Target high-speed and high-precision positioning method based on stripe structured light

Abstract

The embodiment of the invention provides a high-speed and high-precision target positioning method based on stripe structured light. The method comprises the following steps: constructing a high-speed and high-precision target positioning system based on the stripe structured light; when stripe structured light is projected, a stripe mode with a specific code is designed and generated according to characteristics and positioning requirements of a target object; in the image capturing stage, an image processing technology is adopted to pre-process a stripe image, captured by a high-speed camera at a set frame rate, of the surface of a target object, and an edge detection algorithm and a sub-pixel positioning technology are utilized to extract a stripe center line; after the target high-speed and high-precision positioning system is installed, calibration is carried out to obtain parameters including the focal length, the distortion coefficient, the relative position and the rotation angle, and the position and attitude information of the target object is calculated by combining the calibration parameters and the extracted stripe center line information; and outputting the calculated target position and attitude information to provide support for subsequent control and decision.

Description

Technical Field

[0001] The invention relates to the technical field of machine vision and optical measurement, and in particular to a high-speed and high-precision target positioning method based on stripe structured light. Background Art

[0002] In the fields of industrial automation, intelligent manufacturing and robotics, high-speed and high-precision positioning of target objects is crucial. However, although traditional positioning methods such as mechanical contact measurement, ultrasonic or laser ranging can achieve positioning to a certain extent, they are often limited by measurement speed, accuracy and sensitivity to environmental conditions. In addition, these methods usually require physical contact with the target object, which may not only introduce errors but also damage the surface of the target object.

[0003] In recent years, with the rapid development of computer vision technology and the continuous progress of image processing algorithms, positioning methods based on machine vision have gradually become a hot topic of research. Among them, target positioning technology based on stripe structured light has attracted widespread attention due to its non-contact, high precision and flexibility. By projecting stripe structured light with a specific coding pattern onto the surface of the target object, the three-dimensional shape information of the object surface can be obtained, thereby realizing the accurate calculation of its position and posture.

[0004] However, existing positioning methods based on stripe structured light still face some challenges. Under high-speed motion, due to short exposure time, image quality degradation and rapid changes in the surface of the object, traditional stripe structured light positioning methods often have difficulty in accurately capturing the position and posture information of the target object. In addition, for target objects with complex surface structures or textures, the projection of stripe structured light and the selection of image processing algorithms also put forward higher requirements. Summary of the invention

[0005] In view of the above-mentioned problems existing in the prior art, an embodiment of the present invention provides a high-speed and high-precision target positioning method based on stripe structured light, which combines modern computer vision technology, image processing algorithm and optical projection principle, optimizes the design of stripe structured light, improves the image processing algorithm, and combines high-speed camera technology. Specifically, a stripe structured light of a specific pattern is projected onto the surface of the target object, and the deformed stripe image is captured by a high-speed camera. The position and posture information of the target object is extracted through the image processing algorithm, so as to realize fast and accurate positioning of the target object in a high-speed motion state, and provide more reliable and efficient positioning solutions for the fields of industrial automation, intelligent manufacturing, etc.

[0006] The embodiment of the present invention provides a method for high-speed and high-precision positioning of a target based on stripe structured light, comprising:

[0007] Step 1: Build a high-speed and high-precision target positioning system based on stripe structured light;

[0008] Step 2: When projecting the stripe structured light, a stripe pattern with a specific code is designed and generated according to the characteristics of the target object and the positioning requirements, and the stripe pattern at least includes periodic sinusoidal wave stripes and pseudo-randomly coded binary stripes;

[0009] Step 3: In the image capture stage, the stripe image on the surface of the target object captured by the high-speed camera in the target high-speed and high-precision positioning system at a set frame rate is preprocessed by using an image processing technology including at least adaptive threshold segmentation and morphological filtering, and the stripe center line is extracted by using an edge detection algorithm and sub-pixel positioning technology to provide a data basis for subsequent positioning calculations;

[0010] Step 4: After the target high-speed and high-precision positioning system is installed, calibration is performed to obtain parameters including focal length, distortion coefficient, relative position, and rotation angle, and the position and posture information of the target object is calculated by combining the calibration parameters and the extracted fringe centerline information;

[0011] Step 5: Output the calculated target position and attitude information to provide support for subsequent control and decision-making.

