95 results about "Statistical filtering" patented technology

An autonomous warehouse robotics system integrates a multi-sensor platform, adaptive payload handling, dynamic task reallocation, advanced navigation, energy management, and comprehensive safety features into one mobile robot chassis. The system utilizes LiDAR, stereo vision, ultrasonic sensors, mmWave radar, thermal cameras, and event cameras to perform complete environmental sensing and obstacle detection. Sensor fusion combines adaptive weighting, multi-modal data integration, and statistical filtering to create high-confidence maps for reactive path planning and collision avoidance. The robot's payload system features machine vision for item recognition, telescopic lifts, variable-width grippers, and real-time toolhead verification to handle a variety of goods. Fleet management is achieved through dynamic task reallocation that considers robot location, battery level, and operational delays, all coordinated by a cross-platform middleware architecture that ensures standardized communication, remote monitoring, and over-the-air updates among diverse robot brands. Energy management optimizes power usage via predictive routing, autonomous return-to-charge, and auction-based scheduling. Safety is maintained through proximity detection, behavior-based intervention, and human-robot cohabitation protocols while advanced localization is enhanced by fusing ultra-wideband positioning with visual landmark alignment, inertial sensing, and machine learning to deliver high accuracy in non-line-of-sight conditions. An onboard edge AI module further refines navigation and task prioritization through neural network inference, ensuring robust, adaptive operation in dynamic, unstructured warehouse environments.

The invention relates to the technical field of ultrasonic medical robots, and discloses a superficial ultrasonic scanning path planning method and device based on an SVD-improved RRT algorithm. The superficial ultrasonic scanning path planning method is applied to ultrasonic medical robot equipment, and specifically comprises the following steps: S101, receiving a data acquisition request sent by a main control end, controlling a Kinect camera to acquire a superficial depth image, processing 20 groups of calibration board images through a Matlab calibration tool box, and executing a corner detection algorithm to identify checkerboard corners, and optimizing and solving the internal reference matrix of the infrared camera. According to the method, SVD plane fitting and an improved RRT algorithm are fused, the precision and efficiency of an ultrasonic scanning path are remarkably improved, in the point cloud processing stage, statistical filtering and voxel gravity center down-sampling synergistically act, point cloud noise is reduced by 90%, meanwhile, more than 95% of three-dimensional features are reserved, a high-fidelity data basis is provided for path planning, and the method has good application prospects. SVD plane fitting forcibly passes through the center of mass, and the error is smaller than or equal to 0.5 mm

The invention discloses a target vehicle attitude recognition method based on 3D point cloud feature extraction, and the method comprises the steps: carrying out the preprocessing of the point cloud data of a target vehicle obtained through a laser radar, and the preprocessing comprises Gaussian statistical filtering, voxelization sampling, and vehicle tire point cloud extraction based on a region growing algorithm; based on the point cloud data, key points of tire steering features are extracted, and a Harris corner detection method is used for detecting the key points; calculating a fast point feature histogram (FPFH) descriptor based on the extracted key points, and performing coarse registration by adopting an SAC-IA algorithm to obtain a coarse registration matrix; and using a point-to-surface nearest point iteration ICP algorithm to perform fine registration on the point cloud after coarse registration to obtain attitude information of the target vehicle. According to the method, the three-dimensional attitude angle of the target vehicle is accurately estimated by preprocessing the laser radar point cloud, extracting tire key points and carrying out SAC-IA coarse registration and ICP fine registration. Therefore, vehicle attitude recognition with high shielding robustness and high precision is realized, and the safety of automatic driving is enhanced.

The invention relates to a fine-grained target real-time image segmentation method and system based on a dynamic state modeling network, and belongs to the technical field of intelligent image processing. The method comprises the following steps: extracting multi-scale detail features by using a lightweight backbone network; through a dual-scale two-dimensional selective scanning module, the features are divided into a thin branch and a thick branch, and local scanning and global scanning are executed respectively; a dynamic cross-scale feature selection and aggregation module is adopted, redundancy is suppressed through reweighting and statistical filtering, and key target responses are highlighted; at a decoding end, local details and global semantics are fused through jump connection and an edge extractor; and finally, introducing a form-guided pseudo label hierarchical supervision strategy, and improving the structure learning ability of the model by using a coarse-to-fine morphological prior. According to the method, the segmentation precision, the boundary integrity and the tiny target recall rate of the fine-grained target under the scenes of ore separation, industrial defect detection, pavement crack recognition and the like are remarkably improved.

