44 results about "Bundle adjustment" patented technology

The invention relates to the field of computer vision and mobile robot navigation, and discloses an object-level semantic vision SLAM method and system based on a mixed attention mechanism target detection network and an ellipsoid model. The system comprises five parallel thread modules, namely a semantic perception module, a visual tracking and repositioning module, a local mapping and fusion module, a loopback detection module and a global consistency optimization module. According to the method, local and global features of an image are extracted in parallel through a target detection network, and high-precision semantic observation is output; when the tracking is lost, the dual geometric constraint of the 2D internally tangent ellipsoid-3D object ellipsoid is utilized, and the P3P algorithm and the IoU cost function are matched to realize rapid relocation. In the mapping process, Gaussian-Wasserstein distance is introduced to measure semantic re-projection errors, and a joint objective function containing map point geometric errors and object semantic errors is constructed to carry out bundle adjustment. According to the method, the problem of feature extraction failure in motion blur and weak texture scenes is effectively solved, and the construction precision of the semantic map and the survivability of the system are remarkably improved.

The invention discloses an enhanced 3D (three-dimensional) surface reconstruction method based on 3D (three-dimensional) Gaussian Splitting. Global consistent depth priori is obtained through virtual stereo pair rendering; constructing a factor graph and introducing a cross-view geometry / luminosity consistency constraint to form local beam adjustment loss; the prior is used as a learnable parameter to be combined with 3DGS to be optimized, and meanwhile, the Pull loss is assisted to pull low-credibility pixels; and finally, multi-loss function end-to-end training is adopted. The method comprises the following steps of: in Tanksamp; the method has the advantages that F1 is equal to 0.58 and DTU Chamfer is equal to 0.48 mm on a Temples data set, the training time is only 20 min, compared with the prior art, geometric accuracy SOTA and speed magnitude improvement are achieved at the same time, and the method is suitable for VR / AR, robot and industrial measurement scenes.

The invention relates to a structured light three-dimensional measurement technology, and provides a system joint calibration optimization method fusing three-dimensional offset modeling and phase identity constraint for an industrial field low-precision target scene. The method comprises the following core steps: introducing a three-dimensional offset for a target feature point, and compensating spatial deformation and eliminating scale ambiguity through zero-mean constraint; sub-pixel matching is realized based on a phase identity principle, and projection constraint is enhanced to reduce dependence on a target; and constructing a joint reprojection error function, and adjusting and synchronously optimizing system parameters and offset by means of a bundle method. The prior art depends on a high-precision plane target, and when the target has manufacturing errors, installation warping or pasting deformation, calibration errors can be transmitted through an imaging link, so that the measurement precision is reduced, and target deformation and geometric errors of a projector are difficult to compensate at the same time. The method breaks through the limitation of a traditional inverse camera model, does not need to depend on a high-precision glass target, reduces the hardware cost, and constructs a cooperative compensation mechanism of a target manufacturing error and an imaging parameter. Experiments show that after explicit compensation is carried out on the local deformation of the target by adopting the three-dimensional offset factor, the flatness error gt is detected; on a paper target of 10 microns, the re-projection error of the camera is reduced by 79.6%, and the re-projection error of the projector is reduced by 74.8%; sub-pixel matching is achieved through phase identity constraint, multi-parameter collaborative optimization of bundle adjustment is combined, the high-precision measurement requirement of an industrial site is met, and an efficient solution is provided for high-precision three-dimensional measurement.

Techniques are described for automated operations to analyze visual data from images acquired in multiple rooms of a building to generate building information that may include a floor plan for the building, such as by analyzing visual overlap between those images to determine information that includes global inter-image pose and locations of walls and optionally other structural elements, and by using the generated building information in further automated manners. In some situations, the described techniques include using a combination of a trained diffusion transformer machine learning model and a bundle adjustment optimizer to determine global inter-image pose and wall location data and to use that data to generate a resulting floor plan for the building, such as to operate in parallel or with the bundle adjustment optimizer as a layer within the diffusion model that provides guidance for its automated determinations.

