592 results about "Point pair" patented technology

The invention relates to an image processing technology, and discloses an efficient panoramic image splicing method and system based on multi-view fusion, and the method comprises the steps: collecting a plurality of images from different views; performing multi-scale feature extraction on each image, generating a feature descriptor for each extracted feature point, and matching a corresponding feature point pair; performing multi-view geometric constraint screening on the feature point pairs; according to the screened feature point pairs, estimating a homography matrix between adjacent images, and carrying out global optimization on the homography matrix; aligning all the images into the same coordinate system; determining an overlapping region between adjacent images; and according to the pixel information in the overlapping areas, fusing the overlapping areas by adopting a self-adaptive weighted fusion algorithm so as to splice the plurality of images into a panoramic image. The invention further discloses a control device and a computer readable storage medium. The invention aims to improve the efficiency and accuracy of generating the multi-view fused panoramic image.

The invention discloses a cross-source data three-dimensional reconstruction method and system based on improved Gaussian sputtering, and the method comprises the steps: collecting an unmanned plane inclined image and a ground panoramic image of a target region, and constructing a time-space correlation data set; based on multi-view geometric constraints, space-time coding matching point pairs are established through an adaptive feature pyramid, intelligent incremental cross-source data sparse reconstruction is carried out, and point cloud and camera parameters are output; adopting improved Gaussian sputtering, compressing a three-dimensional Gaussian kernel into a two-dimensional Gaussian primitive through double tangent vector constraint, and fitting surface geometry to realize multi-scale reconstruction; and optimizing primitive parameters by using a differentiatable renderer, completing multi-scale fine reconstruction through gradient back propagation, and generating a high-precision three-dimensional model. According to the method, multi-scale accurate geometric prior input and accurate camera poses are provided for three-dimensional reconstruction, the dependence on professional manual operation in a traditional three-dimensional reconstruction method is greatly reduced, and meanwhile, the geometric accuracy and visual fidelity of a reconstruction result are remarkably improved.

The invention discloses an image feature matching optimization method based on intra-class space consistency in the technical field of computer vision and image processing. The method comprises the following steps: feature point extraction and preliminary matching: extracting feature points from a query image and a reference image and performing preliminary matching; initialization and transformation model estimation: initializing a matching point set and a residual error, and calculating an initial transformation model; error calculation and matching point set updating: calculating the error of the matching point pair, and updating the matching point set by adopting a dynamic screening method; performing residual optimization: judging whether the optimal condition is reached or not based on the residual, and deciding whether to continue iteration or not; performing intra-class space consistency clustering and isolated cluster elimination: performing clustering analysis after the optimal residual error is obtained, and eliminating isolated clusters based on an intra-class space consistency separation ion structure; and outputting a result: outputting a matching point set after the isolated clusters are removed. The method solves the problem that a traditional feature matching method is difficult to completely remove mismatching in a complex scene and is sensitive to noise.

Provided are a multimodal image matching method and system, terminal device, and storage medium. The method includes: performing self-supervised feature extraction on an optical image and a synthetic aperture radar (SAR) image to obtain a repetitive feature point between the optical image and the SAR image; segmenting the optical image and the SAR image into a first image block sequence based on the repetitive feature point, and performing feature extraction on the first image block sequence through a dual-branch network to obtain feature description vectors of the optical image and the SAR image respectively, where the dual-branch network includes a first branch network for extracting a global feature and a second branch network for extracting a local feature; and performing feature matching on the optical image and the SAR image based on the feature description vectors to obtain a matching point pair between the optical image and the SAR image.

The invention relates to the technical field of image processing, and provides an OCT (Optical Coherence Tomography) image splicing method, which comprises the following steps: acquiring image pairs and matching point pairs corresponding to the image pairs, the image pairs being adjacent OCT images; calculating a homography matrix based on the matching point pairs so as to align the image pairs, and outputting preliminary aligned image pairs; constructing a fusion weight map, wherein the fusion weight map comprises a weight value of a pixel position and a region type; and according to the weight value and the region type, carrying out fusion splicing on the pixel positions so as to output a panoramic OCT image. According to the method, partition fusion is achieved by fusing the weight map, the problems of tissue dislocation and artifacts in OCT splicing are solved, and then the problem that the splicing effect of OCT image splicing is poor is solved.

