328 results about "Reprojection error" patented technology

The invention discloses a charging robot automobile charging port pose vision measurement method comprising the following steps: 1, controlling a mechanical arm to drive a monocular camera to collect a charging port image, and constructing a charging port image data set; 2, extracting a charging hole contour in each image by adopting an improved Mask R-CNN instance segmentation model; step 3, performing robust ellipse fitting based on each charging hole contour to obtain center coordinates of each charging hole in the two-dimensional image; step 4, establishing a world coordinate system based on charging hole space distribution defined by the automobile charging port standard model, and determining three-dimensional coordinates of each charging hole; and 5, the multi-view two-dimensional center coordinates and the corresponding three-dimensional coordinates are input into the PnP graph optimization model, and the pose of the charging port in the mechanical arm base coordinate system is solved by fusing the re-projection error constraint, the structure prior constraint and the kinematics chain constraint. The invention further discloses a charging robot automobile charging port pose vision measurement charging method and a charging system.

The invention provides a space projection error analysis-combined 4D millimeter wave radar and visual camera automatic calibration method. The method comprises the steps of constructing a sensor internal reference model; designing a composite multifunctional calibration board and a three-dimensional adjustable holder, performing rigid geometrical relationship modeling, and automatically associating physics-features-coordinates; collecting multi-frame synchronization frame pair data, and performing time sequence fine grit alignment and interpolation compensation to obtain a space-time alignment frame pair sequence; extracting cross-modal spatial constraints of the features and automatically screening and matching point pairs to obtain a high-confidence point pair set; performing point-point constraint and point-point error, point-plane error and pixel reprojection error minimization to obtain a standardized error term; and constructing an overall objective function, and performing spatial constraint and time deviation estimation to obtain corrected external parameters and synchronization deviation. According to the invention, through a full-automatic standardized process, the probability of artificial participation and error occurrence is greatly reduced; and multi-modal and multi-scene high-precision calibration is supported, and the flexibility and robustness are greatly improved.

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

The invention discloses an unmanned aerial vehicle camera attitude estimation optimization method and device, and relates to the technical field of unmanned aerial vehicles. The method comprises the following steps: acquiring data by using an unmanned aerial vehicle carrying various sensors, preprocessing, carrying out feature extraction and matching on preprocessed image data, removing mismatching points, and based on an initial point cloud, carrying out sparse reconstruction in an incremental expansion and bundle adjustment optimization stage; integrating the re-projection error term, the flight path constraint, the epipolar geometric constraint and the triangulation constraint into a target function; constructing a model for predicting the pose correction based on the deep residual convolutional neural network, and taking the initial pose of the camera and the corresponding local image as input; and adjusting the weight of each constraint in the target function based on the predicted pose correction and pose confidence, and minimizing the target function to improve the precision of unmanned aerial vehicle camera pose estimation. The problem of reconstruction scene deviation caused by inaccurate camera attitude estimation in the prior art is solved.

The invention discloses a dynamic self-adaptive calibration method and device for hands and eyes of two arms of a robot. The method comprises the steps that calibration plates are fixed to the tail end of a left mechanical arm and the tail end of a right mechanical arm respectively, the mechanical arms are controlled through an automatic program to execute N times of translation and rotation movement in the view field of a camera, and calibration plate images are collected after each time of movement; based on the collected calibration plate image, the angular point position of the calibration plate is extracted, and the pose of the calibration plate in a camera coordinate system is calculated; and the pose of the calibration plate corresponding to each calibration plate image in the mechanical arm coordinate system is obtained, and the rotation matrix and the translation vector of the camera coordinate system and the mechanical arm coordinate system are solved. According to the technical scheme, the pose is corrected in real time through dynamic environment perception and vibration compensation, closed-loop re-projection error optimization and spiral / grid path planning are combined, high-precision robot double-arm hand-eye calibration is achieved, the environment interference calibration failure rate is reduced, and efficiency is improved.

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.

