205 results about "3d localization" patented technology

The invention discloses a power transformer partial discharge positioning method based on multi-sensor array fusion, and the method comprises the following steps: S1, selecting a sensor installation point, and laying a multi-sensor array structure; s2, partial discharge signals of the three types of sensors are collected, and primary signal processing is carried out; s3, calculating propagation time differences between the reference channel and other channels by adopting a generalized cross-correlation weighting algorithm, and generating a time difference matrix; s4, constructing a TDOA model in combination with the layout coordinates and the time difference matrix, and solving three-dimensional initial coordinates of a power supply; s5, establishing a structure correction model, compensating the path deviation, and outputting corrected positioning coordinates; s6, calculating an error and generating a confidence score; s7, mapping a positioning result to the three-dimensional model and generating an image; and S8, writing the positioning information into a database for filing management. According to the method, the multi-frequency sensor and the path correction model are fused, and high-precision three-dimensional positioning of partial discharge of the transformer is realized.

The invention relates to the technical field of image processing, and discloses a vehicle surface defect identification method and system based on artificial intelligence. The method comprises the following steps: acquiring a vehicle surface image through polarization filtering multi-angle imaging; processing the standardized image by applying HSV color conversion and texture equalization; constructing three-dimensional deformation features in combination with parallax analysis, and extracting defect feature vectors; and identifying defects by using the position-aware convolutional neural network, and outputting a severity thermodynamic diagram. According to the invention, in a complex illumination environment, tiny defects of vehicle surfaces with various colors and materials are accurately identified and evaluated, and accurate three-dimensional positioning and severity quantification of the defects are realized at the same time.

The invention discloses an artificial intelligence-assisted standard artificial temporomandibular joint automatic placement method. The method comprises the following steps: training a three-dimensional reconstruction model used for extracting a mandible and zygomatic bone zygomatic arch joint prosthesis placement area; based on an improved 3D U-Net neural network, carrying out segmentation extraction on the interested areas of the glenoid fossa and the mandibular branch, and obtaining a space geometric structure for prosthesis installation; the method comprises the following steps: extracting outer bone surface point cloud data of a zygomatic arch and a mandibular ramus on the basis of a space geometric structure of prosthesis installation, identifying anatomical key points, and generating a standard prosthesis fitting surface through geometric fitting and constrained optimization; determining the relative position relation between the maxillary joint fossa prosthesis and the mandibular branch prosthesis, and determining the installation direction, the hole position definition and the assembly reference point; and determining a contact area, a posture direction and a placement position of the prosthesis, and completing three-dimensional positioning and posture matching of the prosthesis in the patient model as a surgical planning scheme.

The invention belongs to the technical field of unmanned aerial vehicles, and particularly relates to an intelligent aerial 3D printing and spraying system and method based on an unmanned aerial vehicle cluster, and the system comprises a spatial three-dimensional laser ranging scanning modeling module for generating a real-size scene model, a three-dimensional positioning network module, and a three-dimensional positioning network composed of a plurality of laser radar arrays. Wherein the laser radar or the wireless positioning beacon array is arranged on a working plane to construct a three-dimensional space coordinate reference system; the printing module is integrated on the unmanned aerial vehicle, compared with traditional high-altitude operation, the construction efficiency is improved, the constraint of tedious procedures such as scaffold building and manual high-altitude operation is thoroughly eliminated, the unmanned aerial vehicle cluster can be rapidly deployed in a short time, and operation tasks such as large-area single-color or full-color spraying, repairing, modeling and printing are efficiently completed with high precision; the project construction period is greatly shortened, the comprehensive construction cost is reduced, the safety construction level is improved, the engineering income is improved, and the construction accident risk is reduced.