The invention discloses a multi-machine collaborative three-dimensional map updating method based on mobile augmented reality, and belongs to the technical field of three-dimensional reconstruction, and the method comprises the following steps: carrying out the collection of initial collection data of a plurality of target regions through the wearing of AR glasses by a plurality of people; performing frame extraction on the original video image, and calculating to obtain a data grouping result; a sparse pairing list is generated by adopting a hybrid pairing strategy, and sparse point cloud data is obtained by combining incremental SfM calculation; performing distortion removal processing on the frame extraction image, and obtaining pure dense point cloud data through a multi-view solid geometry algorithm, a clustering algorithm and statistical filtering; a triangular mesh model is generated through Poisson surface reconstruction, and a high-precision three-dimensional model is obtained through Markov random field optimization view selection; and constructing a unified space-time reference conversion matrix to obtain a real-time AR visual navigation and map updating result. By adopting the method, the problems that the three-dimensional map is poor in current situation, virtual and real registration is misplaced, the modeling efficiency is low and the precision is insufficient are solved.

The invention belongs to the technical field of three-dimensional modeling, and particularly discloses a pipeline three-dimensional modeling method based on adaptive statistical filtering and ground constraint poses, and the method comprises the steps: controlling a collection device to move along a target pipeline, and collecting the original point cloud data of the target pipeline and the movement distance information of the collection device; on the basis of the moving distance information, continuous pose changes of the collection equipment in the target pipeline are calculated, and a ground constraint pose sequence is generated; taking a matching relationship between the original point cloud data as a constraint between pose nodes of the ground constraint pose sequence, and obtaining an optimized ground constraint pose sequence; on the basis of the optimized ground constraint pose sequence, registering the original point cloud data to obtain a pipeline point cloud model; filtering the pipeline point cloud model; and generating a three-dimensional model of the target pipeline based on the filtered pipeline point cloud model. The method can improve the precision of the generated three-dimensional model.

The invention discloses a sugarcane live-action three-dimensional phenotype extraction method based on a 3D Gaussian splashing technology. The method comprises the following steps of data acquisition, wherein a 360-degree omnibearing shooting mode is executed around a single sugarcane plant for shooting or video recording to obtain complete image or video data of the sugarcane plant; preprocessing the data; 3D model construction comprises the following sub-steps: SfM sparse reconstruction: instance segmentation; the method comprises the following steps: carrying out 3D Gaussian Splicing guided by a mask; point cloud data processing includes the following sub-steps: point cloud preprocessing including voxel downsampling, statistical filtering and radius filtering; segmenting and identifying stalks / leaves; the segmentation comprises RANSAC plane segmentation, DBSCAN clustering and region growth; the phenotype analysis comprises grid reconstruction and area / volume / perimeter / plant height calculation. According to the invention, the problems of difficult data acquisition, large workload and incomplete information acquisition in the prior art are solved.

The invention discloses a distributed detection device and a 4D millimeter wave point cloud registration method based on three radars. According to the device, a distributed detection module comprises left, middle and right 4D millimeter wave radars; the processor comprises an offline calibration and data synchronization module, an original point cloud preprocessing module, a coordinate system unification module, a point cloud coarse registration module and a point cloud precise registration and fusion output module; the distributed detection module, the processor and the communication module are connected in sequence; the method comprises the steps that off-line external parameter calibration is carried out in a combined mode, and accurate pose external parameters of the left radar and the right radar are obtained based on multi-pose point cloud data; executing statistical filtering processing; uniformly mapping the de-noised point clouds of the left radar and the right radar to a coordinate system of the middle reference radar to form a point cloud set of a three-path common reference system; determining an initial pose matrix through coarse registration; and completing accurate alignment and fusion based on the high-precision pose matrix, and outputting dense 4D millimeter wave radar point clouds. According to the invention, dense and high-quality 4D millimeter wave point cloud data can be obtained.