The invention discloses a construction robot positioning and mapping method in a long tunnel environment, and relates to the technical field of computer vision, and the method comprises the steps that a robot obtains data information in the long tunnel environment through a carried sensor, and the data information comprises IMU information, an RGB image and a depth image; the method comprises the following steps: calculating IMU pre-integration according to IMU information; carrying out adaptive ORB point feature extraction and EDlines line feature extraction and optimization on an RGB image; constructing a deep learning network to carry out feature matching on extracted point-line features to obtain a matching result; constructing a point-line feature re-projection error model based on the matching result; preliminarily estimating an inter-frame pose based on the depth image and a point-line feature re-projection error model; the method comprises the following steps: selecting a key frame and detecting an identification code based on a depth image and a preliminarily estimated inter-frame pose, constructing a factor graph model based on the key frame, an identification code detection result and IMU pre-integration, updating global positioning based on a factor graph optimization result, and constructing a global map.

The invention provides a self-supervised end-to-end vision reconstruction method and system, and relates to the technical field of computer vision processing. The method comprises the following steps: firstly, acquiring multi-camera parameters and different-view-angle image data, including focal length, lens distortion and main coordinate point data of each camera, and pixel point coordinates of a current frame of a first camera and a reference frame of a second camera, then constructing an end-to-end training model, and calculating a re-projection error of pixel points of the current frame and the reference frame to obtain a multi-view-angle image; and solving the parameter update quantity by using a Gaussian Newton iteration method, iteratively optimizing the camera pose, the pixel corresponding relation and the depth data, and reconstructing a three-dimensional coordinate by combining the obtained data set after the re-projection error is converged, and converting and splicing to realize three-dimensional scene reconstruction. By implementing the scheme, end-to-end self-supervised training can be realized under the condition of not depending on manual annotation, so that three-dimensional visual reproduction is realized.

The invention relates to an unmanned aerial vehicle dynamic environment sensing method based on a visual large model, electronic equipment and a medium, and the method comprises the steps: carrying out the instance segmentation and optical flow estimation of multiple frames of original images based on the visual large model and an optical flow estimation network, and obtaining a dynamic mask and inter-frame optical flow information; performing feature point classification on the depth information, the dynamic masks and the inter-frame optical flow information corresponding to the multiple frames of images to obtain a classification result; constructing a re-projection error model based on the classification result to obtain an estimated pose; evaluating the quality of the feature points based on the estimated pose to obtain an optimized pose; and constructing a feature point global map based on the world coordinates of the continuous frame road sign points and the optimized poses. Through a multi-information fusion and step-by-step optimization mechanism, the method has relatively high robustness, can adapt to various dynamic environments, and improves environment perception performance and operation reliability in different scenes.

The invention discloses a tight coupling laser inertial vision fusion method based on radiation reconstruction, and relates to the technical field of vision fusion. Comprising the following steps: removing laser radar point cloud motion distortion based on IMU measurement, dividing the laser radar point cloud into multi-scale voxel grids, and obtaining a plane normal vector of the laser radar point cloud contained in each voxel grid to calculate a point-surface residual error; real radiation information is obtained through photometric calibration, meanwhile, a global map is projected to a current frame to obtain a tracking point, the obtained tracking point is tracked through an LK optical flow method, and a frame-to-frame re-projection error and a radiation error are obtained; a multi-constraint factor graph containing IMU pre-integration, laser radar constraint and visual constraint is constructed based on the factor graph, and robustness enhancement of the system in a degradation environment is realized in combination with a key frame sliding window optimization mechanism. The method provided by the invention has high positioning precision and robustness in a highly challenged degradation scene.

The invention provides a non-overlapping field-of-view binocular camera external parameter calibration method and system, and the method is applied to a non-overlapping field-of-view area, and comprises the steps: setting a control point, obtaining a three-dimensional coordinate of the control point through a total station, and collecting an image containing the control point through a binocular camera; constructing a re-projection error function by using the three-dimensional coordinates of the control points to obtain an initial value of the external parameters of the camera; designing an image branch and a point cloud branch in a Transform network, respectively extracting image features and coordinate features, then carrying out cross-modal feature association, constructing a weighted multi-objective loss function, and training to obtain matching information of the image features and the coordinate features; and finally, optimizing and solving the extrinsic parameters through the light beam adjustment method model, and outputting the optimized extrinsic parameters of the camera after training. According to the method, the dependence of a traditional method on the field-of-view coincidence degree is overcome, the problem of scale drift when external parameter calibration is performed by using deep learning is solved, and complex scene calibration is supported.

