552 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 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.

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.

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 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 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.

The invention discloses a machine vision-oriented three-dimensional image coding method, which comprises the following steps of: performing feature extraction on input left and right views by using a three-dimensional image feature extraction module to obtain three-dimensional image multi-scale features containing 3D scene information; a stereo image multi-scale feature coding module is used for coding the stereo image multi-scale features to obtain compact left and right viewpoint potential representations, and stereo entropy coding is carried out on the left and right viewpoint potential representations to obtain code streams for transmission; performing stereo entropy decoding on the transmitted code stream to obtain decoded left and right viewpoint potential representations, and decoding the left and right viewpoint potential representations at a decoding end by using a stereo image multi-scale feature decoding module to obtain reconstructed stereo image multi-scale features; and performing visual analysis on the reconstructed stereo image multi-scale features by using a 3D visual analysis module to obtain a 3D target detection result. According to the invention, the 3D visual task performance and the stereo image compression efficiency are effectively improved.

The invention discloses an intelligent deviation correction control method and device based on 3D vision and a storage medium, and relates to the field of conveying belts. Carrying out edge searching calculation on the belt according to the no-load point cloud data of the belt, and determining a reference detection area and a point cloud reference curve of the fitting belt; screening belt point clouds and material point clouds based on the point cloud reference curve, performing offset detection on the belt point clouds based on the reference detection area, and performing gravity center deviation calculation on the materials based on the material point clouds; and according to the material gravity center deviation and the belt movement speed, the basic deviation correction angle and the flow compensation angle of a deviation correction motor are calculated, and belt deviation correction is conducted. According to the scheme, the reference detection area is established, point cloud screening, offset detection and gravity center deviation calculation in the load state are combined, the angle parameters of the deviation correction motor are dynamically adjusted, and the problems that 2D visual detection is poor in stability, 3D point cloud data processing is insufficient, and multi-motor cooperative control precision is low are solved; the method has the advantages of improving the detection stability, enhancing the data processing capability and optimizing the correction control precision.

The invention provides a 3D point cloud defense method, device and equipment based on CLIP guidance, and a medium, relates to the technical field of point cloud defense, and aims to enhance the robustness and security of 3D point cloud data in the face of adversarial attack by using a contrast language-image pre-training model. According to the method, a new defense framework is provided by combining geometric and semantic features, triggers in backdoor attacks can be effectively identified and inhibited, meanwhile, the geometric structure of point cloud data is repaired, and the classification performance is improved. Specifically, the method includes projecting a 3D point cloud into a multi-view 2D depth map, extracting cross-modal features using a CLIP model, and performing semantically guided 3D reconstruction through a variational auto-encoder (VAE) to generate a repaired point cloud that retains key information and neutralizes perturbations. The method shows excellent performance in various complex scenes, and provides powerful support for the safety of the 3D vision technology in key application.

The invention relates to the technical field of cigarette production, in particular to an automatic auxiliary material tray matching work station suitable for full-specification production. Comprising an industrial robot module, a 3D visual camera module, a code reader module, a tool clamp module, a clamp storage module, a cigarette auxiliary material and a tray. The tool clamp module and the clamp storage module are arranged on the two sides of the industrial robot module respectively, the 3D visual camera module is located above to-be-prepared auxiliary materials to be used for positioning and sending position information, the code reader module is arranged between an industrial robot and the prepared auxiliary materials to be used for scanning codes, and an automatic gun changing disc at the tail end of the industrial robot can switch corresponding clamps. And after the industrial robot receives the position information, the clamp is switched to grab the to-be-prepared auxiliary materials, and the auxiliary materials are placed on the prepared auxiliary material tray after being matched with the code reader to scan codes. Through multi-module cooperation, full-process automation of tray distribution is achieved, manual operation is reduced, production efficiency is improved, meanwhile, by means of accurate positioning and code scanning, the material recognition accuracy is guaranteed, and the workshop automation and intelligence level is remarkably improved.

The invention provides a precise workpiece positioning method and system based on three-dimensional vision, and the method comprises the steps: obtaining a scene three-dimensional vision data set containing a target workpiece, carrying out the multi-modal data fusion processing of the scene three-dimensional vision data set, generating a fusion data set with aligned space coordinates, extracting a workpiece feature identifier set from the fusion data set, and carrying out the recognition of the workpiece feature identifier set. And feature matching association processing is carried out on the workpiece feature identifier set and a pre-stored standard workpiece feature template, a feature matching corresponding relation set is generated, three-dimensional positioning coordinate information of the target workpiece in the scene is calculated based on the feature matching corresponding relation set, and the three-dimensional positioning coordinate information comprises workpiece centroid coordinates and space attitude angle parameters. The precision of precision workpiece machining is improved, and the stability of the positioning process is ensured.

The invention relates to the technical field of warehouse management, in particular to a material inspection method and system for an emergency warehouse. Comprising the following steps that multi-dimensional data of materials in an emergency warehouse are collected in real time through a multi-modal sensor array, and the multi-modal sensor array comprises an RFID reader-writer, an infrared thermal imager and a 3D visual sensor; dynamically grading the materials in the warehouse based on a material priority classification model, wherein the priority classification model considers the validity period, the use frequency and the emergency response level of the materials; constructing a material three-dimensional digital twinborn model, and comparing the collected multi-dimensional data with preset material standard parameters in real time; when the comparison difference exceeds a preset threshold value, abnormal early warning is triggered, and an inspection task is generated; planning an optimal inspection path for the automated guided vehicle based on an improved conflict avoidance algorithm; according to the invention, material inspection with dynamic standard adaptation and intelligent priority adjustment can be realized.