The invention provides a coal mine underground point cloud accurate registration method based on dynamic weight combination, and relates to the technical field of point cloud registration, and the method comprises the steps: constructing an initial point pair set; calculating a point pair distance weight by adopting a Gaussian kernel function; calculating the shape feature weight of the point pair based on the BSC descriptor; based on the point pair distance weight and the point pair shape feature weight, constructing a point pair reliability weight dynamic combination model, and screening high-reliability point pairs from the initial point pair set; and on the basis of the screened high-reliability point pairs, optimal rigid body transformation is calculated, and point cloud accurate registration is realized. According to the invention, based on the double-weight dynamic combination model of the Gaussian kernel function and the BSC descriptor, the reliability of the point pair is accurately evaluated, and the influence of noise and local feature degradation is effectively inhibited. The adaptive weight adjustment strategy dynamically adjusts the distance weight and the shape feature weight in different registration stages, and optimizes the registration process. And an efficient point pair screening and optimization mechanism is combined with KD-tree accelerated search and Gaussian Newton method optimization, so that the registration speed and precision are improved.

The invention discloses a pose estimation method and device based on point pair feature matching, a medium and a product. The method comprises the following steps: performing coarse registration on a fusion scene point cloud and each template point cloud, and calculating point pair features; according to the corresponding hash key values, retrieving from each hash table to establish registration point pairs; calculating a translation vector and a rotation quaternion matched with each registration point pair according to the reference point coordinate system and the template coordinate system, and performing clustering processing on each registration point pair to obtain each initial corresponding set; screening out a target pose candidate transformation matrix and a matched target template point cloud from the pose candidate transformation matrixes matched with the initial corresponding sets respectively; a target optimization function between the fusion scene point cloud and the target template point cloud is constructed, iterative optimization is performed on parameters in the target pose candidate transformation matrix, and the target pose of the fusion scene point cloud is obtained at the end of iteration, so that the precision, real-time performance and reliability of six-dimensional pose estimation are improved.

The invention discloses a three-dimensional point cloud registration method and system based on multi-feature dynamic weighting. The method comprises the following steps: firstly, acquiring a source point cloud and a target point cloud to be registered and performing down-sampling; secondly, extracting geometric features of the source point cloud and the target point cloud, including curvatures and normal angles of all points; extracting a target point from the target point cloud, performing point pair matching by using a multi-constraint weighted scoring mechanism and a dynamic weight optimization strategy, and taking a point pair with a minimum dynamic weighted difference function value as a matching point pair; screening the matching point pairs according to the normal angle difference threshold value and the curvature difference threshold value, if the normal angle difference score is smaller than or equal to the normal angle difference threshold value and the curvature difference score is smaller than or equal to the curvature difference threshold value, reserving the matching point pairs, and otherwise, rejecting the matching point pairs; and finally, solving a rotation matrix and a translation vector according to the reserved matching point pairs to realize point cloud registration. The problems that a traditional ICP algorithm is sensitive to the initial attitude of the point cloud, the anti-noise capability is weak, and feature utilization is insufficient are solved.

The invention discloses a registration method and system for an optical image and an SAR image of the moon, and relates to the technical field of image registration, and the method comprises the steps: firstly determining the same typical moon appearance object in an image to be registered, and enabling the corresponding optical and SAR region images to form a region image pair; performing feature extraction and matching on each regional image pair by using a feature-based registration algorithm to obtain a preliminary matching point pair, and screening out a high-confidence matching point pair; and calculating a region affine transformation matrix of each region image pair based on the high-confidence matching point pairs, constructing a global optimization objective function according to all region affine transformation matrixes, solving an optimal affine transformation matrix, and finally realizing accurate registration of the to-be-registered image.

The invention relates to an image registration method and a real-time tracking method, and the method comprises the steps: dividing a reference frame and a subsequent frame into a plurality of regions according to the same rule, carrying out the registration of the reference frame and the subsequent frame, and obtaining a region combination (a reference frame region and a subsequent frame region) where each pair of registration points are located; screening out matching point pairs according to the distribution condition of the registration point pairs in all region combinations; and calculating a transformation matrix according to the matching point pairs. As the change of the images between the continuous frames is not large, the method calculates the transformation matrix for registration by using the most representative local area, can avoid interference information brought by the influence of the environment and noise on the partial area in the image, can ensure that the screened matching point pair for registration has high reliability, and can improve the registration accuracy. In addition, calculation is simple and convenient, the rapid processing requirement of real-time registration can be met, and the method has wide application scenes and value.

