754 results about "Characteristic point" patented technology

The invention relates to the technical field of digital twinning control, and discloses a composite structure prefabricated segment digital twinning-adaptive assembly correction method and system, and the method comprises the steps: reading BIM geometric model data, and constructing an assembly reference coordinate system and a digital twinning geometry of a prefabricated segment; composite data are collected in real time, and end tooth groove boundary feature point cloud is extracted; matching the actually measured point cloud with the digital twinborn geometry through a dynamic point cloud registration technology, calculating a six-degree-of-freedom pose error vector, and generating a predicted total pose error vector in combination with a pre-constructed pose drift prediction model; based on the error vector, a mechanical fine adjustment jack is driven to execute position and posture adjustment until the position and posture are converged, and then tooth groove precise meshing and mechanical locking are completed; according to the method, through the synergistic effect of the dynamic mapping of the digital twinborn model and the self-adaptive correction algorithm, high-precision dynamic correction of the multi-combination structure segment assembly process is achieved on the premise that an original mechanical connection structure is not changed.

The invention discloses a visual inertial positioning method based on dynamic target detection and semantic information constraint, and belongs to the field of motion estimation and dynamic environment processing. According to the method, a dynamic target detection mechanism is introduced, the dynamic target is effectively detected based on the target detection network, inertial navigation information and geometric constraints, the dynamic target is effectively recognized in the image processing process, the corresponding dynamic feature points are screened out, the mismatching rate of the dynamic features is remarkably reduced, and high-quality observation input is provided for back-end optimization. In the back-end sliding window optimization stage, a semantic information consistency constraint method is constructed, and the estimation stability of the system in a weak texture area or a repeated texture area is enhanced by utilizing the consistency of feature points in the same semantic area on a geometric structure. Visual inertia pose estimation is realized based on dynamic target detection and semantic information constraint, and high-robustness and high-precision pose estimation can still be realized in a complex environment with dynamic interference of pedestrians, vehicles and the like and severe scene change.

The invention discloses a self-adaptive dynamic SLAM method based on Gaussian distribution, and relates to the technical field of dynamic environments. According to the method, the dynamic environment positioning precision is improved, the system can accurately recognize and eliminate interference of dynamic objects through a dynamic target elimination strategy based on CRF in combination with YOLO detection and conditional random field optimization, and a traditional SLAM system is likely to be affected by moving objects in the dynamic environment to generate pose offset, so that the system is not easy to operate. Reprojection error evaluation and feature point weighting processing are introduced, differential modeling is carried out on static points and dynamic points, the dependence of pose estimation on static features is strengthened, estimation errors caused by dynamic interference are effectively reduced, the positioning precision and stability of the system in a complex dynamic scene are improved, and the positioning accuracy of the system in the complex dynamic scene is improved. Moreover, the consistency and integrity of the map are enhanced, and the mapping efficiency and scene adaptability are improved.

The invention relates to a defect positioning method based on fusion of weld defect features and trajectory tracking data, and belongs to the technical field of weld defect detection and positioning. The method comprises the following steps: capturing welding seam track dynamic data and defect feature data, constructing a dynamic coordinate system based on a welding seam initial feature point, and establishing double-data-set reference mapping; performing multi-physics field interference decoupling correction on the trajectory data, and performing cross-modal feature purification and core feature consistency verification on the defect data; converting the preprocessed data into a feature form adaptive to fusion, and constructing a welding process-defect formation mechanism association network to regulate and control fusion weight; and finally, reconstructing a three-dimensional dynamic contour of the welding seam, calling dynamic positioning logic to position the defect, and outputting a result carrying the process-defect causal confidence coefficient. The positioning precision is improved through multi-dimensional data fusion and mechanism association, and technical support is provided for welding quality management and control.

The invention relates to a point cloud registration method based on improved ISS-TOLDI features in combination with ICP, and belongs to the technical field of laser point cloud application. The method comprises the steps of obtaining a source point cloud and a target point cloud, and performing preprocessing; extracting feature points from the point cloud data by using an internal shape descriptor ISS algorithm; performing feature description on the extracted feature point set by using an improved TOLDI algorithm; performing coarse registration on the point cloud by using a sampling consistency initial registration algorithm; and performing fine registration on the coarsely registered point cloud data by using an iterative closest point ICP algorithm to complete point cloud registration. According to the method, the TOLDI algorithm is improved, so that the calculation complexity is reduced, the feature integrity is ensured, and the point cloud registration precision is higher.

