693 results about "Back projection" patented technology

The invention relates to the technical field of electrical equipment fault diagnosis, in particular to an intelligent fault diagnosis method and system for electrical equipment, and the method comprises the steps: constructing a multi-dimensional tensor model, uniformly fusing the equipment state information, electrical distance weighted connection and phase dynamic coupling relation, and extracting an abnormal propagation mode through high-order singular value decomposition; designing a space-time-frequency coupling interference stripping mechanism, and combining structure guide disturbance deconstruction, multi-scale dictionary learning and sparse low-rank decomposition to accurately separate transmissible and non-transmissible interferences; reconstructing a fault trajectory based on a generative adversarial mechanism, coupling a graph structure dynamic encoder, a topology consistency discriminator and a time controllable generator, and restoring a real propagation path; and finally, tensor semantic compression, a three-view graph neural network and fault label back projection interpretation are integrated through a multi-source semantic fusion mechanism. According to the method, cross-space-time and cross-structure fault diagnosis and traceability are realized, and the accuracy and interpretability are improved.

The invention discloses a quadruped robot obstacle avoidance control method and system based on path planning, and relates to the technical field of quadruped robot obstacle avoidance control, and the method comprises the steps: obtaining environment point cloud data, image data and quadruped robot motion speed data, and carrying out the terrain semantic segmentation and semantic pixel back projection of the image data, thereby obtaining a quadruped robot obstacle avoidance result; generating semantic point cloud data, combining the semantic point cloud data with the environment point cloud data subjected to motion compensation, and constructing a semantic annotation grid map; performing global path search on the semantic annotation grid map to generate a global smooth path; when the quadruped robot advances along the global smooth path, path nodes on the global smooth path are extracted at a fixed step pitch, the terrain category and the grid occupation state of each path node are judged according to the semantic annotation grid map, and a local reference path is generated; and executing a multi-stage obstacle avoidance strategy on the local reference path, and generating a foot end track sequence.

The invention provides a multi-view three-dimensional point cloud reconstruction method and device based on DPE-SE depth estimation, and relates to the technical field of computer vision and three-dimensional reconstruction. The method comprises the following steps: acquiring multi-view image data; preprocessing the image; inputting the preprocessed image into a DPE-SE-based depth estimation model, carrying out key constraint on an edge region through a semantic edge guiding mechanism, carrying out adaptive propagation updating on a weak texture region, realizing accurate depth estimation, and generating a multi-view depth result; then geometric consistency check and multi-scale depth fusion are performed on a multi-view depth result, and a dense depth map is constructed; and finally, performing three-dimensional back projection reconstruction and point cloud optimization processing, and outputting high-quality point cloud data containing three-dimensional coordinates and confidence information. According to the method, the problems of edge mismatching and depth voids are remarkably improved in complex illumination, weak texture and shielding environments, the continuity and structural integrity of the point cloud are improved, and technical support is provided for unmanned aerial vehicle surveying and mapping, building detection and digital twin modeling.

The invention relates to a remote sensing image super-resolution reconstruction method based on cross-scale Mama, and belongs to the technical field of remote sensing image super-resolution reconstruction. The method comprises the following steps: introducing a CSRSSG cross-scale residual state space group and a CARR channel attention back projection reconstruction module to improve an original MambaIR model, and obtaining a cross-scale Mamba super-resolution reconstruction model; extracting shallow layer features of the remote sensing image to be reconstructed based on a cross-scale Mama super-resolution reconstruction model; based on a cross-scale Mama super-resolution reconstruction model, deep layer features are extracted from the shallow layer features; and adding the shallow features and the deep features element by element to obtain residual connection features, and inputting the residual connection features and the remote sensing image to be reconstructed into a cross-scale Mamba super-resolution reconstruction model for reconstruction to obtain a super-resolution reconstructed remote sensing image. The method can effectively enhance the extraction and expression capability of the model on associated information among different scale features in the remote sensing image, and improves the super-resolution reconstruction quality of the remote sensing image.

An image reconstruction method and system based on a sparse graph set. The method comprises the following steps: using CT scanning to obtain a sparse angle projection graph set; performing back projection on the sparse angle projection graph set to obtain an initial iteration value of volume data; calculating initial values of three directional gradient fields of the initial iteration value of the volume data; on the basis of the initial iteration value of the volume data and the initial values of the three gradient fields, respectively calculating orthographic projection and residual back projection; performing, by means of a neural network and by using the initial values of the three gradient fields, regularization processing on data having undergone orthographic projection and data having undergone residual back projection; performing addition on an integral of the volume data and the gradient fields to serve as an initial value of next iteration, iterating again until completion and outputting a result. The method can obtain projection data required by reconstruction in a short period of time, and can effectively process the sparse angle / finite angle problem to obtain a high-quality image reconstruction result.

