A laser-assisted positioning system and method based on intelligent control

Abstract

The application provides a laser-assisted positioning system and method based on intelligent control, which collects a polarization image reflected by a surface of a target positioning area; generates a multi-dimensional feature description matrix in a laser projection process according to polarization state distribution and spatial distortion characteristics of each polarization sub-image in the polarization image; constructs a polarization fingerprint map of the target positioning area according to the correlation between laser projection features in different polarization channels and target spatial positions identified by the multi-dimensional feature description matrix; reconstructs the projected laser pattern on the surface of the target positioning area into an optimized image matched with a preset positioning template based on a pose adjustment instruction generated by the polarization fingerprint map; and judges a positioning completion state of the target positioning area based on the optimized image. The technical scheme provided by the application can realize accurate mapping of the laser pattern emitted by the laser under the interference of nonlinear changes in surface optical characteristics.

Description

Technical Field

[0001] This application relates to the field of laser positioning technology, and more specifically, to a laser-assisted positioning system and method based on intelligent control. Background Technology

[0002] The development of laser positioning stems from the demand for high-precision position control in industrial automation. Its core relies on the high directionality, high monochromaticity, and high brightness characteristics of lasers to achieve accurate measurement and tracking of the spatial coordinates of targets. Early laser positioning suffered from problems such as accuracy being affected by mechanical wear and limited measurement range, making it difficult to meet the micron-level precision requirements of scenarios such as semiconductor manufacturing and high-end equipment assembly. With the popularization of laser diodes and CCD / CMOS image sensors, laser positioning has expanded from traditional interferometry to vision guidance, and combined with machine vision algorithms, it has achieved real-time positioning of dynamic targets.

[0003] In existing laser positioning methods, laser positioning is mainly based on triangulation and time-of-flight principles. In triangulation, the distance and orientation between the target and the device are determined by calculating trigonometric functions based on the laser emission direction, the camera optical axis direction, and the fixed geometric relationship between the two. The time-of-flight principle uses the time difference between the laser pulse emission and reception, combined with the constant speed of light, to calculate the target distance. However, in laser-assisted positioning, due to the significant differences in the reflection behavior of the target positioning area surface to the laser, it is difficult to overcome the interference caused by the nonlinear changes in surface optical properties. For example, highly reflective surfaces will produce specular reflection, resulting in artifacts, while rough surfaces will introduce diffuse reflection noise, causing deviations in the spatial mapping of the structured light pattern, leading to positioning failure of the target positioning area. Therefore, how to achieve accurate mapping of the laser pattern emitted by the laser under the interference of nonlinear changes in surface optical properties has become a difficult problem for the industry. Summary of the Invention

[0004] This application provides a laser-assisted positioning system and method based on intelligent control, which can accurately map the laser pattern emitted by the laser under the interference of nonlinear changes in surface optical properties.

[0005] In a first aspect, this application provides a laser-assisted positioning method based on intelligent control, comprising the following steps: A laser pattern with coded information is projected onto the target positioning area by a laser, and a polarization image of the reflection from the surface of the target positioning area is acquired. The polarization image is decoupled to obtain polarization sub-images under different polarization channels. A multi-dimensional feature description matrix is ​​generated based on the polarization state distribution and spatial distortion characteristics of the target positioning area surface in each polarization sub-image. Based on the multidimensional feature description matrix, the correlation between laser projection features and target spatial position under different polarization channels is identified, and a polarization fingerprint map of the target positioning area is constructed according to the correlation between laser projection features and target spatial position under different polarization channels. The polarization fingerprint map is a visual map used to identify the polarization state of all spatial positions within the target positioning area. Based on the polarization fingerprint spectrum, a pose adjustment command including polarization modulation parameters and beam guiding parameters is generated, and then the projected laser pattern is reconstructed on the surface of the target positioning area into an optimized image that matches the preset positioning template according to the pose adjustment command. The positioning completion status of the target positioning area is determined based on the signal-to-noise ratio and contrast distribution of the optimized image under different polarization channels.

[0006] In some embodiments, decoupling the polarization image to obtain polarization sub-images under different polarization channels specifically includes: The polarization image is decomposed into original polariton images corresponding to the 0°, 45°, 90°, and 135° polarization channels; Noise suppression processing is performed on each original polariton image to obtain polariton images under different polarization channels.

[0007] In some embodiments, generating a multi-dimensional feature description matrix for laser projection based on the polarization state distribution and spatial distortion characteristics of the target positioning region surface in each polariton image specifically includes: The linear polarization degree of each pixel on the surface of the target positioning region in each polariton image is determined, thereby forming a polarization state distribution set; The pattern pixel offset of each pixel on the surface of the target positioning region in each polariton image is determined, and then a spatial distortion feature set is generated; The polarization state distribution set is associated and matched with the spatial distortion feature set to obtain a multidimensional feature description matrix.

[0008] In some embodiments, identifying the correlation between laser projection features and target spatial position under different polarization channels based on the multidimensional feature description matrix specifically includes: The multidimensional feature description matrix is ​​grouped according to polarization channel to obtain the feature description matrix corresponding to each polarization channel; Principal component analysis was performed on each feature description matrix to extract key feature parameters of each polarization channel as laser projection features; A three-dimensional spatial coordinate grid is established for the target positioning area. Each laser projection feature is mapped to the coordinate point corresponding to the three-dimensional spatial coordinate network. The mapping coefficient between each laser projection feature and the corresponding coordinate point is calculated, thereby determining the correlation between the laser projection feature and the target spatial position under different polarization channels.

