669 results about "3d vision" patented technology

The invention discloses a point cloud data rapid processing system based on 3D vision, and relates to the technical field of point cloud data processing. The binocular acquisition module generates an anti-interference high-precision point cloud; the preprocessing module is used for reducing noise and dimensionality and retaining key geometric features; the hierarchical feature extraction module fuses curvature weight and a non-maximum suppression strategy, and improves the robustness of an ORB algorithm in rotation, scale and noise scenes; the industrial scene segmentation module accurately separates stacked objects through normal vector clustering and a dynamic region growing algorithm, and secondarily verifies boundaries in combination with a lightweight semantic model; the two-stage registration module dynamically adjusts a threshold value based on error feedback to realize pose optimization from coarse registration to fine registration; the coordinate mapping module establishes a space mapping model through multi-attitude calibration and robot multi-axis linkage trajectory optimization. The problem that in the prior art, a 3D machine vision algorithm is high in time delay is solved, the real-time performance and rapidity of 3D model recognition and feature extraction are improved, and the efficiency requirement of industrial production is met.

The invention discloses a robot 3D visual guide grabbing method, which belongs to the field of robot application, and comprises the following steps of: comprehensively scanning a working environment by using a high-resolution 3D visual sensor to obtain point cloud data of a scene; carrying out object identification on the processed point cloud data by using a deep learning algorithm; a model capable of recognizing a target object is trained, the model can distinguish objects of different types, shapes, colors and textures, point cloud data are obtained through omnibearing scanning of a high-resolution 3D vision sensor, professional preprocessing is carried out, and the working environment can be accurately perceived. The deep learning algorithm is combined with multiple segmentation methods, objects can be accurately recognized and segmented under the complex background and shielding, meanwhile, geometric models of the objects and the environment are created through a specific algorithm, a comprehensive environment map is formed, an accurate basis is provided for follow-up operation, and object characteristics, gripper capacity and multiple grabbing strategies are comprehensively considered.

The invention relates to a non-standard special-shaped part high-precision measurement and verification method based on three-dimensional vision, and belongs to the field of three-dimensional reconstruction and computer vision. Point cloud data of a non-standard special-shaped part are obtained through multi-angle scanning by means of multi-camera-projector collaborative layout in combination with a structured light coding technology and a phase shift code technology; the method comprises the following steps of: acquiring point cloud data, ensuring comprehensiveness and accuracy of the data, realizing high-precision alignment of the point cloud data through matching an underivative optimization algorithm with image feature points, and performing fusion, denoising and registration processing on the acquired point cloud data to ensure that point clouds acquired from a plurality of angles can be effectively fused so as to reconstruct a high-precision three-dimensional model. In the measurement and verification stage, the deviation between the point cloud data and the CAD model is measured through a standard coordinate system, multi-stage registration and a high-precision algorithm, rapid detection and size verification of the geometric deviation of the industrial workpiece are achieved, and reliable technical support is provided for quality control.

The invention provides an object positioning and posture recognition method based on 3D vision. The method comprises the steps that 3D point cloud data and image data corresponding to the 3D point cloud data are collected; preprocessing the image data to obtain a quality evaluation result, and executing image restoration processing based on the quality evaluation result to generate optimized image data; inputting the optimized image data into a pre-constructed conditional diffusion model based on hierarchical feature guidance, and generating feature representation of the optimized image data; and inputting the feature representation of the optimized image data into a differential transformer network to obtain a position and attitude prediction result of the object. According to the invention, through innovative image quality evaluation based on dual representation interaction driving, a conditional diffusion model based on hierarchical feature guidance and the design of a differential transformer network, the precision and robustness of object positioning and posture recognition in a complex environment are improved.