The invention provides a point cloud welding seam recognition method combining a 2D segmentation model and spatial features, relates to the field of welding automation, and solves the technical problem of low welding seam recognition precision in the prior art. The method comprises the following steps: acquiring point cloud data of the surface of a to-be-identified workpiece by utilizing three-dimensional laser scanning equipment; preprocessing the point cloud data, and projecting the preprocessed point cloud data to a two-dimensional image to obtain two-dimensional data; performing semantic segmentation on the two-dimensional data by adopting a pre-trained image segmentation model, and identifying a welding seam region; back-projecting the weld joint area to a point cloud space, and extracting spatial features of adjacent planes of the weld joint; calculating an included angle and a distance between the adjacent planes based on the spatial characteristics of the adjacent planes, and judging the welding seam type; and according to the welding seam type and the spatial characteristics of the adjacent planes, calculating to obtain end point coordinates of the welding seam. The device is used in the industrial welding automatic production process.

The invention relates to a workpiece polishing track generation method and equipment, and the method comprises the steps: collecting a current workpiece surface point cloud, carrying out the plane fitting, obtaining an upper surface equation and an upper surface point cloud, projecting the upper surface point cloud to the upper surface equation, obtaining a two-dimensional projection drawing, obtaining the template workpiece contour of each type of template workpiece, and carrying out the polishing of the workpiece. And calculating the polishing track points of each type of template workpiece, matching the two-dimensional projection drawing with the contour of the template workpiece to obtain the corresponding polishing track points, and back-projecting the polishing track points to the three-dimensional space to obtain the three-dimensional polishing track of the current workpiece. The problem that the workpiece surface polishing track cannot be quickly and effectively recognized in the prior art is solved.

The invention discloses an occlusion state 3D human body posture estimation method based on multistage optimization. According to the method, a plurality of synchronously calibrated cameras are used for acquiring RGB images, and a plurality of data enhancement strategies including random rotation, horizontal overturning, geometric shielding, object shielding and the like are introduced, so that the robustness of a 2D joint point detection network under complex visual angle and shielding conditions is improved. And then an initial three-dimensional human body posture is preliminarily estimated by using a voxel space back projection method through the detected multi-view 2D heat map. For the occlusion problem, a visibility evaluation model fusing autologous occlusion and visual angle occlusion is constructed, and robust and stable human body three-dimensional attitude estimation can still be realized under the severe occlusion condition by introducing multiple constraints such as visual consistency, time sequence continuity, attitude priori and skeleton consistency to optimize and predict a 3D attitude in a multi-stage manner. According to the method, high-precision and shielding-robust 3D joint point detection can be realized only by inputting a multi-view-angle RGB image during operation, and the method is suitable for a real scene with a complex shielding condition.

The invention belongs to the technical field of three-dimensional scene reconstruction, and particularly discloses a multi-view three-dimensional Gaussian densification method and system for adaptive density control, and the method comprises the following steps: collecting a multi-view original image, and carrying out the preprocessing of the multi-view original image; complexity features are extracted, a pixel-level complexity heat map is generated, and a globally unified three-dimensional complexity field is constructed; performing back projection on the reconstruction residual error, high-frequency inconsistency and depth / geometric consistency cost of each view angle, generating three-dimensional error popularity, determining a candidate newly-added set and a candidate pruned set, generating a weak label to train a lightweight multilayer perceptron classifier, outputting a ternary probability corresponding to newly-added / pruned / maintained, and obtaining a new / pruned / maintained three-dimensional perceptron classifier; and performing Gaussian densification operation on the newly added region. By adopting the technical scheme, fine point adding is carried out on the complex area, effective pruning is carried out on the simple area, and meanwhile, the synthesis quality, the global consistency and the calculation efficiency of the new view angle are improved.

The invention relates to the field of 3D rendering, in particular to an intelligent scenic area three-dimensional image rendering method, which introduces a differentiable discrete decision into 3D fusion, supports end-to-end learning of a k value, performs discrete-continuous optimization based on an activation function, predicts an optimal k value of each voxel, introduces feature adaptive fusion based on dynamic neighborhood bidirectional retrieval, and realizes the 3D image rendering of the scenic area. The alignment of the color image and the point cloud is enhanced, the false detection rate of a small target is reduced, the detail reconstruction capability of a large target is improved, and the comprehensive rendering capability of a scenic spot is improved; according to the method, a lightweight grid is adopted to express a scenic spot subject, residual Gaussian is introduced to supplement high-frequency detail features, the number of Gaussian is reduced, rendering efficiency and capability are improved, textures are generated based on initial rendering back projection, fuzzy view angle dependence is avoided, hierarchical clustering and contour extraction from bottom to top are adopted on the basis, and the method is more efficient and efficient. The point cloud vertical structure change is dynamically detected, the point cloud is complemented, the accurate contour is extracted, the number of grid vertexes is reduced, and the rendering integrity is improved.

