is fitted using a space circle fitting algorithm to obtain a plurality of contour center points of the third point cloud data, and the contour centerline data is composed based on the plurality of contour center points.

6. The method according to any one of claims 2 to 5, wherein: Using a contour reconstruction algorithm, based on the end circle feature data and the starting endpoint, the contour of the corrected point cloud data is reconstructed, including: Using the first circle center coordinates in the first end circle feature data as a first reference point, and using the second circle center coordinates in the second end circle feature data as a second reference point; Taking the direction determined by the first endpoint and the first reference point as the starting angle, collecting data points on the first end circle fitting space circle based on a second predetermined angle step as fourth reference points to form a fourth reference point set; Traversing and acquiring each fourth reference point in the fourth reference set, determining a second reference plane based on the first reference point, the second reference point and the fourth reference point, wherein the second reference plane is used to segment the corrected point cloud data; Acquire, from the corrected point cloud data, each data point whose distance value to the second reference plane is less than a second threshold value, to obtain fourth point cloud data; performing density clustering on the fourth point cloud data to obtain multiple clusters of fifth point cloud data; Fitting the fifth point cloud data by using a space circle fitting algorithm to obtain a fifth axial direction vector of the fifth point cloud data, a fifth center coordinate and a fifth radius of the space circle fitted by the fifth point cloud data; Using the fifth circle center coordinate as the reconstruction circle center, the fifth radius as the reconstruction radius, and the fifth axial direction vector as the rotation axis direction, a complete space circle data is generated, wherein the complete space circle data includes a plurality of data points distributed on a circumference; Data points located at the first endpoint and the second endpoint in the complete space circle data are removed to obtain contour reconstruction data.

7. The method according to any one of claims 1 to 6, wherein: The end circle point cloud data is distributed in a spatial spiral.

8. The method according to any one of claims 1 to 7, wherein: The three-dimensional sensor includes one or more of a laser rangefinder, a laser profiler, and a structured light sensor.

9. A suspension spring point cloud data reconstruction device, deployed in a computing device, comprising: A first acquisition unit is configured to acquire first point cloud data of the suspension spring collected by the three-dimensional sensor, wherein the first point cloud data is point cloud data of the suspension spring with some missing data; A determination unit, which determines a minimum bounding rectangle of the first point cloud data, and determines a long side direction vector of the minimum bounding rectangle, wherein the long side direction vector is consistent with a spiral direction of the first point cloud data; a correction unit, which corrects the distribution direction of the first point cloud data based on a first rotation transformation matrix between the long side direction vector and the positive direction vector of the Z axis of the world coordinate system, so as to adjust the spiral direction of the first point cloud data to be consistent with the positive direction of the Z axis, thereby obtaining corrected point cloud data; A second acquisition unit is used to acquire the end circle point cloud data in the correction point cloud data; A fitting unit, which fits the end circle point cloud data using a space circle fitting algorithm to obtain end circle feature data, wherein the end circle feature data includes an end circle axial direction vector, a center coordinate and a radius of the end circle fitting space circle; An endpoint search unit, using an endpoint search algorithm, based on the end circle feature data, obtains a starting endpoint on the contour centerline of the corrected point cloud data, wherein the starting endpoint includes a first endpoint and a second endpoint; The reconstruction unit uses a contour reconstruction algorithm to reconstruct the contour of the corrected point cloud data based on the end circle feature data and the starting end point to obtain contour reconstruction data.

10. A computing device comprising: at least one processor; and A memory storing program instructions, wherein the program instructions are configured to be processed by the at least one processor, and the program instructions include instructions for processing the method according to any one of claims 1 to 8.

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