The invention relates to the technical field of planet remote sensing data processing and three-dimensional reconstruction, and discloses a Mars three-dimensional terrain reconstruction method based on feature enhancement and multi-view stereo matching, which comprises the following steps: firstly, obtaining and preprocessing Mars stereo image data; secondly, extracting texture features based on a gray-level co-occurrence matrix, and performing region segmentation by using local entropy to distinguish a high texture region from a low texture region; then, parameters such as the size of a matching block and a similarity threshold value are adaptively adjusted according to a segmentation result, and a multi-dimensional mixed feature descriptor is generated to perform homonymy point matching; a coupling threshold model of the solar incident angle and the terrain roughness is constructed, and parallax calculation of the shadow and the steep terrain is optimized; and finally, combining cross-scale parallax propagation and global bundle adjustment to reconstruct a three-dimensional terrain model. According to the method, the technical problems that the matching reliability of areas such as weak textures and shadows on the surface of Mars is insufficient and the model is discontinuous are solved, and the integrity, the self-adaptability and the global precision of the reconstruction model are improved.

The invention discloses a fabric-like sewing type cavity self-healing endoscope scene reconstruction method. The method comprises the following steps: carrying out initialization modeling on a two-dimensional Gaussian scene; tracking the pose of the camera; gaussian extension and key frame sampling strategy; a mapping mapping and hole perception completion module; light beam adjustment optimization of fusion smooth constraint is carried out; according to the method, firstly, continuous and dense representation of an endoscope scene is realized through two-dimensional Gaussian scene modeling and a Gaussian extension mechanism; 2, a cavity sensing and complementing mechanism is adopted, so that the mapping process has a fabric-like sewing type self-healing capability; thirdly, light beam adjustment optimization of normal smooth constraint is introduced, and the phenomena of local geometric roughness and Gaussian scene discontinuity are relieved; the overall frame is designed in a modularized mode and can be seamlessly integrated with an existing two-dimensional Gaussian mapping and light beam adjustment system; and 5, the method is suitable for a complex tubular organ scene. According to the method, topological perception and self-healing complementation of the mapping process can be realized in a complex tubular organ scene, and the continuity, stability and geometric quality of an endoscope three-dimensional reconstruction result are improved.

This invention belongs to the field of image generation, specifically relating to a method and system for generating 360° images of subway vehicles based on image stitching. The method includes: acquiring source images collected along the subway vehicle; obtaining effective matching point pairs through feature extraction matching and spatial topological similarity filtering; selecting the camera pair with the strongest geometric constraints as a benchmark; using incremental triangulation to recover the camera pose and 3D point cloud; in global bundle adjustment, optimization is performed by combining reprojection error and linear regularization terms based on the geometric principal axes of principal component analysis, with the regularization term weights adjusted according to the average distance from the point cloud to the principal axes; after optimization, the source images are projected onto a cylindrical canvas, and the optimal image gradient is selected based on gradient information entropy to construct a gradient field; a seamless 360° panoramic image is generated by solving the Poisson equation. This invention can effectively eliminate mismatches, suppress 3D reconstruction offset, and generate high-quality seamless panoramic images.

The invention belongs to the field of space target imaging, and discloses a space target three-dimensional reconstruction method for inhibiting dark color interference through combination of brightness and transparency, which is used for solving the problems of poor imaging quality and inaccurate pose estimation of a space target. According to the method, multiple requirements of improving the quality of an input image, adjusting a pose and training a three-dimensional model are jointly completed, and low-quality imaging of an incomplete imaging system is preprocessed based on lucky imaging; and then, dense feature points are extracted by using a pre-training network based on no detector, estimation of a three-dimensional initial point cloud and an initial pose of a camera is carried out, and pose joint adjustment is carried out by combining RANSAC and Bundle AdJustlement. During training, an MLP-based optimizer is used for calculating the deviation of pose adjustment, meanwhile, training of parameters of three-dimensional Gaussian primitives is carried out, and after various physical constraints are added, a one-stop assembly line can be implemented. According to the method, the good basic shape and the sharp edge of the model are kept, the good anti-noise capability is achieved, and meanwhile the real-time performance and the robustness are achieved.

Examples described herein provide a method for trajectory estimation using an image sequence. The method includes receiving an image sequence of an environment from a camera moving relative to the environment. The method further includes extracting and matching features from images of the image sequence. The method further includes determining a relative orientation of the images of the image sequence. The method further includes determining orientation parameters of the camera using sequential image resection. The method further includes performing, using the orientation parameters, bundle adjustment to generate refined orientation parameters of the camera. The method further includes estimating a trajectory of the camera relative to the environment based at least in part on the refined orientation parameters by performing loop closure.

