123 results about "3d localization" patented technology

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.

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.

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.

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.

The present application provides a kind of complex stratum structure and topographic effect considering slope microseismic event 3D positioning method, the technical scheme includes S1: the theoretical travel time model suitable for complex slope environment is built;S2: the actual travel time difference is extracted from slope monitoring data;S3: based on the 3D positioning of theoretical and observed travel time difference matching, the ambient noise surface wave tomography technology is applied to slope velocity modeling, without artificial seismic source, high resolution velocity structure can be obtained;Realize 3D ray tracing under the constraint of digital elevation model, ensure that wave propagation path meets physical law, positioning accuracy reaches 3 m, can reach 1.5 m in dense array coverage area, compared with traditional method, it is improved by one order of magnitude;Successfully applied to complex environment with more than 700m topographic relief and extreme velocity variation, can be applied to microseismic monitoring in various complex geological environments.

The utility model discloses a three-dimensional spatial calibration module for realizing spatial three-dimensional positioning and camera calibration, which comprises a cubic framework, calibration target surfaces are respectively arranged on six surfaces of the cubic framework, and laser etching checkerboards, dot matrixes or ArUco coding patterns are respectively arranged on the six calibration target surfaces. And the six surfaces of the cubic skeleton are respectively provided with an identification surface coding module. According to the utility model, multi-surface pattern arrangement is adopted, and multi-angle three-dimensional calibration can be realized by a single module; the structural cubic design meets spatial analytic geometry constraints, and the reconstruction stability is improved; a built-in attitude sensing system records calibration attitudes and improves reproduction and error analysis capability; the modular quick-change base design supports quick plugging and mobile calibration; a wireless tag and visual coding combined recognition mechanism enhances the recognition and matching efficiency of an automatic system.

The invention relates to the technical field of computer vision and industrial automation, in particular to a hoisting equipment real-time positioning and trajectory tracking method based on a vision algorithm, which comprises the following steps of: S1, acquiring a video stream in real time through a camera deployed in a hoisting operation area, and preprocessing an image; according to the method, accurate mapping from two-dimensional image features to a global three-dimensional space in a complex hoisting scene is realized through a multi-source constraint fusion self-correction three-dimensional pose solving mechanism, and a trajectory prediction and'trajectory-load 'associated safety system is constructed based on a three-dimensional positioning result; the closed-loop control from passive trajectory tracking to active prediction planning, overload early warning and equipment linkage protection is realized, and the positioning precision, robustness and safety protection level of hoisting operation are greatly improved.

This invention relates to the field of engineering cost technology, and discloses a method and system for generating cost schemes driven by a component system. The method includes: extracting annotation information from 2D CAD drawings and identifying the spatial positioning attributes of components; generating 3D bounding boxes for component positioning based on multi-view geometric projection rules; inferring spatial adjacency relationships of components and calculating adjacency geometric parameters through bounding box intersection detection; expanding the component system to add spatial adjacency trigger dependency definitions; parsing bill of quantities items to match adjacency records and calculating linked quota quantities; and generating a cost combination scheme including linked quotas. This invention solves the technical problem of not being able to automatically identify and measure linked quotas dependent on spatial adjacency relationships when a BIM model is lacking.

The invention discloses a three-dimensional positioning method and system for a bimodal target, and relates to the technical field of indoor positioning, and the method comprises the steps: collecting the phase ranging data of a connected state base station and the received signal strength indication data of a broadcast state base station, and forming a bimodal perception data set; selecting a static positioning model or a dynamic positioning model according to the motion state of a to-be-positioned point, and converting a positioning problem into a nonlinear optimization problem based on the bimodal perception data set; and solving the nonlinear optimization problem by adopting a CBS algorithm to obtain the position estimation of the point to be positioned in the three-dimensional space. According to the method, three-dimensional positioning of a static target and smooth track positioning of a dynamic target are realized at the same time, the optimization range is effectively constrained, the system deployment complexity and the calculation load are reduced, the precision and robustness of indoor three-dimensional positioning are effectively improved, and the method is suitable for application scenes with strict requirements for quick response and low-cost deployment.

The application 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; applying HSV color conversion and texture equalization processing to standardize the image; combining parallax analysis to construct a three-dimensional deformation feature and extract a defect feature vector; and utilizing a position perception convolutional neural network to identify the defect and output a severity heat map. According to the application, in a complex light environment, the tiny defects on the surface of vehicles with various colors and materials can be accurately identified and evaluated, and the accurate three-dimensional positioning and severity quantification of the defects can be realized.