[0012] In some embodiments of the present invention, in step 1, the method specifically includes:

[0013] Build a high-speed and high-precision target positioning system based on fringe structured light, which includes at least a fringe projector, a high-speed camera, a computer processing unit, and a display and storage device;

[0014] Calibrate the fringe projector and the high-speed camera to ensure accurate correspondence between the optical path and image capture;

[0015] The relevant parameters in the computer processing unit are set, including at least the encoding method of the stripe structured light and the selection of the image processing algorithm.

[0016] In some embodiments of the present invention, in step 2, the method further comprises:

[0017] By adjusting the spacing, width and encoding method of the stripes, accurate measurement of the surface details of the target object can be achieved.

[0018] In some embodiments of the present invention, in step 2, the method specifically includes:

[0019] Using a fringe projector to project periodic or binary-coded fringe structured light onto the surface of a target object, so as to form a unique deformation pattern on the surface of the target object, which contains shape and texture information of the target object;

[0020] Design a high-contrast sinusoidal fringe pattern, the mathematical expression is:

[0021]

[0022] Where I(x,y) is the intensity of the fringe pattern at position (x,y), I 0 is the background light intensity, I m is the amplitude of the fringe, f is the frequency of the fringe, p is the frequency of the fringe, is the phase shift, which is used to control the starting position of the stripes;

[0023] By introducing a coding technology including at least binary coding or color coding, each stripe is given a unique identification, and then the stripe projector is started to project the designed stripe structured light onto the surface of the target object.

[0024] In some embodiments of the present invention, in step 3, preprocessing the fringe image includes:

[0025] Remove noise and interference information from fringe images.

[0026] In some embodiments of the present invention, in step 3, the method comprises:

[0027] Use Gaussian filter to remove noise,

[0028]

[0029] When extracting the center line of the stripes, the Canny edge detection algorithm is used;

[0030] Canny edge detection includes the steps of Gaussian filtering, calculating gradient amplitude and direction, non-maximum suppression, double threshold processing and edge tracking, among which the formula for calculating gradient amplitude and direction is:

[0031]

[0032] Among them, and G x , G y They are the gradients of the image in the x and y directions, Magnitude is the gradient magnitude, and Direction is the gradient.

[0033] In some embodiments of the present invention, in step 4, the acquiring of parameters including focal length, distortion coefficient, relative position, and rotation angle specifically includes:

[0034] Obtain the internal parameters including the focal length, principal point coordinates and distortion coefficient of the camera; where:

[0035] The focal length and principal point coordinates are determined by the camera imaging principle and the standard pinhole model;

[0036] The distortion coefficients are obtained and corrected through a specific calibration algorithm, where:

[0037] The radial distortion correction is expressed as:

[0038] x corrected =x(1+k 1 r 2 +k 2 r 4 +k 3 r 6 )

[0039] y corrected =y(1+k 1 r 2 +k 2 r 4 +k 3 r 6 )

[0040] The tangential distortion correction is expressed as:

[0041] x corrected =x+[2p 1 xy+p 2 (r 2 +2x 2 )]

[0042] y corrected =y+[p 1 (r 2 +2y 2 )+2p 2 xy]

[0043] Where (x, y) is the original image coordinate, x corrected ,y corrected is the corrected image coordinate, r is the distance from the image point to the optical center, k 1 , k 2 , k 3 , p 1 , p 2 is the distortion coefficient.