The invention provides a deep learning-based die process die surface automatic supplement generation method and system, and relates to the technical field of deep learning, and the method comprises the steps: extracting the contour line and feature point information of a die surface as a training sample; feature learning is carried out based on a multi-scale image convolutional network and a double-flow network, and optimized local geometric features and global topological features are extracted; generating a supplementary modular surface through the generative adversarial network; and carrying out stress field constraint verification and then fusing with an original model. According to the method, intelligent supplementary generation of the mold surface is realized, and the mold design efficiency and quality are improved.

The invention provides a material data curve identification and fitting method based on deep learning, and relates to the technical field of material data extraction, and the method comprises the steps: obtaining an image file containing a plurality of material data curves; separating the material data curve from the image background by adopting an image segmentation technology, and extracting feature points of the material data curve through a convolutional neural network; and taking the feature points as control points, performing parametric fitting on the curve through polynomial or spline interpolation, and generating a smooth continuous curve. The method further comprises the following steps: detecting the position and boundary of a coordinate axis in the image file through an image processing technology; the OCR technology is used to identify the scale label on the coordinate axis, and the deep learning model is combined to analyze the numerical value and unit of the scale. According to the method, through innovative technologies such as multi-modal feature fusion, self-adaptive preprocessing and intelligent data labeling, precise recognition of complex curves, multi-curve separation and association, intelligent recognition of non-standard coordinate axes and efficient real-time processing are achieved.

The invention relates to the technical field of bolt length detection data processing, in particular to a bolt length detection system, which is characterized in that a three-dimensional data acquisition module generates a high-density point cloud and extracts a thread curvature extreme point, and a dynamic model matching module screens an optimal lead angle parameter based on a Hausdorff distance; and constructing an axial expansion model carrying phase offset gradient features. The projection mapping correction module adopts a genetic algorithm to optimize a compensation coefficient matrix, and generates a dynamic weight correction parameter to suppress geometric distortion; and the phase error correction module combines Fourier spectrum decomposition and an exponential decay weight coefficient to construct a frequency domain transfer function to eliminate an axial accumulative error. According to the method, the problems of projection distortion and phase deviation caused by the lead angle are effectively solved through geometric-phase double-domain correction, dynamic parameter iteration and an error collaborative suppression mechanism, and the spatial positioning precision of the feature points at the tail end of the thread is improved.

The invention discloses an FP-XFat feature device and a typical rescue scene-oriented three-dimensional reconstruction method and system, and relates to the technical field of three-dimensional reconstruction. The FP-XFat feature device is internally provided with a pre-training model, and the pre-training model comprises an improved PIOU loss function; the implementation mode of the improved PIOU loss function is as follows: 1) constructing a dynamically adjusted weight coefficient, reinforcing geometric constraints when the IoU is low, and weakening the constraints when the IoU is high; 2) introducing a shape difference metric item based on an arc tangent function, converting the aspect ratio into an angle dimension, and eliminating the influence of scale difference on shape judgment; and 3) adopting a gradient separation technology to block back propagation of an IoU gradient to a weight coefficient. The FP-XFat feature device introduces an improved PIOU loss function, and through an adaptive weight mechanism and a shape perception penalty term, the accuracy of feature point positioning can be significantly improved.

The invention discloses a dynamic scene robust vision SLAM (Simultaneous Localization and Mapping) method based on multi-feature collaborative optimization, which comprises the following steps of: acquiring an image sequence, carrying out dynamic target detection and segmentation through an instance segmentation network, generating a segmentation mask containing a dynamic region mark, and identifying and separating a dynamic object and a static background; removing feature points corresponding to the dynamic object based on the segmentation mask to obtain static feature points; carrying out pose estimation based on the static feature points, and for the key frame, carrying out feature matching with the previous key frame by minimizing a re-projection error, and solving to obtain the camera pose of each key frame; for non-key frames, performing camera pose tracking and data association on the previous frame by adopting an optical flow algorithm, and accumulating solving results to obtain pose tracks of all the non-key frames; the key frames and the non-key frames are subjected to differential processing by fusing feature matching and an optical flow algorithm, so that the calculation efficiency is remarkably improved while the positioning precision is ensured, and the real-time performance is improved.