[0009] In some embodiments, constructing a polarization fingerprint map of the target positioning region based on the correlation between laser projection characteristics under different polarization channels and the target spatial location specifically includes: The polarization fingerprint identification factor for each polarization channel is determined based on the correlation between the laser projection characteristics under different polarization channels and the target spatial position. The polarization fingerprint identification factor refers to the influence coefficient characterizing the identification of the target positioning area by different polarization channels. The polarization fingerprint spectrum of the target location area is determined based on the polariton image under different polarization channels and the polarization fingerprint identification factor.

[0010] In some embodiments, generating a pose adjustment command containing polarization modulation parameters and beam guiding parameters based on the polarization fingerprint specifically includes: Determine the feature deviation between the polarization fingerprint spectrum and the preset standard fingerprint spectrum; The adjustment increment of the laser polarization state is calculated based on the characteristic deviation to obtain the polarization modulation parameters; The correction value of the laser beam projection angle is obtained by solving the mapping relationship between the characteristic deviation and the target spatial position, and the beam guiding parameters are obtained. The polarization modulation parameters and the beam guiding parameters are encoded and integrated according to a preset protocol to generate a pose adjustment command that includes the polarization modulation parameters and the beam guiding parameters.

[0011] In some embodiments, a polarization image of the surface reflection of the target positioning area is acquired using a polarization camera.

[0012] Secondly, this application provides a laser-assisted positioning system based on intelligent control for executing a laser-assisted positioning method based on intelligent control. The system includes: The acquisition module is used to project a laser pattern with coded information onto the target positioning area through a laser, and to acquire the polarization image reflected from the surface of the target positioning area. The processing module is used to decouple the polarization image to obtain polarization sub-images under different polarization channels, and generate a multi-dimensional feature description matrix for laser projection based on the polarization state distribution and spatial distortion characteristics of the target positioning area surface in each polarization sub-image. The processing module is further configured to identify the correlation between laser projection features and target spatial position under different polarization channels based on the multidimensional feature description matrix, and construct a polarization fingerprint map of the target positioning area based on the correlation between laser projection features and target spatial position under different polarization channels. The polarization fingerprint map is a visual map used to identify the polarization state of all spatial positions within the target positioning area. The processing module is also used to generate a pose adjustment command containing polarization modulation parameters and beam guiding parameters based on the polarization fingerprint spectrum, and then reconstruct the projected laser pattern on the surface of the target positioning area into an optimized image that matches the preset positioning template according to the pose adjustment command. The execution module is used to determine the positioning completion status of the target positioning area based on the signal-to-noise ratio and contrast distribution of the optimized image under different polarization channels.

[0013] Thirdly, this application provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described laser-assisted positioning method based on intelligent control.

[0014] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned laser-assisted positioning method based on intelligent control.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: The laser-assisted positioning system and method based on intelligent control provided in this application firstly projects a laser pattern with coded information onto a target positioning area using a laser, and acquires polarization images reflected from the surface of the target positioning area. Secondly, the polarization images are decoupled to obtain polariton images under different polarization channels. A multi-dimensional feature description matrix is ​​generated based on the polarization state distribution and spatial distortion characteristics of the target positioning area surface in each polariton image. Further, the correlation between laser projection features under different polarization channels and the target spatial position is identified based on the multi-dimensional feature description matrix, and a polarization fingerprint spectrum of the target positioning area is constructed based on the correlation between laser projection features under different polarization channels and the target spatial position. The polarization fingerprint spectrum is a visual spectrum used to identify the polarization state of all spatial positions within the target positioning area. Then, a pose adjustment command containing polarization modulation parameters and beam guiding parameters is generated based on the polarization fingerprint spectrum. Subsequently, the projected laser pattern is reconstructed on the surface of the target positioning area into an optimized image matching a preset positioning template based on the pose adjustment command. Finally, the positioning completion status of the target positioning area is determined based on the signal-to-noise ratio and contrast distribution of the optimized image under different polarization channels.

[0016] Therefore, this application can accurately map laser patterns emitted by a laser under the interference of nonlinear changes in surface optical properties. First, by projecting a laser pattern with coded information and acquiring a reflected polarization image, the coded information can be used to quickly identify the pattern, and the polarization characteristics of the target area can be captured through the polarization image, providing raw data support with both spatial and polarization dimensions for subsequent positioning analysis. Second, the polarization image is decoupled to obtain polarization sub-images of different polarization channels. Then, a multi-dimensional feature description matrix is ​​generated by combining the polarization state distribution and spatial distortion characteristics of the target area in each sub-image. This effectively integrates the two key types of information, polarization and space, avoiding the problem of single feature analysis being easily affected by environmental interference, and laying a high-quality data foundation for subsequent accurate identification of the correlation between polarization and spatial location. Furthermore, the correlation between features and target spatial location under different polarization channels is identified based on the multi-dimensional feature description matrix, and a polarization fingerprint spectrum is constructed based on this correlation, so that each spatial location has a unique "polarization identifier". This process creates a positioning reference that combines spatial topology and unique polarization attributes, effectively improving the stability and discriminativeness of the correspondence between features and positions. Then, based on the polarization fingerprint spectrum, pose adjustment commands containing polarization modulation parameters and beam guidance parameters are generated, transforming abstract feature deviations into specific control parameters that the laser can directly execute. This reconstructs an optimized image that matches the preset positioning template, achieving precise correction of the laser pattern. This avoids structured light pattern mapping deviations caused by nonlinear changes in surface optical properties, ensuring that the projected pattern highly matches the template in both geometry and polarization characteristics. Finally, by analyzing the signal-to-noise ratio and contrast distribution of the optimized image under different polarization channels, the positioning completion status is determined, ensuring that the final laser positioning accuracy meets actual requirements and effectively improving the accuracy, anti-interference capability, and automation level of laser-assisted positioning. In summary, the technical solution provided in this application can achieve precise mapping of laser patterns emitted by a laser under the interference of nonlinear changes in surface optical properties. Attached Figure Description

