35 results about "Essential matrix" patented technology

The invention discloses a projection single attitude calibration method, system and device and a medium, and relates to the technical field of computer vision and projection interaction.The method comprises the steps that a plurality of calibration patterns are generated and projected to a calibration plane, gray level images are collected, corner point detection and sub-pixel refinement processing are carried out, and a projection single attitude calibration result is obtained. The method comprises the following steps: acquiring an angular point corresponding relation between a projector image and a camera image, carrying out iterative solution on a projection matrix of a projector and a projection matrix of a camera, separating an internal parameter matrix and an external parameter matrix, calculating a basic matrix and an essential matrix, and decomposing to obtain a rotation matrix and a translation vector of the camera relative to the projector; camera imaging distortion is estimated, a distortion correction coefficient is obtained, and distortion correction is carried out; and counting a re-projection error generated in the correction process, and adjusting parameters of the projector and the camera until the error meets a preset precision requirement. Projector parameter calculation is completed through projection single posture calibration, posture adjustment is not needed in the calibration process, the automation degree is improved, and meanwhile manual intervention is reduced.

A camera misalignment detection system detecting a misalignment condition of one or more cameras for a vehicle includes one or more controllers that determine a set of matched pixel pairs, determine a feature matching ratio based on the set of matched pixel pairs, and calculate an alignment angle difference of the one or more cameras. In response to determining the feature matching ratio, the essential matrix inlier ratio, and the alignment angle difference each exceed respective threshold values, the controllers add the alignment angle difference to a queue including a sequence of historical alignment angle difference values. The controllers perform statistical filtering to determine a total number of historical alignment angle difference values within the queue that are inliers and determine a misalignment condition of the one or more cameras based on the total number of historical alignment angle difference values within the queue that are inliers.

The invention provides an automatic calibration method and system for internal and external parameters of a satellite-borne binocular camera vibration test system. The method comprises the following steps: extracting and matching feature points of image sequences of a left camera and a right camera; calculating basic matrixes of the left camera and the right camera and a basic matrix between the left camera and the right camera based on the matched feature point pairs; according to the basic matrixes of the left camera and the right camera, an objective function is constructed based on the mapping relation between the essential matrix and the camera internal reference, and an IAO algorithm is adopted to solve the optimal solution of the camera internal reference; calculating a camera extrinsic parameter candidate solution and a projection matrix candidate solution according to the basic matrix between the left camera and the right camera and the camera internal parameters, screening a correct projection matrix through triangularization, and taking the corresponding extrinsic parameters as initial values of the camera extrinsic parameters; and solving the optimal extrinsic parameter of the camera by minimizing the re-projection error and the translation vector modulus length error. The method does not depend on known coordinate characteristics of the earth surface or structural characteristics of a satellite body, does not need a calibration board and manual operation, and can realize automatic real-time high-precision calibration of internal and external parameters of a satellite-borne binocular camera system.

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 invention discloses a multi-view camera pose determination method, system and device, a medium and a product, and relates to the field of computer vision, and the method comprises the steps: constructing coordinate systems of a motion platform at different moments; determining transformation of each camera relative to a coordinate system and a direction vector and a distance vector of a Pluecker straight line corresponding to an image matching point; determining an initial essential matrix between the initial coordinate system and the transformed coordinate system based on a multi-camera pose solving linear method; determining an initial rotation matrix through the initial essential matrix; determining an initial translation vector based on a total least square method or a scaling factor recovery method; further determining an intermediate rotation matrix and an intermediate translation vector of each camera at different moments; finally, determining an optimized rotation matrix and an optimized translation vector between the initial coordinate system and the transformed coordinate system based on Simpson errors and an LM algorithm; according to the invention, the precision and robustness of determining the pose of the multi-view camera can be improved.

The invention relates to the technical field of SLAM, in particular to a dynamic environment robust positioning method based on improved ORB-SLAM2, and the method comprises the steps: obtaining RGB-D scene data containing an indoor moving object, and extracting ORB feature points described by FAST angular points and BRIEF descriptors; extracting feature points with unreliable heights according to geometric features of ORB feature points of adjacent frames; pixel analysis is carried out on the RGB-D scene data based on a Fast-SAM instance segmentation model, and a mask matrix containing environmental object space boundary information is generated; performing point-prompt extraction on the height unreliable feature points and the mask matrix to obtain a dynamic object mask; removing ORB feature points existing in the dynamic object mask to obtain a reliable feature point set; and calculating matching point pairs in the reliable feature point set to obtain an essential matrix and a corresponding rotation matrix and translation vector, and refining the pose based on a nonlinear optimization method.