[0044] In some embodiments of the present invention, in step 4, the acquiring of parameters including focal length, distortion coefficient, relative position, and rotation angle further includes:

[0045] Obtaining external parameters including the relative position and rotation angle between the camera and the fringe projector;

[0046] Specifically, it is obtained by photographing a specific calibration plate and extracting feature points on the calibration plate using image processing technology;

[0047] The calibration of external parameters usually involves solving the homography matrix and calculating the rotation matrix, where:

[0048] The solution formula for the homography matrix is ​​as follows:

[0049]

[0050] Among them, H is the homography matrix, which is solved from multiple pairs of corresponding points by the least squares method;

[0051] Obtaining the homography matrix, we can solve the rotation matrix and translation vector.

[0052] In some embodiments of the present invention, in step 4, when calculating the position and posture information of the target object, the method includes:

[0053] A feature matching algorithm based on deep learning is used to match the stripes in continuous frames, and image datasets containing target objects at different perspectives are collected and annotated;

[0054] Divide the dataset into training, validation and test sets;

[0055] Design a deep convolutional neural network f(.) as a feature extractor with parameter θ;

[0056] Given an input image I, the feature extraction network outputs a feature vector F = f(I; θ);

[0057] Design a feature matching network that takes the feature vectors of two images as input and outputs a similarity score between them;

[0058] Contrast loss is used as the loss function to measure the distance relationship between matching and non-matching feature points. The contrast loss function is defined as:

[0059]

[0060] Make the similarity between their feature representations as high as possible. For unmatched sample pairs, make the similarity between their feature representations as low as possible and at least reach a preset interval margin.

[0061] Assume that the intensity of the deformed stripe light is:

[0062]

[0063] Where h(x,y) is the height of the target at position (x,y);

[0064] Extracting phase information from deformed fringes using phase extraction algorithm The relationship between phase and height is then used to calculate the displacement of the surface of the object:

[0065]

[0066] In some embodiments of the present invention, in step 5,

[0067] The output results are displayed on a display and saved on a storage device for subsequent analysis and processing;

[0068] Integrate positioning results with other systems including robotic control systems and / or automated production lines.

[0069] Compared with the prior art, the beneficial effect of the high-speed and high-precision target positioning method based on stripe structured light provided by the embodiment of the present invention is that it optimizes the design and application of stripe structured light, combines high-speed camera technology and image processing algorithms, and realizes fast and accurate positioning of target objects in high-speed motion. Specifically, by optimizing the design and application of stripe structured light, the ability to capture surface details of target objects is improved, and high-precision positioning in high-speed motion is realized; by combining high-speed camera technology and image processing algorithms, fast and accurate positioning of target objects is realized, and the real-time and stability of the positioning system are improved; at the same time, the above technical solution has broad application prospects and can be applied to industrial automation, intelligent manufacturing, robot navigation and other fields to improve production efficiency, reduce energy consumption, and improve product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 A flowchart of a method for high-speed and high-precision positioning of a target based on stripe structured light provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0071] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0072] Various aspects and features of the present application are described herein with reference to the accompanying drawings.

[0073] These and other characteristics of the present application will become apparent from the following description of a preferred form of embodiment given as a non-limiting example with reference to the accompanying drawings.

[0074] It should also be understood that, although the present application has been described with reference to some specific examples, those skilled in the art will be able to realize many other equivalent forms of the present application that have the features described in the claims and are therefore within the scope of protection defined thereby.

[0075] The above and other aspects, features and advantages of the present application will become more apparent in view of the following detailed description when taken in conjunction with the accompanying drawings.

[0076] Specific embodiments of the present application are described hereinafter with reference to the accompanying drawings; however, it should be understood that the embodiments applied for are merely examples of the present application, which may be implemented in a variety of ways. Well-known and / or repeated functions and structures are not described in detail to determine the true intent based on the user's historical operations and to avoid unnecessary or redundant details that make the present application unclear. Therefore, the specific structural and functional details applied for herein are not intended to be limiting, but are merely used as the basis and representative basis for the claims to teach those skilled in the art to use the present application in a variety of ways with substantially any suitable detailed structure.

[0077] This specification may use the phrases "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," all of which may refer to one or more of the same or different embodiments according to the present application.