The invention relates to the field of metal material nondestructive testing, and provides a non-ferromagnetic metal material surface profile reconstruction method based on a rotating alternating current electromagnetic field, which comprises the following steps of: determining a detection point array of a detection area, acquiring a vertical magnetic field signal z of the detection area by adopting a TMR magnetic sensing chip, and forming an initial Bz numerical matrix Bz0; performing bilateral filtering; obtaining a Bz numerical matrix Bz2 after morphological processing; obtaining a weighted Bz numerical matrix Bz3; a positive value matrix is formed, binarization processing is conducted on the positive value matrix, and a key numerical value matrix area Tkey containing crack related feature points is formed; calculating the center-of-mass coordinates of the disconnected independent areas, which are called nodes; on the basis of an improved Boruvka algorithm, new edge weight design is carried out on connecting edges between nodes, and a generated minimum spanning tree is effective representation of the surface profile of the bifurcated crack of the metal part.

In various examples, systems and methods are disclosed relating to generating each initial feature map of a plurality of initial feature maps based on a respective input image of an input dataset, each initial feature map, incorporating depth data of the respective input image, corresponds to a plurality of pixels of the respective input image, generating a sparse feature point cloud including a plurality of features determined using the plurality of initial feature maps, transforming the sparse feature point cloud into multi-resolution sparse grids, each of the multi-resolution sparse grids comprising a plurality of voxels, modeling, using a plurality of neural networks according to a hierarchal architecture, the multi-resolution sparse grids to construct a hierarchical volume representation, and providing constructed content based on the hierarchical volume representation.

In a dynamic environment, a visual SLAM (Simultaneous Localization and Mapping) system often causes the problems of large positioning error and inaccurate map construction due to dynamic target interference. In order to improve the robustness and precision of the system, the invention provides a dynamic scene SLAM optimization method based on improved YOLOv11 and geometric consistency constraint. Firstly, ORB features in a scene are extracted, and meanwhile a prior dynamic object and feature points on the prior dynamic object are removed through a YOLOv11 semantic segmentation model; secondly, eliminating feature points on the potential dynamic object by utilizing geometric consistency constraint, and recovering a background shielded by the dynamic object through a semantic perception Gaussian filter; and finally, selecting a high-quality key frame and applying the key frame to loopback detection and global optimization, constructing a basic Gaussian graph through a group of determined poses and point clouds, and finally fusing repair frame information to realize new view rendering and three-dimensional scene optimization.

The invention discloses a 6D pose estimation method and device based on online 3D Gaussian reconstruction and a medium, and the method comprises the steps: firstly obtaining the initial pose of a target object in an input RGB-D image through a feature point method, then optimizing the initial pose according to the depth corresponding to a matched pixel between two images, putting a new frame and the optimized pose into a local image, and carrying out the optimization of the initial pose. The method comprises the following steps that: firstly, a local image is obtained by a thread, the local image is matched with other frames by the other thread, the pose is further optimized, finally, the 3D Gaussian is initialized and optimized according to the image and the pose in the local image, and an obtained model provides new loss item auxiliary optimization for the previous pose optimization process in turn. The invention provides a method for solving the 6D pose estimation problem of any object, any model information related to the target object does not need to be provided during operation, and the universality of the 6D pose estimation method is improved.

The invention discloses a 3D GS cultural relic digital reconstruction method and system based on a block chain, and the method comprises the steps: collecting the RGB image data and depth perception data of a cultural relic, eliminating the influence of different shooting conditions through an illumination separation processing technology, and building a standardized image data set; recognizing a surface area suitable for reconstruction based on image analysis, determining feature point distribution by using kernel density estimation, and generating initial three-dimensional representation through Gaussian ellipsoid fitting; performing gradient calculation and feature extraction on the depth data, and combining with Gaussian representation to form a geometric constraint mechanism; self-adaptive encryption based on visual importance is executed for a sparse region, and a layered rendering effect is achieved through opacity parameter adjustment; the rendering characteristics and the conversion relation of different view angles are analyzed, key observation points are determined through stability analysis, and a smooth multi-view-angle display sequence is constructed; and integrating multi-view rendering information to generate volumetric representation, and completing right confirmation of the high-quality three-dimensional digital model through digital signature.