The invention relates to a multi-modal image matching method and system based on learning features and epipolar geometric constraints. The method comprises the following steps: carrying out edge enhancement processing on an input image through wavelet transform; extracting a multi-scale dense feature map based on the transformed convolutional neural network, and generating a feature descriptor with rotation and scale invariance in combination with principal direction normalization; adopting an FLANN algorithm and dynamic distance constraint to realize preliminary feature matching; and introducing a basic matrix construction and epipolar geometric consistency verification mechanism, and eliminating mismatching point pairs in combination with an RANSAC affine constraint model. According to the method, image enhancement, deep learning and geometric verification strategies are fused, the problems of radiation nonlinearity and geometric distortion caused by imaging mechanism differences among multi-modal images are effectively solved, the matching precision and robustness are improved, and the method is suitable for remote sensing application scenes such as optical-SAR registration, multi-source image fusion and earth surface change detection.

The invention discloses a camera offset detection and dynamic labeling method based on feature point matching. The method comprises the following steps: constructing a target object feature point data model; acquiring a camera monitoring picture image in real time, performing feature point extraction on the picture and the video frame of the target object, and performing matching with the target object feature point data model by using a feature point matching algorithm to obtain a matched feature point pair; calculating the offset of the camera according to the matched feature point pairs, including the translation amount and the rotation amount; correcting the video frame according to the offset of the camera, and identifying and positioning a target object in the corrected video frame; and generating a corresponding label according to the identification and positioning result of the target object, and dynamically displaying the label in the video frame. The method can effectively solve the problems of poor real-time performance, low accuracy, poor robustness and the like in the prior art, and has a wide application prospect.

The invention discloses a satellite angular rate identification method and related equipment, and relates to the technical field of rocket launching, and the method comprises the steps: obtaining a rocket telemetry video frame sequence after satellite-rocket separation; for each frame of image of the rocket telemetry video frame sequence, determining a dynamic image area of the target satellite based on the static area mask; based on a feature detection algorithm, binary descriptors are extracted from dynamic image areas of two adjacent frames of images; based on the binary descriptor, matching corresponding feature point pairs between two adjacent frames of images; estimating a rotation matrix between two adjacent frames of images through an iterative optimization algorithm based on the corresponding feature point pairs; and determining the angular rate of the target satellite according to the rotation matrix and the time interval between the two adjacent frames of images. According to the method, rapid and high-precision identification of the satellite angular rate is realized, the problem that the traditional technology depends on indirect data and is high in delay is solved, task success or failure can be judged in real time, and the launching task evaluation efficiency is improved.

Disclosed in the present invention are a multi-modal image matching method and system, and a terminal device and a storage medium. The method comprises: performing self-supervised feature extraction on an optical image and an SAR image, so as to obtain repeatable feature points between the optical image and the SAR image; on the basis of the repeatable feature points, segmenting the optical image and the SAR image into a first image block sequence, and performing feature extraction on the first image block sequence by means of a double-branch network, so as to respectively obtain a feature description vector of the optical image and a feature description vector of the SAR image, wherein the double-branch network comprises a first branch network used for extracting global features and a second branch network used for extracting local features; and on the basis of the feature description vectors, performing feature matching on the optical image and the SAR image, so as to obtain matching point pairs between the optical image and the SAR image. In the present invention, a two-stage training method is used, in which method training is performed first so as to obtain robust SAR-optical image feature description vectors, and learning is then performed in view of the feature description vectors so as to obtain feature points having high matching performance.

The invention relates to the technical field of remote sensing surveying and mapping, in particular to a multi-frame wide-area InSAR deformation result fusion method, which aims at the splicing problem of multi-frame InSAR deformation under a large spatial scale, performs optimization on the basis of a double-layer triangulation network and a local extraction method, and performs splicing according to dot-shaped and planar deformation storage characteristics. LOS deformation of each frame of image is converted into a vertical direction; and then on the basis of the double-layer triangulation network model, a genetic algorithm and PSO optimization are used as an improved algorithm for dotted deformation data homonymy point pairing to solve the many-for-one condition during registration. And under the condition of ensuring that the space position offset of the overlapping area is small, the offset of adjacent planar data is calculated by utilizing an optimized local extraction method, so that the deviation of adjacent deformation results in a large range is solved. And finally, generating a wide-area InSAR deformation product with consistent space reference and high availability based on two optimization algorithms.