The invention relates to the technical field of large casting surface cleaning, in particular to a large casting surface cleaning robot system and method based on 3D vision. According to the technical scheme, the large casting surface cleaning robot system comprises a rotary working platform, a rotary assembly, a robot and a processing assembly, and the processing assembly is arranged on a swing arm of the robot; the system further comprises a visual scanning assembly, a comparative analysis module and a control module, the robot drives the visual scanning assembly to scan a casting, and three-dimensional grid data of the casting to be machined are obtained; the contrastive analysis module receives the three-dimensional grid data and carries out contrastive analysis on the three-dimensional grid data to obtain processing path data, the control module receives the processing path data and forms a control signal based on the processing path data, and the control signal is sent to the rotating assembly, the robot and the processing assembly. The casting surface defect treatment method has the effect of improving the casting surface defect treatment efficiency and the casting surface defect treatment precision.

The invention provides a massage robot multi-mode fusion treatment system and method based on 3D vision, and relates to the technical field of massage robots. The region recognition module is used for establishing a human body recognition model and recognizing human body features in the processed image; the massage terminal is used for carrying out massage operation according to parts needing to be massaged and human body characteristics and monitoring skin pressure of the massaged parts in real time; the pain sense recognition module is used for human body pain sense recognition; the force adjusting module is used for adjusting skin pressure. According to the method, pertinence and safety of massage operation are remarkably improved through multi-modal image preprocessing and an initialized human body recognition model of a fusion segmentation model and a key point detection model; a global path strategy is generated and optimized through a hybrid ant colony algorithm, efficient and accurate motion control of the mechanical arm is achieved, through multi-modal data fusion analysis, the pain feeling of a user is sensed in real time, the massage strength is dynamically adjusted, and the comfort and the safety coefficient are improved.

The invention discloses a full-automatic grinding method and system for steering knuckle flash based on 3D vision, and relates to the technical field of casting post-processing automation. A trimming-free direct polishing process is innovatively provided, a casting is scanned in real time through a high-precision 3D visual system, a flash space molded line is accurately captured, and one-key calibration of a camera, automatic generation and dynamic simulation of a flash path, multi-tool intelligent switching and self-adaptive adjustment of residual height are achieved in combination with self-developed off-line programming software. According to the system, a robot is adopted for grabbing workpieces to cooperate with a fixed type multi-cutter work station, a disordered feeding technology is integrated for 2D / 3D visual guiding grabbing, workpiece deformation and die difference are effectively dealt with, and flash residues are thoroughly eliminated. The method is characterized in that visual scanning, path planning and execution control are seamlessly fused, the dependence of traditional automatic grinding on the trimming process is broken through, the yield and efficiency are remarkably improved, and the industrial problem of full-process automation of casting flash is solved.

The invention discloses a multi-mode sensing multi-mechanical-arm cooperative control method and system and a robot. Task types are judged according to power consumption and path requirements of a target task; performing module selection according to the task type; data such as material positions, material shapes, obstacle positions and obstacle shapes are collected in real time; the real-time data collected by the sensing module is fused, and the motion path, the motion speed, the motion acceleration, the grabbing posture and the grabbing force of the mechanical arm are determined; storing the execution data and task feedback information, analyzing an operation effect, and optimizing modal selection and parameter setting of a next task; through 3D vision, 2D vision and instruction set multi-modal perception and data fusion, the motion path of the robot is continuously optimized, multi-source data are integrated in real time, and the accuracy and robustness of material recognition and state description are improved.

In estimating a position of a vehicle utilizing a global navigation satellite system (GNSS), it is desirable to exclude outliner GNSS measurements due to navigation signals via non-light-of-sight paths from the GNSS to the vehicle, but it leads to a distorted satellite geometry distribution. Complementariness between low-lying visual landmarks and healthy but high-elevation satellite measurements is explored to improve the geometry constraint. Measurements of an inertial measurement unit, low-lying visual landmarks captured by a forward-looking camera onboard the vehicle, and healthy but high-elevation satellite measurements are tightly-coupled integrated via sliding window optimization of system states used in a factor graph. To improve estimation performance, good initial guesses of system states are important. As such, initial guesses of velocity set and position set inside a sliding window are estimated simultaneously based on data of Doppler measurement, double-differenced (DD) pseudorange measurement and DD carrier-phase measurement as obtained in GNSS measurements.