This invention discloses a novel target localization method for a stitched mapping camera, comprising: obtaining the interior orientation elements of each sub-image of the camera based on the calibration parameters of the stitched mapping camera; obtaining the exterior orientation elements of each sub-image based on the POS data of each exposure point in the aerial photography operation and the transformation relationship between each sub-image; establishing an overlap relationship table between each sub-image based on the image overlap rate and the transformation relationship between each sub-image; finding each pair of sub-images with an overlap relationship based on the overlap relationship table, extracting corresponding feature points within the overlapping area of ​​the sub-images, and performing feature matching to obtain the image plane coordinates of the corresponding feature points in each sub-image; treating each sub-image with obtained image plane coordinates as an independent camera, performing aerial triangulation, and using bundle adjustment to jointly solve for the ground positioning result. This invention avoids the accuracy loss caused by the near single-center projection processing of the stitched mapping camera, thus improving the ground positioning accuracy.

The invention relates to the technical field of image processing, and discloses a landslide surface crack three-dimensional evolution unmanned aerial vehicle image extraction and quantification method and system, and the method comprises the steps: carrying out the bundle adjustment of an ultrahigh overlapping degree aerial image of a landslide surface, and obtaining an image shooting position, an image attitude parameter and a sparse three-dimensional point cloud; constructing a high-precision digital earth surface model; performing digital mosaic on the aerial image with the ultrahigh overlapping degree to obtain an orthoimage; performing semantic segmentation on the orthoimage to obtain a crack binary segmentation map of the orthoimage so as to identify a main crack and a secondary crack; performing three-dimensional geometric parameter analysis on the main crack and the secondary crack to obtain a crack three-dimensional space attribute parameter; carrying out evolution trajectory tracking on the crack three-dimensional space attribute parameters to obtain evolution data so as to construct a visual map of the landslide surface; according to the invention, the efficiency of extracting and quantifying the three-dimensional evolution unmanned aerial vehicle image of the landslide surface crack can be improved.

Methods, systems, and apparatus for performing bundling adjustment using epipolar constraints. A method includes receiving image data from a headset for a particular pose. The image data includes a first image from a first camera of the headset and a second image from a second camera of the headset. The method includes identifying at least one key point in a three-dimensional model of an environment at least partly represented in the first image and the second image and performing bundle adjustment. Bundle adjustment is performed by jointly optimizing a reprojection error for the at least one key point and an epipolar error for the at least one key point. Results of the bundle adjustment are used to perform at least one of (i) updating the three-dimensional model, (ii) determining a position of the headset at the particular pose, or (iii) determining extrinsic parameters of the first camera and second camera.

The present application relates to the technical field of map saliency localization, and particularly relates to a saliency localization method based on learning. The saliency localization method based on learning provided by the present application adopts a saliency prediction model to simulate the mechanism in a SLAM framework. A saliency map is predicted according to a saliency model, and the model can capture scene semantics and geometric information. The value of the saliency map is used as the weight of a feature point in a traditional bundle adjustment method. Detailed experiments conducted on the KITTI and EuRoc datasets with the most advanced algorithms show that the algorithm proposed by the present application is superior to existing algorithms in indoor and outdoor environments, and significantly improves the positioning accuracy and robustness.

The invention belongs to the technical field of visual simultaneous localization and mapping, and particularly discloses a binocular visual simultaneous localization and mapping method and device based on bionic divergence. According to the invention, the method comprises the steps: carrying out the equal cutting of the overlapping region of a plurality of images according to the cutting width, and carrying out the independent feature extraction of an effective cutting region; after a re-projection error is calculated by combining an observed camera device, parameter optimization is carried out according to the re-projection error based on a double-map beam adjustment optimization algorithm of an external parameter constraint. Through the above mode, a bionic divergent layout is taken as a core, a divergent binocular configuration is constructed through two camera devices which swing outwards and partially overlap view fields, overall view field expansion is realized, equivalence of virtual camera devices is completed by adopting a divergent binocular modeling strategy, and depth estimation and wide-angle sensing capability are optimized. In combination with an external parameter constrained double map beam adjustment optimization algorithm, the system structure consistency is ensured and the calculation redundancy is reduced, so that the positioning precision and the rotation robustness can be effectively improved.

