327 results about "Binocular stereo" patented technology

The invention discloses a double-station robot sorting optimization method and system based on digital twinning and a terminal, double improvement of sorting efficiency and safety is achieved by constructing a digital twinning driven collaborative operation system, and the method comprises the steps that firstly, a laser radar and a polarization camera are used for forming a composite sensing unit; geometric morphology, material reflection characteristics and spatial pose data of a target object are synchronously obtained, and are input into a digital twin engine after time synchronization processing; an engine constructs a virtual sorting scene containing a material attribute database based on a physical rendering technology, sub-millimeter-level space registration is achieved through feature fusion of a binocular stereoscopic vision depth map and geometric parameters, high-precision digital twin stations are generated, a system plans a space-time constraint trajectory of a double-station robot in a virtual environment, and a target object is obtained. And an integrated discrete event simulation engine performs operation time sequence conflict prediction, and when a space overlapping risk is detected, an alternative path containing a dynamic obstacle avoidance strategy is generated through a trajectory re-planning algorithm.

The invention discloses a multi-modal fusion real-time environment monitoring visual robot system, and relates to the technical field of real-time vision. The system comprises a multi-mode sensing module, and is equipped with various sensors such as a binocular stereo camera and a laser radar to collect environment data. The heterogeneous data preprocessing unit cleans and downsamples data such as images and point clouds; the space-time alignment fusion module realizes multi-source data space-time registration and synchronization; the environment semantic understanding engine constructs an environment semantic graph through a multi-branch network in combination with an attention mechanism; the abnormal event detection unit identifies abnormity based on a historical data model; the path planning and decision-making module integrates multiple targets to generate an optimal path; the autonomous movement execution module controls the robot to move and operate; and the cloud cooperative control center supports model updating and remote intervention. According to the invention, through cooperative work of all the modules, full-process intelligentization of environment monitoring data acquisition, processing, analysis and decision making is realized.

A slope monitoring method based on optical flow estimation and binocular vision belongs to the technical field of deformation monitoring and measurement, and adopts the technical scheme that a binocular stereoscopic vision system is built, and camera acquisition parameters are calibrated; transmitting the image to a server in real time through the Internet of Things; distortion correction and stereo matching are carried out to obtain a depth map; monitoring points are manually or automatically arranged on the corrected image, and dense optical flow estimation tracking pixel point movement is carried out; world coordinates of observation points and movement conditions of each frame are obtained, and displacement changes of slope monitoring points are obtained by combining coordinates before and after displacement; and obtaining a displacement rate by combining monitoring time and displacement, judging slope safety, obtaining a displacement track, and performing linear interpolation to obtain a slope deformation cloud picture, thereby realizing the monitoring purpose. The slope displacement monitoring method has the advantages that slope monitoring can be carried out more flexibly and efficiently, the safety of workers is improved, the slope monitoring cost is reduced, real-time slope displacement monitoring is achieved, and the intensive degree of slope monitoring points is greatly improved.

The invention belongs to the technical field of plate shape monitoring and visual analysis, and discloses a stainless steel medium-thickness plate double-face contour measurement method and system, and the method comprises the steps: S1, completing the calibration of a double-line-scan digital camera, and obtaining the upper and lower surface images of a medium-thickness plate; s2, carrying out binocular stereoscopic vision three-dimensional reconstruction to generate a point cloud picture, and converting the point cloud picture into a point cloud picture under the same coordinate system for display; s3, eliminating transverse rotation and deviation according to point cloud center line fitting and plane fitting of the upper surface and the lower surface of the medium-thickness plate; s4, comparing the geometrical relationship between the point clouds of the upper and lower surfaces to quantify local vibration and thickness change, correcting vibration errors, and obtaining the thickness of the medium-thickness plate; and S5, extracting an edge point set from the point cloud image to obtain the overall contour of the medium-thickness plate, and obtaining the position of a shear line based on the width and edge linearity detection of the medium-thickness plate. By the adoption of the technical scheme, the overall contour, overall thickness distribution and accurate shearing position of the medium-thickness plate can be obtained.

