Method and system for aligning virtual models for automotive design with physical device coordinates

Abstract

This invention proposes a method and system for aligning the coordinates of a virtual model and a physical device for automotive design, belonging to the field of automotive 3D design technology. The method includes: extracting multiple sets of corresponding key points from both the virtual model and the motion capture coordinate system; performing rigid transformations on these points using a rigid transformation algorithm to determine the rotation matrix and translation vector; and determining whether the error factor meets the transformation requirements. When the error factor meets the transformation requirements, the coordinate system of the virtual model is transformed to the motion capture coordinate system, and the transformation error is verified using VR equipment. This invention enables precise alignment of coordinates between the virtual model and the physical device, providing effective data support for design review and other stages in automotive manufacturing.

Description

Technical Field

[0001] This invention belongs to the field of automotive 3D design technology, and particularly relates to a method and system for aligning the coordinates of virtual models and physical devices used in automotive design. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] With the continuous improvement of digitalization in automotive industrial design, 3D modeling and virtual reality technologies are being used more and more widely in the vehicle design review stage. Designers can build virtual vehicle models using 3D design software and conduct immersive reviews in a virtual environment using VR systems, thereby identifying and correcting problems early in the design process and improving development efficiency.

[0004] However, aligning virtual models with real physical devices presents a technical challenge due to inconsistencies in coordinate systems. Virtual models are constructed using the coordinate system of 3D design software, while physical devices are based on the physical space coordinate system defined by the motion capture system; therefore, a consistent coordinate reference cannot be established between the two. Currently, common alignment methods primarily rely on manual adjustment or traditional rigid transformation algorithms. Manual adjustment methods depend on the operator's experience, requiring repeated measurements and trial-and-error to achieve coordinate alignment, resulting in low efficiency, poor consistency, and susceptibility to subjective errors.

[0005] Traditional rigid transformation algorithms (such as the Kabsch algorithm) can achieve a certain degree of coordinate transformation, but they still have obvious limitations in practical applications. For example, on the one hand, when performing coordinate alignment based on such methods, the accuracy and distribution of the input point set are required to be high, resulting in poor robustness when there is noise interference or poor point set quality. On the other hand, these methods lack a systematic error evaluation and feedback mechanism for the transformation results, making it difficult for users to intuitively judge whether the alignment effect meets the review accuracy requirements. Summary of the Invention

[0006] To overcome the shortcomings of the prior art, this invention provides a method and system for aligning the coordinates of a virtual model and a physical device for automobile design. This method and system can achieve precise alignment of coordinates between the virtual model and the physical device, providing effective data support for design review and other stages during automobile manufacturing.

[0007] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: The first aspect of this invention provides a method for aligning the coordinates of a virtual model with physical equipment for automobile design.

[0008] Methods for aligning virtual models with physical device coordinates in automotive design include: Multiple sets of corresponding key points are extracted from the motion capture coordinate system of the virtual model and physical device used for automobile design. The key points include at least three non-collinear points among the steering wheel center, seat center and pedal center. The rigid transformation algorithm is used to perform rigid transformations on the obtained sets of corresponding key points to determine the rotation matrix and translation vector, and to determine whether the error factor meets the transformation requirements; wherein, the rigid transformation includes centroid normalization, covariance matrix construction, singular value decomposition and reflection detection; When the error factor does not meet the conversion requirements, key point extraction and rigid transformation are performed again; when the error factor meets the conversion requirements, the coordinate system under the virtual model is transformed to the motion capture coordinate system according to the obtained rotation matrix and translation vector, and the conversion error is checked using VR equipment to achieve coordinate alignment between the virtual model and the physical device.

[0009] Furthermore, the selection of the key points is based on the measurable characteristics of the physical device and the geometric structure of the virtual model.

[0010] Furthermore, the reflection detection includes: when a reflection is detected, adjusting the matrix elements in the singular value decomposition result to correct the rotation matrix so that the rotation matrix always represents a pure rotation.

[0011] Furthermore, the error factor is calculated based on the Euclidean norm of the conversion error, and the error factor is compared with a preset conversion threshold to determine whether the error factor meets the conversion requirements.

[0012] Furthermore, the verification criteria for the transformation error include: displaying the error distribution of the maximum error, minimum error, and root mean square error generated when transforming the coordinate system under the virtual model to the motion capture coordinate system through a graphical interface to assist users in making decisions and optimizations.

[0013] Furthermore, the coordinate alignment method is applied to the automotive design review process, and the physical device is an automated flexible test bench.

