50 results about "Geometric verification" patented technology

The invention relates to a multi-scale feature fusion-based facade crack detection method, apparatus and device, and a medium. The method comprises the steps of performing multi-dimensional complexity evaluation on a to-be-detected image, and generating a classification label by combining information entropy, edge density, texture and gradient variance; dividing the image into high / low-complexity data based on a dynamic threshold, and processing the high / low-complexity data by adopting parameterized enhancement and multi-modal feature fusion strategies: for the low-complexity image, suppressing noise through multi-threshold segmentation and morphological optimization, and extracting fine crack features; for a high-complexity image, in combination with multi-scale feature extraction, asymmetric convolution solution and attention weight fusion, crack response under a complex background is enhanced; and finally, carrying out normalization and geometric verification on the two types of probability graphs, and outputting accurate crack positions and forms. According to the invention, through a complexity-driven differential processing mechanism, the detection robustness in a complex illumination and texture interference scene is significantly improved, and the consumption of computing resources is reduced.

The invention discloses an image recognition system for industrial part defect detection, and particularly relates to the field of part defect detection, which comprises a multi-axis controllable light source array module, a high-speed polarization camera module, an edge computing node module, a double-branch semantic segmentation network module and a physical constraint post-processing module, according to the invention, through combination of time-sharing stroboscopic illumination and polarization image sequence acquisition, multi-dimensional perception of surface topography and material differences is realized; generating an elevation map and a normal map by using photometric stereo solution, constructing a differential rendering layer reverse matching CAD model, and extracting flash sensitive features; a double-branch U-Net network is adopted to fuse geometric and polarization characteristics, the characterization capability is enhanced through a trans-attention mechanism, and a pixel-level mask is output; and finally, mapping a two-dimensional result to a three-dimensional coordinate system by means of calibration parameters, carrying out geometric verification in combination with a tolerance zone and a height threshold value, and automatically generating a structured defect report containing position, size, grade and visual information.

The invention discloses a LiDAR-IMU-camera tight coupling positioning and mapping method and device for a mobile platform, and belongs to the technical field of robot SLAM. Synchronously triggering the color camera, the laser radar and the IMU through hardware, and unifying timestamps; performing compensation and distortion removal on the laser point cloud motion by the IMU data; based on curvature, intensity and density self-adaptive downsampling, local plane fitting errors are used for distributing observation weights; the IMU pre-integration pose is used as an initial value, point-to-surface registration of the weighted point cloud and the local map is carried out, and a laser-IMU tight coupling odometer factor is obtained; the color image and the laser intensity graph are fused into a multi-mode loopback descriptor, and loopback factors are generated through global retrieval and geometric verification; and inputting an odometer, an IMU, a laser loopback factor and a visual loopback factor into an increment factor graph optimizer, jointly solving a global optimal key frame pose, and outputting a dense laser point cloud map and a color visual point cloud map. The method can be operated in real time on an embedded platform, effectively inhibits drifting, and realizes centimeter-level global consistent positioning and mapping.

The invention relates to a multi-modal image matching method and system based on learning features and epipolar geometric constraints. The method comprises the following steps: carrying out edge enhancement processing on an input image through wavelet transform; extracting a multi-scale dense feature map based on the transformed convolutional neural network, and generating a feature descriptor with rotation and scale invariance in combination with principal direction normalization; adopting an FLANN algorithm and dynamic distance constraint to realize preliminary feature matching; and introducing a basic matrix construction and epipolar geometric consistency verification mechanism, and eliminating mismatching point pairs in combination with an RANSAC affine constraint model. According to the method, image enhancement, deep learning and geometric verification strategies are fused, the problems of radiation nonlinearity and geometric distortion caused by imaging mechanism differences among multi-modal images are effectively solved, the matching precision and robustness are improved, and the method is suitable for remote sensing application scenes such as optical-SAR registration, multi-source image fusion and earth surface change detection.

The invention provides a data processing method which can be applied to the technical field of computers. The data processing method comprises the steps that in response to data writing operation, written data are verified according to geometric verification rules corresponding to all verification disks, verification blocks obtained through verification are stored in the corresponding verification disks, and a mapping relation exists between the verification blocks and the written data stored in the multiple data disks; if it is detected that the disk fails, determining a recovery mode according to the type of the failed disk and the number of the failed disks; and based on the mapping relationship, according to the recovery mode, forwards and / or reversely calling the geometric verification rule so as to recover the data of the fault disk. The invention further provides a data processing device, equipment, a medium and a program product.