A kind of open pit model dynamic updating method based on real-time positioning of excavator and SDF belongs to the technical field of digital mine. The initial pit triangular mesh model is voxelized to generate a three-dimensional symbolic distance field based on the definition of vertical coordinate symbol. The three-dimensional positioning data of the center of rotation of the excavator is collected in real time through the GNSS-RTK positioning module, and the working state is judged based on the preset time window and displacement threshold. When the excavator switches from displacement state to digging state, a spherical mining influence domain is constructed with the current positioning data as the center of the sphere and the maximum digging radius as the radius, and the voxel symbol distance field value in the domain is set to 0. A control point set is constructed by boundary constraint points and background constraint points, and global symbol distance field smoothing reconstruction is realized by using radial basis function interpolation. The zero value isosurface is extracted by moving cube algorithm to generate the updated pit model. The present application realizes high-frequency model updating synchronized with operation, has low deployment cost and strong robustness, and solves the problems of model updating lag and visualization penetration.

The application provides a deep foundation pit anti-floating risk assessment method based on monitoring data, and relates to the technical field of deep foundation pit anti-floating. The method comprises the following steps: carrying out layered multi-source heterogeneous monitoring on the deep foundation pit, obtaining a water pressure deformation data set and extracting spatial node and time sequence parameters; calculating the spatial distribution gradient of buoyancy and the dynamic resistance threshold; then establishing a local mechanical coordinate system in the effective stress coupling area, extracting a structure deformation mutation sequence from the data set; subsequently constructing a space-time coupling graph, generating a nonlinear attenuation path covering a weak area through a feature fusion algorithm; finally extracting a local anti-floating risk set along the path, generating a global anti-floating evolution graph through time domain smoothing and three-dimensional space mapping, and outputting an evaluation result containing three-dimensional positioning and automatic hardware control instructions. The application realizes space-time linkage analysis of foundation water pressure and structure deformation, and significantly improves the fine early warning and entity control ability of anti-floating risk under complex working conditions of deep foundation pit.

This application discloses a multimodal data collaborative intelligent detection and 3D localization method for maritime vessels, comprising: fine preprocessing of collected multi-source heterogeneous data and preliminary target detection; dynamic weighted fusion of the preprocessed multi-source heterogeneous data, combined with depth data from lidar, to achieve accurate coordinate recovery of the vessel target in 3D space; and prediction and updating of the motion state of the identified target based on a multi-tracking mechanism collaborative strategy. By fully utilizing the rich texture information of visible light images, the anti-interference capability of thermal imaging, and the precise spatial depth data of lidar through multimodal data perception and preliminary processing, heterogeneous data deep fusion and 3D localization, and advanced multi-target continuous tracking and state estimation, this method integrates a multi-tracking mechanism collaborative strategy to predict and update the motion state of the identified target, effectively addressing the problem of tracking interruption caused by nonlinear motion or temporary occlusion of maritime targets.

This invention provides a method, system, and medium for three-dimensional positioning and displacement measurement that integrates microwave and vision technologies. The method includes: rigidly connecting at least one microwave sensor and at least one vision sensor as a joint sensing unit; establishing a spatial transformation relationship between the coordinate systems of the two sensors through calibration; fusing the target's radial distance and angle information provided by the microwave sensor with the target's two-dimensional imaging information provided by the vision sensor; and directly solving for the target's three-dimensional coordinates in the selected coordinate system using an optimization algorithm; further combining the high-precision one-dimensional displacement information from the microwave sensor with the two-dimensional displacement information from the vision sensor to reconstruct the target's complete displacement vector in three-dimensional space. This invention, through a compact heterogeneous sensor fusion architecture, effectively solves the problems of limited dimensionality of single-sensor modes, high cost of multi-sensor networking, system complexity, and poor engineering applicability, achieving high-precision, low-cost, and full-dimensional non-contact three-dimensional positioning and displacement measurement.

This invention relates to the fields of artificial intelligence and computer image recognition technology, and discloses an intelligent method and system for detecting the quality of interior decoration. The method includes: applying a composite acoustic signal through a multi-band acoustic excitation device, and simultaneously acquiring multi-channel acoustic response, infrared thermal imaging sequences, and electromagnetic induction data; extracting acoustic impedance abrupt changes, thermal conduction anomalies, and electromagnetic continuity features respectively; generating multimodal embedding vectors using three convolutional neural network encoders, and generating a unified representation through a cross-modal attention fusion module; finally decoding and outputting defect type, confidence level, and three-dimensional positioning information, and superimposing it onto a building information model to generate a visual report. This system integrates a multi-physics sensing unit and an edge computing module to achieve non-invasive, fully automatic, and high-precision interior decoration quality detection. This invention improves defect detection rate and positioning accuracy through multimodal fusion and deep learning, increasing detection efficiency compared to manual methods.