[0014] Furthermore, the method for aligning the coordinates of a virtual model and a physical device for automotive design also includes: tracking the pose of the physical device in real time through motion capture in a virtual reality environment and dynamically updating the coordinate system transformation results to achieve real-time alignment between the virtual model and the physical device.

[0015] A second aspect of the present invention provides a system for aligning the coordinates of a virtual model with physical equipment for automobile design.

[0016] A system for aligning virtual models with physical device coordinates for automotive design includes: The key point extraction module is configured to extract multiple sets of corresponding key points from the virtual model and motion capture coordinate system of the physical device used for automobile design, respectively. The key points include at least three non-collinear points among the steering wheel center, seat center and pedal center. The rigid transformation module is configured to: perform rigid transformation on the obtained sets of corresponding key points based on the rigid transformation algorithm to determine the rotation matrix and translation vector, and determine whether the error factor meets the transformation requirements; wherein, the rigid transformation includes centroid normalization, covariance matrix construction, singular value decomposition and reflection detection; The coordinate alignment module is configured to: when the error factor does not meet the transformation requirements, re-extract key points and perform rigid transformation; when the error factor meets the transformation requirements, transform the coordinate system under the virtual model to the motion capture coordinate system according to the obtained rotation matrix and translation vector, and use the VR device to check the transformation error, so as to achieve coordinate alignment between the virtual model and the physical device. A third aspect of the present invention provides a computer-readable storage medium having a program stored thereon that, when executed by a processor, implements the steps in the method for aligning the coordinates of a virtual model with physical devices for automotive design as described in the first aspect of the present invention.

[0017] The fourth aspect of the present invention provides an electronic device including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the method for aligning the coordinates of a virtual model for automobile design with physical device coordinates as described in the first aspect of the present invention.

[0018] The above one or more technical solutions have the following beneficial effects: This invention extracts multiple sets of corresponding key points from virtual models used for automotive design and motion capture coordinate systems of physical devices, and performs rigid transformations on these key points based on a rigid transformation algorithm. Through a series of steps including centroid normalization, covariance matrix construction, singular value decomposition, and reflection detection, the local errors and noise interference of the point set data can be effectively adjusted. Furthermore, this invention introduces an iterative control mechanism that guides the user to reselect key points or adjust calculation parameters when the transformation error does not meet requirements. This ensures stable and reliable transformation performance even when faced with unavoidable measurement noise and equipment errors in real industrial scenarios. In addition, this invention can utilize VR devices to verify and visualize the transformation error, allowing for intuitive judgment of whether the alignment effect meets the review accuracy requirements and enabling adaptive adjustments.

[0019] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0020] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0021] Figure 1 This is a flowchart of the method for aligning the coordinates of a virtual model and a physical device used in automobile design, as described in Embodiment 1 of the present invention. Detailed Implementation

[0022] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0023] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0024] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0025] Example 1 This embodiment discloses a method for aligning the coordinates of a virtual model with physical equipment used in automobile design.

[0026] like Figure 1 As shown, the method for aligning the coordinates of a virtual model with physical equipment used in automotive design includes: Step S1: Extract multiple sets of corresponding key points from the virtual model and motion capture coordinate system of the physical device used for automobile design. The key points include at least three non-collinear points among the steering wheel center, seat center and pedal center. Step S2: Based on the rigid transformation algorithm, perform rigid transformation on the obtained sets of corresponding key points to determine the rotation matrix and translation vector, and determine whether the error factor meets the transformation requirements; wherein, the rigid transformation includes centroid normalization, covariance matrix construction, singular value decomposition and reflection detection; Step S3: When the error factor does not meet the conversion requirements, key point extraction and rigid transformation are performed again; when the error factor meets the conversion requirements, the coordinate system under the virtual model is transformed to the motion capture coordinate system according to the obtained rotation matrix and translation vector, and the conversion error is checked using VR equipment to achieve coordinate alignment between the virtual model and the physical device.

[0027] Based on the above process, this invention can achieve precise alignment of coordinates between the virtual model and the physical device, providing effective data support for design review and other stages in automobile manufacturing. To facilitate understanding of the technical solution of this invention, the specific implementation methods of this invention will be further explained and described below.

[0028] In step S1, multiple sets of corresponding key points are extracted from the virtual model used for automobile design and the motion capture coordinate system under the physical device; wherein, the key points include at least three non-collinear points among the steering wheel center, seat center and pedal center.