Systems and techniques are provided for image processing for calibration. For example, a computing device can obtain image frames of a three-dimensional (3D) scene, each image frame including a plurality of two-dimensional (2D) points. Each 2D point corresponds to a 3D point. The computing device can determine a subset of 3D points by applying bundle adjustment on a set of 3D points (distributed over a field of view of the camera) and on fixed parameters of a camera. The computing device can determine inlier points from the subset of 3D points with a reprojection error less than a threshold value. The computing device can determine a final 3D points and final parameters of the camera by applying the bundle adjustment on the inlier points and on a prior set of camera parameters. The computing device can apply, to the camera, final intrinsic parameters of the final parameters of the camera.

The application relates to the field of computer vision and mobile robot navigation, and discloses an object-level semantic visual SLAM method and system based on a hybrid attention mechanism target detection network and an ellipsoid model. The system comprises five parallel thread modules of semantic perception, visual tracking and repositioning, local mapping and fusion, loop detection and global consistency optimization. The application extracts local and global features of an image in parallel through a target detection network, and outputs high-precision semantic observations; when tracking is lost, a 2D inscribed ellipse-3D object ellipsoid dual geometric constraint is used to realize fast repositioning in cooperation with a P3P algorithm and an IoU cost function. In the mapping process, a Gaussian-Wasserstein distance is introduced to measure semantic re-projection errors, and a joint objective function containing map point geometric errors and object semantic errors is constructed to perform bundle adjustment. The application effectively solves the problem of feature extraction failure in motion blur and weak texture scenes, and significantly improves the construction accuracy of a semantic map and the survival ability of the system.

This invention discloses a calibration-free panoramic camera and laser point cloud registration method and system. The method includes: acquiring laser point clouds and a panoramic image; splitting the panoramic image into multiple pinhole images and performing feature extraction and matching, then recalculating the matching results back into the panoramic image; reconstructing the sparse point cloud using a motion recovery structure and obtaining the initial pose of the panoramic image; registering the sparse point cloud and the laser point cloud using an iterative nearest-point algorithm, calculating seven parameters to achieve preliminary alignment; and improving the bundle adjustment method by combining the plane normal information corresponding to the sparse point cloud with the geometric constraints of the laser point cloud to achieve precise alignment between the panoramic image and the laser point cloud. This invention does not rely on any external calibration parameters, IMU information, or prior scene information. Through image self-reconstruction and laser constraint fusion, it significantly improves the registration accuracy and geometric consistency of 3D reconstruction, and is suitable for multi-source point cloud fusion reconstruction in scenarios without auxiliary information.

The invention provides a fringe projection trinocular vision calibration method based on phase reliability weighting, and the method comprises the steps: constructing a three-dimensional point set for each feature circle by taking a pixel coordinate in a neighborhood of the feature circle as a plane coordinate and taking a corresponding phase value as a height coordinate; carrying out plane fitting on the point set and obtaining a normal vector; the phase normal vector difference of each point is obtained, a frequency statistical graph is generated, curve fitting is carried out, and a self-adaptive segmentation threshold value is obtained according to a preset exponential decay curve; dividing the error curve into three sections by utilizing a self-adaptive segmentation threshold value and endowing the three sections with weights; on the basis of accurate geometric constraint provided by a calibration plate, 'phase normal vector difference 'is introduced as a physical criterion of the quality of feature points, a weight model is constructed through a piecewise function, and adaptive weighting is performed on the feature points with different qualities in multi-view beam adjustment optimization, so that the multi-view beam adjustment optimization is realized. Therefore, the calibration precision of the projector in the trinocular system is obviously improved.

The application discloses a camera and object point combined parallax angle parameterized bundle adjustment method and system, comprising the following steps: 1) obtaining remote sensing images by aerial photography of a measurement area, extracting and matching homonymous image point observation values of all remote sensing images; 2) arranging two prior anchor points to form a baseline and establish an angle reference frame; 3) uniformly parameterizing a camera pose, a photographic center position and a three-dimensional connection point position by a direction angle, an elevation angle and a parallax angle; 4) constructing a collinear observation equation to perform bundle adjustment calculation and jointly obtain the camera pose, the photographic center coordinates and the ground point coordinates. On the basis of parallax angle parameterization of the three-dimensional connection point, the photographic center and the camera pose are further uniformly incorporated into the angle parameterization system, so that the ill-conditioned and numerical instability problems under small parallax and weak control conditions are effectively improved, and the convergence and stability of the adjustment calculation are improved. The application is suitable for regional network adjustment of aerospace remote sensing images.