The invention relates to the technical field of optical fiber sensing signal processing, in particular to a DAS signal floor noise elimination method, system and equipment based on statistical analysis, and the method comprises the steps: initializing system parameters, and loading a DAS application field data matrix; performing slicing and frequency domain transformation on the DAS original data; dynamically determining a peak threshold value, and performing adaptive peak searching according to the peak threshold value; sorting the identified and screened local maximum values in a descending order, and selecting frequencies corresponding to the local maximum values as main frequency candidates; performing clustering analysis on the selected dominant frequency candidates; calculating the cumulative proportion of each main frequency in the total statistics, selecting a center frequency, and forming the multi-frequency band elimination of the monitoring unit by using band elimination frequency bands formed by different center frequencies; and adding the multi-frequency band elimination to a statistical filtering frequency band set of all the point locations, storing the statistical filtering frequency band set of all the point locations as a statistical model, and performing background noise elimination on the DAS original signal based on the statistical model.

The invention relates to the technical field of industrial internet-of-things monitoring, and discloses an internet-of-things-based remote monitoring method and system for asphalt mixture stirring equipment. The method comprises the following steps: acquiring key component operation parameters and external environment variables, and performing time domain statistical filtering to obtain an initial operation sequence; performing adaptive window length smoothing and frequency domain noise suppression to obtain a clean operation sequence; performing environment equivalent correction on the clean operation sequence by using the external environment variable to obtain a calibration feature vector; performing multi-dimensional feature correlation analysis according to the calibration feature vector, and determining a working condition switching label; determining a dynamic reference range according to the working condition switching label; calculating a preliminary abnormal probability according to the calibration feature vector and the dynamic reference range; and performing historical trend weighting and abnormal integration to obtain a final health assessment result. According to the method, through environment correction and dynamic reference following, environment interference and working condition misjudgment are eliminated, and accurate evaluation of the equipment health state is achieved.

The invention discloses an existing bridge high-precision three-dimensional reconstruction method based on laser point cloud data, and relates to the technical field of bridge engineering digital reconstruction. Multi-source laser point cloud cooperatively collects detail point cloud of key components of an existing bridge, point cloud of a bridge floor and an upper structure and point cloud of a shielding part; multi-source point cloud coarse registration is completed based on a target ball of a preset specification, and then fine registration is carried out by adopting an improved iterative nearest point algorithm of integrated normal vector constraint; and adopting a layered denoising strategy to carry out statistical filtering denoising on the flat area, and carrying out adaptive radius filtering on the vulnerable area to reserve cracks and peel off disease points. Through collaborative design of multi-source acquisition, precise processing, intelligent segmentation, parameterized modeling and hierarchical verification, data integrity improvement, consideration of point cloud processing precision and disease retention, semantic segmentation and instance identification precision optimization, BIM model engineering value improvement and achievement adaptability enhancement are realized.

The invention provides a moving ship detection method based on spatial-temporal characteristics, which comprises the following steps: firstly, eliminating land and thick cloud regions through dual-threshold segmentation preprocessing, and adaptively extracting weak ship candidate targets in a water area by combining top-hat transform with sequential statistical filtering; combining detection results of each wave band, expanding the area of a ship candidate target, realizing signal enhancement and eliminating static false alarms, combining multiple frames of candidate targets, and carrying out filtering processing by utilizing a multi-direction rotation linear kernel by utilizing position offset characteristics of a moving target in five wave bands of a Gaofen-4 satellite due to imaging time difference; according to the method, real ships in smooth track distribution are reserved, isolated and randomly distributed false alarms or non-coherent track false alarms are eliminated, and stable and high-precision ship detection is effectively guaranteed while the low false alarm rate is kept.

The invention provides a marine target detection method based on an unmanned aerial vehicle, and the method comprises the following steps: 1, carrying out the time-space synchronization of point cloud original data collected by a plurality of sensors on the unmanned aerial vehicle, and obtaining the point cloud original data after the time-space synchronization; step 2, preprocessing the point cloud original data after time-space synchronization acquired by each sensor, and obtaining preprocessed single-sensor point cloud data through voxel filtering and statistical filtering; step 3, based on a K-dimensional tree and a DBSCAN clustering method, performing clustering segmentation on the point cloud data of the single sensor to obtain a single sensor target detection result; and 4, performing comprehensive analysis on all single-sensor target detection results by adopting a multi-source point cloud information decision level information fusion method to obtain a final target detection result.