[0029] Multiple sets of corresponding key points are extracted from both the virtual model used for automotive design and the motion capture coordinate system of the physical equipment. The selection of key points is based on the measurable features of the physical equipment and the geometry of the virtual model. Specifically, in the virtual model, the 3D coordinates of feature points such as the steering wheel rotation center, seat cushion geometric center, and pedal mounting plane center are directly obtained in the software coordinate system using the coordinate reading tool of 3D design software (such as CATIA or Blender). On the physical equipment side, optical reflective markers are installed on corresponding components of the automated flexible platform. A high-precision optical motion capture system (such as Vicon or OptiTrack) captures the 3D coordinates of these markers in physical space in real time, thereby establishing a one-to-one correspondence between virtual and physical points.

[0030] The extracted sets of corresponding key points are used for subsequent coordinate alignment between the virtual model and the physical device. The coordinate alignment method provided by this invention is applied to the automotive design review process. The virtual model involved in this method is a 3D automotive design model, and the physical device is an automated flexible test bench. Specifically, this automated flexible test bench is an adjustable physical platform used to simulate the layout of a real vehicle's cockpit. It includes an electrically or manually adjustable steering wheel module, a seat rail and adjuster module, a floor lifting module, a roof module, and pedal mounting brackets. By adjusting the spatial positions of these modules, the interior layout of different vehicle models can be matched, providing a realistic physical reference for design review.

[0031] In step S2, the rigid transformation algorithm is used to perform rigid transformations on the obtained sets of corresponding key points to determine the rotation matrix and translation vector, and to determine whether the error factor meets the transformation requirements. The rigid transformation includes centroid normalization, covariance matrix construction, singular value decomposition, and reflection detection. Specifically, this can be achieved through the following methods: Step S2-1: Apply rigid transformation to the obtained sets of corresponding key points based on the rigid transformation algorithm.

[0032] The first step is to perform centroid normalization, that is: first, calculate the centroids of point set A and point set B: ; ; in, This represents the centroid, or geometric center, of point set A; point set A is the set of key points in the virtual model. Indicates the number of key points. represents the point in point set A The three-dimensional coordinates of the key points; This represents the centroid, or geometric center, of point set B; point set B is the key point set in the physical model. In point set B, the first... The three-dimensional coordinates of the key points.

[0033] Then, a decentralized operation is performed: ; ; in, This represents the result of decentering the point set A, i.e., the offset vector of each point relative to the centroid; This represents the result of decentering the point set B, i.e., the offset vector of each point relative to the centroid.

[0034] The second step is to construct the covariance matrix. And perform singular value decomposition (SVD), that is: ; in, This represents the transpose of AA; This is a left singular vector matrix used to represent the principal components along the AA direction; It is a singular value diagonal matrix used to represent the "energy" or "importance" in each direction; It is the transpose of the right singular vector, used to represent the principal component in the BB direction; This represents the singular value decomposition function, used to decompose a matrix into three parts: rotation, scaling, and rotation.

[0035] The third step is to calculate the rotation matrix, i.e.: ; in, This represents the final rotation matrix (3×3), used to rotate the virtual point set A to align with the direction of the physical point set B; is the right singular vector matrix from SVD, used to represent the target direction (BB); is the transpose of the left singular vector matrix, used to represent the source direction (AA).

[0036] After calculating the rotation matrix, reflection detection is performed immediately: first, the determinant of R is calculated. ;like This indicates that unnecessary reflection transformations have been introduced into the current decomposition result. At this point, only the right singular vector matrix obtained from the singular value decomposition is considered. To make corrections, the specific method is to... The last column (i.e. Invert the sign of the third row vector to obtain the correction matrix. The rotation matrix is ​​then recalculated using this correction matrix. The corrected R satisfies This ensures that it represents only pure rotation and contains no reflection components, thereby maintaining physical consistency between the coordinate transformation between the virtual model and the physical device.

[0037] Fourth step, calculate the translation vector. And perform conversion error calculation: ; in, The error matrix (N×3) is used to represent the deviation between each key point and the target point after transformation; This represents the transpose of the rotation matrix, used to rotate point set A to the target direction.

[0038] Building upon this, this invention introduces the concept of an error factor. By calculating the Euclidean norm of the error matrix, the error is quantified into a factor. The closer the error factor is to 1, the smaller the error, allowing for a more intuitive assessment of the reliability of the conversion result. ; in, The error factor is a dimensionless numerical value used to quantify the overall accuracy of coordinate transformation. The closer it is to 1, the smaller the error and the more accurate the transformation. The Frobenius norm (also called the Euclidean norm) of the error matrix is ​​used to represent the "total size" of the errors at all points.