The invention discloses an infrared image three-dimensional reconstruction method and system based on a neural radiation field, and belongs to the technical field of computer vision and three-dimensional reconstruction. According to the method, the image quality is improved through dual-stage enhancement preprocessing, accurate estimation of the camera pose is realized by combining SIFT feature extraction and a beam adjustment method, a local-global joint infrared radiation field model is constructed, and a light adaptive SDF optimization strategy is introduced to balance the radiation field and symbol distance function convergence process. According to the method, an infrared imaging process is simulated through cumulative transmissivity modulation, and total loss function optimization model parameters including control loss and structure loss are designed. Experiments show that the infrared scene three-dimensional geometric reconstruction precision and the new view synthesis quality can be remarkably improved, the average chamfering distance is reduced by 87.31%, the peak signal-to-noise ratio is improved by 55.03%, and an efficient solution is provided for infrared three-dimensional reconstruction in complex scenes such as low visibility.

This invention discloses a method and system for calibrating fisheye lenses using combined 2D and 3D information, belonging to the field of computer vision and photogrammetry. The method includes: acquiring multi-angle video data from a 2D checkerboard pattern to estimate initial camera intrinsic parameters; acquiring 3D calibration field images and identifying AprilTag tags to obtain 3D-2D information of control points; performing distortion correction on the control points using the initial intrinsic parameters; estimating initial extrinsic parameters using SVD and RQ decomposition methods; constructing a bundle adjustment optimization model; and performing global optimization using an MEI camera model combined with 2D checkerboard and 3D calibration field observation information to obtain the final calibration parameters. This invention overcomes the shortcomings of 2D methods lacking absolute spatial constraints and 3D methods being complex to operate, achieving high-precision, high-efficiency, and fully automated fisheye camera calibration.

The application relates to the technical field of geographic information, in particular to a three-dimensional map construction method and system based on multi-modal data, which comprises the following steps: acquiring a candidate image sequence and a flight log collected by a UAV, and acquiring a three-dimensional point cloud of a target position, a matching image sequence and UAV initial pose data of each matching image in the matching image sequence based on the candidate image sequence and initial camera internal parameters; constructing a camera optical center prior position corresponding to each matching image in the matching image sequence and an allocation weight corresponding to each camera optical center prior position based on the flight log; and optimizing the initial three-dimensional point cloud set, the initial camera internal parameters and each UAV initial pose data based on the camera optical center prior position corresponding to each matching image in the matching image sequence, the allocation weight corresponding to each camera optical center prior position and a pre-set bundle adjustment algorithm, so as to acquire a three-dimensional map of the target position. The method improves the stability, accuracy and practicability of three-dimensional map construction.

Methods, systems, and apparatus for performing bundling adjustment using epipolar constraints. A method includes receiving image data from a headset for a particular pose. The image data includes a first image from a first camera of the headset and a second image from a second camera of the headset. The method includes identifying at least one key point in a three-dimensional model of an environment at least partly represented in the first image and the second image and performing bundle adjustment. Bundle adjustment is performed by jointly optimizing a reprojection error for the at least one key point and an epipolar error for the at least one key point. Results of the bundle adjustment are used to perform at least one of (i) updating the three-dimensional model, (ii) determining a position of the headset at the particular pose, or (iii) determining extrinsic parameters of the first camera and second camera.

The present application relates to the field of photogrammetry, and discloses a land space image analysis method based on photogrammetry, comprising: acquiring aerial images and attitude data of a survey area to generate sparse point clouds; distinguishing rigid and non-rigid feature points based on semantic segmentation results; establishing a local vertical reference system for non-rigid points and configuring anisotropic parameters with transverse variance greater than longitudinal variance; mapping the parameters to a global weight matrix using a coordinate rotation matrix, and substituting them into a bundle adjustment model to complete iterative solution, the present application effectively realizes the automatic suppression of non-rigid disturbance of vegetation and water area by constructing an anisotropic weight determination mechanism coupled with physical properties and geometric constraints, and improves the geometric accuracy and stability of three-dimensional reconstruction in complex land space scenes.