The invention discloses a visual SLAM method, device and equipment and a storage medium, and relates to the technical field of visual SLAM, and the method comprises the following steps: carrying out the target detection and feature point extraction of each frame of RGB image, and obtaining a plurality of targets and corresponding target detection frames; comparing the weighted values of the plurality of targets in the next frame of RGB image with a weighted threshold value, updating the initial dynamic levels of the plurality of targets according to a comparison result, and determining whether the plurality of targets are non-static targets or not according to an updating result; obtaining chi-square values of feature points contained in a target detection frame corresponding to the non-static target based on the multi-frame depth image, comparing the chi-square value of each feature point with a set threshold value, and rejecting the feature points smaller than the set threshold value to obtain static feature points; and constructing a three-dimensional map based on the static feature points. The method can adapt to the dynamic environment, and it is guaranteed that the constructed map is consistent with the actual dynamic environment.

The invention relates to a method for detecting the gradient of a plane and optimizing the machining precision, and the method comprises the following steps: (1), carrying out the preliminary path collection processing of the plane at the current position before the actual machining; (2) carrying out optimal feature point selection based on the acquired data set; (3) carrying out plane fitting processing based on the selected optimal feature point; (4) calculating a normal vector of the current position plane and a corresponding base vector based on the selected optimal feature point; (5) constructing a local coordinate system based on the current position, and calculating transformation between the local coordinate system and the workpiece global coordinate system; and (6) optimizing the machining path of the cutting head according to the local coordinate system. By adopting the method for detecting the inclination of the plane and optimizing the machining precision, the machining path is optimized according to the local coordinate system, so that the machining precision is effectively improved, and errors caused by inclination of the workpiece are reduced.

The invention relates to the technical field of ship detection, and discloses a ship lockage safety detection method and system, and the method comprises the steps: obtaining and analyzing ship image data in real time, and separating a ship target; key feature points representing the core form and structure of the ship target are identified, and a spatial distribution diagram containing the relative position relation of the key feature points is constructed; matching the spatial distribution map with a pre-stored standard ship three-dimensional model library, and acquiring passing information of the ship target according to a matched standard three-dimensional model; according to the method, lockage safety evaluation is carried out based on passing information, deep analysis is carried out on ship image data, key feature points are identified, a spatial distribution diagram is constructed, and then matching with a standard three-dimensional model is carried out, so that accurate ship passing information is obtained; the problem of size and position information deviation caused by inaccurate hull contour extraction in the prior art is effectively solved, and the reliability of lockage safety assessment is remarkably improved.

The present invention discloses an intelligent detection method for determining forming defects by the offset of the blank contour of a stamping part, belonging to the field of sheet metal forming quality detection. In the present invention, the offset amounts of the contour feature points of the blank in the defective state and the defect-free state are obtained through finite element simulation of the stamping process. After manually annotating the defect types, a training data set and a validation data set are obtained, and based on this, a BP neural network model is constructed and trained to obtain a defect detection model. At the stamping site, a laser rangefinder is used to measure the offset amount of the actual stamping blank contour feature points along the material inflow direction, and the offset amount is input into the BP neural network defect detection model to output the defect judgment result. The method of the present invention greatly alleviates the current manual dependence on stamping forming quality detection and has the advantages of high detection efficiency, high accuracy, and low cost.