The invention discloses a two-stage object six-degree-of-freedom pose estimation method based on a monocular camera, solves the problem of how to carry out high-precision and high-efficiency pose estimation on weak-texture industrial parts, and belongs to the technical field of monocular camera pose estimation. The invention relates to a coarse-to-fine two-stage pose estimation method. The coarse pose estimation network roughly estimates the pose value of six degrees of freedom based on mask feature and 2D position feature fusion, and the coarse pose estimation stage provides an initial value for precise pose estimation, so that the optimization process of the precise pose estimation algorithm is prevented from falling into a local optimal value. A dynamic snakelike convolutional layer is introduced into an accurate pose estimation network to construct an object contour feature extraction network, the contour feature generation quality is improved, and a background contour is filtered out. Through a differentiable Gaussian-Newton optimization method, iterative optimization is carried out on multiple scales, the re-projection error of the projection contour and the object contour is minimized, and accurate pose estimation is realized.

The invention discloses an industrial automobile crane positioning and mapping method and device based on multi-sensing fusion, and relates to the field of machine sensing, and the method comprises the following steps: initializing a pose and offset of an inertial measurement unit by using an input laser point cloud and a pre-integration result of the inertial measurement unit; correcting laser point cloud motion distortion by using an inertial measurement unit, extracting a dynamic object point cloud, and synchronously extracting laser features and visual features with depth; calculating a key frame pose in real time by minimizing a visual re-projection error and an inertial measurement unit pre-integration error, and performing frame-to-map matching by taking the key frame pose as an initial value to further optimize the pose; a visual word bag model is utilized to quickly retrieve a closed-loop candidate frame, after laser passes geometric matching to verify a closed loop, a laser radar factor, a visual factor, an inertial measurement unit factor and a closed-loop factor are fused, a global factor graph is constructed and optimized, and a pose and point cloud map is updated. According to the method, the environment sensing capability of the industrial automobile crane can be improved, and the sensing accuracy and robustness are improved.

To locate and model a moving 3D object captured in a time sequence of multiple images, semantic keypoint detection is applied to each image, in order to generate a set of 2D semantic keypoint detections in an image plane of the image. For each image, a camera pose and an initial object pose is received. Based on the initial 3D model, and the initial object pose and the camera pose for each image, a set of keypoint projections is computed in the image plane of the image, the keypoint projections being 2D projections of 3D semantic keypoints of the initial 3D model. A new 3D model and a new object pose are determined for each image based on an aggregate reprojection error between the 2D semantic keypoint detections and the keypoint projections across the time sequence of multiple images.

The invention discloses a camera self-calibration method and system in a short-track speed skating scene, and belongs to the technical field of computer vision in a short-track speed skating motion scene. The method is used for solving the problem that a calibration object or other constraint conditions need to be prepared in advance in the existing camera calibration technology. The method comprises the following steps: acquiring two-dimensional feature points of an image; obtaining an initial value M0 of a projection matrix by using a corresponding relation between the two-dimensional feature points of the image and the known three-dimensional points; calculating a re-projection error between a predicted coordinate and an actual image coordinate according to M0, and minimizing the re-projection error to find an optimal solution M of a projection matrix, namely optimal solutions K, R and T of internal and external parameters of the self-calibration camera; according to the camera self-calibration method and system in the short-track speed skating scene, a more convenient and accurate calibration mode can be provided.

The invention discloses an omnidirectional fisheye image feature tracking and extracting method based on a non-parallel virtual binocular, and the method comprises the steps: constructing a four-eye fisheye camera system, obtaining the relative pose transformation based on a pre-calibration external parameter, predicting the pose based on a uniform speed model or IMU pre-integration, and constructing a re-projection error function through common-view feature points. According to the invention, by constructing four groups of virtual binocular combinations, on the premise of not increasing the number of physical cameras, eight-view-angle equivalent coverage is realized, so that a high-overlapping common-view area is formed between adjacent cameras; on the basis of a pre-calibrated external parameter and an MEI fisheye model, a spherical epipolar line is directly generated for a distorted image to carry out matching search, and meanwhile, a time sequence-space two-dimensional fault-tolerant mechanism is put forward for challenges of shielding and environmental interference: according to a dynamic direction switching design, when a tracking feature number is lower than a threshold value due to shielding in a camera direction, the tracking feature number is smaller than the threshold value; the camera direction is actively switched to serve as a main tracking source, new feature points are supplemented at the same time, and system collapse caused by local view failure is avoided.