The invention relates to the technical field of agent deep learning, and particularly discloses a single-agent three-dimensional visual positioning method and system based on deep learning, and the method comprises the steps: generating an optical flow field through a deep learning optical flow prediction model, judging the prediction accuracy in combination with analysis process parameters, guaranteeing the input quality, and providing a reliable basis for subsequent three-dimensional visual positioning; an essential matrix is constructed and decomposed, an initial three-dimensional visual positioning result is obtained in combination with constraints, positioning precision is improved, when the initial three-dimensional visual positioning result is unreliable, inertial measurement unit data is fused for space-time alignment, fusion reliability is verified through process parameters, and positioning robustness in a complex scene is enhanced. And the accuracy and the stability are considered, and the accuracy and the reliability of single-agent three-dimensional visual positioning are effectively improved.

Embodiments are directed to the association of concurrent tracks across multiple views for sensing objects. A sensing system that employs signal beams to scan paths across an object may be provided such that two or more sensors separately detect signals reflected by the object. Events may be determined based on the detected signals such that the events include pairs of events that correspond to pairs of sensors. Essential matrices may be generated based on the positions of each pair of sensors. The pairs of events associated with the pairs of sensors may be compared based on the essential matrices of the sensors. Scores for the pairs of events may be provided based on the comparison. If a score for a pair of events may be less than a threshold value, each event in the pair of events may be associated with a same location on the object.

The present invention discloses a visual odometry method and system based on image depth prediction and monocular geometry. The method includes inputting two consecutive image frames, detecting and describing local features of the images using the Scale-Invariant Feature Transform (SIFT) algorithm, and then using the Fast Library for Approximate Nearest Neighbors (FLANN) algorithm to match corresponding feature point pairs between the two frames; solving for the essential matrix using epipolar geometry constraints to obtain the relative pose transformation of the camera; constructing and training a monocular depth prediction model to predict dense depth information for each input image frame; if the number of valid depth information pairs formed by two consecutive image frames is greater than a given threshold, using triangulation to estimate the scale factor to obtain the corrected relative pose transformation, otherwise using a combination of the Perspective-n-Point (PnP) projection algorithm, the Random Sample Consensus (RANSAC) algorithm, and local non-linear optimization to solve for the absolute pose transformation. The present invention effectively integrates the advantages of deep learning and traditional geometric methods, can adapt to dynamic environments, and improves the robustness and accuracy of monocular visual odometry.

The invention provides a feature matching method and system based on image block comparison scoring. The method comprises the following steps: A, giving a group of image pairs formed by two images; b, intercepting image blocks of different sizes of each matching pair in the original image through the coordinates of the key points, and evaluating the difference between the image blocks of each matching pair; and C, selecting a part of matching pairs with small difference. And D, constructing a shallow KNN neighbor graph and a deep KNN neighbor graph. And E, enhancing the feature representation of the current feature through a feature enhancer with a three-branch structure. And F, executing a self-attention operation to capture global context information. Step C to Step F are regarded as a whole, that is, a pair pruning module is matched, and the module is iteratively executed twice. In the process, wrong matching pairs are removed step by step, an essential matrix is regressed, and the network is guided to be trained by calculating matching pair classification loss and essential matrix regression loss, so that a high-performance feature matching model is obtained.

This application relates to a method, apparatus, device, and storage medium for detecting camera angle shift, and pertains to the field of computer technology. The method includes: acquiring a standard image captured by the camera at a first time point and an image to be detected captured by the camera at a second time point; matching first image feature points of the standard image with second image feature points of the image to be detected; if the number of successful matches between the first and second image feature points is greater than or equal to a preset number, then for any set of successfully matched first and second image feature points, determining an essential matrix based on the first image coordinates of the first image feature points and the second image coordinates of the second image feature points; and based on the essential matrix, determining whether an angle shift has occurred in the camera between the first and second time points. This method can improve the accuracy of camera angle shift detection.