[0017] Figure 1 This is an exemplary flowchart of a laser-assisted positioning method based on intelligent control, according to some embodiments of this application; Figure 2 This is an exemplary flowchart illustrating the determination of a multidimensional feature description matrix according to some embodiments of this application; Figure 3 This is a schematic diagram of a laser-assisted positioning system based on intelligent control, according to some embodiments of this application. Figure 4 This is a schematic diagram of the structure of a computer device that implements a laser-assisted positioning method based on intelligent control, according to some embodiments of this application. Detailed Implementation

[0018] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] refer to Figure 1 The figure is an exemplary flowchart of a laser-assisted positioning method based on intelligent control according to some embodiments of this application. The figure mainly includes the following steps: In step S101, a laser pattern with coded information is projected onto the target positioning area by a laser, and a polarization image of the surface reflection of the target positioning area is acquired.

[0020] In practice, a laser pattern with coded information is projected onto the target positioning area by a laser. The coded information refers to the digital identification information that is pre-set and embedded in the laser pattern. The coded information includes, but is not limited to, the three-dimensional coordinate reference code of the target positioning area (used to establish a spatial coordinate system reference), the pattern frame synchronization encoding (to ensure the timing matching of image acquisition and pattern projection), and the laser operating parameter encoding (such as output power and wavelength). The specific coded information can be set according to the actual needs of target positioning, which will not be elaborated here.

[0021] It should be noted that, in this application, the laser pattern refers to a laser pattern projected by a laser with a preset spatial arrangement rule. The laser pattern includes, but is not limited to, checkerboard, sine stripes, and random speckle. The specific laser pattern can be selected according to actual needs and is not limited here.

[0022] In practice, a polarization image of the surface reflection of the target positioning area can be acquired by a polarization camera. The polarization camera can simultaneously capture polarization images in four different directions (0°, 90°, 45° or 135°).

[0023] It should be noted that, in this application, the polarization image refers to an image that records the polarization state of the laser during the reflection of the laser on the surface of the target positioning area. In laser positioning, by determining the polarization image, the differences in the polarization state of the laser reflected from different material areas on the target surface can be effectively identified, clearly distinguishing the target object from the background interference within the positioning area, avoiding the influence of non-target objects on positioning accuracy. In addition, it can enhance the edge contour and detail recognition of the laser pattern, ensure accurate extraction of encoded information, and further improve the accuracy and stability of laser positioning.

[0024] In step S102, the polarization image is decoupled to obtain polarization sub-images under different polarization channels. A multi-dimensional feature description matrix is ​​generated based on the polarization state distribution and spatial distortion characteristics of the target positioning area surface in each polarization sub-image during the laser projection process.

[0025] In some embodiments, the decoupling of the polarization image to obtain polarization sub-images under different polarization channels is specifically achieved through the following steps: The polarization image is decomposed into original polariton images corresponding to the 0°, 45°, 90°, and 135° polarization channels; Noise suppression processing is performed on each original polariton image to obtain polariton images under different polarization channels.

[0026] In specific implementation, firstly, when decoupling the polarization image, the pixel signals corresponding to the 0°, 45°, 90°, and 135° polarization directions in the polarization image are classified accordingly. Then, the missing pixels in each polarization direction are supplemented by interpolation to form the original polariton images corresponding to the 0°, 45°, 90°, and 135° polarization channels. The interpolation method can be bilinear interpolation, which is based on the gray values ​​of the four neighboring pixels around the pixel and calculates the weighted average value of the target pixel in the horizontal and vertical directions to achieve pixel supplementation. This will not be elaborated here. The original polariton image refers to the channel image separated from different polarization directions. Then, existing Gaussian filtering is used to perform noise suppression processing on each original polariton image, and the noise-suppressed original polariton image is used as the polariton image, thereby obtaining the polariton images under different polarization channels.

[0027] It should be noted that, in this application, the polariton image refers to an image with a single polarization direction obtained by decoupling from a polarization image containing information of multiple polarization directions. By determining the polariton image, the spatial geometric features of the laser pattern are preserved, and the polarization state difference information generated by the interaction between the target surface material and the laser is also included. This provides basic data for the subsequent extraction of the polarization state distribution (such as degree of polarization and polarization angle) of the target positioning area. The polarization response difference of different material areas can be accurately captured by the image with a single polarization direction, avoiding feature confusion caused by the superposition of information of multiple polarization directions.

[0028] In some embodiments, reference Figure 2 As shown, this figure is an exemplary flowchart of determining a multidimensional feature description matrix according to some embodiments of this application. In this embodiment, generating a multidimensional feature description matrix during laser projection based on the polarization state distribution and spatial distortion characteristics of the target positioning region surface in each polariton image can be achieved by the following steps: First, in step S1021, the linear polarization degree of each pixel on the surface of the target positioning region in each polarimetric image is determined, thereby forming a polarization state distribution set; Then, in step S1022, the pattern pixel offset of each pixel on the surface of the target positioning region in each polarizer image is determined, thereby generating a spatial distortion feature set; Finally, in step S1023, the polarization state distribution set is associated and matched with the spatial distortion feature set to obtain a multidimensional feature description matrix.