The invention relates to the technical field of agricultural machinery control, in particular to a multi-variety vegetable harvester dynamic identification and feeding control system based on image processing, which comprises an image acquisition module, an environment sensing module, an image processing module, a data fusion module, a dynamic identification and tracking module and a feeding control module. Through binocular stereo vision and millimeter wave radar fusion, three-dimensional space positioning and dynamic tracking of the brassica oleracea corm are realized. Track prediction is carried out in combination with extended Kalman filtering and a long-short-term memory network, and the clamping path of the mechanical arm is optimized; the clamping force is adaptively adjusted through fuzzy PID control, and accurate feeding of the cabbages is achieved in combination with speed control of the conveying belt. The automatic cabbage harvesting machine improves the automation level of cabbage harvesting, effectively reduces the damage rate, improves the operation stability and harvesting efficiency, and is suitable for intelligent harvesting operation of a large-scale cabbage planting base.

The invention discloses a transcranial magnetic stimulation target region recommendation method based on craniocerebral position estimation. The method comprises the following steps: constructing a face key point cloud based on binocular stereo vision; reconstructing the face key point cloud into a cranial surface model by using a pre-trained generative model; the method comprises the following steps: collecting magnetic resonance imaging data of patients with various diseases, establishing a transcranial magnetic stimulation target region standardized template with disease specificity, and determining a stimulation target region of each disease in a standard space; and based on the transcranial magnetic stimulation target region standardization template, mapping the corresponding stimulation target region from the standard space brain region to the scalp of the patient according to the disease type of the patient so as to realize recommendation. According to the method, the graph neural network is innovatively combined with the SHAP algorithm, the scientificity and interpretability of target spot selection are improved, important brain region target spots are obtained by calculating the contribution value of classification model features to classification decision of each sample, and then intervention target spots are sorted and selected to obtain an optimal treatment target spot recommendation scheme.

The invention discloses a pavement disease size accurate quantification method based on binocular vision, and the method specifically comprises the steps: collecting pavement image data through a vehicle-mounted binocular camera, achieving the real-time detection of a disease target through an improved RT-DETR model, and outputting the disease type and detection frame information; after a pavement disease target is detected, pixel-level segmentation is carried out on a disease area in the detection frame based on a semantic segmentation model, and a disease contour mask is extracted; an improved IGEV-Stereo stereo matching algorithm is used for calculating a disparity map, depth information is output based on camera calibration parameters and a disparity estimation result, and conversion from pixel coordinates to three-dimensional coordinates is achieved; and the binocular depth information and a disease detection segmentation result are combined to realize accurate size quantification of typical road surface diseases with different characters. Through the binocular stereoscopic vision technology, automatic detection and precise quantification of pavement diseases can be realized, the cost can be effectively reduced while the disease quantitative evaluation precision is improved, and decision support is provided for road management and maintenance.

The invention provides a macrobrachium rosenbergii quality grading system based on machine vision. The macrobrachium rosenbergii quality grading system based on machine vision comprises a prawn body placing platform used for placing macrobrachium rosenbergii to be tested; the three-dimensional acquisition module comprises a structured light projection device, a binocular stereo camera and a flight time depth sensor; and the three-dimensional reconstruction module is used for constructing the data of the three-dimensional acquisition module into a complete three-dimensional point cloud model through a data fusion algorithm and a point cloud registration algorithm. According to the macrobrachium rosenbergii quality grading system based on machine vision, the overall scheme has robustness and expandability, the system is suitable for various large-scale farms and grading scenes, and the intelligent level of macrobrachium rosenbergii quality control is greatly improved.