The invention relates to a method for fusing visual odometry with inertial measurement unit (IMU) data of an ego-vehicle (1) with the following steps: - Capturing an environment of the ego-vehicle (1) by means of at least one optical sensor (2) and generating sensor data, wherein the optical sensor (2) is a camera and the sensor data are consecutively captured camera images; - Applying semantic segmentation on the camera images; - Computing a visual odometry by means of optical flow; - Observing a shape and position of a road (F) and / or road elements (4) and deriving a tilt angle between an optical axis of the optical sensor (z) and the road (F); - Estimating a three axis rotation between two consecutive camera images by means of an essential matrix, which was computed through single value decomposition; - Fusing a delta rotation between the two consecutive camera images with accelerometers of the inertial measurement unit, wherein the fusion is conducted using a filter; - Adjusting rotation parameters of the essential matrix and transforming the detected objects (3) based on the fusion of the delta rotation.

The application discloses a monocular image sequence satellite attitude estimation method, and belongs to the technical field of space target perception, and comprises the following steps: acquiring satellite sequence images shot by a space-based monocular camera as input images; completing segmentation of a solar panel and a load main body; obtaining a single-frame satellite attitude based on a segmentation result; screening feature points in the satellite sequence images based on a feature extraction deep learning network and a preliminary semantic segmentation result; guiding feature point matching based on semantic information of an extracted mask; iteratively optimizing the feature point matching by using a polar line constraint; obtaining an essential matrix between adjacent images; calculating satellite attitude changes in the satellite sequence images; and combining the single-frame satellite attitude to obtain a series of attitudes of the satellite in the satellite sequence images. According to the technical scheme, the attitude estimation task can be better completed without labeled data, and the generalization performance for a non-cooperative target satellite is improved.

The application relates to a monocular vision inertial odometer method based on feature point depth, which comprises the following steps: acquiring input images of two adjacent frames and extracting feature points in the images respectively; matching the extracted feature points; predicting first camera pose changes by using acceleration measurement data and angular rate measurement data output by an inertial measurement sensor; obtaining the depth of each pair of matched feature points based on the first camera pose changes; if the number of feature points meets a preset condition, constructing a weight for each pair of feature points and obtaining a weight matrix; constructing a weighted observation equation set based on an epipolar geometry constraint equation and the weight matrix; constructing a quadratic form based on the weighted observation equation set and solving the quadratic form to obtain an essential matrix of the matched feature points in the least square sense; obtaining a second rotation matrix and a second translation vector by singular value decomposition based on the essential matrix and the second rotation matrix and the second translation vector, and obtaining the representation of actual camera pose changes.

The disclosure describes a method for calculating an essential matrix of a capsule endoscope in a tissue cavity, including: collecting a first image of a reconstruction region in the tissue cavity along a first direction in the tissue cavity having a wrinkled inner wall, and causing the capsule endoscope to collect a second image along a second direction forming an included angle with the first direction in the tissue cavity; calculating an optical flow of a first pixel region of the reconstruction region in the first image and a second pixel region corresponding to the first pixel region of the reconstruction region in the second image by a neural network based on a convolution operation, wherein the neural network based on the convolution operation extracts a first feature point set including a plurality of feature points at the first pixel region, and obtains a second feature point set corresponding to the first feature point set at the second pixel region based on the first feature point set and the optical flow; and calculating the essential matrix of the capsule endoscope based on the first feature point set and the second feature point set. Thus, the essential matrix of the capsule endoscope in the tissue cavity can be obtained.

The application discloses an image feature matching method based on gradual neighbor consistency mining, and comprises the following steps: 1, all features are constructed into an initial image feature matching set S; 2, the initial image feature matching set S is pruned to obtain a reliable candidate image feature matching set and corresponding inlier weight; 3, the corresponding essential matrix is predicted according to the candidate image feature matching set and the corresponding inlier weight; 4, the inlier probability w of all input initial image feature matching is obtained according to the essential matrix; finally, the essential matrix of two matching images and the probability of correct matching inliers are estimated. The application has obtained significant performance improvement in image matching.