The invention provides a panoramic image intelligent splicing method based on context semantics. The method comprises the following steps: firstly, obtaining and preprocessing a sequence image; extracting a depth feature map of the image by using a pre-trained depth network; performing semantic-guided feature matching and image alignment based on the feature map; an optimal suture line is generated according to the semantic region boundary, and fusion is carried out by adopting a semantic weighted multi-band fusion algorithm; and finally, detecting and repairing the semantic inconsistent region to ensure the semantic coherence of the panoramic image. According to the method, matching is carried out by using the high-dimensional feature tensor rich in semantic information extracted by the deep neural network, the problem of feature matching ambiguity caused by repeated textures and weak texture regions such as sky or white walls is solved, the semantic information is used as a strong constraint, regions with similar appearances but different semantics can be effectively distinguished, and the accuracy of feature matching is improved. Therefore, correct matching point pairs with consistent semantics are screened out from massive candidates, and the matching accuracy is greatly improved.

The invention relates to a pathological section splicing method, system and device and a storage medium, and the method comprises the steps: collecting a pathological section image, converting the pathological section image into a plurality of modes, and carrying out the histogram equalization and denoising processing; calculating an entropy value of each pixel point region in the image, and extracting pixel points with high entropy values as feature points; carrying out preliminary matching on the image under different scales, and carrying out bidirectional matching operation in a matching region to obtain a feature point pair; constructing a deformation model according to the feature point pairs, calculating parameters of the deformation model, and enabling the to-be-registered image to deform based on the deformation model through minimizing deformation energy; gradient information of an image overlapping region is calculated, a weight is allocated to each pixel point in the overlapping region according to a weight allocation rule, and the edge of the image overlapping region is processed to obtain a spliced image; and carrying out quantitative evaluation on the spliced image. According to the method, the problems of low registration precision, large calculation amount and poor adaptability to complex backgrounds in the prior art can be solved.

The invention relates to the technical field of multi-view three-dimensional reconstruction, and discloses a multi-view three-dimensional reconstruction method, device and equipment and a storage medium, and the method comprises the steps: obtaining a multi-view image, generating a diffusion feature map for the multi-view image through a diffusion model, carrying out the feature matching according to the diffusion feature map, and generating a matching point pair; calculating the pose of a camera through a Colmap algorithm according to the matching point pairs, extracting the deep features of the multi-view image by using a convolutional neural network to obtain a deep feature map, projecting the deep feature map to a three-dimensional space according to the pose of the camera to generate a preliminary three-dimensional feature map, and extracting the preliminary three-dimensional feature map according to the preliminary three-dimensional feature map. Fusing the preliminary three-dimensional feature map by adopting a U-Net network model to obtain a fused three-dimensional feature map, generating a three-dimensional voxel grid according to the fused three-dimensional feature map, generating a three-dimensional model according to the three-dimensional voxel grid, and finally outputting a globally optimal solution to improve generalization ability and precision.

The invention discloses an underwater target identification and positioning method based on fusion of a physical model and deep learning, and belongs to the technical field of computer vision, and the method comprises the steps: carrying out the transmissivity estimation and physical restoration of a collected underwater monocular image according to an underwater light propagation model, and carrying out the image correction; key feature points are extracted, and high-quality matching point pairs are screened in combination with the joint similarity; further deriving a basic matrix through the high-quality matching point pairs meeting the epipolar geometric constraint relation, solving an essential matrix, and obtaining relative attitude parameters between the cameras in combination with weighted re-projection-LM optimization; and finally, refraction correction triangulation is carried out according to the Snell's law, pixel-level fusion is carried out after scale normalization and space alignment are carried out on the refraction correction triangulation and dense depth output by the MiDaS, three-dimensional space coordinates of the target are inverted, and high-precision recognition and positioning of the underwater target are achieved. The system is light in structure, efficient in calculation, suitable for being integrated on various underwater autonomous or remote control robot platforms and used for tasks such as target recognition, tracking and positioning.