The invention relates to a binocular stereoscopic vision three-dimensional reconstruction method and system based on a polarization state. The method comprises the following steps: acquiring polarization images of a polarization camera under different polarization degrees; according to the obtained polarization image, the angle of a normal vector is calculated based on the Fresnel theory, and the angle of the normal vector comprises the zenith angle and the azimuth angle of the surface of the object; the ambiguity of the azimuth angle is eliminated through the projection included angle of the normal vector on the YOZ plane, and mismatching is eliminated by using the angle constraint of the normal vector; and performing gradient integration on the angle of the normal vector after mismatching elimination to complete three-dimensional reconstruction. According to the method, the projection included angle is utilized to assist in judging the quadrant of the normal, so that mismatching of the corresponding points is eliminated, and the measurement efficiency is improved.

The invention relates to a vehicle-mounted road defect detection system and method based on binocular vision and deep fusion, and the system comprises a binocular camera module, a main control platform, a positioning module, a communication module and a power supply control module. The system realizes automatic detection and severity quantitative evaluation of various pavement defects such as cracks, pits and ruts, obtains defect positions in combination with a positioning module, and uploads the defect positions to a cloud platform in real time through a communication module, thereby forming a closed-loop urban road defect information perception and management system. According to the method, the quantitative evaluation capability of the identification accuracy and severity of the road defects is remarkably improved, and automatic identification, accurate positioning and real-time cloud synchronous uploading of the road defects are realized; the method has the advantages of flexible deployment, efficient operation, high adaptability and the like, is particularly suitable for urban road intelligent inspection and maintenance management scenes, and has good engineering application prospects and popularization values.

The invention relates to the technical field of computer vision, and particularly provides a tunnel video stream three-dimensional modeling system based on binocular stereo matching and SLAM (Simultaneous Localization and Mapping), which comprises a multi-modal image acquisition unit, a binocular infrared camera and an RGB (Red, Green and Blue) camera are configured to synchronously acquire an infrared image pair, an RGB image pair and inertial measurement unit data of a tunnel environment, the multi-source data is uploaded to the cloud processing platform in real time through the wireless transmission module; based on a dynamic switching mechanism of environmental perception, performing adaptive fusion pose estimation of a feature point method and a direct method on input multi-modal image data; the stereo matching and dense reconstruction unit is used for calculating a disparity map by adopting an improved self-adaptive window stereo matching algorithm, generating a three-dimensional point cloud in combination with the pose information output by the pose estimation unit, and constructing a global consistency point cloud model of the tunnel scene through a time sequence point cloud registration and splicing algorithm; and a multi-algorithm target detection and semantic fusion unit. The method can meet the requirements of precision and robustness of tunnel reconstruction.

The invention provides a multi-modal image fusion endoscope operation target positioning device and method, and the method comprises the steps: calibrating internal and external parameters of a binocular camera and a fluorescence camera, and obtaining a pose conversion matrix between the fluorescence camera and the binocular camera; using a U-Net network model to identify and track the position of the fluorescent mark point in the fluorescent image in real time, and obtaining the two-dimensional image coordinate of the fluorescent mark point; processing a left image and a right image of the binocular camera by using a region-based local stereo matching method in binocular stereo imaging to obtain a point cloud image of a target space; and obtaining three-dimensional pose information of the fluorescent mark point by combining the two-dimensional image coordinate of the fluorescent mark point and the point cloud image of the target space. The binocular stereoscopic vision and the fluorescence imaging technology are combined, the three-dimensional pose and depth information of the fluorescence mark point is obtained by registering the information between the fluorescence camera and the binocular camera, more accurate and real-time vision assistance is provided for an operation, and the safety and efficiency of the operation are improved.