This invention discloses a location privacy protection method based on recurrent reinforcement learning for 3D localization errors, belonging to the fields of location services and information security. The method uses the precise coordinates of landmarks around the user as anchor points; the user generates a perturbed location for the current time slot based on their own positioning error and uploads it to the server, obtaining an assessment of privacy protection level and service quality; the perturbed location of the current time slot with positioning error is modeled as a partially observable Markov decision process, employing a dynamic location privacy protection mechanism combining a long short-term memory network and a dual-delay deep deterministic policy gradient algorithm to protect user location privacy even with positioning errors. The method is simple, effective, and achieves an effective balance between service quality and privacy protection needs, maximizing user utility.

The invention discloses a tunnel vehicle positioning method and device fusing trajectory learning and map constraint, and belongs to the technical field of traffic positioning. The method comprises the following steps: constructing a multi-source time sequence observation vector by synchronously collecting inertial navigation and ultra-wideband ranging data, and preprocessing through data cleaning and generative adversarial network interpolation; learning a vehicle motion mode by using a deep learning model based on a gating circulation unit and an attention mechanism, and outputting a preliminary three-dimensional positioning coordinate; meanwhile, geometric simplification is carried out on the tunnel digital map to extract a structured road section; and finally, the predicted coordinates are corrected to a map road section through a geometric map matching algorithm, threshold judgment and interpolation restoration are carried out according to a preset tolerance, and high-precision and high-reliability continuous positioning of the vehicle in the tunnel environment is realized. According to the invention, the precision and robustness of vehicle positioning in a complex tunnel scene are effectively improved.

The invention relates to the technical field of building detection, and provides a house indoor defect intelligent detection and graphic file output method and system. The method comprises the following steps: acquiring millimeter-level three-dimensional point cloud data through handheld SLAM laser scanning, generating an orthoimage after preprocessing, carrying out defect identification and classification by adopting an unsupervised-weak supervised neural network in which a MobileNetV3-SOM and a stacked auto-encoder are connected in series, calculating three-dimensional coordinates and quantization parameters of a defect center through pixel-point cloud back projection, and carrying out defect identification and classification on the defect center. And finally generating a CAD vector file containing defect types, three-dimensional coordinates and quantization parameters. According to the method, only normal wall surface sample training is needed, the problem of insufficient model generalization caused by defect sample scarcity is solved, meanwhile, accurate three-dimensional positioning and complete parameter description of defects are achieved, the output standardized CAD file can be directly used for repairing design, the detection efficiency is improved, and the application range is widened.

The present application relates to a kind of low slow small target fast identification tracking method and system;The method comprises: constructing the video device array for target area;Each video device has first state and second state, and independently and in parallel video stream acquisition and local processing are carried out;Based on current motion target information and historical motion target information, each motion target is carried out multi-view data association and three-dimensional positioning, and the associated video device set of each motion target is determined in real time;The advantage video device group of each motion target is determined in real time, and based on advantage video device group team motion target, fine identification and tracking are carried out.The present application realizes early warning, real-time identification, continuous tracking and threat assessment to unmanned aerial vehicle, model airplane, bird and other low slow small targets.The system can be widely applied to key scenes such as important place air defense, border patrol, major event security, airport clearance protection.

The invention provides a nuclear power embedded part intelligent detection method based on data fusion, belongs to the technical field of intelligent detection, is based on multi-source data fusion of 2D images and 3D point clouds, combines AI deep learning and homography matrix projection transformation technologies, and aims to break through the technical bottleneck of three-dimensional positioning and accurate measurement of embedded parts in complex scenes. A high-precision sub-pixel angular point extraction algorithm based on Hough straight line fitting is designed, and precise recognition of the edge of the embedded part is achieved; high-precision data fusion of the 2D image and the 3D point cloud is realized through joint calibration parameters of the camera and the laser radar; accurate conversion from 2D image angular points on the surface of the embedded part to 3D space coordinates is realized based on homography matrix projection conversion; according to the method, the BIM design drawing data is combined, automatic calculation and evaluation of the installation deviation of the embedded part are achieved, the detection precision and efficiency are improved, the detection cost is reduced, and the detection safety is guaranteed.

The invention provides a TDOA positioning error correction method based on robust cascade optimization, and relates to the technical field of wireless positioning and signal processing. The TDOA positioning error correction method based on robust cascade optimization comprises the following steps: S1, establishing a three-dimensional distance difference measurement model and a weight matrix; s2, based on the measurement model, a Chan two-step weighted least square method is adopted to obtain an initial value of a target position; s3, constructing a unified weighted residual cost function; s4, based on the cost function, adopting an improved RIME swarm intelligence algorithm to refine the target position; and S5, on the basis of an RIME refinement result, carrying out pull-back optimization by adopting a robust LM-IRLS method with priori constraint, and obtaining a final three-dimensional positioning result. By adopting a 'Chan + RIME + robust LM' cascade structure, positioning is transited from a closed fast initial value to swarm intelligence global refinement, and then high-noise pull-back is performed by robust local iteration with prior, so that positioning precision and stability are considered under different noise conditions, and divergence and failure probabilities are reduced.