[0078] The embodiment of the present invention provides a method for high-speed and high-precision positioning of a target based on stripe structured light. Figure 1 As shown, the method includes:

[0079] Step 1: Build a high-speed and high-precision target positioning system based on stripe structured light;

[0080] Step 2: When projecting the stripe structured light, a stripe pattern with a specific code is designed and generated according to the characteristics of the target object and the positioning requirements, and the stripe pattern at least includes periodic sinusoidal wave stripes and pseudo-randomly coded binary stripes;

[0081] Step 3: In the image capture stage, the stripe image on the surface of the target object captured by the high-speed camera in the target high-speed and high-precision positioning system at a set frame rate is preprocessed by using an image processing technology including at least adaptive threshold segmentation and morphological filtering, and the stripe center line is extracted by using an edge detection algorithm and sub-pixel positioning technology to provide a data basis for subsequent positioning calculations;

[0082] Step 4: After the target high-speed and high-precision positioning system is installed, calibration is performed to obtain parameters including focal length, distortion coefficient, relative position, and rotation angle, and the position and posture information of the target object is calculated by combining the calibration parameters and the extracted fringe centerline information;

[0083] Step 5: Output the calculated target position and attitude information to provide support for subsequent control and decision-making.

[0084] Specifically, in order to facilitate the understanding of the above technical solution, the following is a detailed description with reference to the accompanying drawings of the specification as follows:

[0085] System construction and initialization: When building a high-speed and high-precision target positioning system based on stripe structured light, we need to carefully select the models and specifications of the stripe projector and high-speed camera to ensure that their performance meets the requirements of positioning accuracy. In addition, the installation and calibration process of the system is crucial. We use precise calibration plates and high-precision measuring tools to accurately calibrate the projector and camera to ensure that their optical axes are parallel and their focal lengths are consistent, thereby eliminating positioning deviations caused by installation errors.

[0086] Specifically, a high-speed and high-precision target positioning system based on stripe structured light is built, which includes a stripe projector, a high-speed camera, a computer processing unit, and display and storage devices. The stripe projector and high-speed camera are calibrated to ensure the precise correspondence between the optical path and image capture. The relevant parameters in the computer processing unit are set, including the encoding method of the stripe structured light and the selection of the image processing algorithm.

[0087] Stripe structured light projection: When projecting stripe structured light, we design and generate stripe patterns with specific codes according to the characteristics of the target object and positioning requirements. These stripe patterns can be periodic sinusoidal stripes, pseudo-randomly coded binary stripes, or other complex coded stripes. By adjusting the spacing, width, and coding method of the stripes, we can achieve accurate measurement of the surface details of the target object.

[0088] Specifically, a fringe projector is used to project periodic or binary-coded fringe structured light onto the surface of the target object. These fringe structures form a unique deformation pattern on the surface of the target object, which contains the shape and texture information of the target object. The mathematical expression for designing a high-contrast sinusoidal fringe pattern is:

[0089]

[0090] Where I(x,y) is the intensity of the fringe pattern at position (x,y), I 0 is the background light intensity, I m is the amplitude of the fringe, f is the frequency of the fringe, p is the frequency of the fringe, is the phase offset, which is used to control the starting position of the fringes.

[0091] Coding technology, such as binary coding or color coding, is introduced to give each stripe a unique identifier. Then, the stripe projector is started to project the designed stripe structured light onto the surface of the target object.

[0092] Image capture and processing: During the image capture phase, a high-speed camera captures the stripe image on the surface of the target object at a very high frame rate. To ensure image quality, we use advanced image processing techniques, such as adaptive threshold segmentation and morphological filtering, to pre-process the captured image and remove noise and interference information. Subsequently, edge detection algorithms and sub-pixel positioning technology are used to accurately extract the stripe centerline, providing an accurate data basis for subsequent positioning calculations.