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 invention discloses a flow field key structure detection and feature point extraction method based on an optimized YOLO model, and the method comprises the steps: S1, obtaining flow field time sequence schlieren images of an air-breathing aircraft under different working conditions through a high-speed schlieren collection system in a wind tunnel test; s2, preprocessing the flow field time sequence schlieren image to obtain a data set corresponding to the strong shock wave of the isolation section of the air-breathing aircraft, and dividing the data set into a training set, a verification set and a test set in proportion; s3, constructing an optimized YOLO target detection model, and obtaining an optimal weight model after iterative training and verification; and S4, adopting the optimal weight model to complete target detection and feature point extraction of the test set or the schlieren image to be detected. According to the method, the target area is focused through area cutting, meanwhile, a targeted data enhancement strategy of Gaussian noise, brightness adjustment, contrast ratio adjustment and saturation is designed, a high-quality annotation data set is constructed, and the model detection precision is effectively improved.

This invention discloses a method and system for numerically defining a rebar-concrete interface element under both monotonic and cyclic loading, comprising: first, establishing a finite element model of reinforced concrete component, and generating a solver input file within the finite element software; then, adding a series of user-defined interface elements in the solver input file; then, inputting parameters of characteristic points of the axial bond-slip curve between rebar and concrete, the diameter of rebar, and the characteristic parameters of fibers associated with the user-defined element (UEL) in the solver input file; then, setting the element stiffness matrix, the coordinate transformation matrix and residual in the UEL subroutine; finally, submitting the solver input file in the finite element software, and calling the UEL subroutine for calculation.

The invention relates to a bridge structure linear change detection method based on a three-dimensional laser point cloud. The method comprises the following steps: 1) cutting a component section point cloud; 2) generating a section template library; 3) matching a section template; 4) fitting accurate key points; and 5) detecting the variable quantity of the characteristic line. According to the parameterized section template library generation method based on the large language model, dependence of design prior information is reduced, the posture size of a component is assisted in positioning in advance, meanwhile, template geometric information is considered, accurate feature points are positioned and screened out, point cloud quality is dynamically adapted, and the requirement that in the non-ideal environment of point cloud scanning in a project, the precision of the component is greatly improved can be met. The method solves the problem of local position missing of the point cloud, provides a general solution thought for automatic detection of the bridge line shape, and can be applied to bridge line shape detection in other bridge scenes.

The invention discloses a packaging box folding path planning method based on an intelligent optimization algorithm, and the method comprises the steps: collecting the unfolding state data and folding target state data of a packaging box, building a mathematical mapping model between an unfolding state and a folding target state through feature point marks, determining a state transition constraint condition in a folding process, and carrying out the folding of the packaging box. On the basis of the model, an optimization function with the shortest folding path length, the minimum number of structural interference times and the optimal action sequence as targets is constructed; the optimization function is solved through an intelligent optimization algorithm, an optimal folding path is iteratively generated, simulation analysis is conducted on the path, structural interference possibly existing in the folding process is recognized and corrected, the corrected path is imported into automatic execution equipment, and automatic folding of the packaging box is completed through driving equipment; according to the method, the problems of insufficient path planning precision and low automation degree in the prior art are effectively solved, and the intelligence and production efficiency of packaging box folding operation are improved.

The invention provides an ore recognition method and system based on a large model algorithm, and is applied to the technical field of computer vision and data processing, and the method comprises the steps: obtaining an ore microscopic image, and generating a feature point location candidate set through a convolutional neural network model; calculating a spectral value and a morphological parameter of each feature point location to determine a multi-dimensional data point; clustering the multi-dimensional data points to obtain a feature point cluster; and when the spatial distance between the adjacent feature point position clusters is smaller than a first preset threshold value and the spectral similarity is higher than a second preset threshold value, combining the adjacent feature point position clusters into the same mineral region. The segmentation precision and the type identification accuracy of the complex symbiotic mineral area can be obviously improved.

The invention discloses a non-standard mould base batch forming method and system, and relates to the technical field of mould base forming, and the method comprises the following steps: firstly, clamping a plurality of non-standard mould base workpieces to be machined on a machining platform of machining equipment, constructing a workpiece coordinate system according to a workpiece design reference, and determining a machining center coordinate system according to an equipment fixed reference point; then acquiring a reference angle and coordinate system parameters when the workpiece is initially clamped, measuring coordinate data of at least three feature points in a measurement coordinate system, and calculating a conversion matrix of an actual coordinate system relative to a machining center coordinate system according to the coordinate data; identifying features of to-be-processed parts of workpieces, selecting an adaptive processing strategy from a standard database, generating an actual processing path in combination with a conversion matrix, gathering the same tool processing parts according to the processing strategy, generating a tool path instruction in combination with cutting parameters, and processing a plurality of workpieces in batches according to the path and the instruction, so that accumulation of coordinate conversion errors is effectively avoided; therefore, the position precision of the to-be-machined part is ensured and the rejection rate is reduced.