The invention relates to the technical field of computer vision and smart cities, and provides a hidden space garbage stacking identification method based on improved deep learning, and the method comprises the steps: collecting under-bridge image video data, carrying out the data preprocessing, and generating an optimized to-be-detected image; performing junk heap target identification on the to-be-detected image by using a pre-trained target detection model to obtain a detection result set; filtering the detection result set through a multi-stage filtering mechanism, performing three-dimensional positioning, and outputting two-dimensional detection and three-dimensional space positioning results; and performing visual display, information recording and alarm output based on two-dimensional detection and three-dimensional space positioning results. According to the method, the recognition precision and robustness of garbage heaps under small targets, shielded targets and complex backgrounds are remarkably enhanced, the garbage patrol efficiency and the intelligent level under the bridge are greatly improved, and the bridge safety and the environmental sanitation are effectively guaranteed.

This invention provides a method and apparatus for visual detection and harvesting control of guava maturity, relating to the field of visual detection technology. It acquires multi-view time-series images from a drone and extracts individual guava image blocks and depth information to determine spatial location. A feature encoder extracts feature vectors and performs clustering to determine maturity levels. Then, an optimization problem is constructed to plan the harvesting path, aiming to maximize harvesting revenue per unit time. Finally, the drone and robotic arm are controlled for flexible harvesting. This method can achieve automatic identification of guava maturity, 3D positioning, revenue-driven target selection and path planning, and precise flexible harvesting even in the absence of numerous manual maturity labels. It forms an integrated autonomous harvesting system with a closed loop of perception, understanding, decision-making, and execution, effectively solving problems such as difficult feature extraction, high manual labeling costs, low planning efficiency, and fruit damage in greenhouse guava harvesting.

The application discloses a kind of multi-angle spaceborne SAR target three-dimensional positioning method and device.Method includes: obtaining the multi-azimuth angle spaceborne SAR image sequence of simulation scene;Certain angle image in image sequence is regarded as main image, and the height estimation sensitivity of each other angle image is calculated based on main image;According to the target height range of simulation scene, determine multiple sampling heights;At each sampling height, the correlation coefficient between each target point in main image and its corresponding matching point in each other angle image is calculated;Based on height estimation sensitivity and the correlation coefficient under each sampling height, construct the single-angle height estimation probability density function of each other angle image of each target point in main image based on each single-angle height estimation probability density function, the height estimation value of each target point in main image is calculated;Based on the focusing position and height estimation value of each target point in main image, the three-dimensional position estimation result of the target point is calculated.The application can accurately invert the three-dimensional position of target.

The invention relates to the technical field of intelligent building construction, and discloses a model-based pipeline comprehensive arrangement three-dimensional positioning construction method, which comprises the following steps: checking an LOD400 precision BIM model in combination with field three-dimensional scanning, constructing a global environment map unified with model coordinates, and establishing a high-fidelity digital reference; a task library is generated based on the reference, and the BIM data and scheduling server dynamically allocates tasks according to the real-time poses of the intelligent terminals and issues a laser projection instruction; a constructor carries out installation according to a positioning line, and carries out dual quality verification through a mixed reality technology and high-precision scanning comparison, so as to form a closed loop. According to the method, a high-precision model and on-site scanning are fused to build an accurate reference, dynamic cooperative guidance is achieved through three-dimensional laser positioning, closed-loop verification is completed by combining BIM-MR and scanning comparison, the problems of inaccurate positioning, low cooperative efficiency, quality control lag and the like are solved, and the precision and efficiency of pipeline construction are remarkably improved.

This invention discloses a machine learning-based method, device, and medium for 3D model object recognition, relating to the field of 3D model recognition technology. The method includes: extracting topological features from a set of 3D ROI (Region of Interest) bodies to generate topological constraint codes; simultaneously performing shape decomposition to generate shape syntax codes; integrating the topological constraint codes and shape syntax codes to generate a manufacturing syntax dual-code description package; classifying the manufacturing syntax dual-code description package and interpreting it to generate 3D semantic segmentation; calculating the overlap degree by projecting onto a standard CAD view to generate a manufacturing conformity certificate; extracting the 3D semantic segmentation from the manufacturing conformity certificate; mapping the 3D semantic segmentation to the CAD 3D model to generate a direction annotation layer; associating this layer with the manufacturing conformity certificate to generate a manufacturing semantic object recognition list. This invention achieves structured encoding of manufacturing semantics, enhancing the interpretability and industrial applicability of 3D model recognition.