The invention provides a three-dimensional positioning method and system for a satellite-borne multi-base radar system based on weighted linear iteration, and the method comprises the steps: obtaining radar echo data of a plurality of receivers, carrying out the effective detection of a target, and carrying out the searching of a pitch angle and a space taper angle of a target signal through a two-dimensional adaptive filtering technology, obtaining two-dimensional angle estimation information of the target at different bases and different visual angles; constructing a nonlinear equation set according to two-dimensional angle information and slope distance information of the target under different visual angles, and performing first-order Taylor expansion on the nonlinear equation set to obtain a corresponding linear overdetermined equation set; and forming a precision matrix according to the known measurement precision of the radar system, and carrying out normalization operation and iterative solution on the linear overdetermined equation to obtain the three-dimensional coordinate of the target in the earth-centered earth-fixed coordinate system, thereby obtaining the latitude, longitude and height information of the target, and realizing the high-precision three-dimensional positioning of the multi-static radar system.

The invention discloses a dual-machine target positioning method based on semantic prior and spatial intersection constraint. The method comprises the following steps: 1, acquiring images of a camera 1 and a camera 2, and determining the spatial orientation of a camera coordinate system relative to a reference world coordinate system; step 2, obtaining a target normalized coordinate under a coordinate system of the camera 1; step 3, obtaining a target normalized coordinate under a coordinate system of the camera 2; 4, converting the normalized coordinates of the target into geometric rays in a three-dimensional space; 5, constructing an optimization function with the minimum distance sum of squares; step 6, performing semantic-level foreground and background division on the image content through a YOLOv5 network, and constructing a three-dimensional space feature point library of a semantic background; and 7, performing single-view semantic neighborhood retrieval and depth compensation by using the three-dimensional space feature point library of the semantic background constructed in the step 6, and realizing target three-dimensional positioning in a high-altitude long-distance complex scene. According to the invention, the detection robustness and the positioning precision of the high-altitude long-distance target in the environment are improved.

The invention discloses a digital twinning-based three-dimensional visual management system and a digital twinning-based three-dimensional visual management method for an anode assembly workshop. The system comprises a triple binding index module, a unified time axis alignment module, a view cone inverse solution positioning module, a safety linkage arrangement module and a resume playback module, and is provided with a process template library and a network isolation / main / standby module. Three-dimensional positioning and PTZ linkage of an AI / threshold event are realized by establishing mapping of a three-dimensional object, a camera and a PLC point location; the batch / bracket / station events, the PLC time sequence and the video slices are indexed in a unified mode; shadow verification, interlocking check, instruction issuing, read-back and video consistency confirmation and trace leaving are executed according to the process, and a safe closed loop from warning to disposal is formed. The method comprises the steps of access and calibration, triple binding, time axis alignment, event positioning and stream taking, linkage processing and resume filing. The positioning and handling efficiency can be improved, the misoperation risk is reduced, and cross-production-line reuse and tracing evidence obtaining are supported.

The invention discloses a physical object three-dimensional positioning method and system based on video data, and relates to the technical field of three-dimensional positioning. The method is used for solving the problems of low object space positioning precision, unstable pose estimation and poor time sequence continuity in a video scene. Firstly, an input video stream is analyzed, an object segmentation mask is generated, feature points are extracted, camera motion parameters are calculated through inter-frame matching, and a scene sparse three-dimensional point cloud is reconstructed; a candidate three-dimensional bounding box is generated according to the segmentation mask and the point cloud, an optimal bounding box is selected by combining geometric matching degree and feature similarity evaluation, and a preliminary three-dimensional positioning result of the object is obtained; a positioning result and historical frame motion data are fused, a space-time constraint optimization model is constructed, and the six-degree-of-freedom pose of the object is solved; and finally, neural radiation field representation is established based on the pose, the pose and neural parameters are optimized through combination of micro rendering and back propagation, continuous and accurate updating of the three-dimensional position of the object is realized, and the positioning stability and robustness in a dynamic scene are improved.

The present invention provides a visual guidance system for a mobile grasping device, comprising a data acquisition and preprocessing module, the data acquisition and preprocessing module being used to acquire image data and preprocess the acquired image data, and transmitting the preprocessed image data to an instance segmentation module, the instance segmentation module being used to identify and segment a target object in the preprocessed image data, and outputting the pixel coordinate position and mask information of the target object to a three-dimensional positioning module, the three-dimensional positioning module being used to calculate the spatial position of the target object, and transmitting the spatial position information of the target object to an execution module, which accurately guides the grasping of the target object through the spatial position information of the target object. The visual guidance system for a mobile grasping device in the present invention can more accurately perform spatial positioning of the target object, and due to the use of technologies such as deep learning, instance segmentation, and spatial point cloud, the time required for the visual guidance system to adapt to the mobile grasping device is shorter.