The invention discloses a semantic vision SLAM (Simultaneous Localization and Mapping) method and a semantic vision SLAM system for an indoor low-texture and dynamic environment, which are characterized in that a visual odometer, a dynamic point elimination algorithm, a loopback detection algorithm and the like are integrated into a visual SLAM system, so that data in an indoor low-texture and dynamic scene are processed in real time, and high-precision positioning and high-quality map construction are provided. A semantic information acquisition thread and a dynamic point elimination module are added to the whole system, and a loopback detection module is improved. According to the method, the problems of poor positioning precision, low robustness, poor map quality and the like of a visual SLAM system in a low-texture environment and a dynamic environment in the prior art are solved, and the feature point extraction and matching precision in the low-texture environment is improved; the interference of a dynamic object on the SLAM system is eliminated, and the positioning precision and the loopback detection performance are improved; the map construction quality is improved, and the semantic map with high readability is constructed by fusing semantic information.

The invention discloses a method for identifying interference signals of weld defects under different lifts-off conditions, which comprises the following steps of: S1, de-trending processing: carrying out de-trending processing on original detection signals; s2, Gaussian wavelet transform: carrying out Gaussian wavelet transform on the detrended signal; s3, optimal wavelet basis selection: by calculating correlation coefficients or energy ratios of different wavelet basis and defect signals, selecting the wavelet basis with the highest matching degree for reconstruction; s4, envelope processing: extracting a signal envelope based on Hilbert transform; s5, mean filtering: applying sliding window mean filtering to the envelope signal; and S6, threshold processing: setting a self-adaptive threshold screening signal, and retaining the feature points of which the amplitudes exceed the threshold. According to the method, de-trending and Gaussian wavelet transform are used for de-noising enhancement. According to the method, wavelet functions of different orders are constructed and matched with defects, secondary signal enhancement is carried out by selecting a filtering method, finally, threshold stripping interference is calculated, reliable defect identification is carried out, and technical support is provided for uneven welding seam quality monitoring.

The invention discloses a feature point detection method based on edge saliency and scale sensitivity. The method comprises the following steps of performing Gaussian smooth denoising on an input image; calculating the edge saliency of the pixel points through a Laplace operator; calculating a texture change degree based on the local gradient magnitude; generating a scale sensitivity weighted value through multi-scale analysis; candidate points are screened in combination with edge saliency, texture weighting and scale weighted values, and mismatching points are removed through non-maximum suppression; and finally, key points are enhanced and marked. According to the method, the edge structure, the texture information and the multi-scale features are fused, the robustness and accuracy of feature point detection are improved, and the method is particularly suitable for image matching and target recognition tasks in complex scenes.

The invention discloses a self-adaptive feature point compensation method for visual SLAM in a dynamic scene, and the method comprises the steps: firstly obtaining a dynamic environment image, recognizing a dynamic target in the image through a YOLO11s target detection model, and obtaining the type of a target detection frame and the coordinate information of the target detection frame; then, a dynamic confidence scoring criterion is constructed based on target prior motion characteristics, high-dynamic object detection frame categories are distinguished, and interference feature points in the high-dynamic object detection frames are accurately eliminated; secondly, designing an adaptive threshold by combining the space coverage rate of a dynamic detection frame and the time distribution characteristic of the number of historical feature points, and dynamically compensating the number of the feature points; and finally, when the number of the feature points is lower than an adaptive threshold, delimiting a compensation area by taking the edge of the high-dynamic detection frame as a reference, compensating the ORB feature points, and ensuring that the number of the feature points is stable. By adopting the technical scheme of the invention, the robustness of the number of feature points and the positioning precision in a dynamic scene are remarkably improved while the real-time performance is ensured.