[0093] Specifically, the captured image may contain noise due to ambient light interference or device noise. A filtering algorithm can be used to remove the noise. For example, using a Gaussian filter:

[0094]

[0095] In order to extract the center line of the stripes, the Canny edge detection algorithm can be used. Canny edge detection includes Gaussian filtering, calculating gradient amplitude and direction, non-maximum suppression, double threshold processing and edge tracking. Among them, the formula for calculating gradient amplitude and direction is:

[0096]

[0097] Among them and G x ,G y They are the gradients of the image in the x and y directions, Magnitude is the gradient magnitude, and Direction is the gradient.

[0098] Feature matching and displacement calculation: After the fringe projector and camera are installed, they need to be calibrated to obtain their internal and external parameters. These parameters include focal length, distortion coefficient, relative position, rotation angle, etc. By combining the calibration parameters and the extracted fringe centerline information, the position and posture information of the target object can be calculated. In order to improve the matching accuracy and calculation efficiency, we use a feature matching algorithm based on deep learning and an optimized displacement calculation model to ensure the accuracy and real-time performance of the results.

[0099] In this embodiment, internal and external parameters are calibrated, and the internal parameters mainly include the focal length, principal point coordinates, and distortion coefficient of the camera. The focal length and principal point coordinates can be determined by the camera imaging principle and the standard pinhole model. The acquisition of the distortion coefficient requires complex calculations and tests. Due to manufacturing and assembly errors, camera lenses often have radial and tangential distortions, which need to be corrected by specific calibration algorithms.

[0100] The radial distortion correction can be expressed as:

[0101] x corrected =x(1+k 1 r 2 +k2 r 4 +k 3 r 6 )

[0102] y corrected =y(1+k 1 r 2 +k 2 r 4 +k 3 r 6 )

[0103] The tangential distortion correction can be expressed as:

[0104] x corrected =x+[2p 1 xy+p 2 (r 2 +2x 2 )]

[0105] y corrected =y+[p 1 (r 2 +2y 2 )+2p 2 xy]

[0106] Where (x, y) is the original image coordinate, x corrected ,y corrected is the corrected image coordinate, r is the distance from the image point to the optical center, k 1 , k 2 , k 3 , p 1 , p 2 is the distortion coefficient.

[0107] The external parameters mainly include the relative position and rotation angle between the camera and the fringe projector. These parameters can be obtained by photographing a specific calibration plate and extracting the feature points on the calibration plate using image processing technology. The calibration of external parameters usually involves solving the homography matrix and calculating the rotation matrix.

[0108] The solution formula for the homography matrix is ​​as follows:

[0109]

[0110] Among them, H is the homography matrix, which can be solved from multiple pairs of corresponding points by the least squares method. Once the homography matrix is ​​obtained, the rotation matrix and translation vector can be further solved.

[0111] A feature matching algorithm based on deep learning is used to match stripes in consecutive frames. A dataset of images containing target objects at different perspectives is collected and annotated. The dataset is divided into training set, validation set and test set. A deep convolutional neural network f(.) is designed as a feature extractor with parameter θ. Given an input image I, the feature extraction network outputs a feature vector F=f(I;θ). A feature matching network is designed that accepts the feature vectors of two images as input and outputs a similarity score between them. Contrastive Loss is used as the loss function to measure the distance relationship between matching and non-matching feature points. The contrast loss function is defined as:

[0112]

[0113] We hope that the similarity between their feature representations is as high as possible (i.e., the distance is as small as possible). For unmatched sample pairs, we hope that the similarity between their feature representations is as low as possible (i.e., the distance is as large as possible), and at least reaches a preset interval margin.

[0114] Assume that the intensity of the deformed stripe light is:

[0115]

[0116] where h(x,y) is the height of the target at position (x,y).

[0117] Extract phase information from the deformed fringes using a phase extraction algorithm (such as Fourier transform or phase shift method) The relationship between phase and height is then used to calculate the displacement of the surface of the object:

[0118]

[0119] Result output: The calculated target position and posture information is output to provide support for subsequent control and decision-making. The output results can be displayed and saved through displays, storage devices, etc. In addition, the positioning results can be integrated with other systems (such as robot control systems, automated production lines, etc.) to achieve a higher level of automation and intelligence.