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.

According to the plug cord injection molding on-line real-time detection method, an industrial camera array is integrated at key nodes of an injection molding machine, technological parameters of mold clamping force and screw rotating speed are synchronously collected, and a comprehensive signal collection system is constructed. By developing a defect recognition algorithm and combining a copper sheet three-dimensional positioning model and a plastic filling dynamic simulation database, micro-offset detection and early warning of bubble formation are realized. An image feature extraction and quantification technology and a three-dimensional reconstruction and micro offset extraction method are utilized to carry out high-precision analysis on the injection molding process. And a parameter adjustment mapping relation is established based on the defect type and severity, an injection molding parameter PID controller is developed, and online correction of the mold locking force and the screw rotating speed is achieved.

The invention relates to the technical field of GIS partial discharge high-precision three-dimensional positioning, and discloses a multi-stage iteration GIS partial discharge high-precision three-dimensional positioning method, and the technical scheme is characterized in that S1, an electromagnetic simulation model is constructed based on a GIS equipment structure, and the installation position of an ultrahigh frequency sensor is optimized through electromagnetic simulation; s2, performing wide-band adaptive sub-band reconstruction and multi-resolution feature enhancement processing on the acquired signals; s3, calculating three-dimensional focusing energy distribution by adopting a multi-band time inversion method, and extracting candidate discharge positions; s4, screening candidate positions; s5, performing multi-stage hybrid optimization on the screening result to obtain refined three-dimensional positioning coordinates; and S6, outputting a three-dimensional positioning result, and performing performance evaluation based on a confidence index. According to the method, a time delay consistency screening mechanism is introduced, false candidate points which do not conform to an actual signal propagation rule are effectively eliminated, and the accuracy and reliability of a positioning result are remarkably improved.

The invention relates to the technical field of unmanned aerial vehicle detection and computer vision, in particular to an application method of a multi-scene throwing object recognition FOD algorithm carried by an unmanned aerial vehicle, and the method comprises the steps: dynamically fusing data through a multispectral camera and a laser radar, carrying out the scene self-adaptive defogging and illumination equalization preprocessing, inputting an improved twin network-FPN model, and extracting features; then, a target frame is screened by adopting a DIOU-NMS containing a scene complexity coefficient; and three-dimensional positioning is realized by combining RTK-GPS and a laser radar, the data is transmitted to a ground terminal through 5G and is subjected to secondary verification, and the models can be dynamically switched according to the flight height. The method solves the problems of missing and false detection, poor environmental adaptability and the like of complex scene FOD recognition, data integrity is improved through multi-sensor fusion, robustness is enhanced through scene self-adaption, the model and dynamic strategy balance precision and real-time performance are improved, multi-scene adaptability is high, and the recognition effective rate exceeds 98%.

The invention discloses a 3D digital asphalt paving system and a paving quality monitoring method. The 3D digital asphalt paving system comprises an asphalt paving machine and a 3D paving control system, the 3D paving control system comprises a three-dimensional positioning system, a signal acquisition system and a vehicle-mounted control system; the three-dimensional positioning system comprises a data control system, a GPS base station, a GPS moving station, a laser receiver and a laser transmitter. The signal acquisition system comprises a vibration sensor mounted on the screed, a data line connected with the vibration sensor, a data memory and a signal processing module; the vehicle-mounted control system comprises a vehicle-mounted analyzer, a three-dimensional quality parameter processing module, a quality parameter calculation module, a slope sensor and a handheld controller; the output end of the data control system and the output end of the quality parameter calculation module are connected to the three-dimensional quality parameter processing module, and the three-dimensional quality parameter processing module and the handheld controller are both connected with the vehicle-mounted analyzer. The problems that the construction efficiency is low, the influence of human factors is large, and paving quality control lags behind are solved.