[0029] In specific implementation, firstly, the target positioning region (i.e., the region where the laser pattern is located) corresponding to each polariton image is extracted using the Otsu method in existing image processing methods. For the target positioning region corresponding to each polariton image, the ratio of the light intensity of each pixel in the target positioning region under the corresponding polarization direction to the reference light intensity is calculated to obtain the linear polarization degree. The linear polarization degree of each pixel is combined to form the polarization state distribution corresponding to the polariton image. Then, all polarization state distributions are combined into a polarization state distribution set. The reference light intensity can be set according to actual needs and is not limited here. The linear polarization degree is a physical quantity that measures the degree of polarization of light. The polarization state distribution refers to a vector containing the linear polarization degree at different positions in the target positioning region. The polarization state distribution set refers to a set containing the polarization state distribution of each polariton image in the target positioning region, which can intuitively reflect the polarization differences of different regions. Secondly, for the laser pattern corresponding to the surface of the target positioning region in each polariton image, the laser pattern is compared with a preset standard pattern at the pixel level. The coordinate deviation of the corresponding pixel points is calculated to obtain the pattern pixel offset of each pixel point. The pattern pixel offset of each pixel point is combined to form the spatial distortion of the corresponding polariton image. The spatial distortion features of each polariton image are then combined into a spatial distortion feature set. The spatial distortion feature refers to the vector of positional offset generated after the laser pattern is projected onto the target positioning area, used to characterize the spatial distortion of the pattern. The pattern pixel offset refers to the coordinate difference between the corresponding pixel points of the actual laser pattern and the preset standard pattern in the image coordinate system. The spatial distortion feature set refers to the collection of spatial distortion features of different polariton images. Finally, using the spatial coordinates of the target positioning area as a reference, the linear polarization degree parameter of each coordinate point in the polarization state distribution set is bound one-to-one with the pattern pixel offset parameter of the corresponding coordinate point in the spatial distortion feature set. The bound parameters are then combined according to preset dimensions (such as the linear polarization degree parameter dimension and the pattern pixel offset parameter dimension) to obtain the projection feature vector corresponding to each coordinate point. The projection feature vector includes the linear polarization degree and the pattern pixel offset. After normalization, the projection feature vectors corresponding to each coordinate point are weighted and summed (where the weight values ​​of the linear polarization degree and the pattern pixel offset can be set according to their influence on laser positioning; for example, the weight values ​​of the linear polarization degree and the pattern pixel offset can be set to 0.4 and 0, respectively).6) The weighted summation result is used as the projection feature description value for the corresponding coordinate point, resulting in a projection feature description value for each coordinate point. Each projection feature description value is then arranged according to its corresponding coordinate to obtain the feature description matrix for the corresponding polariton image. All feature description matrices are then combined into a multi-dimensional feature description matrix. This multi-dimensional feature description matrix contains feature description matrices corresponding to different polariton images; that is, one polariton image corresponds to one feature description matrix. The feature description matrix refers to a matrix-based data set containing projection feature information of the target positioning area under a single polarization channel. Furthermore, in this embodiment, the projection feature vectors are weighted and summed to obtain a unique feature description value for each coordinate point. This facilitates the rapid construction of the multi-dimensional feature description matrix and reduces the computational complexity of multiple parameters when identifying the correlation between polarization and spatial location and constructing a polarization fingerprint map. Simultaneously, it ensures that the feature values ​​accurately characterize the comprehensive impact of the coordinate point on positioning, providing focused and efficient basic data for subsequent positioning adjustments.

[0030] It should be noted that the multidimensional feature description matrix in this application refers to a set of parameter matrices containing the polarization characteristics of the target region and the spatial distortion information of the pattern under different polariton image dimensions. By identifying the shortcomings of the multidimensional feature description matrix, the polarization characteristics of the target positioning region and the spatial distortion characteristics of the laser pattern can be deeply integrated to form a comprehensive feature set that simultaneously covers the polarization dimension and the spatial dimension. This breaks the information limitations of a single polarization feature or a single spatial feature, avoiding the problem of not being able to accurately locate the spatial position by relying solely on polarization information, and also solving the defects of relying solely on spatial features being easily interfered with by ambient light and having difficulty distinguishing the differences between the target and the background material.

[0031] In step S103, the correlation between laser projection features and target spatial position under different polarization channels is identified based on the multidimensional feature description matrix, and a polarization fingerprint map of the target positioning area is constructed based on the correlation between laser projection features and target spatial position under different polarization channels. The polarization fingerprint map is a visual map used to identify the polarization state of all spatial positions within the target positioning area.

[0032] In some embodiments, identifying the correlation between laser projection features and target spatial position under different polarization channels based on the multidimensional feature description matrix is ​​specifically achieved through the following steps: The multidimensional feature description matrix is ​​grouped according to polarization channel to obtain the feature description matrix corresponding to each polarization channel; Principal component analysis was performed on each feature description matrix to extract key feature parameters of each polarization channel as laser projection features; A three-dimensional spatial coordinate grid is established for the target positioning area. Each laser projection feature is mapped to the coordinate point corresponding to the three-dimensional spatial coordinate network. The mapping coefficient between each laser projection feature and the corresponding coordinate point is calculated, thereby determining the correlation between the laser projection feature and the target spatial position under different polarization channels.