A high-speed rail pantograph carbon contact strip abrasion detection method comprises the steps that carbon contact strip point cloud profile data of a high-speed rail pantograph are obtained through a single-line laser sensor; processing the data by adopting a motion blur correction algorithm based on spatial range screening and reflection intensity screening to obtain effective point cloud data; statistical filtering is carried out on the effective point cloud data, then normalization processing is carried out, and normalized effective point cloud data are obtained; obtaining a first demarcation point and a second demarcation point of the pantograph horn and the carbon contact strip by adopting a reflection characteristic identification method and a characteristic point identification method respectively; screening and integrating to obtain a final demarcation point, and intercepting to obtain an actually measured carbon slide plate point cloud data set; and comparing and registering the actually measured point cloud data set of the carbon contact strip and the point cloud data set of the standard carbon contact strip by adopting a point-to-surface ICP (Inductively Coupled Plasma) registration method so as to calculate the abrasion value and the over-limit abrasion area of the pantograph carbon contact strip. According to the invention, high-efficiency and high-precision detection of high-speed railway pantograph carbon contact strip abrasion can be realized.

The invention belongs to the technical field of thermal design of integrated circuits, and particularly relates to a chip wiring three-resistor thermal resistance extraction method based on separated grids. The method solves the problems of local temperature gradient distortion and geometric interface characteristic deficiency near the wiring caused by an existing equivalent thermal resistance model (such as a volume equivalent method and a homogenization method), and avoids the technical problem of overhigh computing resource consumption caused by full-chip fine grids. The method comprises the following steps: analyzing a chip structure to generate a JSON file; the method comprises the following steps: dividing a chip into a plurality of layers by adopting a separated grid technology, embedding wires in a three-dimensional grid to serve as a one-dimensional structure, and marking an interface; the calculated key parameters comprise a total area, a metal coverage area, a dielectric coverage area and an adjacent unit distance; calculating total thermal resistance formed by series connection of in-vivo thermal conduction resistance and interface penetration thermal resistance based on the thermal conductivity; and after statistical filtering, a circuit simulator is imported to output temperature distribution. The method is high in precision, and the calculation cost is remarkably reduced.

The application discloses a power pipeline point cloud semantic recognition and three-dimensional modeling method based on PointNet++, and belongs to the technical field of three-dimensional laser point cloud data processing, deep learning semantic segmentation and three-dimensional modeling of electric power engineering. The method builds a full-process automatic processing system from original point cloud input to complete three-dimensional model output, and completes modeling through four links of point cloud preprocessing, semantic recognition, parameterized fitting and topological reconstruction. In the point cloud preprocessing link, statistical filtering is combined with radius filtering denoising, FPFH feature matching and ICP precise registration, and voxel downsampling technology is used to generate a standardized point cloud dataset. The application solves the problems of low automation degree, poor precision and weak scene adaptability of three-dimensional modeling of electric power pipelines in the prior art, can reduce manual intervention by more than 80%, the average semantic segmentation precision of components reaches 95.2%, is fully suitable for various electric power pipeline scenes such as transformer substations, cable pipe corridors and power transmission lines, and provides technical support for digital management of the whole life cycle of electric power pipelines.

The invention relates to a big data information collection method. The method comprises the following steps: collecting retrieval data, browsing data, consulting data and auxiliary data of multiple users in an application system; invalid data removal and noise data filtering are carried out on the collected data, invalid data removal comprises null value and meaningless data removal, duplicate data de-duplication and format standardization processing, and noise data filtering comprises regular filtering and statistical filtering; and constructing an invalid and noise data judgment model based on the processed valid data and invalid and noise data. According to the method, through data preprocessing and double screening of invalid and noise data judgment models, invalid data and noise data can be effectively eliminated, so that the accuracy of data information collection is improved; through automatic judgment of invalid and noise data judgment models and an incremental updating mechanism, the models are continuously optimized along with data accumulation, the manual screening workload of data information collection can be reduced, and the misjudgment rate of automatic judgment is reduced.