The invention relates to an image matching method based on corresponding relation pruning and structured context aggregation. The method comprises the following steps: modeling an image matching task into a mixed problem combining classification and regression; feature coding is carried out on the matching point set through a vision-space fusion network, vision-space embedded features F0 are extracted and then input into a corresponding relation pruning network CPFormer, the CPFormer network constructs a feature graph firstly, then the feature graph is processed through a semantic graph network module, and local geometric features and global context features are extracted and spliced; then feature aggregation and coding are carried out, a multi-head self-attention mechanism is used for modeling a dependency relationship between features, and further processing is carried out to obtain discriminative feature representation; estimating multi-stage interior point confidence based on features output by the CPFormer network, and further calculating an essential matrix; and calculating a symmetric polar line distance of each matching point based on the essential matrix, and when the symmetric polar line distance is smaller than a preset threshold value, determining that the matching point is an inner point, thereby completing image matching. The method is beneficial to improving the image matching precision.

The invention discloses an RGB-T dual-light camera system parameter self-calibration method based on shape context, and relates to the technical field of camera parameter self-calibration, and the method comprises the steps: synchronously collecting a plurality of frames of RGB images and thermal imaging images of an object at different visual angles, respectively extracting the outlines of the images through employing a contour extraction algorithm, carrying out the sampling of the outlines, and carrying out the self-calibration of the parameters of the RGB-T dual-light camera system. Obtaining a contour point set and calculating a shape context histogram; comparing the shape context histograms to obtain a matching point set, and when the number of matching points in the set exceeds a set threshold value, obtaining a matching point essential matrix based on an RANSAC algorithm; and decomposing the essential matrix through SVD to obtain decomposed external parameters, optimizing the decomposed external parameters through a nonlinear optimization technology, and completing RGB-T dual-light camera system parameter self-calibration by using the optimized external parameters. According to the method, the calculation of the relative pose external parameters between the cameras can be realized without special processing, and the calibration of the RGB-T dual-light camera system is facilitated.

The invention is suitable for the technical field of computer vision, and provides a GraphMama-based dual-view image matching method, which comprises the following steps of: constructing a model which comprises an InitialProjection unit, a Preminarial Local ContextEncoding unit, a Consensus-Aware Learning Block unit, a GraphMama unit and an Inlier Predictor unit, and constructing a model which comprises an InitialProjection unit, a Preminarial Location unit, a Consensus-Aware Learning Block unit, a GraphMama unit and an Inlier Predictor unit; the method comprises the following steps of: performing high-dimensional feature mapping, local neighborhood enhancement, global consistency optimization and long-range dependence modeling by taking initial matching point pairs of a double-view image extracted by SIFT (Scale Invariant Feature Transform) as input, and performing Consensus-Aware Learning Block and GraphMama iteration for four times; and training the model by using supervised data sets such as YFCC100M, SUN3D and the like, and finally outputting correct matching point pairs and an essential matrix. According to the GraphMama-based double-view-angle image matching method, through the limitation that traditional MLP independently processes matching pairs and RANSAC robustness is insufficient, long-range dependence of long-sequence matching pairs is accurately captured, on YFCC100M and SUN3D data sets, mAP indexes of relative attitude estimation and feature matching are remarkably superior to those in the prior art, complex scenes such as view angle changes and shielding can be dealt with, and the method has the advantages of being high in adaptability, high in robustness and the like. The method can adapt to computer vision tasks such as image stitching and vision positioning.

The invention provides a double-view feature matching method based on a paired motion field, and the method comprises the following design steps: 1, for a given image feature matching image pair, employing an SIFT algorithm to extract feature points; 2, designing a pairwise motion vector field construction module; 3, designing a feature fusion module; and 4, further predicting the probability of each matching pair serving as a real matching point (namely an inner point) by using the initial matching pair processed by the constructed paired motion vector field and the feature fusion module. And 5, taking the obtained probability set and the corresponding set as input, and estimating an essential matrix by using a weighted eight-point algorithm. And F, iteratively executing the step C to the step E for five times, calculating cross entropy loss according to a predicted classification result and a real category result in each iteration, calculating regression loss in combination with a predicted essential matrix and a real essential matrix to guide network training, and finally obtaining a double-view feature matching model with optimal performance.