The invention provides a power distribution construction safety distance monitoring method, device and equipment and a storage medium, and the method comprises the steps: inputting an obtained power distribution operation scene image into a stereo matching neural network model for binocular stereo matching calculation, outputting a three-dimensional information disparity map, and converting the three-dimensional information disparity map into a power distribution operation depth map; based on the power distribution operation scene image and the target detection neural network model, key operation elements and two-dimensional coordinate information are determined; screening the depth values of the pixel points in the key job element target frame to obtain image coordinates of the screened pixel points; performing coordinate system conversion on the image coordinates of the screened pixel points and the depth values to obtain initial three-dimensional point cloud data of the key job elements; clustering and screening the initial three-dimensional point cloud data to obtain effective three-dimensional point cloud data; and determining a first nearest point coordinate, a second nearest point coordinate and a third nearest point coordinate from the effective three-dimensional point cloud data, and determining a safety distance among the operator, the construction machinery and the electrified equipment.

The present invention relates to a vehicle control method, a binocular stereoscopic vision-based road unevenness feature detection method, system and device, and a computer readable storage medium. The binocular stereoscopic vision-based road unevenness feature detection method comprises the steps: S1, road surface unevenness feature detection; S2, generating a binocular disparity point cloud; S3, feature region point cloud projection; and S4, region feature calculation: calculating unevenness information of a corresponding region point cloud. The vehicle control method comprises the steps: T1, performing ego-vehicle trajectory prediction on the basis of the motion state of the vehicle; T2, on the basis of the predicted ego-vehicle trajectory, calculating a degree of correlation to a corresponding region point cloud; and T3, sending to an ADAS controller the concavity and convexity and height position of the corresponding region point cloud and the acquired degree of correlation as summarized information of each road unevenness feature. The present invention can effectively identify road unevenness information, reduce calculation resource consumption, and thus improve the comfort of intelligent driving.

The invention provides a cross-domain binocular stereo matching method based on multi-scale information dynamic fusion and feature deviation correction, and belongs to the field of computer vision. Specifically, the model adaptively fuses a convolutional neural network (CNN) and a Transform structure according to dynamic change of a scene so as to capture local detail information and global context dependence at the same time. Secondly, through a multi-stage progressive cost body fusion and aggregation mechanism, full fusion of multi-scale matching cost bodies is promoted, redundant information is effectively inhibited, and matching precision is improved; and finally, guiding the model to learn more stable domain invariant representation by gradually reducing the deviation between different domain features, thereby remarkably enhancing the cross-domain robustness and adaptability. Compared with the most advanced method for performing cross-domain experiments in different scenes, the method disclosed by the invention shows better performance compared with an existing advanced model: 1) the cross-domain stereo matching precision is remarkably improved in a complex scene, and stronger domain migration capability and generalization stability are shown; and 2) while the cross-domain robustness is maintained, relatively high accuracy and calculation efficiency are maintained in a non-cross-domain environment, and double consideration of cross-domain and non-cross-domain performance is realized.

The invention relates to the technical field of computer vision, in particular to a three-dimensional reconstruction method and device based on optical polarization and stereoscopic vision principles. The three-dimensional reconstruction method sequentially comprises the steps of image preprocessing, polarization parameter extraction, binocular stereo matching, disparity map generation and optimization, normal vector correction, depth information fusion and three-dimensional point cloud reconstruction and modeling generation. A polarization normal vector field is constrained and corrected as a'skeleton ', and the azimuth angle pi ambiguity problem which puzzles polarization three-dimensional reconstruction for a long time is solved; high-frequency surface normal details contained in corrected polarization information are used as textures to enhance and fill up depth information loss of binocular vision in weak texture and repeated texture areas, the inherent limitation of a single sensing technology in a three-dimensional reconstruction task is overcome, and the three-dimensional reconstruction of the surface of an object, especially a diffuse reflection object, is realized. And high-precision and high-integrity three-dimensional shape recovery is realized.

The invention discloses a pet size intelligent measurement system and method based on a multi-task segmentation network. According to the system, the three-dimensional visual perception capability of a scene is constructed by using a binocular stereoscopic vision technology, and accurate depth information is obtained; meanwhile, an advanced multi-task segmentation network is adopted to process the pet image, efficient target detection, category recognition and pixel-level instance segmentation are achieved, and the pet contour (especially the hair edge) is accurately extracted; and finally, depth data provided by three-dimensional vision and the accurate contour output by the multi-task segmentation network are fused, and the real three-dimensional size of the pet is calculated. According to the method, the problems of low precision and poor contact stress and dynamic edge processing of a traditional method are solved, and non-contact and high-precision pet size intelligent measurement is realized.