[0033] In specific implementation, firstly, the multidimensional feature description matrix contains feature description matrices corresponding to different polariton images, with one polariton image corresponding to one polarization channel. Then, the multidimensional feature description matrix is ​​grouped according to polarization channels to obtain the feature description matrix corresponding to each polarization channel. The feature description matrix refers to a matrix-based data set containing projection feature information of the target positioning area under a single polarization channel. Next, for the feature description matrix corresponding to each polarization channel, principal component analysis is used to calculate the covariance matrix of all feature parameters within the feature description matrix. By solving for the eigenvalues ​​and eigenvectors of the covariance matrix, the eigenvector corresponding to the largest eigenvalue is selected as the key feature parameter (i.e., laser projection feature) of the corresponding polarization channel. The feature refers to the characteristic parameter that characterizes the projection characteristics of a laser under a single polarization channel. Finally, a three-dimensional spatial coordinate grid of the target positioning area is established using a three-dimensional modeling tool (such as CAD modeling software). The three-dimensional spatial coordinate grid is composed of uniform spatial grid cells, and each spatial grid cell corresponds to a unique three-dimensional coordinate point. Then, each laser projection feature is matched to a specific coordinate point in the three-dimensional spatial coordinate grid according to the pixel spatial position corresponding to its acquisition. Then, a linear regression algorithm is used (with the three-dimensional coordinate value of the coordinate point as the independent variable and the laser projection feature as the dependent variable, a linear regression equation is constructed and the equation coefficients are solved) to calculate the mapping coefficient between each laser projection feature and the corresponding coordinate point. The mapping coefficient is used to describe the correlation between the laser projection feature and the target spatial position.

[0034] It should be noted that in this application, correlation refers to the quantitative result reflecting the correspondence between laser projection features and target spatial position. By determining the correlation between laser projection features and target spatial position under different polarization channels, a precise correspondence between the core feature parameters of a single polarization channel and the three-dimensional spatial coordinates of the target positioning area can be established. The core function of this correlation is to break the information separation between polarization features and spatial position. This avoids the limitation that it is impossible to locate specific spatial coordinates by relying solely on laser projection features, and also solves the problem that it is difficult to distinguish feature differences under different polarization channels by relying solely on spatial position.

[0035] In some embodiments, constructing a polarization fingerprint map of the target positioning region based on the correlation between laser projection characteristics under different polarization channels and the target spatial location is specifically achieved through the following steps: The polarization fingerprint identification factor for each polarization channel is determined based on the correlation between the laser projection characteristics under different polarization channels and the target spatial position. The polarization fingerprint identification factor refers to the influence coefficient characterizing the identification of the target positioning area by different polarization channels. The polarization fingerprint spectrum of the target location area is determined based on the polariton image under different polarization channels and the polarization fingerprint identification factor.

[0036] In specific implementation, firstly, the polarization fingerprint identification factor for each polarization channel is determined based on the correlation between the laser projection characteristics under different polarization channels and the target spatial position. That is, the correlation between the laser projection characteristics under different polarization channels and the target spatial position is normalized using min-max normalization, and the result obtained after normalization is used as the polarization fingerprint identification factor for the corresponding polarization channel. The polarization fingerprint identification factor refers to the influence coefficient characterizing the identification of the target positioning area by different polarization channels, which is used to distinguish the polarization differences of different polarization channels. Secondly, the polarization fingerprint spectrum of the target positioning area is determined based on the polariton images under different polarization channels and the polarization fingerprint identification factor. That is, the polariton images under different polarization channels are weighted and fused using the polarization fingerprint identification factor as the weight value, and the weighted fused image is used as the polarization fingerprint spectrum.

[0037] It should be noted that the polarization fingerprint spectrum in this application refers to a visual spectrum used to identify the polarization state of all spatial locations within the target positioning area. By determining the polarization fingerprint spectrum, a "reference system" with both spatial topological attributes and unique polarization characteristics can be constructed for the target positioning process. This breaks the information isolation between polarization characteristics and spatial location, and accurately binds key polarization parameters (such as polarization fingerprint identification factors) under different polarization channels with the three-dimensional spatial coordinates of the target area. This gives each spatial location a unique "polarization identity," allowing for direct reverse positioning of specific spatial locations through polarization characteristics. This avoids the problem of relying solely on visual images being susceptible to interference from ambient light and occlusion, thereby guiding the laser to accurately adjust polarization modulation parameters and beam guiding parameters.

[0038] In step S104, a pose adjustment command containing polarization modulation parameters and beam guiding parameters is generated based on the polarization fingerprint spectrum. Then, according to the pose adjustment command, the projected laser pattern is reconstructed on the surface of the target positioning area into an optimized image that matches the preset positioning template.

[0039] In some embodiments, generating pose adjustment commands containing polarization modulation parameters and beam guiding parameters based on the polarization fingerprint spectrum is specifically achieved through the following steps: Determine the feature deviation between the polarization fingerprint spectrum and the preset standard fingerprint spectrum; The adjustment increment of the laser polarization state is calculated based on the characteristic deviation to obtain the polarization modulation parameters; The correction value of the laser beam projection angle is obtained by solving the mapping relationship between the characteristic deviation and the target spatial position, and the beam guiding parameters are obtained. The polarization modulation parameters and the beam guiding parameters are encoded and integrated according to a preset protocol to generate a pose adjustment command that includes the polarization modulation parameters and the beam guiding parameters.

[0040] In specific implementation, firstly, the pixel values ​​of each spatial coordinate point in the polarization fingerprint spectrum are extracted and compared with the corresponding pixel values ​​in the preset standard fingerprint spectrum. Then, the Euclidean distance between the two is calculated to obtain the feature difference value of each coordinate point. The difference values ​​of all coordinate points are integrated according to their spatial distribution to form a feature deviation quantity reflecting the overall deviation. The feature deviation quantity refers to the set of differences between pixels in the polarization fingerprint spectrum and the preset standard fingerprint spectrum at the same spatial position, used to quantify the degree of deviation between the two. The preset standard fingerprint spectrum is a standard spectrum set in advance according to actual needs. Secondly, the feature deviation quantity is converted into the adjustment increment of the laser polarization state using an existing proportional-integral algorithm to obtain the polarization modulation parameter. The polarization modulation parameter refers to the control parameter used to adjust the output polarization state of the laser. Then, the mapping relationship between the feature deviation quantity and the target spatial position is obtained. The spatial position offset (e.g., X, Y, Z) corresponding to the feature deviation quantity is determined through the mapping relationship. The positional deviation along the axis is calculated, and then, based on the relative installation position parameters (initial angle) between the laser and the target positioning area, trigonometric functions are used to convert the spatial position offset into a correction value for the laser beam projection angle. This correction value is then used as the beam guiding parameter. The projection relationship is obtained by solving a linear regression equation with standard spatial coordinates as the independent variable and the corresponding characteristic deviation as the dependent variable (i.e., the slope of the linear regression equation is used as the projection relationship). The beam guiding parameter refers to the angle control parameter used to adjust the laser beam projection direction. Finally, the polarization modulation parameter and the beam guiding parameter are encoded and integrated according to a preset protocol (such as the Modbus protocol commonly used in industrial control). This involves first standardizing the format of each parameter (e.g., converting it to fixed-length binary data), and then arranging them according to the protocol to form a data stream that conforms to the laser's recognition format. This data stream is the pose adjustment command containing the polarization modulation parameter and the beam guiding parameter.