The invention discloses an underwater robot deep learning adaptive data processing method and system, and relates to the technical field of deep learning. Sonar data, a visual image and a robot motion state of an underwater environment are collected; enhancing the definition of a visual image by using a convolutional neural network, marking autonomous moving elements, and constructing a two-dimensional coordinate system to complete preliminary positioning; the element depth is calculated based on the pixel value and the time delay of the sonar data, and the two-dimensional coordinates are fused to construct a three-dimensional coordinate system; and unifying the track in the three-dimensional space, calculating the distance between the elements and the robot, and planning the moving route and speed. The system comprises a data acquisition module, a visual processing module, an acoustic fusion module and a path planning module, and realizes multi-source data integration, three-dimensional positioning and path planning. Through visual and acoustic data fusion and a deep learning model, the underwater environment stereoscopic perception precision and the path planning efficiency are improved, and the adaptability and the operation safety of the underwater robot in a complex water area are enhanced.

The invention relates to a personnel identification and behavior detection method and device and a security robot, and the method achieves the whole-process intelligent processing from threat perception to precise intervention through a multi-module cooperative working mechanism and an innovative algorithm design. According to the method, the problem of balance between threat detection precision and real-time performance in the prior art is solved, and accurate three-dimensional positioning and tracking of a threat target are realized through effective integration of depth information. According to the method, through technologies of deep learning algorithm optimization, three-dimensional space positioning and the like, the threat detection capability, the response speed and the intervention accuracy of the security robot in a complex environment are comprehensively improved, powerful technical support is provided for high-risk scene safety prevention and control, and the method has remarkable practical value and popularization prospects.

An automatic end effector tip position initialization mechanism for a micromanipulation system is provided. The method includes: S1, positioning a macro end effector tip, calculating a camera pose by using ArUco Tag, and realizing a three-dimensional positioning of the end effector tip by using a triangulation method, and moving the end effector to a area near a center of a focal plane based on a visual servo; S2, positioning a micro end effector tip; S3, acquiring calibration data based on a visual servo, calibrating the end effector tip and the petri dish, and obtaining a left calibration matrix and a right calibration matrix reflecting the transformation relationship between left and right micromanipulator coordinates and image coordinates; generating an intuitive human-computer interaction interface based on a mouse and a keyboard according to acquired position information. The method has obvious advantages in terms of operation accuracy, operation efficiency and repeatability.

The invention discloses a single-sensor type robust high-precision three-dimensional positioning method and system, and aims to improve the positioning precision and stability of a moving target in a three-dimensional scene. According to the method, a geometric constraint structure formed by a multi-label sensor is constructed, rich observation information is fused, and simultaneous acquisition of three-dimensional position and attitude information is realized. The optimal layout coordinates of a base station sensor and a label sensor are calculated by adopting a particle swarm optimization algorithm, and high-precision three-dimensional position and attitude calculation is realized by combining a weighted least square algorithm of single-point positioning, a weighted least square algorithm of rigid body positioning, robust M estimation, gross error detection and a Kalman filtering backward smoothing algorithm. According to the method, in a complex environment of non-line-of-sight signal interference, the positioning robustness can be remarkably enhanced, the elevation error is reduced, and the application feasibility and the precision reliability of a single type of sensor in an actual scene are improved.

The present invention relates to a systems and methods to perform 3D localization of target objects in point cloud data using a corresponding 2D image. According to an illustrative embodiment of the present disclosure, a target environment is imaged with a camera to generate a 2D panorama and a scanner to generate a 3D point cloud. The 2D panorama is mapped to the point cloud with a 1 to 1 grid map. The target objects are detected and localized in 2D before being mapped back to the 3D point cloud.

The invention relates to a power distribution network tower AI adaptive visual inspection method and system based on edge calculation. The method comprises the following steps: acquiring a tower image and pose data through an unmanned aerial vehicle carrying an edge calculation node, and realizing flight path optimization in combination with visual SLAM and optical flow tracking; a target detection model which fuses a knowledge graph and spatial relationship modeling is adopted to identify an electric power component, and the small target detection precision is improved through image partitioning and a feature enhancement strategy; carrying out defect discrimination by utilizing a special heterogeneous classification model and multi-scale feature fusion; three-dimensional accurate positioning of the defective part is realized based on reverse ray tracing and cross-modal feature matching; and the edge nodes adopt multi-target reinforcement learning resource scheduling and space-time diagram neural network fault detection to realize system resource optimization and self-healing protection. According to the invention, intelligentization, precision and full-process automation of electric power tower inspection are realized.