The invention discloses an unmanned aerial vehicle automatic inspection method and system for a photovoltaic power station, and the method comprises the steps: carrying out the geometric verification and luminosity verification of a preliminarily recognized potential abnormality through fusing multi-dimensional information, including an infrared image, a visible light image, the precise pose of an unmanned aerial vehicle and a timestamp, and combining solar ephemeris data and a photovoltaic panel geometric model; therefore, accurate discrimination and decoupling of true and false abnormities are realized. In this way, the accuracy and reliability of anomaly detection are remarkably improved, operation and maintenance personnel can more concentrate on handling real faults, and therefore the overall inspection efficiency and benefits are improved.

The invention discloses an SLAM (Simultaneous Localization and Mapping) optimization mapping method and device combining geometric verification and constraint of a reflector, relates to the technical field of industrial-grade mobile robot navigation, and solves the problem that in the prior art, a continuous constraint mechanism cannot be established in a mapping process to correct accumulative errors in motion. The method comprises the following steps: acquiring an original laser radar point cloud containing a reflector, screening out reflection points with the distance smaller than a preset distance, and carrying out clustering and quintuple geometric verification on the screened points by adopting a density clustering algorithm, so as to obtain a multi-dimensional geometric verification mechanism based on PCA (Principal Component Analysis); local coordinates of the center of the reflector are calculated and converted into global coordinates, the reflector is used as a stable Landmark depth to be fused into an SLAM back-end graph optimization framework, and optimization nodes including a timestamp, a robot pose, a reflector ID and an observation pose are constructed based on a global coordinate system to participate in graph optimization; and the matching problem caused by insufficient natural characteristics or environment change in a long corridor scene can be solved in a targeted manner.

The invention relates to the technical field of hardware design, in particular to a differential wiring verification method for a PCB and electronic equipment, and the method comprises the steps: determining a differential wiring topology model in response to an obtained PCB design file; acquiring an electrical rule defined by a user, and performing geometric verification based on the differential routing topology model and the electrical rule to position geometric violation points; performing integrity analysis on a preset key structure in the differential routing topology model to determine a signal distortion risk point; and generating a visual view according to the geometric violation points and the signal distortion risk points. The method has the advantages that hard geometric violation and hidden electrical risks of differential wiring in PCB design are found, and verification accuracy and design reliability are remarkably improved.

The invention relates to the technical field of robots and automation, and discloses a dynamic environment-oriented laser SLAM (simultaneous localization and mapping) system and an efficient loopback detection method thereof, the system comprises a data preprocessing module, a probability persistence map management module, a persistence weighted front-end odometer module, a loopback detection and state reverse correction module and a rear-end pose map optimization module, static and dynamic elements in an environment are distinguished by utilizing a probability persistent map, structural conflicts in the environment are intelligently identified by comparing the similarity of a structural anchoring descriptor synthesized by high-persistent voxels with an error of geometric verification, and when the structural conflicts are identified, a system performs reverse correction on old structural information in the map; and when the geometric verification is passed, generating a loopback constraint. According to the method, transient dynamic object interference and long-term structural change of the environment can be effectively dealt with, and the robustness, loopback detection accuracy and map long-term consistency of the SLAM system in the complex dynamic environment are improved.

The invention relates to an elastic sheet structure generation method and device, computer equipment and a storage medium. The method comprises the following steps: generating an initial structure control parameter of an elastic sheet based on a preset spatial constraint parameter; fitting is carried out based on the initial structure control parameters, and a three-dimensional elastic piece model is obtained; the elastic sheet three-dimensional model is an elastic sheet three-dimensional model conforming to a preset geometric verification rule; performing performance simulation on the elastic sheet three-dimensional model to obtain a performance simulation result; and based on the performance simulation result, performing iterative optimization on the initial structure control parameter to obtain a target structure control parameter and a target elastic sheet three-dimensional model corresponding to the target structure control parameter. By adopting the method, the elastic sheet design efficiency can be improved, the labor cost is reduced, and the elastic sheet performance is improved.