[0120] Specifically, the calculated position and posture information of the target object can be displayed in real time on the display so that the operator can monitor the motion state of the target object in real time. At the same time, the positioning data is stored in the storage device for subsequent analysis and processing. In addition, an interface with other systems is provided to achieve real-time sharing and collaborative work of positioning data.

[0121] It can be seen from the above technical solutions that the high-speed and high-precision positioning method for targets based on stripe structured light provided by the above embodiments of the present invention realizes fast and accurate positioning of target objects in high-speed motion by optimizing the design and application of stripe structured light and combining high-speed camera technology and image processing algorithms. Specifically, by optimizing the design and application of stripe structured light, the ability to capture surface details of target objects is improved, and high-precision positioning in high-speed motion is realized; by combining high-speed camera technology and image processing algorithms, fast and accurate positioning of target objects is realized, and the real-time and stability of the positioning system are improved; at the same time, the above technical solutions have broad application prospects and can be applied to industrial automation, intelligent manufacturing, robot navigation and other fields to improve production efficiency, reduce energy consumption, and improve product quality.

[0122] The above embodiments are only exemplary embodiments of the present invention and are not intended to limit the present invention. The protection scope of the present invention is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present invention within the essence and protection scope of the present invention, and such modifications or equivalent substitutions shall also be deemed to fall within the protection scope of the present invention.

Claims

1. A high-speed and high-precision target positioning method based on stripe structured light, characterized in that: include: Step 1: Build a high-speed and high-precision target positioning system based on stripe structured light; Step 2: When projecting the stripe structured light, a stripe pattern with a specific code is designed and generated according to the characteristics of the target object and the positioning requirements, and the stripe pattern at least includes periodic sinusoidal wave stripes and pseudo-randomly coded binary stripes; Step 3: In the image capture stage, the stripe image on the surface of the target object captured by the high-speed camera in the target high-speed and high-precision positioning system at a set frame rate is preprocessed by using an image processing technology including at least adaptive threshold segmentation and morphological filtering, and the stripe center line is extracted by using an edge detection algorithm and sub-pixel positioning technology to provide a data basis for subsequent positioning calculations; Step 4: After the target high-speed and high-precision positioning system is installed, calibration is performed to obtain parameters including focal length, distortion coefficient, relative position, and rotation angle, and the position and posture information of the target object is calculated by combining the calibration parameters and the extracted fringe centerline information; Step 5: Output the calculated target position and attitude information to provide support for subsequent control and decision-making.

2. The method for high-speed and high-precision positioning of a target based on stripe structured light according to claim 1, characterized in that: In step 1, the method specifically includes: Build a high-speed and high-precision target positioning system based on fringe structured light, which includes at least a fringe projector, a high-speed camera, a computer processing unit, and a display and storage device; Calibrate the fringe projector and the high-speed camera to ensure accurate correspondence between the optical path and image capture; The relevant parameters in the computer processing unit are set, including at least the encoding method of the stripe structured light and the selection of the image processing algorithm.

3. The method for high-speed and high-precision positioning of a target based on stripe structured light according to claim 2, characterized in that: In step 2, the method further comprises: By adjusting the spacing, width and encoding method of the stripes, accurate measurement of the surface details of the target object can be achieved.

4. The method for high-speed and high-precision positioning of a target based on stripe structured light according to claim 3, characterized in that: In step 2, the method specifically includes: Using a fringe projector to project periodic or binary-coded fringe structured light onto the surface of a target object, so as to form a unique deformation pattern on the surface of the target object, which contains shape and texture information of the target object; Design a high-contrast sinusoidal fringe pattern, the mathematical expression is: Where I(x,y) is the intensity of the fringe pattern at position (x,y), I0 is the background intensity, and I m is the amplitude of the fringe, f is the frequency of the fringe, p is the frequency of the fringe, is the phase shift, which is used to control the starting position of the stripes; By introducing a coding technology including at least binary coding or color coding, each stripe is given a unique identification, and then the stripe projector is started to project the designed stripe structured light onto the surface of the target object.