The invention relates to the technical field of ore body three-dimensional positioning prediction, and discloses a hidden ore body three-dimensional positioning prediction method based on artificial intelligence, and the method comprises the steps: firstly processing geological data through data preprocessing and feature extraction, and then carrying out the training and prediction through a constructed neural network model, and three-dimensional positioning of the concealed ore body is realized. The method comprises the steps of data normalization, missing value filling, feature extraction, neural network model construction, gradient descent updating, three-dimensional coordinate conversion, ore body scale estimation and the like. Through prediction precision evaluation and model optimization, the accuracy and efficiency of concealed ore body positioning are improved. The invention provides an efficient and accurate three-dimensional positioning prediction method for mineral resource exploration, and the method has a wide application prospect.

The embodiment of the invention discloses a target position global optimization method and system based on gradient search and TDOA-AOA cooperation, and aims to solve the problem of low positioning precision caused by poor high-dimensional observation of a pure time difference of arrival positioning system under the condition of ground station arrangement. A fitness function containing a distance difference and an angle residual error is constructed, a nonlinear positioning equation is solved and converted into an objective function minimization problem, and a gradient optimizer is introduced for global optimization. According to the method, exploration and development capabilities are balanced through a gradient search rule, and local optimum is effectively avoided by using a local escape operator. Simulation shows that the height estimation error is remarkably reduced while the horizontal positioning precision is kept, and the overall performance of three-dimensional positioning is improved.

The present application belongs to the technical field of image processing, and particularly relates to a real-time positioning method and system for mechanical arm operation, which comprises the following steps: calculating an initial matching cost volume of a stereo image pair; generating an adaptive aggregation path field according to a local structure tensor of the image, and determining an adaptive penalty term based on a structure strength; performing cost aggregation along the adaptive path field by using the adaptive penalty term to obtain an aggregated cost volume; determining a disparity map according to the aggregated cost volume and solving a three-dimensional pose of a target point to be drilled. The present application generates an aggregation path that conforms to the scene geometry and dynamically adjusts the smoothing constraint, thereby significantly improving the accuracy and robustness of three-dimensional positioning in a complex environment and providing reliable real-time pose guidance for high-precision operation of the mechanical arm.

This invention discloses an assisted firefighting method and system based on UAV information and image processing technology. The method includes: step S1, using a UAV equipped with preset equipment to collect images of a fire scene; step S2, performing smoke removal preprocessing on the fire scene images to obtain a preprocessed image; step S3, performing target detection on the preprocessed image to obtain a target detection result; step S4, performing visual target localization at the fire scene based on the UAV's gimbal pose information and the target detection result; and step S5, using the target detection result and the visual target localization to assist in the scheduling of firefighting resources, thus completing the assisted firefighting based on UAV information and image processing technology. The improved YOLOv8 model is used to perform target detection on the preprocessed image. Based on the camera imaging principle, and combining the target detection result with the UAV's monocular gimbal sensor data, the fire source target is three-dimensionally located to assist in the scheduling of firefighting resources.

The application discloses a real-time leakage positioning monitoring system and method based on electrical resistance tomography, relates to the technical field of process tomography and industrial safety monitoring, and solves the technical problem that it is difficult to intelligently identify, three-dimensionally position and quantitatively evaluate leakage events; the system comprises a sensor array, a multi-channel data acquisition and excitation module, a real-time data processing and image reconstruction module, a leakage intelligent identification and positioning module, a man-machine interaction and data management module; the core lies in that a multi-layer nested annular array and a sparse measurement strategy are adopted to improve data acquisition efficiency, a physical information neural network is introduced to reconstruct an image, a measurement topological coding matrix and a physical residual correction module are used to reconstruct the image, and finally, a time-space dual-domain leakage probability field model is constructed, a virtual sensor network and an improved D-S evidence reasoning framework are combined to intelligently identify and position a leakage diffusion mode.