The invention provides a camera calibration method and device, electronic equipment and a storage medium, and belongs to the technical field of data processing, and the method comprises the steps: obtaining laser radar point cloud data, inertial measurement unit data and reference camera image data of a vehicle, and obtaining initial calibration parameters of a to-be-calibrated camera; based on the laser radar point cloud data, the inertial measurement unit data and the reference camera image data, constructing a point cloud map containing color textures and a pose track of a vehicle, and converting the point cloud map into a surface grid model; according to the pose track and the initial calibration parameters, the surface grid model is rendered to a visual angle of the to-be-calibrated camera, and a rendered image and a rendered depth map corresponding to the to-be-calibrated camera are generated; based on the real image data, the rendered image and the rendered depth map, establishing a corresponding relationship between two-dimensional image features and three-dimensional space points in the real image data; and constructing a re-projection error objective function based on the corresponding relationship, and optimizing the external reference and the internal reference of the camera to be calibrated by minimizing the objective function.

The invention discloses a visual camera calibration method and system applied to large-view-field optical measurement, and belongs to the technical field of visual measurement, and the method comprises the steps: firstly obtaining a multi-view-angle image sequence and a camera three-dimensional position corresponding to each frame of image; obtaining an initial camera external parameter with a real scale and a three-dimensional scene structure through a motion recovery structure and in combination with a camera position; selecting and initializing a large-view-field camera projection model containing an internal reference matrix and a lens distortion parameter; iteratively optimizing lens distortion parameters, camera external parameters and three-dimensional point coordinates; and constructing and minimizing a global cost function containing a re-projection error and camera position constraint, and jointly optimizing all parameters to obtain a final calibration result. According to the method, through comprehensive model optimization, by applying surveying and mapping services except satellite application services, the calibration precision and model completeness of large-view-field complex distortion are remarkably improved, and the problem that distortion correction is insufficient due to dependence on a few points is solved; and the robustness and the intelligent level of calibration and the reliability and the traceability of a result are improved.

The invention belongs to the technical field of computer vision, and particularly relates to a camera external parameter calibration method, device and equipment. The camera extrinsic parameter calibration method comprises the steps of determining initial extrinsic parameters of a camera, constructing 2D-3D matching feature point pairs in 2D pixel points in a 2D image and 3D points in a 3D point cloud for edge lines of a static target in a target area based on each initial extrinsic parameter in an initial extrinsic parameter set, and determining final extrinsic parameters based on the 2D-3D matching feature point pairs, the 2D image is acquired by the camera; and determining a re-projection error of projecting 3D feature points in the 2D-3D matching feature point pair to the 2D image based on the final external parameter, when the re-projection error meets a preset standard, taking the final external parameter as a calibration external parameter of the camera, otherwise, taking the final external parameter as an initial external parameter and adding the initial external parameter into the initial external parameter set, and repeating the steps until the calibration external parameters are obtained.

The invention discloses a rapid robust monocular vision inertial positioning method and system. The method comprises the following steps: configuring IMU and monocular camera sensor parameters; preprocessing the IMU data; iMU attitude initialization is carried out; image features of the monocular camera are extracted, abnormal matching points are removed, and IMU pre-integration is carried out; monocular vision initialization is carried out; odometer attitude optimization: judging whether the feature points are mismatched or not based on an IMU pre-integration result, constructing a feature point re-projection error jacobian matrix, and performing blocking and diagonalization processing on the re-projection error jacobian matrix to respectively optimize inverse depths and image frame attitudes of the feature points; selecting a key frame based on a key frame identification rule after the current frame attitude update is completed; and judging according to the current frame, and removing a certain frame in the sliding window to reserve a sliding window space for adding the latest frame. According to the method, the inverse depth of the feature points and the image frame attitude are optimized respectively based on the counterweight projection error Jacobian matrix partitioning and diagonalization processing, and the calculation complexity is reduced.