The invention relates to a structured light-assisted binocular stereo matching fusion method, system and device and a medium. The method comprises the following steps: projecting a plurality of groups of high-frequency sine stripes to the surface of a target object, and superimposing and projecting a Gray code pattern; shooting the surface of the target object to obtain left and right images; and extracting a wrapped phase from the left and right images, and calculating to obtain an absolute phase according to the wrapped phase and the decoded information of the Gray code pattern. And calculating the matching cost of each pixel in the left image and the right image under different parallax, and carrying out cost aggregation processing in a parallax space by taking an absolute phase as a constraint to obtain a parallax map. And optimizing the disparity map to obtain the optimal disparity. And generating a three-dimensional point cloud according to the optimal parallax and the calibration parameters. According to the method, texture information can be enhanced, and the reconstruction integrity and geometric accuracy of weak-texture and high-reflection non-abandoned products are remarkably improved.

The invention discloses a PCB soldering paste printing three-dimensional defect detection system based on multispectral imaging, and relates to the field of machine vision detection, and the system comprises a multispectral image acquisition module, a four-channel industrial camera based on RGB and near infrared, and a tunable LED light source array; acquiring reflection characteristic data under different penetration depths through a wavelength switching mechanism; the motion control trigger module is used for realizing positioning based on an XYZ three-axis objective table driven by a servo motor in cooperation with feedback of an encoder; a pulse width modulation signal is adopted to coordinate the moving speed of a camera shutter and a platform; according to the method, RGB and NIR four-channel imaging is combined with Beer-Lambert law modeling, so that double verification of material component quantitative analysis and three-dimensional shape reconstruction is realized, and limitation of monocular vision is avoided; structured light projection and binocular stereo matching technologies are adopted, cross-frame data alignment is realized in cooperation with an ICP algorithm, and detail defects can be better detected.

The invention relates to the technical field of image processing, and discloses a speckle structured light binocular stereo matching method and system based on multi-cost fusion, a computer readable storage medium and a computer program product. The method comprises the following steps: respectively carrying out epipolar correction on an obtained left camera image and an obtained right camera image; performing boundary segmentation on the corrected left camera image to obtain a corresponding boundary image; calculating a Census cost and a BT cost by using the corrected left camera image and right camera image, and carrying out weighted fusion to obtain a matched cost body; performing cost aggregation on the obtained cost body by using the boundary image as prior information; and calculating a parallax image corresponding to the left camera image through the aggregated cost body. According to the invention, abnormal matching points in a speckle image stereo matching process are reduced, and the real-time performance and robustness of speckle image stereo matching are effectively improved.

The application provides a three-dimensional reconstruction method based on binocular stereo matching, and relates to the technical field of binocular stereo vision. The method combines binocular stereo matching algorithm, triangulation algorithm and surface texture mapping, and can realize surface reconstruction of scene objects in different environments. After stereo calibration is performed on a binocular camera, the internal and external parameters of the binocular camera are obtained, stereo correction is performed, then an image disparity map of the corresponding scene object is generated according to an optimized semi-global stereo matching algorithm, after the disparity information is obtained, the point cloud information converted by the object disparity is triangulated on the surface according to the triangulation algorithm, and finally three-dimensional surface reconstruction of the object is realized through the texture mapping technology. The application can reduce the influence of noise in the disparity map generation process, i.e. the semi-global stereo matching algorithm, improve the accuracy of the disparity information, and complete the three-dimensional surface reconstruction task of the object with little sacrifice of time performance.