The present invention discloses a method, system, device and medium for projection single-pose calibration, which relates to the field of computer vision and projection interaction technology. The method includes: generating multiple calibration patterns, projecting them onto a calibration plane, collecting grayscale images, performing corner detection and sub-pixel refinement processing, obtaining the corner point correspondence between the projector image and the camera image, iteratively solving the projection matrix of the projector and the camera, separating the intrinsic parameter matrix and the extrinsic parameter matrix, calculating the basic matrix and the essential matrix, and decomposing the rotation matrix and translation vector of the camera relative to the projector; estimating the camera imaging distortion, obtaining the distortion correction coefficient, and performing distortion correction; statistically calculating the reprojection error generated during the correction process, and adjusting the parameters of the projector and camera until the error meets the preset accuracy requirements. Projector parameter calculation is completed by projection single-pose calibration, and the calibration process does not require posture adjustment, thereby improving the degree of automation and reducing manual intervention.

This invention discloses a method for 3D reconstruction of asphalt pavement texture, belonging to the fields of road detection and computer vision technology. The method first calibrates an industrial camera to obtain intrinsic parameter matrices and distortion coefficients, and then continuously acquires pavement images at short intervals of 1 to 2 centimeters on a moving vehicle. Next, the images undergo preprocessing including cropping, Gaussian kernel convolution for noise reduction, and 8 to 12 low-pass filtering convolution enhancements. Feature points are matched using the SIFT algorithm combined with a limited neighborhood range strategy of 80 to 120 pixels. Subsequently, based on the matched points, the fundamental and essential matrices are solved using the RANSAC algorithm and the 8-point method. The camera pose is optimized using bundle adjustment to obtain an initial point cloud, followed by kdtree clustering and statistical filtering for dual noise reduction. Finally, bending deformation is corrected through quadratic polynomial surface fitting, tilt is corrected using projection transformation, and the model scale is adjusted according to the scaling factor to obtain a standardized 3D model. This method effectively solves the problem of weak asphalt texture matching and achieves efficient and high-precision pavement texture reconstruction.

The application discloses a kind of methods for indoor positioning of uncalibrated camera using Aruco code.The application uses a special two-dimensional code mark-Aruco code to carry out the self-calibration of camera, and the mapping and camera positioning of indoor environment.Through the corresponding relationship of a group of Aruco code corner points under different viewing angles, the distortion coefficient of the camera is solved using epipolar geometry constraint.The camera intrinsic parameters are calculated using the special properties of essential matrix after removing the distortion of the image.In an indoor scene with a group of Aruco codes pasted on a wall and a ceiling, the positional relationship between all codes is calculated using the pose relationship between a single code and the camera, thereby establishing an indoor map and completing the indoor positioning task of the camera.

The present invention discloses a target tracking method applicable to vehicle-mounted environments. The method uses a target detection algorithm to obtain the target detection results Pre_Detections and Cur_Detections in the previous frame and the current frame images, and extracts appearance feature vectors; calculates the essential matrix F of the epipolar geometry model for the two consecutive frame images; predicts its position G_Tracks in the current frame image according to Pre_Detections by using the essential matrix F; calculates the Mahalanobis distance correlation metric matrix corresponding to all targets in G_Tracks and Cur_Detections, and at the same time calculates the appearance feature cosine distance correlation metric matrix corresponding to all targets in Pre_Features and Cur_Features, and uses the metric matrix to make a decision on the correlation information between the targets in the front and rear frames, so as to achieve multi-target tracking. The design of the present invention can extract the appearance feature information of the target through a convolutional neural network, and at the same time calculate the motion information of the target in the video, and combine the two to achieve multi-target association between frames in the video, so as to achieve the effect of target tracking.

The invention discloses a point pricking control method for a PTZ camera image set, a medium and electronic equipment, and the method comprises the steps: extracting and describing feature points from the image set, and carrying out the matching of the extracted feature points; based on a matching result, estimating motion between the images by using an essential matrix, and performing rotation averaging on all the images to obtain an image relative orientation result; performing bundle adjustment to optimize a relative orientation result; querying a relative orientation result, and obtaining an internal parameter matrix and an external parameter between the images; calculating a conversion relation of corresponding points between the images by using the internal reference matrix and the external reference between the images; and according to the conversion relation, calculating and marking the predicted position of the control point on the non-pricking point image. Through the computer vision technology and the SFM technology, automatic control point pricking of the PTZ camera image set is achieved, the working efficiency and precision are improved, and the method is suitable for various monitoring and measurement application scenes.