The invention discloses a robot unstacking and stacking support system based on a 3D visual system, and belongs to the technical field of 3D visual systems. Comprising a 3D visual data acquisition preprocessing module, a target positioning priority analysis module, a box type intelligent identification matching module, a dynamic path planning obstacle avoidance module, a self-adaptive stacking strategy generation module, an information binding tracing management module and a feedback optimization module. The problems of low efficiency and conflict caused by a single grabbing sequence in traditional stacking and unstacking operation are solved, the priority weight is dynamically adjusted by combining the inclination angle of the carton, weight distribution and reachability verification, and the optimality and safety of the grabbing action of the robot are ensured.

The invention relates to the technical field of 3D vision, and discloses a manipulator dispensing path optimization method and device based on 3D vision, and the method comprises the steps: carrying out the multi-angle three-dimensional vision scanning of a dispensing workpiece, and obtaining the point cloud data of the dispensing workpiece; performing semantic segmentation on the point cloud data, and decomposing the dispensing workpiece into a plurality of functional parts; according to the semantic identifier and the surface feature of the functional component, identifying a target area to be dispensed and a dispensing path constraint condition; generating an initial dispensing path based on the target area to be dispensed and the dispensing path constraint condition; and performing iterative linear secondary adjustment control on the initial dispensing path to generate a motion control instruction. According to the invention, the self-adaptive optimization adjustment of the dispensing path is realized, the problem that the traditional dispensing system cannot cope with the actual state change of the workpiece is solved, and the adaptability and stability of the dispensing system are greatly improved.

The invention provides a disordered material identification and positioning method and system based on a 3D visual technology. The method comprises the steps that S1, a 3D model of a material object is converted into point cloud data, downsampling processing is conducted on the point cloud data, template point cloud of the material object is generated, and PPF features of the template point cloud of the material object are calculated; s2, in a disorderly stacked material scene, based on the obtained RGB image and depth image of the material scene, performing material segmentation by using an SAM model to obtain a material point cloud of each instance, and calculating a PPF feature of the material point cloud in the material scene; and S3, matching the PPF features of the material point clouds in the material scene with the PPF features of the template point clouds to obtain a 6D pose of each material, and realizing recognition and positioning of disordered materials through single matching. According to the method, the deep learning model and the 3D vision technology are combined, the accuracy and efficiency of disorderly stacked material recognition are improved, and the method is widely applied to automatic grabbing and assembling tasks in storage, production line and other complex environments.

The invention relates to an intelligent robot control method and system for unstacking and stacking, which is applied to the technical field of intelligent robots and comprises the following steps: initializing an intelligent robot and accessory equipment thereof through a driving program integrated by an intelligent robot system to obtain an initial parameter file; receiving a mode selection instruction from an administrator terminal, and determining an execution scheme according to the mode execution instruction; a continuous image taking instruction is sent to the 3D vision imaging device, and a to-be-detected target image is obtained; identifying the to-be-detected target image, labeling a work area in the to-be-detected target image, and generating a labeling box; automatically positioning the central position of the labeling box according to a preset graph center recognition algorithm; and according to the execution scheme, the labeling box, the to-be-detected target image and the center position, an intelligent robot is controlled to grab a target object to execute unstacking work.

A system and method for estimating a 3D box model of a generally cuboidal 3D object imaged by 3D vision system, which provides a 3D image having a set of 3D points representing surfaces of the cuboidal 3D object to a processor is provided. An input process provides an approximate box as a region of interest (ROI), to the processor, the ROI defining a search volume within the 3D image. An identification process identifies the 3D points that are within the search volume. A coarse estimation process estimates 3D box dimensions that approximate a box shape of the cuboidal object based upon the identified 3D points. A refinement process refines the coarse box shape by processing the 3D points based on the 3D box dimensions that correspond to each of a plurality of imaged faces of the cuboidal object to derive an estimated result for the cuboidal object.