The invention relates to the field of mapping, in particular to a three-dimensional mapping method, which comprises the following steps of: initializing a system; oRB features are extracted from the left camera and the right camera; constructing an initial key frame: sending image data of a first frame of a left camera to a semantic thread, and setting the frame as the initial key frame; performing a mask prediction mechanism, key frame tracking and dual-stage tracking on the image of the left camera, and alternately performing key frame tracking and static tracking on the right camera; and binocular stereo matching is carried out according to results of the left camera and the right camera, map points are updated, and an obtained result is a three-dimensional diagram. The method has practical significance when being practically applied to rapid three-dimensional mapping of a small computing unit or a large scene, dynamic objects are filtered out of the mapping in the mode, the real-time performance and the mapping efficiency of the mapping are guaranteed, three-dimensional mapping can be rapidly completed on a low-computing-power platform, and the method is suitable for large-scale popularization and application. And the method has good practical performance and application potential.

The invention discloses a binocular stereo vision-based three-dimensional measurement and positioning method and system and a bird repelling linkage method, and relates to the technical field of bird repelling. Comprising the steps of collecting left-eye and right-eye images and obtaining original visual data; respectively carrying out bird target detection in the left and right eye images, and determining a bird target object to be processed; binocular correction parameters are calculated, and errors caused by unparallel optical axes of left and right eye images are corrected; on the basis of linear constraint, bird targets in the left-eye image and the right-eye image are matched, and the same bird targets are found; calculating a parallax value of the same bird target; calculating the distance data of the bird target according to the distance measurement parameter and the parallax value; and calculating the spatial three-dimensional information of the bird target according to the longitude and latitude of the system deployment place and the azimuth pitching distance of the optical system. According to the invention, the three-dimensional information of all bird targets in the field of view is determined through optical binocular calibration, stereo matching and depth calculation, and the problem that an optical bird detection system cannot acquire the spatial position information of the bird targets is solved.

The invention discloses a point cloud data enhancement method based on a false point cloud technology, and the method comprises the steps: innovatively designing a binocular depth estimation method for rapidly inspecting a residual structure, and extracting the feature information of three levels through a real-time binocular stereo matching network RealtimeStereo; a rapid feature aggregation module with an attention mechanism aggregates the features to achieve the performance of a high-cost feature extraction network; a residual structure aggregation cost volume is designed, a two-stage depth optimization module is introduced, the two-stage depth optimization module comprises an accelerated expansion depth self-optimization module and a KNN-based point cloud depth correction algorithm, the accelerated expansion depth self-optimization module carries out self-optimization on a depth map, and the KNN-based point cloud depth correction algorithm introduces point cloud information to optimize the depth to generate a false point cloud with higher quality; an auxiliary module is innovatively designed to remove invalid information; and finally, a high-quality enhanced point cloud is generated through a depth projection module and a point cloud merging module, so that the quality and practicability of point cloud data are improved.

The invention discloses a lightweight image recognition method and system for power plant safety, and relates to the field of image recognition, and the method comprises the steps: constructing a multi-scale adaptive perception image anomaly detection model, and recognizing the violation behaviors of power plant operators in power plant real-time video data through the image anomaly detection model; constructing a multi-stage binocular stereo area distribution model to match the feature points in the binocular image of the power equipment, and calculating the safety distance between the power plant operating personnel and the electrified body based on the matching result; and constructing an edge equipment reasoning model of lightweight knowledge distillation in combination with illegal behaviors and safety distances of power plant operating personnel, and monitoring potential safety hazards of the power plant in real time by using the edge equipment reasoning model. According to the method, the teacher model with high expression ability is deployed on the cloud for training, semantic information and structural knowledge are transmitted to the student model on the edge device through the dynamic adaptation strategy, and the recognition ability of the edge model in a computing power limited scene is remarkably improved.