The present application relates to the technical field of industrial internet of things monitoring, and discloses a remote monitoring method and system for asphalt mixture mixing equipment based on the internet of things.The method comprises the following steps: collecting key component operating parameters and external environmental variables, performing time domain statistical filtering to obtain an initial operating sequence; performing adaptive window length smoothing and frequency domain noise suppression to obtain a clean operating sequence; using the external environmental variables to perform environmental equivalent correction on the clean operating sequence to obtain a calibration characteristic vector; performing multi-dimensional feature correlation analysis according to the calibration characteristic vector to determine a working condition switching label; determining a dynamic reference range according to the working condition switching label; calculating a preliminary abnormality probability according to the calibration characteristic vector and the dynamic reference range; performing historical trend weighting and abnormality integration to obtain a final health assessment result.This method eliminates environmental interference and working condition misjudgment through environmental correction and dynamic reference following, and realizes accurate assessment of the health state of the equipment.

The invention belongs to the technical field of image data processing, and discloses an image data processing method for embedding and positioning a decoration panel, which comprises the following steps of: S1, synchronously acquiring two-dimensional image data and three-dimensional point cloud data of the decoration panel and an insert; s2, performing gray correction, 3 * 3 kernel Gaussian filtering and adaptive threshold segmentation preprocessing on the two-dimensional image data, performing denoising and downsampling on the three-dimensional point cloud data by adopting a statistical filtering algorithm, then performing coordinate registration with the preprocessed two-dimensional image data, and establishing a mapping relation between pixel coordinates and physical coordinates; s3, inputting the preprocessed two-dimensional image data and three-dimensional point cloud data into a CNN convolutional neural network model trained by a decoration panel mosaic scene sample; s4, an XYA reference coordinate library is established based on a decoration panel design drawing, independent detection coordinates are calculated for the four types of feature information, and final detection coordinates are output after fusion through a weighted average algorithm; and S5, calculating an uncompensated offset value of the final detection coordinate and the reference coordinate.

The invention discloses a ship backside completion method based on dynamic screening and registration optimization of a medial plane, and belongs to the technical field of three-dimensional point cloud data processing. The method comprises the following steps: step 1, carrying out dynamic adaptive statistical filtering noise reduction on an input original point cloud, dynamically adjusting a neighborhood size and a threshold value according to a local point cloud density, and adaptively distinguishing noise and effective points; step 2, screening out a medial surface according to different ship types, and optimizing the medial surface; 3, generating back-side missing data from the complete front-side point cloud data through symmetric mapping by using the inherent symmetry of a ship structure; step 4, through quantitative analysis of the spatial matching degree between the completion point cloud and the original point cloud, screening out a completion result closest to the real geometric distribution; step 5, performing registration based on the optimal completion point cloud screened out in the step 4 to obtain a point cloud alignment result; and step 6, optimizing and adjusting the medial surface by using the transformation matrix obtained by ICP registration so as to approach the real medial surface of the ship.

The invention discloses a self-adaptive point cloud registration method based on mixed geometric constraint and statistical filtering, and belongs to the technical field of machine vision and three-dimensional point cloud processing. According to the method, an improved coarse-to-fine matching strategy is provided for solving the problems that a traditional distance signature algorithm is large in calculation redundancy, sensitive to noise and lack of scale adaptability. The method comprises the following specific steps: firstly, calculating normal vectors of a source point cloud and a target point cloud, carrying out rapid pre-screening by utilizing a normal included angle constraint, and removing candidate point pairs with inconsistent geometric features; secondly, a robust distance signature based on bilateral percentile truncation is constructed, and outlier noisy points in a distance spectrum are stripped through a statistical method; and finally, adopting a normalized dynamic matching threshold as a criterion to realize adaptive matching of point clouds with different densities, and solving a rigid transformation matrix through singular value decomposition (SVD). According to the method, the robustness and the calculation efficiency of point cloud registration in noise interference and partially overlapped scenes are remarkably improved.

The application discloses a kind of ship slotting V-type groove weld parameter extraction method and system, the method includes: using the statistical filtering method based on percentile to eliminate abnormal points, and based on neighborhood z coordinate range realizes positive side weld point cloud separation;Then for the geometric characteristics of two kinds of structures of blunt edge V-type weld and butt V-type weld, rectangular surface recognition algorithm and boundary detection algorithm are designed respectively, accurate separation and boundary positioning of groove point cloud are realized;Finally, the adaptive extraction of groove angle parameters is completed by fusing RANSAC straight line fitting and endpoint method.The application can be suitable for the above two kinds of structures simultaneously, can provide accurate groove geometric parameters for ship welding robot, effectively improve the automation welding level of ship and marine equipment.

