10 results about "Essential matrix" patented technology

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 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.

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 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 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.

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 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 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.

The invention provides an image feature matching method and device based on selective context awareness, and a medium, and relates to the technical field of feature matching. The method comprises the following steps: acquiring a group of to-be-matched image pairs, extracting image feature points and descriptors by using a feature extraction algorithm, and generating an initial corresponding point set based on descriptor similarity; inputting the initial corresponding point set into a pre-constructed pruning network for processing; the pruning network adopts a two-stage progressive pruning strategy, global context information and local context information are captured and subjected to interactive fusion refining, so that inner point weights of corresponding points are predicted, an initial corresponding point set is pruned according to the weights, and a reliable candidate point set is obtained; calculating an essential matrix through a weighted eight-point algorithm by using the reliable candidate point set; and based on the essential matrix, utilizing a full-size verification module to calculate epipolar distances of all the corresponding points, and classifying the corresponding points of which the epipolar distances are smaller than a preset threshold value as inner points to complete image feature matching.