The invention provides a camera calibration error analysis method and system based on Kinect V2. The method comprises the following steps: calculating internal parameters and external parameters of a camera; applying a calibration result to a new image, calculating a distance between a re-projection point and an actual observation point, evaluating the calibration result, carrying out statistical analysis on re-projection errors of all images, and calculating statistical indexes such as an average error and a maximum error; the method comprises the following steps: adjusting input parameters such as a calibration plate position and a camera attitude, observing the change condition of a calibration result, determining the influence degree of each input parameter on the calibration result, carrying out eigenvalue decomposition on a covariance matrix of a calibration error, and analyzing an error source and the influence degree of the error source on the calibration result; if some error sources are found, calibration is carried out again by adding calibration points or changing calibration conditions. The system comprises a parameter calculation module, an error analysis module and a calibration optimization module. The accuracy and reliability of the calibration result are improved.

The invention discloses a mobile robot pose estimation method based on mixed point and twin line feature re-projection joint optimization, which comprises the following steps: acquiring an RGB-D image, and extracting point and line features from two continuous frames; calculating point and line feature descriptors based on a point and line feature description algorithm, and constructing point and line feature data association; obtaining point and line feature matching pairs; performing verification in combination with depth information, dividing the point feature matching pairs into 3D-2D matching pairs and 3D-3D matching pairs, and constructing a mixed point feature re-projection error function; a virtual right eye line is constructed for the line feature matching pair, RGB and depth clues are considered at the same time, and a twin line feature re-projection error function is constructed; and constructing a joint unified error optimization model, correcting point and line reprojection errors, and estimating the optimal pose of the robot. According to the method, the optimal pose estimation with complementary advantages of point-line feature fusion is realized, and the trajectory estimation accuracy and self-positioning robustness of the robot in a complex environment are ensured.

The invention discloses a dynamic visual guidance method and system for cooperative assembly of multiple mechanical arms, and belongs to the technical field of industrial robot visual guidance, and the method comprises the steps: synchronously collecting an image sequence through a binocular visual system, synchronously recording the pose sequence of each mechanical arm through a robot controller, and obtaining an observation data set; obtaining a two-dimensional trajectory flow of vehicle body assembly area feature points and instrument body feature points through an optical flow tracking algorithm, and obtaining a hand-eye matrix correction through a nonlinear optimization algorithm; obtaining an updated hand-eye matrix and a local pixel compensation field through a spatial interpolation algorithm based on the re-projection error of each feature point; according to the method, self-adaptive compensation of flexible deformation of a vehicle body and dynamic reflection of an instrument is realized by adopting unsupervised clustering and spatial interpolation according to a re-projection error, and the precision of cooperative assembly of multiple mechanical arms and the self-adaptive capability of a system in a complex industrial environment are improved.

The invention provides a three-dimensional calibration method and system based on a self-adaptive multi-view optimization algorithm and a medium, and the method comprises the steps: automatically shooting images of a calibration target surface at multiple angles based on a camera, and obtaining a multi-view image; extracting feature points based on the multi-view image, and extracting positions of calibration points in the image based on the feature points; searching feature points corresponding to the same calibration point in different images based on a feature matching algorithm; analyzing the density and distribution of image feature points based on a feature matching result, evaluating the current posture of the camera, and dynamically adjusting a shooting angle according to an evaluation result; analyzing a re-projection error of the same calibration point based on a feature matching result, and dynamically adjusting internal and external parameters of the camera based on the re-projection error; the method comprises the following steps: shooting a multi-view image, performing full-view scanning on a calibration target surface, and analyzing calibrated feature matching and reprojection errors to dynamically adjust internal and external parameters of a camera, thereby realizing a high-precision, high-adaptability and high-efficiency calibration process.