The invention discloses a dynamic Voronoi skeleton constrained multi-modal door body detection and position correction method and system. The method comprises the following steps: acquiring sensor data through an RGBD camera and a multi-line laser radar; processing the RGB image by using an improved YOLO deep learning network to realize coarse positioning of the door body, and outputting a three-dimensional initial coordinate of a door frame; carrying out three-dimensional geometric verification by combining point cloud data fusion, wherein the three-dimensional geometric verification comprises ground point cloud elimination, door frame plane fitting and point cloud density analysis so as to judge the opening and closing state of a door body; constructing a dynamic Voronoi skeleton model, and correcting an environment topological structure in real time through an incremental distance field updating algorithm; a curvature constraint projection algorithm is adopted to map a detection result to a skeleton center line; and outputting coordinates of the center of the door body on the two-dimensional grid map. According to the method, the robustness of door body positioning and the navigation reliability in a complex dynamic environment are remarkably improved.

The present invention discloses a semantic positioning method based on environmental objects, which relates to the field of robotics. It includes semantic mapping, closed-loop detection, and positioning correction. The semantic information of the target object in the image is extracted by a convolutional neural network, and the camera's position information is estimated using a visual odometry. The label information of the target object and the position and size of its 2D bounding box in the image are extracted by a convolutional neural network; a two-way comparison and matching method is used to detect whether the camera has passed through a historical location. If so, geometric verification is performed to confirm whether the camera has reached the closed-loop position; finally, the relative position relationship of the objects is calculated and the position of the camera is corrected through pose graph optimization. The present invention can achieve centimeter-level positioning and map construction in complex environments such as those with changes in lighting and seasons and changes in camera observation angles.

The application discloses a dynamic Voronoi skeleton constrained multi-modal door detection and position correction method and system, and the method comprises the following steps: acquiring sensor data through an RGBD camera and a multi-line laser radar; processing an RGB image by using an improved YOLO deep learning network, realizing coarse positioning of a door body, and outputting three-dimensional initial coordinates of a door frame; combining point cloud data fusion to perform three-dimensional geometric verification, including ground point cloud elimination, door frame plane fitting and point cloud density analysis to determine the opening and closing state of the door body; constructing a dynamic Voronoi skeleton model, and correcting the environmental topological structure in real time through an incremental distance field updating algorithm; adopting a curvature constraint projection algorithm to map the detection result to the skeleton center line; and outputting the coordinates of the door body center on a two-dimensional grid map. The application significantly improves the robustness and navigation reliability of door body positioning in a complex dynamic environment.

This invention relates to the field of data verification technology, and particularly to a method, apparatus, device, and storage medium for rapid geometric verification of hyperspectral data. The method includes: acquiring a high-resolution visible light area array image and preprocessing it to obtain a visible light orthorectified image; acquiring a raw hyperspectral image and performing coarse geometric correction to obtain a coarsely geometrically corrected hyperspectral orthorectified image; performing feature extraction and matching processing on the two images obtained above, constructing a large number of pseudo-control points based on the matching results to obtain a set of point pairs including geographic coordinates; calculating the true scan vector of each scanned pixel in the coordinates of a digital land model, and calculating the corresponding geographic coordinates; constructing a cost function based on the set of point pairs and the calculated geographic coordinates; and iteratively solving the hyperspectral geometric verification parameters with the goal of minimizing the cost function to perform geometric correction on the raw hyperspectral image. The method disclosed in this application can significantly improve the efficiency of geometric verification and reduce the verification cost.

The present invention discloses a laser SLAM loop detection method based on semantic information, comprising: performing spherical projection on the laser point cloud in the current environment and obtaining the depth information of each point in the two-dimensional image generated after the projection; performing semantic segmentation through a fully convolutional neural network and generating a weight matrix using the obtained semantic labels; aggregating and rotating the weight matrix through a sliding window to generate a global descriptor with rotation invariance; constructing a Kd-Tree to search historical frames and obtain candidate frames based on time thresholds and distance thresholds; obtaining the angular difference between two frames of point clouds using the global descriptor, and performing geometric verification through an ICP algorithm with an initial angle to determine the final loop frame and obtain a closed-loop position. The present invention solves the problem that the existing technology does not fully utilize the semantic information of the environment when performing loop detection using pure laser, resulting in low algorithm efficiency.