The embodiment of the invention provides a three-dimensional space change detection method and device, equipment and a storage medium, and relates to the field of digital image processing. The method comprises the following steps: acquiring image sequences of the same area shot by the unmanned aerial vehicle in two periods; for each image sequence, performing point cloud reconstruction on the region based on the image sequence to obtain the group of point cloud data; point cloud registration is carried out according to the similarity between the feature descriptors of all the points in the two sets of point cloud data, a matching point pair set comprising a plurality of matching point pairs is obtained, and two points in each matching point pair belong to two pieces of point cloud data and are closest to each other; and for each matching point pair, geometric information of two points in the matching point pair in respective point cloud data is acquired, and if it is determined that the difference between the two pieces of geometric information meets a preset difference condition, it is determined that the corresponding position of the matching point pair in the region is changed. According to the embodiment of the invention, the problem of insufficient spatial change detection precision in a complex scene is solved.

The application is suitable for the field of visual measurement, and provides a target, an information detection method and device, a terminal and a storage medium, wherein the method comprises: controlling a camera to take a picture of the target to obtain a first imaging image; based on the first imaging image, extracting a first pixel point pair with the farthest relative distance on the inner circumference of a circular ring, a second pixel point pair with the farthest relative distance on the outer circumference of the circular ring, a center pixel point of three circles and a vertex pixel point of a first triangle; based on the first pixel point pair and the second pixel point pair, determining a first straight line where the long diameter of the circular ring in the first imaging image is located; based on the center pixel point and the vertex pixel point, determining a second straight line where the bottom side of the first triangle in the first imaging image is located; and determining the intersection of the first straight line and the second straight line as the imaging center point of the target in the first imaging image. The scheme can improve the image data processing efficiency and the practicability of the target.

The embodiment of the invention provides an underwater concrete scene three-dimensional reconstruction method, system and device and a storage medium, and relates to the technical field of underwater three-dimensional reconstruction, and the method comprises the steps: extracting feature points in each key frame image from a plurality of key frame images of video data, and obtaining feature matching point pairs; calculating relative poses of adjacent key frame images and three-dimensional coordinates of the matched feature points according to the pixel coordinates of the same matched feature point; calculating a pixel depth value of each pixel point according to the relative pose; and performing multi-view fusion according to the relative pose, the three-dimensional coordinates of the matched feature points and the pixel depth value of each pixel point, and generating a three-dimensional grid model of the underwater concrete scene. According to the method, the feature points in the enhanced video image are obtained, the moving distance and the translation vector of the shooting camera are calculated, and three-dimensional reconstruction is carried out in combination with the pixel depth value, so that the image acquisition quality is improved, point cloud data are saved, and the precision and efficiency of three-dimensional reconstruction of a large-range underwater scene are improved.

The invention discloses a rapid positioning method for a scanning electron microscope. The rapid positioning method comprises the following steps: acquiring a reference image and a target image of a sample; decomposing the reference image and the target image into a plurality of areas, and respectively extracting target points of each area in the reference image and the target image; matching the target point pairs in the same area of the reference image and the target image, calculating the deformation tensor of the area where the target point is located in the target image, and distinguishing the deformation type of each area in the target image; according to the local deformation type, affine or thin-plate spline transformation models are dynamically allocated to a plurality of areas of the target image; splicing the plurality of sub-models to obtain a complete three-dimensional model of the sample; based on the obtained three-dimensional model, mapping coordinates of a target point in real time; according to the method, the accurate sample three-dimensional model is obtained by using the transformation model according to the deformation tensors of different areas of the target image, so that high-precision reconstruction of the target point at the position of the warped edge curved surface is realized, and the positioning precision of the target point in the three-dimensional space is improved.

The invention discloses a weighted iteration PnP pose resolving method based on mismatching point pair elimination. The method comprises the following steps: firstly, solving to obtain an initial pose of a camera through a PnP method according to known m groups of 2D-3D point pairs, then calculating a re-projection error of each group of point pairs according to the initial pose, and calculating respective weight and elimination threshold according to corresponding re-projection error values; and finally, updating the resolving weight of the point pair and the elimination threshold value of the mismatching point pair, and continuously iterating until the conditions are met. According to the method, the self-adaptive updating of the point pair weight and the mismatching point pair rejection threshold value can be realized through the re-projection error, the pose resolving precision of the camera is greatly improved, and the resolving speed is improved to a certain extent. The method is suitable for obtaining the camera pose information through the feature point two-dimensional pixel coordinates obtained through image matching and the corresponding world three-dimensional coordinates.