5. The method for high-speed and high-precision target positioning based on stripe structured light according to claim 4, characterized in that: In step 3, preprocessing the fringe image includes: Remove noise and interference information from fringe images.

6. The method for high-speed and high-precision positioning of a target based on stripe structured light according to claim 5, characterized in that: In step 3, the method comprises: Use Gaussian filter to remove noise, When extracting the center line of the stripes, the Canny edge detection algorithm is used; Canny edge detection includes the steps of Gaussian filtering, calculating gradient amplitude and direction, non-maximum suppression, double threshold processing and edge tracking, among which the formula for calculating gradient amplitude and direction is: Among them, and G x ,G y They are the gradients of the image in the x and y directions, Magnitude is the gradient magnitude, and Direction is the gradient.

7. The method for high-speed and high-precision positioning of a target based on stripe structured light according to claim 6, characterized in that: In step 4, the acquisition includes parameters of focal length, distortion coefficient, relative position, and rotation angle, specifically including: Obtain the internal parameters including the focal length, principal point coordinates and distortion coefficient of the camera; wherein, The focal length and principal point coordinates are determined by the camera imaging principle and the standard pinhole model; The distortion coefficients are obtained and corrected through a specific calibration algorithm, where: The radial distortion correction is expressed as: x corrected =x(1+k1r 2 +k2r 4 +k3r 6 ) and corrected =y(1+k1r 2 +k2r 4 +k3r 6 ) The tangential distortion correction is expressed as: x corrected =x+[2p1xy+p2(r 2 +2x 2 )] y corrected =y+[p1(r 2 +2y 2 )+2p2xy] Among them, (x, y) is the original image coordinates, x corrected ,y corrected are the corrected image coordinates, r is the distance from the image point to the optical center, k1, k2, k3, p1, p2 are the distortion coefficients.

8. The method for high-speed and high-precision positioning of a target based on stripe structured light according to claim 7, characterized in that: In step 4, the acquisition includes parameters of focal length, distortion coefficient, relative position, and rotation angle, and further includes: Obtaining external parameters including the relative position and rotation angle between the camera and the fringe projector; Specifically, it is obtained by photographing a specific calibration plate and extracting feature points on the calibration plate using image processing technology; The calibration of external parameters usually involves solving the homography matrix and calculating the rotation matrix, where: The solution formula for the homography matrix is ​​as follows: Among them, H is the homography matrix, which is solved from multiple pairs of corresponding points by the least squares method; Obtaining the homography matrix, we can solve the rotation matrix and translation vector.

9. The method for high-speed and high-precision positioning of a target based on stripe structured light according to claim 8, characterized in that: In step 4, when calculating the position and posture information of the target object, the method includes: A feature matching algorithm based on deep learning is used to match the stripes in continuous frames, and image datasets containing target objects at different perspectives are collected and annotated; Divide the dataset into training, validation and test sets; Design a deep convolutional neural network f(.) as a feature extractor with parameter θ; Given an input image I, the feature extraction network outputs a feature vector F = f(I; θ); Design a feature matching network that takes the feature vectors of two images as input and outputs a similarity score between them; Contrast loss is used as the loss function to measure the distance relationship between matching and non-matching feature points. The contrast loss function is defined as: Make the similarity between their feature representations as high as possible. For unmatched sample pairs, make the similarity between their feature representations as low as possible and at least reach a preset interval margin. Assume that the intensity of the deformed stripe light is: Where h(x,y) is the height of the target at position (x,y); Extracting phase information from deformed fringes using phase extraction algorithm The relationship between phase and height is then used to calculate the displacement of the surface of the object:

10. The method for high-speed and high-precision positioning of a target based on stripe structured light according to claim 9, characterized in that: In step 5, The output results are displayed on a display and saved on a storage device for subsequent analysis and processing; Integrate positioning results with other systems including robotic control systems and / or automated production lines.

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