This invention discloses a multi-view skeletal reconstruction method and system for digital evaluation of canine training behavior, relating to the fields of computer vision and canine behavior recognition. The method includes: acquiring image signals; detecting two-dimensional keypoints using a neural network integrating a canine attention mechanism; obtaining a three-dimensional skeletal sequence through three-dimensional reconstruction and bundle adjustment optimization; performing anomaly detection and interpolation repair to obtain a standardized skeletal sequence; extracting features using a spatiotemporal graph convolutional network and generating behavioral feature vectors through a cross-attention mechanism; obtaining semantic action segments through frame-level primitive classification and grammatical parsing; and performing spatiotemporal alignment and deviation analysis between the action segments and a standard digital twin model to generate phenotypic vectors and a behavior evaluation report. Through multi-view skeletal reconstruction and fusion, high-precision capture, semantic segmentation, and multi-dimensional performance quantification evaluation of canine training behavior are achieved.

The application provides a multi-level fusion optimization method and system for oblique photogrammetry space three processing, and relates to the technical field of photogrammetry, wherein the method comprises: acquiring multi-view image data and constructing an image connection graph, using weighted spectral clustering combined with multi-constraint spectral clustering for dynamic blocking to form a subnetwork with internal connection and overlapping relationship. Based on the image quantity, point cloud scale and matching complexity evaluation and calculation load, the subnetwork is distributed to distributed computing nodes, and the subnetwork parameters are optimized in parallel. The target overlapping area is determined through multi-level subnetwork fusion, the public points and camera pose are extracted to construct a pose graph model, and the Gauss-Newton method is used to solve the global consistent transformation parameters. Under the constraint of ground control points, overall optimization is carried out through weighted least squares adjustment, a global model is finally constructed, light beam adjustment is performed, and the space three processing result is generated. The application improves the automation degree and three-dimensional reconstruction precision of oblique photogrammetry data processing.

This application provides a coordinate estimation method and system for matching UAV photos with 3D ground real-world scenes, belonging to the field of geographic coordinate estimation technology. The method includes: acquiring aerial images of the task area using an UAV's onboard camera; identifying and masking dynamic interference areas in the aerial images using semantic segmentation and motion detection to obtain static background images and extracting features to obtain two-dimensional image features; extracting features from a preset static urban real-world 3D model to obtain 3D model features; matching the 2D image features with the 3D model features to establish a correspondence between 2D image points and 3D model points; based on the correspondence, using the PnP algorithm to calculate the initial exterior orientation elements of the UAV in the absolute geographic coordinate system, and optimizing it using bundle adjustment to obtain the optimized UAV visual pose; fusing it with the observation data from the inertial measurement unit to obtain the final 3D position and attitude of the UAV in the absolute geographic coordinate system.

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 application discloses a variable-scale moving body pose refinement method based on adaptive sliding window incremental adjustment, which comprises the following steps: acquiring high-frame-frequency sequence images of a variable-scale moving body; performing incremental Schur complement edge-based bundle adjustment on the sequence images in an optimal sliding window; optimizing the continuous pose based on a weighted pseudo-observation value constraint model; and outputting the refined pose parameters. The application has high flexibility, relies on an airborne moving platform to realize large field observation, has high efficiency and strong adaptability, balances the calculation efficiency and precision through an adaptive sliding window, processes data quickly, has high precision, improves the variable-scale moving body pose refinement precision with the aid of an optimal window, Schur complement edge-based bundle adjustment and weighted constraint, and is suitable for deep space exploration and other scenes.

According to the method, high-precision zero sample depth estimation becomes possible through the proposal of a'depth estimation basic model ', and a good application prospect is shown in a monocular vision inertial synchronous localization and mapping (SLAM) method in which depth information is difficult to directly obtain. However, the model generally only outputs a scale-free relative depth relationship in a reasoning stage, and the rich depth prior information contained in the model cannot be directly introduced for a monocular vision inertial SLAM method. Based on the challenges, the invention provides a synchronous positioning and mapping method for monocular vision inertia of an explicit fusion depth estimation basic model. The method comprises the following steps: an SLAM front end adjusts a feature point extraction threshold based on a parallax gradient map generated by a depth estimation basic model and establishes feature point tracking based on optical flow; a depth constraint residual error is added to the rear end of the SLAM, and depth mapping parameters are obtained through bundle adjustment optimization; and recovering a metric depth map according to the depth mapping parameters and performing dense mapping. According to the method, depth priori contained in a depth estimation basic model is embedded into a monocular vision inertial SLAM method in an explicit mode, cooperative gain of functional modules is achieved, then the overall precision of a platform in positioning and mapping tasks is systematically improved, and dense mapping is achieved.