The invention discloses an SLAM dynamic point semantic filtering method based on DDMA-SAM, and belongs to the technical field of synchronous positioning and mapping. A decoupling distillation mechanism is introduced, an image encoder in an original SAM model is subjected to lightweight optimization, a DDMA-SAM semantic network integrating a multi-scale aggregation detection module and an efficient mask decoding module is constructed, and the SLAM dynamic point semantic filtering method based on DDMA-SAM is obtained. And the segmentation performance is improved while the model parameters are greatly compressed. Based on the semantic network, providing a semantic prior and geometric consistency combined-driven double filtering strategy; based on a semantic mask and a confidence threshold, carrying out preliminary dynamic point identification; in combination with the epipolar geometric constraint and the triangulation reprojection error of random sampling consistency estimation, fine elimination of dynamic feature points is realized, and only static points are reserved to participate in camera pose estimation. According to the method, the real-time performance of the system is kept, and meanwhile, the mapping quality and the track stability in a dynamic scene are effectively improved.

The embodiment of the invention provides a high-precision machine vision calibration method and system based on multi-modal feature fusion and a medium, and the method comprises the steps: collecting calibration plate shooting based on a multi-modal sensor, and obtaining a multi-modal calibration plate image and point cloud information; extracting multi-modal image features based on the multi-modal calibration plate image, and extracting point cloud features based on the point cloud information; carrying out fusion processing on the multi-modal image features and the point cloud features based on a multi-modal fusion network; performing initial calibration on the multi-modal sensor based on a calibration algorithm, inputting the fusion feature vector into the calibration algorithm, calculating a re-projection error and a point cloud matching error, and performing optimization adjustment on an initial calibration parameter to obtain a calibration result; through multi-modal feature fusion processing, the relation between image features and point cloud features is obtained, the representation ability of the features is enhanced, a re-projection error and a point cloud matching error are analyzed based on a calibration algorithm, calibration parameters are dynamically adjusted, and the calibration precision is improved.

The invention provides a three-dimensional calibration method using a speckle image as a calibration object and combining weight parameters. The method comprises the following steps: acquiring a standard grid image and the speckle image through a camera; determining angular point positions in the standard grid image through an angular point detection algorithm; calibrating the stereoscopic vision system by using the obtained angular points to obtain an initial calibration result; calculating 3D coordinates of the camera calibration board in the world by using the disparity map; determining a subset of the reference image corresponding to the checkerboard angular points in the target image through a digital image cross-correlation algorithm; calculating a weight parameter of each calibration point based on the re-projection error; solving internal and external parameters of the camera by using the weight parameters, radial alignment constraint and a least square method; optimizing the main point of the camera by using the weight parameters; and if the re-projection error or the number of iterations does not meet the requirement, repeatedly calculating the weight parameter of the calibration point and the subsequent steps, otherwise, obtaining a camera calibration result, and obtaining the distortion curved surface of the camera lens.

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 belongs to the technical field of computer vision, and discloses an underwater binocular positioning method and system based on optical target prior constraint. The method comprises the following steps: establishing a binocular stereo imaging model considering camera distortion; the method comprises the following steps: constructing an image data set of an underwater prior optical target, extracting potential feature points through feature transformation identification, performing feature point matching on a denoised image in the image data set of the underwater prior optical target, and performing verification by using prior position information of an optical target point as a depth constraint condition. Eliminating wrong matching with overlarge position deviation in the matching feature points; and combining the projection position of the target to be measured with the prior projection position of the target to establish a re-projection error function, and iteratively optimizing the parameters of the binocular stereoscopic vision model by taking the collinear relationship generated by the optical target as a multi-target geometric constraint condition until the re-projection error meets the precision requirement. According to the method, the feature point identification and positioning precision of the underwater robot in a complex water body environment is improved.

The invention relates to the technical field of optical measurement, and discloses a biaxial galvanometer error calibration method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining a camera internal reference containing an internal reference matrix and a distortion coefficient through a calibration board; establishing an ideal geometric transformation model of the biaxial galvanometer, and defining a first galvanometer transformation matrix determined by the rotation angle of the first galvanometer and the first distance and a second galvanometer transformation matrix determined by the rotation angle of the second galvanometer and the second distance; installing error parameters are introduced, and a complete coordinate transformation model used for describing a complete light path transformation relation from an actual camera coordinate system to a final imaging coordinate system is formed; calibration data are collected, galvanometer system parameters and installation error parameters are optimized based on re-projection errors, re-projection residual errors are constructed, the re-projection residual errors are minimized through a nonlinear optimization algorithm, and optimal parameters are obtained. The installation error can be accurately calibrated and effectively compensated.