[0041] It should be noted that the pose adjustment command in this application refers to the control command used to control the laser to adjust the polarization state and beam direction. By determining the pose adjustment command, the abstract feature deviation obtained from polarization fingerprint spectrum analysis can be transformed into specific control parameters that the laser can execute. This builds a key bridge between positioning analysis and equipment execution, which solves the problem that the laser adjustment cannot be directly guided by feature deviation alone, and also avoids the insufficient adjustment accuracy caused by the mismatch between control parameters and positioning requirements.

[0042] In some embodiments, reconstructing the projected laser pattern on the surface of the target positioning area into an optimized image matching a preset positioning template according to the pose adjustment command is specifically achieved through the following steps: Extract polarization modulation parameters and beam guiding parameters from the pose adjustment command; The polarizer angle of the laser is adjusted according to the polarization modulation parameters to obtain the adjusted polarization state. The beam deflection unit is driven according to the beam guiding parameters to adjust the azimuth angle of the laser projection, thereby obtaining the adjusted laser beam direction. Based on the adjusted polarization state and beam direction, a new laser pattern is generated and projected onto the target positioning area to obtain an optimized image that matches the preset positioning template.

[0043] In specific implementation, firstly, the polarization modulation parameters and beam guiding parameters are extracted from the pose adjustment command through the protocol decoding module; secondly, a drive signal for the polarization motor is generated based on the polarization modulation parameters, and the drive signal drives the polarization motor inside the laser to rotate the polarizer to the target angle, thus obtaining the adjusted polarization state. The adjusted polarization state refers to the polarization state of the laser output light that meets the polarization characteristics requirements of the target positioning area after the polarizer angle is adjusted. The polarizer is the component that controls the laser polarization state; then, the beam guiding parameters are received through the beam deflection unit, and a rotation angle signal for the galvanometer motor is generated, driving the X-axis and Y-axis galvanometers to rotate to the corresponding angles, thus changing the polarization state. The propagation direction of the laser beam is changed to obtain the adjusted laser beam direction, which refers to the direction of the laser beam propagation that can be aligned with the target positioning area after adjustment by the beam deflection unit. The galvanometer is a component that adjusts the laser propagation direction. Finally, based on the adjusted polarization state and beam direction, the laser pattern is regenerated by the laser pattern generation module (the pattern generation module modulates the intensity of the laser beam according to the pattern encoding information of the preset positioning template and the adjusted polarization state to form a geometric pattern consistent with the template). The generated laser pattern is then projected onto the target positioning area through the adjusted beam direction to obtain an optimized image that matches the preset positioning template.

[0044] It should be noted that the optimized image in this application refers to a laser pattern image formed on the surface of the target positioning area that meets the requirements of the preset positioning template. By determining the optimized image, the execution effect of the pose adjustment command can be transformed into a laser pattern on the surface of the target positioning area that meets the preset requirements. This not only eliminates the distortion of pattern polarization information caused by polarization state deviation in the early stage, but also corrects the spatial position offset of the pattern caused by beam pointing deviation, ensuring that the laser pattern can accurately cover the key coordinate points of the target positioning area.

[0045] In step S105, the positioning completion status of the target positioning area is determined based on the signal-to-noise ratio and contrast distribution of the optimized image under different polarization channels.

[0046] In some embodiments, determining the positioning completion status of the target positioning area based on the signal-to-noise ratio and contrast distribution of the optimized image under different polarization channels is specifically achieved through the following steps: The optimized image is subjected to polarization channel separation to obtain sub-images corresponding to each polarization channel; Calculate the signal-to-noise ratio and contrast of each sub-image, and generate the signal-to-noise ratio distribution matrix and contrast distribution matrix; Extract the feature statistics from the signal-to-noise ratio distribution matrix and the contrast distribution matrix to obtain the global signal-to-noise ratio and the average contrast. The global signal-to-noise ratio and average contrast are compared with preset thresholds to generate comparison results. The positioning completion status of the target positioning area is determined based on the comparison results. Positioning is considered complete when both the global signal-to-noise ratio and average contrast reach the preset thresholds.