The invention discloses a camera self-calibration method, device and equipment, a medium and a program product, and relates to the technical field of camera self-calibration, and the method comprises the steps: constructing at least one adjacent image pair according to a continuous image sequence collected by a target camera; the adjacent image pair comprises two images; the collection moments of the two images in the adjacent image pair are adjacent; for each adjacent image pair, determining a target basis matrix of the adjacent image pair; according to the target basic matrix of each adjacent image pair, determining a target function based on essential matrix manifold constraint, and optimizing the target function to obtain an optimal internal reference of the target camera; and determining the pitch angle of the target camera according to the optimal internal reference and the continuous image sequence. According to the embodiment of the invention, joint calibration of the internal reference and the pitch angle of the camera is realized, and pitch angle matching in the camera is ensured, so that the accuracy of visual navigation by using the internal reference and the pitch angle of the camera is improved.

The invention discloses an SAR three-dimensional false target suppression method and system based on an essential matrix and neighborhood consistency constraint, and relates to the field of false target suppression. The objective of the invention is to solve the problem that an existing false target elimination method is poor in false target discrimination capability. The method comprises the following steps: acquiring two-dimensional coordinates of to-be-estimated pixel points on a main image and an auxiliary image and a two-dimensional coordinate set of neighborhood pixel points, and acquiring three-dimensional coordinates of the to-be-estimated pixel points and three-dimensional coordinates of neighborhood pixel points by utilizing the two-dimensional coordinates of the to-be-estimated pixel points on the main image and the auxiliary image and the two-dimensional coordinate set of the neighborhood pixel points; constructing a measurement function by using the three-dimensional coordinates of the to-be-estimated pixel and the three-dimensional coordinates of the neighborhood pixel points so as to obtain a measurement value; and determining and storing the pixels of the real target according to the measurement function value. The method is used for suppressing false targets.

The invention relates to a method for fusing visual odometry with data from an inertial measurement unit (IMU) of a self-propelled vehicle (1) comprising the following steps: - Capturing the environment of the vehicle (1) by means of at least one optical sensor (2) and generating sensor data, wherein the optical sensor (2) is a camera and the sensor data are successively recorded camera images; - Performing a semantic segmentation of the camera images; - Calculating a visual odometry based on the optical flux; - Observing the shape and position of a roadway (F) and / or roadway elements (4) and deriving an angle of inclination between an optical axis of the optical sensor (z) and the roadway (F); - Estimating a three-axis rotation between two successive camera images using an essential matrix calculated by single-value decomposition; - Fusion of a delta rotation between the two successive camera images using accelerometers of the inertial measurement unit, wherein the fusion is performed using a filter; - Adjusting rotation parameters of the essential matrix and transforming the detected objects (3) based on the fusion of the delta rotation.

This invention discloses a non-contact strain gauge identification and precise coordinate positioning method, comprising the following steps: acquiring strain gauge image data; converting the strain gauge image data to grayscale and acquiring filtered strain gauge contour image data; determining the target strain gauge contour data; determining the essential matrix of the image acquisition device based on the image rotation matrix and translation vector; identifying the same strain gauge in the target strain gauge contour data based on the essential matrix, and performing coordinate positioning of the same strain gauge based on binocular vision, transforming it to the coordinates under the image acquisition device to obtain the coordinate information of the strain gauge under the image acquisition device; establishing the transformation relationship between the coordinate information and the target coordinate system, and transforming the coordinate information to the target coordinate system to obtain the strain gauge identification result in the target coordinate system; repeating steps S1-S5 to obtain the strain gauge identification results for all structural tests. This invention can improve the accuracy of strain gauge position coordinates.

The application relates to a robust visual odometer method, which comprises the following steps: acquiring input images of two adjacent frames based on a monocular camera, and extracting feature points in the two input images respectively; acquiring a plurality of pairs of feature points, wherein the feature points are expressed in pixel plane coordinates; constructing an epipolar geometry constraint equation based on the pairs of feature points, and solving to obtain an initial essential matrix; using the initial essential matrix, and calculating an initial rotation matrix and an initial translation vector of the monocular camera between the two adjacent input images through singular value decomposition; converting the pixel plane coordinates of the pairs of feature points into three-dimensional world coordinates based on the initial rotation matrix and the initial translation vector; calculating the difference between the feature points in the pairs of feature points based on the three-dimensional world coordinates; judging whether the feature points are static feature points based on the difference, and if yes, the feature points are reserved; re-estimating the actual essential matrix of the monocular camera based on the static feature points, and obtaining an actual rotation matrix and an actual translation vector.