[0039] The error factor is compared with a preset conversion threshold to determine whether the error factor meets the conversion requirements. Specifically, a preset error factor threshold range is used, for example, [1.01, 1.05]. If the calculated... If the result falls within this range, the conversion result is deemed to meet the accuracy requirements; if it exceeds this range, it is deemed not to meet the requirements, and the point set B needs to be re-measured.

[0040] In step S3, when the error factor does not meet the transformation requirements, the key point extraction and rigid transformation are repeated; when the error factor meets the transformation requirements, the coordinate system under the virtual model is transformed to the motion capture coordinate system according to the obtained rotation matrix and translation vector, and the transformation error is checked using VR equipment to achieve coordinate alignment between the virtual model and the physical device.

[0041] If the error factor does not meet the conversion requirements, steps S1 and S2 are repeated for key point extraction and rigid transformation until the error factor meets the conversion requirements. When the error factor meets the conversion requirements, the obtained rotation matrix and translation vector are input into the virtual reality transfer software (Techviz). This allows the coordinate system of the virtual model to be automatically converted to the motion capture coordinate system when it is transferred to the VR headset, based on the input rotation matrix and translation vector. Subsequently, the positions of components such as the steering wheel, seat, and pedals on the automated flexible platform are adjusted to match the relative positions of the virtual model, ensuring that the reviewer can accurately simulate the real vehicle's state in the VR environment.

[0042] After transforming the coordinate system of the virtual model to the motion capture coordinate system, VR equipment is used to check whether the transformation error meets the inspection criteria. If it does not, steps S1 and S2 are repeated to extract key points and perform rigid transformation until the inspection criteria are met. At this point, the coordinates of the virtual model and the physical device are aligned. Once the coordinates are aligned, vehicle reviewers can use VR equipment to conduct immersive VR reviews by combining virtual data with the physical test bench. Because the virtual and real coordinate systems are aligned, vehicle reviewers can wear VR headsets and sit in the driver's seat, passenger seat, or second or third row seats on the physical test bench to roam freely within the virtual vehicle and observe issues such as interior visibility, styling, ease of operation, and space. This method is more intuitive and convenient for reviewers, greatly improving the problem identification rate and vehicle development efficiency during the vehicle design process.

[0043] The verification criteria for conversion error include the maximum error, minimum error, and root mean square error generated when converting the coordinate system under the virtual model to the motion capture coordinate system, which helps users understand the accuracy of the conversion results.

[0044] ; ; ; in, Indicates the maximum error. Indicates the minimum error. This represents the root mean square error.

[0045] Subsequently, the error distribution is displayed through a graphical interface to assist users in making decisions and optimizations.

[0046] Furthermore, the method for aligning virtual models with physical devices in automotive design also includes: real-time tracking of the physical device's pose in a virtual reality environment using motion capture, and dynamically updating the coordinate system transformation results to achieve real-time alignment between the virtual model and the physical device. Specifically, during the review process, the optical motion capture system continuously tracks the real-time pose of marked points on an automated flexible rig at a high frequency (e.g., 100Hz). Once displacement of a rig component is detected (e.g., due to reviewer operation), the system immediately inputs the rig's new pose data into the pre-calibrated transformation relationship, dynamically calculates the pose adjustment amount of the virtual model, and updates the virtual model's display position in real-time in the VR rendering engine. This process forms a closed loop of "physical movement - dynamic tracking - virtual update," enabling the virtual model to maintain precise spatial registration and alignment with the moving physical rig at all times.

[0047] Example 2 This embodiment discloses a system for aligning the coordinates of a virtual model with physical equipment for automobile design.

[0048] A system for aligning virtual models with physical device coordinates for automotive design includes: The key point extraction module is configured to extract multiple sets of corresponding key points from the virtual model and motion capture coordinate system of the physical device used for automobile design, respectively. The key points include at least three non-collinear points among the steering wheel center, seat center and pedal center. The rigid transformation module is configured to: perform rigid transformation on the obtained sets of corresponding key points based on the rigid transformation algorithm to determine the rotation matrix and translation vector, and determine whether the error factor meets the transformation requirements; wherein, the rigid transformation includes centroid normalization, covariance matrix construction, singular value decomposition and reflection detection; The coordinate alignment module is configured to: when the error factor does not meet the transformation requirements, re-extract key points and perform rigid transformation; when the error factor meets the transformation requirements, transform the coordinate system under the virtual model to the motion capture coordinate system according to the obtained rotation matrix and translation vector, and use the VR device to check the transformation error, so as to achieve coordinate alignment between the virtual model and the physical device. Example 3 The purpose of this embodiment is to provide a computer-readable storage medium.