[0047] In specific implementation, firstly, sub-images of the 0°, 45°, 90°, and 135° polarization channels are extracted from the optimized image. Each sub-image corresponding to a polarization channel refers to an image containing laser pattern information in a single polarization direction separated from the optimized image. Secondly, for each sub-image, the signal-to-noise ratio (SNR) and contrast are calculated using a signal-to-noise ratio (SNR) calculation algorithm and a contrast calculation algorithm, respectively. When calculating the SNR, the signal region (laser pattern region) and noise region (background region) in the sub-image are first separated using a grayscale thresholding method. Then, the average grayscale value of the signal region and the average grayscale value of the noise region are calculated. The standard deviation and the ratio of the two are the signal-to-noise ratio (SNR) of the sub-image. Contrast is calculated using the existing Michelson contrast formula, dividing the gray-level difference between the signal region and the background region by the sum of their gray levels to obtain the sub-image's contrast. Then, the SNR of all sub-images is arranged according to their spatial coordinates to form a matrix, which is the SNR distribution matrix. Similarly, the contrast is arranged according to spatial coordinates to form a contrast distribution matrix. The SNR distribution matrix contains the SNR data for different spatial locations of each polarization channel sub-image, while the contrast distribution matrix contains the SNR data for different spatial locations of each polarization channel sub-image. The system generates a matrix of spatial contrast data. Further, a statistical analysis algorithm is used to extract feature statistics from the signal-to-noise ratio (SNR) distribution matrix and the contrast distribution matrix. Specifically, the arithmetic mean of all data in the SNR distribution matrix is ​​calculated to obtain the global SNR reflecting the overall SNR level, and the arithmetic mean of all data in the contrast distribution matrix is ​​calculated to obtain the average contrast reflecting the overall contrast level. The global SNR is the average of the SNR at each spatial location in all polarization channel sub-images, used to quantify and optimize the overall noise resistance of the image. The average contrast is the average of the contrast at each spatial location in all polarization channel sub-images, used to quantify and optimize the distinction between the laser pattern and the background in the image. Finally, the global SNR is compared with a preset SNR threshold, and the average contrast is compared with a preset contrast threshold. This generates a judgment criterion containing two comparison results (if the global SNR ≥ the SNR threshold and the average contrast ≥ the contrast threshold, the comparison result is "meets requirements"; otherwise, it is "does not meet requirements"). Based on this comparison result, the positioning completion status of the target positioning area is determined. When the comparison result is recorded as "meets requirements," the positioning is considered complete.

[0048] It should be noted that the positioning completion status of the target positioning area in this application refers to the judgment result used to characterize whether the optimized image quality of the surface of the target positioning area meets the preset standard and whether the positioning process can be terminated.

[0049] Furthermore, in another aspect of this application, in some embodiments, this application provides a laser-assisted positioning system based on intelligent control, referring to... Figure 3The figure is a schematic diagram of a laser-assisted positioning system based on intelligent control, according to some embodiments of this application. The laser-assisted positioning system based on intelligent control includes: a data acquisition module 201, a processing module 202, and an execution module 203, which are described below: Acquisition module 201, in this application, is mainly used to project a laser pattern with coded information onto the target positioning area through a laser, and to acquire the polarization image reflected from the surface of the target positioning area; Processing module 202, in this application, is mainly used to decouple the polarization image to obtain polarization sub-images under different polarization channels, and generate a multi-dimensional feature description matrix for laser projection based on the polarization state distribution and spatial distortion characteristics of the target positioning area surface in each polarization sub-image. The processing module 202 is further configured to identify the correlation between laser projection features and target spatial position under different polarization channels based on the multidimensional feature description matrix, and construct a polarization fingerprint map of the target positioning area based on the correlation between laser projection features and target spatial position under different polarization channels. The polarization fingerprint map is a visual map used to identify the polarization state of all spatial positions within the target positioning area. In addition, the processing module 202 is also used to generate a pose adjustment command containing polarization modulation parameters and beam guiding parameters based on the polarization fingerprint spectrum, and then reconstruct the projected laser pattern on the surface of the target positioning area into an optimized image that matches the preset positioning template according to the pose adjustment command. The execution module 203 in this application is mainly used to determine the positioning completion status of the target positioning area based on the signal-to-noise ratio and contrast distribution of the optimized image under different polarization channels.

[0050] In addition, this application also provides a computer device, the computer device including a memory and a processor, the memory storing code, the processor being configured to acquire the code and execute the above-described laser-assisted positioning method based on intelligent control.

[0051] In some embodiments, reference Figure 4 The figure is a schematic diagram of the structure of a computer device implementing a laser-assisted positioning method based on intelligent control, according to some embodiments of this application. The laser-assisted positioning method based on intelligent control in the above embodiments can... Figure 4 The computer device shown is used to implement this, and the computer device includes at least one processor 301, a communication bus 302, a memory 303, and at least one communication interface 304.

[0052] The processor 301 can be a general-purpose central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more devices used to control the execution of the laser-assisted positioning method based on intelligent control in this application.

[0053] The communication bus 302 can be used to transmit information between the aforementioned components.

[0054] The memory 303 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 303 may exist independently and be connected to the processor 301 via the communication bus 302. The memory 303 may also be integrated with the processor 301.

[0055] The memory 303 stores program code for executing the scheme of this application, and its execution is controlled by the processor 301. The processor 301 executes the program code stored in the memory 303. The program code may include one or more software modules. In the above embodiments, the determination of the laser-assisted positioning method based on intelligent control can be implemented by the processor 301 and one or more software modules in the program code in the memory 303.

[0056] Communication interface 304 uses any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0057] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0058] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0059] In addition, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described laser-assisted positioning method based on intelligent control.