The invention discloses a stacked workpiece disordered grabbing method and system based on a 3D visual system, and relates to the technical field of computer vision, and the method comprises the steps: obtaining an RGB image and a depth image of a stacked workpiece through a depth camera, and generating point cloud data; the improved Mask R-CNN model integrated with the MSDA module is used for performing instance segmentation on RGB and depth images, and workpiece pose estimation is realized by combining an ICP algorithm and template point cloud registration; establishing a coordinate relation between the camera and the mechanical arm through hand-eye calibration, and obtaining a 6D pose of the workpiece under a mechanical arm base coordinate system; and according to the point cloud centroid distance and the point number, the grabbing priority is calculated, the optimal target is selected for grabbing, and disordered grabbing of all the workpieces is completed circularly. According to the method, the improved Mask R-CNN model integrating the MSDA module and the ICP algorithm are combined with the 3D visual system, so that accurate instance segmentation and pose estimation of stacked workpieces are realized, and therefore, an unordered grabbing task is accurately completed.

A point cloud component segmentation and registration-based battery replacement robot pose estimation method comprises the following steps: 1, placing a 3D visual sensor below an electric vehicle chassis to shoot a to-be-replaced battery pack, and obtaining an RGB color image and a depth image containing a battery pack locking and unlocking device in a scene; 2, acquiring a locking and unlocking instance segmentation mask of the acquired color image by using a convolutional neural network, and projecting a locking and unlocking instance into a locking and unlocking point cloud in combination with the acquired depth map and camera internal reference; 3, statistical filtering and voxel filtering are carried out on the locking and unlocking point cloud, point cloud noisy points are removed, downsampling is carried out, and the point cloud data quality is improved; 4, segmenting a point cloud of the locking and unlocking lock through a Point Net point cloud segmentation network; and 5, solving the important part of the locking and unlocking lock point cloud by using a principal component analysis method, calculating the FPFH feature of the important part, searching the corresponding relationship between the scene lock point cloud and the template lock point cloud through an RANSAC method to complete coarse registration, and finally, optimizing the registration result by using ICP to obtain the estimated pose of locking and unlocking.

The invention belongs to the technical field of 3D visual reconstruction, and particularly provides a 3DGS segmentation method and system based on boundary adaptive Gaussian splitting, and aims to solve the problem of incomplete structure caused by directly deleting boundary Gaussian in the prior art by accurately positioning and segmenting the boundary Gaussian through a gradient consistency index through a boundary adaptive Gaussian segmentation technology. The boundary mBIoT is improved by 1.3%; meanwhile, a semantic-vision joint alternating optimization mechanism is innovatively proposed, continuous mask labels and alpha are utilized for synthetic rendering, and semantic loss and vision loss are combined for alternating optimization, so that compared with a traditional scheme singly depending on mask or feature optimization, the PSNR is improved by 0.29 dB, the texture definition is improved by 15%, and the segmentation precision and the vision quality are both considered; besides, through small-scale fuzzy Gaussian recognition and a targeted optimization strategy, mIoU is only reduced by 1.1% when the mask error rate reaches 20%, robustness to error masks is remarkably enhanced, and the bottleneck that an existing method is sensitive to pre-training mask errors is broken through.