The invention provides an abnormal region screening method and device, equipment, a medium and a program product, contour point extraction processing is performed on a to-be-selected image to obtain a plurality of contour points, a target point pair corresponding to the contour points is determined based on the contour points, and the target point pair comprises a first point and a second point. The first point and the second point are located on the two sides of the partial contour where the contour point is located respectively, and the first gray level of the first point and the second gray level of the second point are subjected to gray level comparison to obtain a first comparison result of the target point pair. As the gray scale difference between the real abnormal area and the periphery of the real abnormal area is larger, and the gray scale difference between the false abnormal area and the periphery of the false abnormal area is smaller, when the first comparison results meeting the preset number all indicate that the difference between the first gray scale and the second gray scale reaches the preset difference, the false abnormal area is judged. The gray scale differences between the inner sides and the outer sides of most contour points are large enough, the to-be-selected area is accurately indicated to be the abnormal area, false detection of a false abnormal area as the abnormal area is avoided, and the accuracy of abnormal area screening is improved.

The invention discloses an intelligent robot vision SLAM method suitable for a lawn dynamic scene. The method comprises the following steps: extracting global feature points of an image; performing point pair information matching on the feature points of the two adjacent frames of images; performing dynamic feature point detection in combination with target detection and epipolar geometry, and filtering potential dynamic feature points included in global feature points; a visual odometer part of the SLAM system is designed into a double-thread parallel method, and information interaction of the two threads in the same host is realized through sockets. The navigation performance of the robot in a complex and dynamic environment is improved, and it is ensured that the robot can accurately understand and respond to changes of the surrounding environment.

The invention is applicable to the technical field of 3D printing, and provides a 3D printing adaptive support algorithm, which comprises the following steps of: performing vertex clustering simplification and Laplacian smooth denoising on an OFF format three-dimensional model, reducing the number of vertexes and patches by utilizing space division and centroid substitution, and calculating and optimizing surface quality in combination with an iterative mean value; recognizing a suspension surface and clustering adjacent surfaces through normal vector analysis and breadth-first search, and performing three-dimensional mapping and duplicate removal processing after generating supporting points; selecting an optimal supporting point pair by adopting a greedy algorithm, calculating lower-layer intersection points to form a tree-shaped skeleton, and avoiding collision by combining an AABB tree and a ray detection technology; a supporting cone, a cylinder and a base are generated, and a complete supporting structure is formed. According to the algorithm, the stability and the printing efficiency of the supporting structure are remarkably improved through multi-stage optimization, and an innovative solution is provided for efficient 3D printing of a complex model.

The disclosure provides a method of relocalization, including: in response to a determination that a current image frame satisfies a relocalization condition, acquiring feature points of the current image frame and descriptors of the feature points; performing, based on the feature points of the current image frame and the descriptor of each feature point, feature matching on the current image frame and each stored key frame respectively to obtain feature point pairs after matching the current image frame with each key frame respectively; determining a matching degree of the current image frame and each key frame respectively based on the feature point pairs; determining a key frame with the highest matching degree with the current image frame as a target key frame; and replacing a camera pose corresponding to the current image frame with a camera pose corresponding to the target key frame.

The invention relates to the technical field of internet big data, in particular to an image registration method for extracting image deep feature information based on deep learning, which comprises the following steps: S1, acquiring a first source image and a second source image to be registered; s2, inputting the first source image and the second source image into a trained deep feature extraction model, and outputting a feature description graph; s3, performing feature point pair matching on the feature description graphs of the first source image and the second source image through similarity calculation; s4, generating a homography transformation matrix as a spatial mapping relation between the first source image and the second source image based on the successfully matched feature point pairs; and S5, registering the first source image and the second source image based on the spatial mapping relation to obtain a registered image. According to the method, the deep features of the image are extracted and optimized through the deep learning model, and meanwhile, the homography transformation matrix is generated based on the successfully matched feature point pairs to complete image registration.

The invention relates to the technical field of three-dimensional map construction, in particular to a three-dimensional map reconstruction method and device. The method comprises the following steps: acquiring a plurality of image data and performing feature extraction to generate initial sub-maps, performing local sparse reconstruction on the plurality of initial sub-maps with overlapped visual angles to generate a plurality of sub-maps, performing global feature extraction on the sub-maps to generate a global feature database, and determining similar image pairs according to the similarity of the sub-maps. According to the method, local feature matching is carried out on similar image pairs, corresponding feature points are determined, and for the 3D-3D corresponding relation, namely when corresponding 3D feature points exist in the two sub-maps, the system adopts a rigid body transformation solving algorithm, and the relative pose between the two sub-maps can be accurately calculated by minimizing the distance between the 3D feature point pairs.