[0060] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0061] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A laser-assisted positioning method based on intelligent control, characterized in that, Includes the following steps: A laser pattern with coded information is projected onto the target positioning area by a laser, and a polarization image of the reflection from the surface of the target positioning area is acquired. The polarization image is decoupled to obtain polarization sub-images under different polarization channels. A multi-dimensional feature description matrix is ​​generated based on the polarization state distribution and spatial distortion characteristics of the target positioning area surface in each polarization sub-image. The process of identifying the correlation between laser projection features and target spatial location under different polarization channels based on the multidimensional feature description matrix, and constructing a polarization fingerprint map of the target positioning area based on the correlation between laser projection features and target spatial location under different polarization channels, specifically includes: determining the polarization fingerprint identification factor under each polarization channel based on the correlation between laser projection features and target spatial location under different polarization channels, wherein the polarization fingerprint identification factor refers to the influence coefficient characterizing the identification of the target positioning area by different polarization channels; determining the polarization fingerprint map of the target positioning area based on the polariton image and polarization fingerprint identification factor under different polarization channels, wherein the polarization fingerprint map refers to the visual map used to identify the polarization state of all spatial locations within the target positioning area; determining the polarization fingerprint map establishes a benchmark reference system with both spatial topological attributes and unique polarization features for the target positioning process; binding the key polarization parameters under different polarization channels with the three-dimensional spatial coordinates of the target positioning area, so that each spatial location has a polarization identity identifier; Generating a pose adjustment command containing polarization modulation parameters and beam guidance parameters based on the polarization fingerprint spectrum, and then reconstructing an optimized image matching a preset positioning template on the surface of the target positioning area according to the pose adjustment command, specifically includes: extracting polarization modulation parameters and beam guidance parameters from the pose adjustment command; adjusting the polarizer angle of the laser according to the polarization modulation parameters to obtain the adjusted polarization state; driving the beam deflection unit to adjust the azimuth angle of the laser projection according to the beam guidance parameters to obtain the adjusted laser beam direction; and regenerating the laser pattern based on the adjusted polarization state and the adjusted laser beam direction and projecting it onto the target positioning area to obtain an optimized image matching a preset positioning template. The positioning completion status of the target positioning area is determined based on the signal-to-noise ratio and contrast distribution of the optimized image under different polarization channels.

2. The method as described in claim 1, characterized in that, Decoupling the polarization image to obtain polariton images under different polarization channels specifically includes: The polarization image is decomposed into original polariton images corresponding to the 0°, 45°, 90°, and 135° polarization channels; Noise suppression processing is performed on each original polariton image to obtain polariton images under different polarization channels.

3. The method as described in claim 1, characterized in that, The multi-dimensional feature description matrix generated based on the polarization state distribution and spatial distortion characteristics of the target positioning region surface in each polariton image specifically includes: The linear polarization degree of each pixel on the surface of the target positioning region in each polariton image is determined, thereby forming a polarization state distribution set; The pattern pixel offset of each pixel on the surface of the target positioning region in each polariton image is determined, and then a spatial distortion feature set is generated; The polarization state distribution set is associated and matched with the spatial distortion feature set to obtain a multidimensional feature description matrix.

4. The method as described in claim 1, characterized in that, The correlation between laser projection features and target spatial position under different polarization channels is identified based on the multidimensional feature description matrix, specifically including: The multidimensional feature description matrix is ​​grouped according to polarization channel to obtain the feature description matrix corresponding to each polarization channel; Principal component analysis was performed on each feature description matrix to extract key feature parameters of each polarization channel as laser projection features; A three-dimensional spatial coordinate grid is established for the target positioning area. Each laser projection feature is mapped to the coordinate point corresponding to the three-dimensional spatial coordinate grid, and the mapping coefficient between each laser projection feature and the corresponding coordinate point is calculated. In this way, the correlation between the laser projection feature and the target spatial position under different polarization channels is determined.

5. The method as described in claim 1, characterized in that, The specific steps for generating pose adjustment commands based on the polarization fingerprint spectrum, including polarization modulation parameters and beam guiding parameters, are as follows: Determine the feature deviation between the polarization fingerprint spectrum and the preset standard fingerprint spectrum; The adjustment increment of the laser polarization state is calculated based on the characteristic deviation to obtain the polarization modulation parameters; The correction value of the laser beam projection angle is obtained by solving the mapping relationship between the characteristic deviation and the target spatial position, and the beam guiding parameters are obtained. The polarization modulation parameters and the beam guiding parameters are encoded and integrated according to a preset protocol to generate a pose adjustment command that includes the polarization modulation parameters and the beam guiding parameters.

6. The method as described in claim 1, characterized in that, A polarization image of the surface reflection of the target positioning area is acquired using a polarization camera.

7. A laser-assisted positioning system based on intelligent control, used to execute the laser-assisted positioning method based on intelligent control as described in any one of claims 1 to 6, characterized in that, The system includes: The acquisition module is used to project a laser pattern with coded information onto the target positioning area through a laser, and to acquire the polarization image reflected from the surface of the target positioning area. The processing module is used to decouple the polarization image to obtain polarization sub-images under different polarization channels, and generate a multi-dimensional feature description matrix for laser projection based on the polarization state distribution and spatial distortion characteristics of the target positioning area surface in each polarization sub-image. The processing module is further configured to identify the correlation between laser projection features and target spatial position under different polarization channels based on the multidimensional feature description matrix, and construct a polarization fingerprint map of the target positioning area based on the correlation between laser projection features and target spatial position under different polarization channels. The polarization fingerprint map is a visual map used to identify the polarization state of all spatial positions within the target positioning area. The processing module is also used to generate a pose adjustment command containing polarization modulation parameters and beam guiding parameters based on the polarization fingerprint spectrum, and then reconstruct the projected laser pattern on the surface of the target positioning area into an optimized image that matches the preset positioning template according to the pose adjustment command. The execution module is used to determine the positioning completion status of the target positioning area based on the signal-to-noise ratio and contrast distribution of the optimized image under different polarization channels.

8. A computer device, characterized in that, The computer device includes a memory and a processor, the memory storing code, and the processor being configured to retrieve the code and execute the laser-assisted positioning method based on intelligent control as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the laser-assisted positioning method based on intelligent control as described in any one of claims 1 to 6.

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