The invention relates to a method for automatically generating a track of a ceramic bathroom surface glazing robot based on 3D vision. The method comprises the following steps: acquiring a surface point cloud model of a target ceramic bathroom; inputting the surface point cloud model into a pre-trained point cloud segmentation model, and outputting S1 and S2 type curved surfaces; generating a preliminary fitting path curve based on the S1 and S2 curved surfaces; performing interpolation processing on the preliminary fitting path curve to obtain path scatter points on the path curve after interpolation; performing path attitude estimation based on path scatter points on the interpolated path curve to obtain a path attitude estimation result; and generating the track of the glazing robot based on the path attitude estimation result. According to the method, a personalized path generation scheme can be intelligently formulated according to different types of glazing curved surfaces on the surface of the ceramic bathroom workpiece. The key influence of the position and posture of the spray gun on the glazing effect is also considered, and the uniformity and high quality of the glazing process are ensured through fine position and posture control and path optimization.

The invention discloses a three-dimensional reconstruction method and system applied to 3D vision, two-dimensional image data used for three-dimensional reconstruction are obtained through shooting of a camera with multiple visual angles, more comprehensive scene information can be provided through the multi-angle image data, the accuracy and integrity of subsequent three-dimensional reconstruction are facilitated, and meanwhile, the three-dimensional reconstruction method and system applied to 3D vision can be applied to 3D reconstruction. The feature points are key information points in the images, association between the images of different view angles can be established through matching of the feature points, the accuracy of three-dimensional reconstruction is improved, in addition, structural information of a three-dimensional space can be effectively recovered from the images of multiple view angles according to the multi-view geometric principle, dense point cloud data are generated, and the accuracy of three-dimensional reconstruction is improved. The point cloud data is an important basis for subsequent three-dimensional reconstruction, the overall quality of the point cloud data can be improved by optimizing the preliminarily recovered point cloud data, and in addition, a three-dimensional model can be presented to a user in a visual and vivid manner through visual operation, so that the user can conveniently observe, analyze and interact.

The invention discloses a grinding device and a grinding method for curved surface component damage repair, and belongs to the technical field of machining equipment. The polishing method for curved surface component damage repair comprises the following steps that S1, hand-eye calibration and tool calibration are conducted, specifically, a transformation matrix of a 3D visual system coordinate system relative to a mechanical arm tail end center coordinate system is obtained, and a transformation matrix of a tool coordinate system relative to the mechanical arm tail end center coordinate system is obtained; s2, setting a polishing initial state: setting the polishing initial state of the mechanical arm by utilizing a dragging teaching function; s3, polishing path planning, wherein a point cloud model of the surface of the workpiece is obtained, and polishing path point information is obtained; s4, a mechanical arm grinding program is generated, specifically, a mechanical arm grinding motion control program is generated; and S5, a polishing procedure. According to the grinding device and the grinding method for damage repair of the curved-surface component, constant-force grinding can be conducted on the irregular surface of the curved-surface component, and it is guaranteed that the surface of the curved-surface component is ground more evenly.

The invention discloses an inverted coplanarity detection method based on 3D vision and refraction compensation, and the method comprises the following steps: 1, selecting an optical measuring head of which the measuring range can cover the whole bottom surface of a measured object according to the size of the bottom surface of the measured object, installing the optical measuring head below glass, and enabling light to penetrate through the glass from bottom to top; 2, establishing a glass refraction compensation model; 3, correcting the glass refraction compensation model; 4, collecting three-dimensional point cloud data of the bottom surface of the measured object; 5, point cloud extraction and processing; step 6, calculating and evaluating the coplanarity; and 7, repeating the steps 4-6 to realize coplanarity analysis under different conditions. Through fusion of a theoretical model and experimental calibration, nonlinear error measurement caused by factors such as glass refraction and non-ideal installation is solved, the measurement precision is improved, the reference plane is established by adopting a minimum area method, the coplanarity evaluation reasonability is improved, and the method can be expanded and applied to all occasions needing to measure the coplanarity of the bottom structure from bottom to top.

