4 results about "Essential matrix" patented technology

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.

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 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.