The invention discloses an aerial image three-dimensional modeling method fusing image distortion removal and pose priori constraint, and relates to the technical field of three-dimensional modeling. The aerial image three-dimensional modeling method fusing image distortion removal and pose priori constraint comprises the following steps: S1, collecting aerial image data to construct an aerial image original input set; s2, converting into a camera observation vector and a camera group internal reference candidate set; s3, performing texture complexity quantization, generating a parameter compiling basis, and selecting a camera model; s4, constructing a candidate image pair set, and executing geometric verification; and S5, frame-level covariance adaptive scaling is carried out. According to the method, the robustness and reproducibility of aerial image three-dimensional modeling under a complex flight condition are effectively improved, and the problem that noise injection and constraint loss coexist due to pose priori quality fluctuation in an aerial scene in an existing method is solved.

The application provides a DFM Pattern Match hardware accelerator based on FPGA and a method thereof, which comprises a data distribution module, a hash calculation module, a hash comparison module, a candidate FIFO, a target area generation module, and an edge screening module.The data distribution module is used for receiving and transmitting different groups of edge data to the hash calculation module in parallel.The hash calculation module is used for performing direction bucket division and calculating corresponding hash values in parallel for each edge.The hash comparison module is used for performing parallel comparison between the hash values of each direction bucket and the hash values of a target pattern, and generating candidate items.The candidate FIFO is used for decoupling the pre-screening stage and the accurate geometric verification stage.The target area generation module is used for obtaining a target search area.The edge screening module is used for screening all edges located in the target search area from a layout.The accurate matching module is used for mapping the edges output by the edge screening module to a target pattern coordinate system, and performing parallel comparison between the edges and each edge of the target pattern, so as to determine whether the target search area is consistent with the target pattern.The application can efficiently perform pattern matching on a circuit layout in integrated circuit physical verification.

A system for chromatic aberration and geometry calibration and validation of VR HMDs has a left eye display and a right eye display, comprises a measurement device with a left (11) and a right (12) camera. A control unit, connected to the two cameras, is configured to deliver control signals to the HMD when attached to the measurement device and with the camera IPD is equal to the HMD IPD and a virtual IPD, the control unit is configured to effect a measurement comprising the steps of: creating a virtual target object having am image center identifying element (523) for each camera (11, 12) and a plurality of predetermined points (525) within the FOV of the cameras, calculating and transmitting the images of said virtual target object to the displays of the HMD, taking the images of the displays with the cameras, detecting the virtual object (535) within the images, determining the shift (555) for the plurality of predetermined points (525) of the virtual object, for creating a chromatic aberration heatmap and a subsequent chromatic aberration validation, or for calculating the distortion of the predetermined points within the FOV of the cameras for creating a geometric correction mapping for the HMD for a geometry calibration of the HMD; or for using the shift for creating a heatmap for the HMD and determining the geometry validation or denying the geometry validation.

The application discloses a kind of multi-modal information comprehensive retrieval street view image positioning method and system, by establishing multi-modal information database, using visual feature extraction model and scene text recognition model establish multi-modal information database;By fusion feature retrieval, global feature and local feature are fused and preliminary recall search is carried out;By geometric verification rearrangement, the geometric verification algorithm based on local feature is used to rearrange preliminary recall search result, and obtain refined retrieval result;By scene text retrieval, the scene text set overlap degree of query picture and database picture is calculated, and the final retrieval result is obtained based on this score ranking. The application considers the comprehensive retrieval scheme of multi-modal information, can greatly improve the image positioning accuracy in practical application, and to a certain extent, solves the complete positioning failure problem of single modal information in some scenes.

The invention relates to a multi-view geometric and spatial distribution consistency-based relocation result screening method and hardware. The method comprises the following steps of: obtaining a plurality of candidate results which are generated by a relocation algorithm and represent the pose of a multi-view vision system; scoring the candidate results by using a multi-dimensional screening model fusing multi-view geometry and spatial distribution consistency, and screening an optimal pose; the system comprises a data acquisition module, a spatial distribution analysis module, a multi-view geometric verification module and a feature fusion and decision module. The multi-view vision positioning equipment comprises a multi-view camera array, a processor and a memory, wherein a computer program executes the method in the memory; a medium is also implemented based on the method. The error selection risk of a single error result is remarkably reduced, the overall robustness of the system in a complex environment is greatly enhanced, the output pose has higher consistency guarantee in statistical significance and physical geometry, the method stably operates in an embedded platform with limited resources or a real-time system needing high-frequency positioning, and the method is suitable for large-scale popularization and application. And high precision and high real-time performance are effectively considered.