[0049] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps in the method for aligning virtual models with physical device coordinates for automobile design as described in Embodiment 1 of this disclosure.

[0050] Example 4 The purpose of this embodiment is to provide an electronic device.

[0051] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the method for aligning the coordinates of a virtual model with physical device coordinates for automotive design as described in Embodiment 1 of this disclosure.

[0052] The steps and methods involved in the apparatuses of Embodiments 2, 3, and 4 above correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0053] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0054] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for aligning the coordinates of a virtual model with physical equipment used in automobile design, characterized in that, include: Multiple sets of corresponding key points are extracted from the motion capture coordinate system of the virtual model and physical device used for automobile design. The key points include at least three non-collinear points among the steering wheel center, seat center and pedal center. The rigid transformation algorithm is used to perform rigid transformations on the obtained sets of corresponding key points to determine the rotation matrix and translation vector, and to determine whether the error factor meets the transformation requirements; wherein, the rigid transformation includes centroid normalization, covariance matrix construction, singular value decomposition and reflection detection; When the error factor does not meet the conversion requirements, key point extraction and rigid transformation are performed again; when the error factor meets the conversion requirements, the coordinate system under the virtual model is transformed to the motion capture coordinate system according to the obtained rotation matrix and translation vector, and the conversion error is checked using VR equipment to achieve coordinate alignment between the virtual model and the physical device.

2. The method for aligning the coordinates of a virtual model and physical equipment for automobile design as described in claim 1, characterized in that, The selection of key points is based on the measurable characteristics of the physical device and the geometric structure of the virtual model.

3. The method for aligning the coordinates of a virtual model and physical equipment for automobile design as described in claim 1, characterized in that, The reflection detection includes: when a reflection is detected, adjusting the matrix elements in the singular value decomposition result to correct the rotation matrix so that the rotation matrix always represents a pure rotation.

4. The method for aligning the coordinates of a virtual model and physical equipment for automobile design as described in claim 1, characterized in that, The error factor is calculated based on the Euclidean norm of the conversion error, and the error factor is compared with a preset conversion threshold to determine whether the error factor meets the conversion requirements.

5. The method for aligning the coordinates of a virtual model and a physical device for automobile design as described in claim 1, characterized in that, The verification criteria for the transformation error include: displaying the maximum error, minimum error, and root mean square error generated when transforming the coordinate system under the virtual model to the motion capture coordinate system through a graphical interface to assist users in decision-making and optimization.

6. The method for aligning the coordinates of a virtual model and a physical device for automobile design as described in claim 1, characterized in that, The coordinate alignment method is applied in the automotive design review process, and the physical equipment is an automated flexible test bench.

7. The method for aligning the coordinates of a virtual model and a physical device for automobile design as described in claim 1, characterized in that, Also includes: In a virtual reality environment, motion capture is used to track the pose of physical devices in real time and dynamically update the coordinate system transformation results to achieve real-time alignment between the virtual model and the physical device.

8. A virtual model and physical device coordinate alignment system for automobile design, characterized in that, include: The key point extraction module is configured to extract multiple sets of corresponding key points from the virtual model and motion capture coordinate system of the physical device used for automobile design, respectively. The key points include at least three non-collinear points among the steering wheel center, seat center and pedal center. The rigid transformation module is configured to: perform rigid transformation on the obtained sets of corresponding key points based on the rigid transformation algorithm to determine the rotation matrix and translation vector, and determine whether the error factor meets the transformation requirements; wherein, the rigid transformation includes centroid normalization, covariance matrix construction, singular value decomposition and reflection detection; The coordinate alignment module is configured to: when the error factor does not meet the transformation requirements, re-extract key points and perform rigid transformation; when the error factor meets the transformation requirements, transform the coordinate system under the virtual model to the motion capture coordinate system according to the obtained rotation matrix and translation vector, and use the VR device to check the transformation error, so as to achieve coordinate alignment between the virtual model and the physical device.

9. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the method for aligning the coordinates of a virtual model with physical equipment for automotive design as described in any one of claims 1-7.

10. An electronic device, comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the method for aligning the coordinates of a virtual model and a physical device for automobile design as described in any one of claims 1-7.

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