A rack is provided with a belt conveying device, a cutting device, a 3D visual scanning device, a control system and an angle adjusting mechanism for adjusting the cutting angle, the 3D visual scanning device is located above the upstream of the belt conveying device, and the 3D visual scanning device, the angle adjusting mechanism, the belt conveying device and the cutting device are electrically connected with the control system; the 3D visual scanning device is used for scanning the raw material individuals conveyed by the belt conveying device to determine parameters of the raw material individuals and transmitting the parameters of the raw material individuals to the control system, a plurality of cutting schemes are preset in the control system, and the control system selectively executes one cutting scheme. The angle adjusting mechanism is controlled to adjust the angle of the cutting device according to the selected cutting scheme, and then the cutting device is controlled to conduct cutting. When the intelligent angle cutting machine is used for cutting raw material individuals, the cutting angle and thickness can be automatically adjusted in time according to parameters of the raw material individuals, various cutting schemes are implemented, and intelligence and the product utilization rate are improved.

The invention relates to the technical field of 3D vision measurement, and discloses a 3D binocular line laser weld contour data acquisition method and system. Two core steps of obtaining surface reflection characteristic information of a to-be-scanned area and adjusting output intensity of laser rays projected by a line laser device at different positions of the to-be-scanned area according to the surface reflection characteristic information are introduced, so that the problem that when a high-reflection welding seam is processed by a traditional method, the laser rays cannot be projected by the line laser device is effectively solved; the problem of image overexposure or underexposure caused by dramatic change of illumination characteristics is solved.

The invention relates to the field of three-dimensional vision measurement, in particular to an aluminum alloy CNC product defect detection method and system based on structured light scanning. The aluminum alloy CNC product defect detection system based on structured light scanning comprises an image acquisition and analysis module, a self-adaptive illumination control module, a point cloud reconstruction module, a defect detection module and a report generation module. According to the method, through texture direction perception and matrix type light source dynamic angle modulation, and in combination with a light intensity-wavelength self-adaptive scheme of saturated pixel grading, mirror surface glare of the aluminum alloy high-reflection surface is greatly reduced; in cooperation with multi-exposure gray scale weighted fusion, a stripe image which is uniform in contrast and free of overexposure can be obtained in single scanning; in the same scanning time, compared with a fixed illumination scheme, the point cloud integrity rate can be improved, and a more reliable three-dimensional data basis is provided for subsequent defect detection.

The invention provides a welding seam identification and trajectory optimization method and system based on three-dimensional vision, and the method specifically comprises the following steps: firstly, collecting the three-dimensional point cloud data of a welding seam, extracting the local geometric features of the welding seam, and carrying out the feature enhancement of the local geometric features, so as to screen out representative effective edge points; and then, collecting multi-source point cloud data of the welding seam area, constructing a multi-scale welding seam feature recognition network, inputting effective edge points obtained by screening into the network, realizing accurate recognition of the welding seam area, and further extracting a linear welding seam area. According to the recognition result, a fitting algorithm is adopted for conducting curve fitting on the weld joint area, and a preliminary welding track is generated; and for the condition that multiple sections of welding seams are intersected, an optimal fitting point searching mechanism is further introduced, the preliminary track is optimized, and the continuity and integrity of a welding path are ensured. And finally, based on the center line of the welding seam, the posture of the welding gun is precisely planned in combination with the dihedral structure model, and therefore high-precision track control and posture adjustment are achieved.

The present invention discloses a flatness detection method for a tower flange of a wind turbine generator set, comprising two steps: a flange plane 3D point cloud acquisition stage and a flatness calculation stage; wherein: the flange plane 3D point cloud acquisition stage comprises the following steps: rotation control comprises the following steps: the 3D vision detection module is accurately positioned; the 3D vision detection module performs laser line scanning on the annular surface to be detected by the flange; the 3D vision detection module performs 3D point cloud acquisition on the flange surface to be detected; the flatness calculation stage comprises the following steps: flatness calculation is performed according to the 3D points output by the 3D vision detection module; and the result is output; compared with the prior art, the flatness detection method for the tower flange of a wind turbine generator set of the present invention integrates motion control and 3D vision detection technology, provides an automated detection method, innovates the current single instrument manual detection method, and improves the efficiency and speed of tower flange flatness detection.