The invention relates to the field of pavement crack detection, in particular to a pavement crack detection device and method based on machine vision, and the device comprises a multi-modal data acquisition module, a lighting unit, a central processing unit, a display module, a storage module, a positioning and communication unit, a power supply module and a supporting vibration reduction structure. The multi-modal data acquisition module comprises a high-definition RGB camera module, a binocular stereoscopic vision module and an infrared thermal imaging module; the detection method comprises the following steps of S01, data acquisition and preprocessing, S02, multi-modal fusion and input preparation, S03, crack identification processing, S04, post-processing and parameter extraction, and S05, result output and storage. The problems that in the prior art, crack detection is poor in adaptability, insufficient in real-time performance, high in false detection and omission ratio, incomplete in quantitative measurement and the like are solved.

The present invention discloses a pipeline defect detection, positioning and ranging system based on binocular stereo vision, including a binocular camera image capture module for capturing and collecting image data of the left and right cameras; a camera calibration module for establishing a camera imaging geometric model and correcting lens distortion, and outputting internal and external camera parameters and distortion coefficients; a stereo rectification module for achieving coplanar row alignment of the left and right images, making the left and right image planes parallel to the baseline, and corresponding points in the left and right images on the same horizontal epipolar line; a deep learning object detection module for training a deep learning network based on an object detection algorithm to achieve detection and recognition of pipeline defects; and a stereo matching and depth calculation module for achieving positioning and ranging of pipeline defects. It can achieve non-contact measurement of pipeline defects, and has the advantages of a wide monitoring range, good real-time performance, high accuracy, and accurate positioning.

The invention provides a binocular stereo matching method and system, and the method comprises the steps: obtaining a left and right stereo image pair captured by a binocular camera, and carrying out the feature extraction and enhancement through a depth separable convolution basic network and an ECANet attention model; carrying out topological structure transformation by using a TopoAug feature enhancement strategy, constructing a topological consistency loss function and carrying out adaptive feature fusion; a large neighborhood search strategy and a hybrid node-destructor model are adopted to construct an initial cost body, and cost aggregation is carried out through capacity routing; and constructing an M uniform loss grid and a grid motion model to carry out parallax estimation, and obtaining a final parallax map through unsupervised consistency optimization. Through the technologies of lightweight network design, topology perception feature enhancement, hybrid node optimization cost body construction, unsupervised consistency optimization and the like, the calculation complexity is reduced while high precision is kept, and the method is particularly suitable for real-time monitoring scenes in resource-constrained environments such as substations and the like.

The invention relates to the field of binocular stereo vision, and discloses a binocular stereo matching method and system based on multi-scale iterative optimization and related equipment. The method comprises the steps of performing semantic structure feature extraction on a corrected left view and a corrected right view through a feature extraction network; determining a cost space pyramid and a probability matrix according to the left view feature map and the right view feature map output by the feature extraction network; extracting a multi-scale context feature of the left view through a context sensing network, and initializing the multi-scale context feature to obtain a hidden state and input of a recursive network; during recursive network iteration, according to the probability matrix, a multi-range search strategy is adopted to index local cost from the cost space pyramid; and inputting the local cost into the recursive network, combining the hidden state obtained by the context features and the input, and carrying out iterative updating on the parallax value to obtain a parallax map, thereby improving the accuracy of parallax calculation.

The invention discloses a visual navigation method of a binocular backpack AGV (Automatic Guided Vehicle), which comprises the following steps: a domain controller detects and matches X angular points in real time according to image information, and calculates image pixel coordinates and image physical coordinates of all current X angular points; the domain controller calculates the real-time length from the current AGV to the visual label and the real-time angle of the current AGV relative to the visual label according to the camera coordinate and the label coordinate of the current X corner point; the domain controller sends a corresponding driving signal to the driving motor in real time according to the calculation result of the length and the angle; the driving motor adjusts the position and the vehicle body angle of the AGV in real time according to the driving signal; the navigation effect of the AGV is remarkably improved by arranging the binocular stereo camera to shoot the visual label in real time and combining the domain controller to perform image analysis and processing in real time, so that the AGV runs more stably and safely, and the environmental suitability of the AGV is remarkably improved.