The present disclosure provides a laser point cloud loop detection method and system based on a triangular pyramid local descriptor, which relates to the field of loop detection technology and is used to retrieve the loop frame of the current key frame from the key frame sequence. The method comprises: constructing a global descriptor and a triangular pyramid local descriptor for each key frame in the key frame sequence; based on the global descriptor, coarsely searching for a preliminary candidate loop frame set from the key frame sequence; based on the triangular pyramid local descriptor, finely searching for a final candidate loop frame set from the preliminary candidate loop frame set; filtering the loop frame of the current key frame from the final candidate loop frame set through geometric verification and validity judgment; wherein the triangular pyramid local descriptor performs density clustering on the point cloud of the key frame based on the number of points in the neighborhood, constructs a triangular pyramid using the clustering result, and encodes the information of the triangular pyramid as the triangular pyramid local descriptor of the key frame. The present invention has the advantages of high precision, strong robustness, and simple calculation.

The invention discloses a three-dimensional point cloud reconstruction method and system of a depth camera-encoder, and belongs to the technical field of computer vision and three-dimensional imaging. According to the method, periodic radial deviation characteristics of characteristic point tracks are extracted, the actual rotation center offset is recognized through least square fitting, and a concentricity compensation matrix is constructed to correct camera space coordinates in real time; and then multi-degree-of-freedom attitude sensing is realized in combination with Kalman filtering, a scanning path is optimized through dynamic coordinate system correction and trajectory control, finally point cloud reconstruction is completed based on the corrected pose, and precision is ensured by adopting a geometric verification closed loop. According to the method, the mechanical assembly tolerance is converted into a technical variable which can be compensated on line, the coupling influence of concentricity deviation and attitude error is fundamentally eliminated through error cooperative compensation and an active control mechanism, and the precision and long-term stability of three-dimensional point cloud reconstruction are remarkably improved.

The application relates to the field of outdoor AR positioning and anchoring technology, in particular to a kind of persistence anchoring method and system of outdoor large scene augmented reality content, comprising: collecting image sequence of target area in different time periods and different weather, and carrying out semantic filtering and feature extraction, and constructing multi-dimensional feature index library;When starting AR application, online semantic enhancement hierarchical positioning is carried out, specifically including: LBS coarse screening and context perception, key frame selection and semantic mask, global visual retrieval, local feature matching and geometric verification, gravity constraint PnP solution, and pose fusion and smoothing;AR image is input into lightweight depth estimation network to obtain depth map, and is written into depth buffer to realize the visual effect that virtual object is correctly occluded by real object;Incremental feature fusion and old feature elimination are carried out using image data.The application realizes high-precision, long-time, cross-session stable anchoring and depth virtual-real fusion AR positioning technology.

This invention discloses a dynamic environment SLAM localization method based on multi-source sensor data fusion, comprising the following steps: acquiring visual, inertial, and laser data and calibrating the buffer; performing HRNet segmentation and ORB-SLAM tracking on the visual data to obtain the initial pose; fusing the inertial and laser results to obtain the current pose; determining and recalculating the divergence of time window data to update the current pose; inserting keyframes and generating a semantic skeleton map; encoding and generating a semantic topology string and associating it with the keyframe index; retrieving loop closure candidate frames and performing geometric verification followed by map optimization to obtain the localization result and map result. This invention achieves highly robust localization and mapping in dynamic environments and improves the accuracy of loop closure recognition and map consistency.

The invention relates to the technical field of house information processing, in particular to a three-dimensional house type model analysis method for generating a house surveying and mapping plane graph, which comprises the following steps of: recording spatial data of a three-dimensional house type model under a plurality of house structures based on the house surveying and mapping plane graph; based on the spatial data of the house structure, carrying out cross validation on a wall body at the position of the house structure, and marking difference classification between each wall body and the standard model; constructing a random change matrix according to the difference classification corresponding to each wall body, and determining boundary features of each wall body at a horizontal position and a vertical position; taking the coordinates corresponding to the boundary features as input, carrying out geometric verification on the walls, and determining a coordinate point set of each wall when the geometric verification is established; and recoding the three-dimensional house type model on the basis of difference classification and boundary features of the coordinate point set, and taking the recoded house structure as an output three-dimensional house type model. And the accuracy and efficiency of the house model are improved.