The application relates to a Beidou RTK measurement point cloud self-adaptive filtering processing method, belonging to the field of high-precision three-dimensional positioning technology based on the Beidou system. The method firstly determines whether it is suitable to directly take the median filtering result as the filtering output according to the data amplitude span state of any measurement point in the Beidou RTK measurement point cloud and the effective satellite quantity when the point cloud is acquired, and quantifies the reliability of the median filtering result through the data fluctuation and direction change of the local neighborhood of the measurement point when it is not suitable, so that the fusion filtering result determination of the median filtering result and the original measurement value is completed in the case that the direct median filtering result is not suitable for filtering output. The application can avoid the reverse filtering optimization effect of the median filtering in the Beidou RTK measurement point cloud, and improve the RTK measurement point cloud data filtering accuracy of the median filtering.

The present invention relates to the technical field of three-dimensional modeling, and particularly to a three-dimensional positioning and surgical navigation system for gastrointestinal stromal tumors with multi-modal image fusion. The system includes: a model initial construction module, which is used to obtain CT images, MRI images and endoscopic images of gastrointestinal stromal tumors, and generate an initial three-dimensional model from multi-layer CT images; a model correction module, which is used to increase the number of triangular meshes in the initial three-dimensional model according to the analysis of the gastrointestinal stromal tumor region in the MRI image to obtain a mesh three-dimensional model; a texture matching module, which is used to determine the best matching pixel corresponding to each triangular mesh in the mesh three-dimensional model, and the best matching pixel belongs to the endoscopic image; a texture rendering module, which is used to perform texture rendering on the mesh three-dimensional model according to the best matching pixel to obtain a three-dimensional positioning model of the gastrointestinal stromal tumor. The present invention combines various types of images to reconstruct a three-dimensional model, improving the detail rendering of the gastrointestinal stromal tumor model.

This invention relates to the field of orchard navigation technology, specifically a 3D method for orchard operations based on a grid map. The method includes: constructing an orchard point cloud map using a mapping algorithm; filtering the acquired 3D point cloud map and constructing a triangular grid map of the orchard, embedding the orchard's GPS information into the triangular grid map; based on the triangular grid map with GPS information, performing ICP registration between the point cloud data scanned by the LiDAR and the points on the triangular grid map using the RMCL 3D positioning algorithm, outputting the 3D pose of the operation robot in the orchard; updating the 3D pose of the operation robot in the orchard using GNSS information; planning the navigation path of the operation robot using grid navigation based on the GPS information of the target point; and controlling the operation robot to dynamically avoid obstacles and navigate to the target point. This invention enables precise positioning and autonomous navigation of the operation robot in hilly orchards, allowing it to complete tasks such as orchard inspection, plant protection, harvesting, and transportation.

This invention discloses a method for autonomous operation of humanoid robots based on ROS. It employs a decision-making and control framework that fuses hierarchical finite state machines with YOLOv8 vision to achieve continuous autonomous execution of multiple tasks in complex unstructured environments. The method includes: system initialization and standardized processing of multimodal perception data; target 3D localization fused with YOLOv8 and coordinate transformation; task scheduling and state transitions using hierarchical finite state machines; segmented navigation and motion planning of joint trajectories; and ROS command issuance and full-process closed-loop control. This invention addresses the rigidity of traditional script-based control by fusing hierarchical state machines with visual servoing. Combined with a full-process anomaly handling mechanism and multi-target scene processing rules, it can dynamically adapt to changes in complex unstructured environments, effectively avoiding problems such as state freezes and erroneous grasping.

A method may include receiving, by a processor, a three-dimensional (3D) location of each of a plurality of electrodes of a catheter from one or more sensors during an ablation session; generating, by the processor, a 3D graphical representation that identifies a location of each electrode of the plurality of electrodes with a first indication; and connecting, by the processor, pairs of adjacent first indications in the constellation-like 3D graphical representation with second indications. These second indications may indicate a location between the electrodes where energy is delivered. The method may include displaying, by the processor, a 3D graphical representation including the first indication and the second indication on a display device. The method may include identifying a pair of adjacent first and third indications not connected by a second indication, the third indication may indicate a location between the electrodes where no energy is delivered.