The invention relates to a collaborative robot line structured light 3D vision hand-eye calibration method which comprises the following steps: acquiring a robot motion space, selecting a fixed welding seam feature point in the robot motion space, and determining coordinate values of the fixed welding seam feature point in a robot base coordinate system and a sensor coordinate system; based on the coordinate values of the fixed welding seam feature points under the robot base coordinate system and the sensor coordinate system, the conversion relation from the camera coordinate system to the robot base coordinate system is obtained; obtaining a robot expression posture mode, and determining a conversion matrix between a robot tail end coordinate system and a robot base coordinate system and a conversion matrix between a sensor coordinate system and the robot tail end coordinate system based on the robot expression posture mode, namely a hand-eye matrix; and performing hand-eye calibration through a conversion relation from a camera coordinate system to a robot base coordinate system, a conversion matrix from a robot tail end coordinate system to a robot base coordinate system and a hand-eye matrix.

The invention discloses a numerical control machine tool autonomous tool setting method based on 3D visual guidance. The method comprises the following steps: firstly, acquiring three-dimensional point cloud data of a cutter and a workpiece by using a 3D visual sensor and preprocessing; then, segmenting the point clouds through a region growing algorithm, realizing the separation of the tool point clouds and the workpiece point clouds, and extracting the point clouds of each cylindrical section of the workpiece; thirdly, based on a cylinder fitting algorithm, cylinder model fitting is conducted on the segmented tool and workpiece point cloud, and geometric parameters such as the axis direction, the radius and the center coordinate are obtained; calculating a relative position relation between the tool and the workpiece according to the geometric parameters, generating a tool setting path and converting the tool setting path into an NC code; and finally, the NC code is transmitted to a grinding machine control system through a grinding machine communication module, and tool setting operation is completed. The automatic tool setting device has the advantages that the relative position relation between the workpiece and the tool can be automatically measured in the machining process, automatic and rapid tool setting is completed, and the machining quality and efficiency are guaranteed.

The invention discloses a badminton tracking method and system based on three-dimensional vision, and relates to computer vision. Badminton motion video data of a left view and a right view are synchronously collected through a binocular camera, a left view frame sequence and a right view frame sequence which are aligned in time are obtained, and the collected video data are preprocessed; obtaining standardized left and right view input frame data; according to input frame data, constructing a 2D key frame detection model based on an improved YOLOv8 network, and extracting the sum of 2D key frame coordinates of left and right views; according to the 2D key frame coordinate sum of the left view and the right view, 3D space positioning is carried out on the badminton through stereo matching and track prediction, 3D track data are obtained, according to the 3D track data, a broken track is repaired through a frame leakage compensation mechanism, and a final 3D track is obtained. In order to solve the problem that in the prior art, key frames are prone to being lost and track breakage is caused during high-speed movement such as smash, the breakage track of the shuttlecock is repaired.

The invention provides a multi-specification nut rapid locking method and device based on 3D vision, and relates to the technical field of automatic assembly, and the method comprises the steps: obtaining the three-dimensional point cloud data of a target nut through the scanning of a 3D vision module; determining a target nut locking strategy according to the nut code identifier; inputting the simulation result into a nut simulation model to carry out locking simulation, and carrying out quality analysis on the simulation result; if the target locking index does not meet the preset standard, optimizing a target nut locking strategy; and performing automatic locking on the target nut based on the target locking optimal strategy. The technical problem that in the prior art, due to the fact that nuts of different materials or specifications have different requirements for torque in the locking process, unified torque control and precision are difficult to achieve, and consequently the nut locking precision is low can be solved, the locking strategy is determined through the three-dimensional point cloud data, the locking strategy is optimized in combination with analogue simulation, and the nut locking precision is improved. And the nut locking precision and efficiency are improved.