The invention discloses a grid star map identification method and system based on weighted voting and geometric verification, and the method specifically comprises the steps: firstly, constructing a feature mode database containing all-day navigation stars, and storing the rasterized geometric mode of an adjacent star sky region of each navigation star and the inverse document frequency (IDF) weight of each grid unit in the database; secondly, extracting a plurality of brightest observation stars as main stars from an observation star map obtained by a star map sensor, and generating an observation geometric mode for each main star; inquiring an offline database by utilizing an observation mode, generating a candidate navigation star list, dynamically filtering low-credibility candidate stars through an adaptive threshold strategy, and sorting the candidate stars by adopting a mixed scoring function; and finally, carrying out pose consistency check on the plurality of candidate stars with the highest scores through a geometric verification method, and identifying the most reliable matching result. On the premise of ensuring the real-time performance, the grid star map recognition robustness and accuracy under various noises and interferences are improved.

The present application relates to the field of industrial visual inspection and intelligent manufacturing, in particular to a power distribution cabinet wiring compliance verification system based on image detection, comprising an image and process data acquisition module, an image processing and detection scheduling module, a compliance analysis and judgment module, and a result interaction and feedback output module; receiving power distribution cabinet images of production line stations, wiring process data and hardware state data of a manufacturing execution system, extracting image state information and performing test item classification and detection scheduling, extracting cable regions using an image segmentation algorithm, performing multi-stage verification and geometric verification in combination with wiring process data, obtaining wiring compliance verification results, generating quality inspection reports and control instructions and pushing them to an external manufacturing execution system, and converting image recognition results into process compliance results that can be directly used for production line judgment.

The embodiment of the invention provides a polygon topology calculation method and system suitable for line segment intersection judgment of a GIS (Geographic Information System), and the method comprises the steps: obtaining spatial topology data of a designated region, and constructing an intersection point set based on the spatial topology data through a geometric verification method based on a dynamic tolerance and endpoint position relation; and sorting the intersection points in the set, and inserting the edges associated with the sorted points into the AVL Tree in a reverse order, and obtaining the polygon topology related data structure with the efficient retrieval and topology analysis capability after the insertion is finished. The implementation of the method can solve the problem that two line segments are in an intersection state in reality but do not have an intersection relationship in the calculation process, and finally, the two line segments are ensured to also intersect in the calculation process by setting the tolerance.

The invention provides a data sampling and abnormal point removing method and system based on reinforcement learning. The method comprises the following steps: extracting multi-modal features, constructing corresponding relation features, extracting key points and descriptors thereof from two input images, generating an initial corresponding relation set, and performing coordinate normalization processing; constructing a reinforcement learning agent, and training the agent to enable the agent to learn an optimal strategy of data sampling and outlier removal; constructing a feature point relation graph, and dynamically segmenting normal matching points and outliers based on a clustering result represented by nodes and a confidence threshold; and carrying out multi-scale geometric verification on the screened high-confidence matching points, eliminating local inconsistent points, estimating an affine transformation matrix, and introducing illumination invariance constraint to optimize the image alignment precision. According to the technical scheme, through interaction with the environment, the reinforcement learning model can learn that the most representative strategy is selected under specific conditions, and the utilization efficiency of data and the performance of image matching are improved.

The invention relates to the technical field of electric vehicle charging, and discloses a slow charging pile automatic detection and automatic charging method based on AI identification, comprising the following steps: collecting a vehicle image and identifying identity information, evaluating a vehicle parking attitude, and performing pose compensation on a preset vehicle digital twin model; real-time three-dimensional space geometric information of a charging gun is obtained, and matching verification is carried out on the real-time three-dimensional space geometric information and charging port three-dimensional space geometric features contained in the compensated vehicle digital twin model so as to determine the validity of charging connection; charging is automatically started on the basis of effective connection, images of the charging cable are continuously collected and analyzed in the charging process, the visual posture characteristics of the charging cable are obtained, and the charging power is dynamically and adaptively adjusted according to the characteristics. According to the method, the reliability of connection judgment is improved through three-dimensional geometric verification, the safety of the charging process is enhanced through visual posture monitoring, automation from vehicle parking, charging starting to process control is achieved, and the intelligent level of charging is improved.