The invention provides a GIS equipment X-ray detection method for realizing space pose automatic compensation, and belongs to the technical field of GIS equipment detection.Point cloud data of GIS equipment are acquired through laser radar multi-view scanning, noise is eliminated by adopting self-adaptive multi-scale statistical filtering, and the spatial pose automatic compensation is realized. Non-rigid point cloud registration is realized by using a space registration optimization model combining a deep expansion network and sparse coding, the position of an internal conductor is corrected based on an electromagnetic field inverse problem inversion algorithm, and the space position and the ray angle of an X-ray machine are automatically adjusted through an attitude compensation adjustment function according to a pose deviation vector. The technical problem that the detection pose cannot be automatically compensated due to equipment deformation and positioning errors during X-ray detection of GIS equipment is solved.

This invention provides a method for training a curved surface intelligent spraying model, a spraying method, and a system thereof, relating to the field of robot path planning technology. The curved surface intelligent spraying method includes: acquiring point cloud data of an object using a depth camera; performing voxel filtering, statistical filtering, and RANSAC plane segmentation preprocessing on the point cloud data; calculating the centroid and PCA principal component orientation of the preprocessed point cloud; rasterizing the space containing the point cloud according to a preset grid size and number of rows and columns to generate a grid map; using a DQN model for path planning; and having the robot perform spraying operations according to the planned path. This invention also provides a method for training a curved surface intelligent spraying model and a curved surface intelligent spraying system to achieve optimal path coverage of all sprayable areas, avoiding unnecessary movement.

The invention discloses a joint compression method for building point cloud data based on geometric features, and the method comprises the steps: firstly carrying out the sampling and statistical filtering preprocessing of the building point cloud data under an octree, so as to remove noise and reduce the data density; secondly, accurately identifying and retaining key contour and structural features of the building through an edge point extraction algorithm based on curvature calculation, and then performing redundancy removal on non-edge points by adopting an adaptive multi-scale normal differential segmentation technology (DoN) which can adaptively adjust scale radius parameters according to geometric complexity of the surface of the building; and structure key points are effectively distinguished and reserved. And finally, merging the edge points and the points subjected to normal differential segmentation to form compressed point cloud data. Through verification of a precision evaluation index, the method can remarkably improve the quality of reconstructed point cloud while keeping a low compression ratio, and provides powerful support for efficient storage and transmission of building point cloud data.

The application provides a kind of underwater fish length measurement method based on binocular vision and attitude estimation, belongs to image recognition and underwater measurement technical field.The method constructs a new type of cascaded perception measurement framework combined with underwater context attention enhancement module and geometric statistical filtering engine;The framework is based on a top-down target detection and key point positioning cascaded network, introduces an underwater context attention enhancement module at the key point feature fusion end, and introduces a spatial orthogonal circular arc compensation and double-stage time signal filtering mechanism at the three-dimensional object understanding algorithm end, through the collaborative optimization of visual feature topology perception and space-time joint filtering, realizes the accurate capture of fish key points under light and shadow interference, and effectively suppresses the non-rigid swimming deformation and depth noise jump, effectively solves the distortion problem of underwater dynamic length measurement, significantly improves the anti-distortion ability and overall output stability of measurement in underwater dynamic environment.

The present application relates to the technical field, especially to a kind of based on three-dimensional information's forming weld appearance defect extraction method.The method specifically includes: the three-dimensional point cloud data of weld is collected;Three-dimensional point cloud data is preprocessed, including using statistical filtering method to remove outlier, and using the point closest to the voxel grid centroid in voxel grid is selected to carry out point cloud data downsampling;After three-dimensional point cloud data is preprocessed, through LMedS adaptive threshold segmentation method, the extraction of weld is carried out, in the weld point cloud that extraction is completed, the framework of region growing method is established, seed point is selected, whether the point in the neighborhood of seed point belongs to the same category according to normal angle threshold and curvature threshold is judged, the extraction of weld surface defect point cloud is realized.The method provided by the present application can more completely obtain three-dimensional defect geometric information, improve the overall operation efficiency of system, and be more conducive to the complete extraction of weld defect.