The invention provides an automatic operation process management method and device based on multi-sensor fusion, and relates to the technical field of automatic processes, and the method comprises the steps: constructing an instrument pose flow and operation field geometric model under a unified coordinate system through unified timestamp alignment and spatial geometry solution of multi-source sensor data; semantic segmentation and three-dimensional reprojection are combined to generate a shielding probability graph, cross-channel compensation reconstruction is carried out, and the image integrity is improved. And forming a conflict risk set through voxel field discretization and short-time prediction, and performing combined solution with standardized operation process constraints to generate a scheduling instruction and a safety time window. And finally, mapping the image, the pose and the physiological signal to a knowledge graph, and outputting consistency judgment and deviation correction suggestions in combination with a time sequence attention recognition process node. According to the method, the problem that the operation process management is not accurate enough due to the problems of view overlapping and the like of multi-source sensor data can be solved.

The invention provides an unmanned aerial vehicle pose visual angle optimization method and system for fracture refined shooting. The method comprises the steps of performing fracture detection and boundary extraction on a coarse inspection image; recovering a camera track and sparse point cloud based on multi-view three-dimensional reconstruction, carrying out back projection and estimating a normal vector of a crack surface; constructing a shooting spherical shell with limited inner and outer radiuses by taking the crack point as a center, and generating a view cone and spherical shell intersection region allowed to be shot as a candidate set in combination with a normal vector; sampling in the candidate area to generate a plurality of candidate shooting points, and synchronously resolving the flight and holder integrated pose of the corresponding unmanned aerial vehicle; constructing a multi-target cost function including path length, attitude, pan-tilt angle and shooting error, and generating an optimal shooting point sequence and an inspection path through an optimization algorithm; the system realizes fine, efficient and automatic shooting of cracks in a complex structure environment through cooperation of multiple modules, and effectively improves the imaging quality and the detection precision.

A secret image sharing method based on deep compressed sensing image steganography belongs to the field of image information security, and comprises the steps of training set preprocessing and model training; performing compressed sensing sampling on the carrier image and the secret image by using a sampling network to obtain corresponding measurement values; mapping the measured value into a proxy image through a forward projection process; hiding the proxy secret image into the proxy carrier image by using a reversible neural network to generate a high-dimensional proxy steganographic image; mapping the proxy steganographic image back to a low-dimensional measurement value through a back projection process, and mapping the proxy steganographic image into a high-dimensional proxy steganographic image through a forward projection process; performing reverse revelation of secret information by using a reversible neural network to recover a proxy image; and reconstructing the recovered proxy image through the reconstruction network to obtain a recovered carrier image and a secret image, and completing sharing. According to the invention, the storage and transmission overhead is reduced, the transmission efficiency is improved, the shared image capacity is increased, and safe and efficient secret image sharing is realized.

The invention provides a semantic aerial view visual relocation method and device in a non-exposed scene, electronic equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a multi-view image sequence under a non-exposed scene (such as a tunnel, an underground pipe gallery or an underground parking lot); semantic recognition is carried out based on a pre-trained semantic target detection model, and spatial consistency semantic features are extracted through a semantic-geometric dual-channel fusion mechanism combining a semantic mask and geometric constraints; the method comprises the following steps of: realizing three-dimensional reconstruction by using a voxel micro-renderable modeling method (VGGT), and generating a dense three-dimensional semantic point cloud fusing semantics and a geometric structure; two-dimensional semantics are mapped to a three-dimensional space through a projection and back projection relation, and point cloud semantics are endowed; main structure planes such as the ground, the left wall surface and the right wall surface are extracted, and a two-dimensional semantic aerial view with semantic annotation is generated; and pose estimation is carried out based on a reciprocal matching strategy guided by a semantic mask, so that visual repositioning with high precision, high robustness and semantic interpretability is realized. The method breaks through the problems of low precision, sparse features and poor semantic consistency of traditional visual repositioning in a non-exposed environment, and can be widely applied to the fields of intelligent transportation, underground inspection and unmanned system positioning.

The invention discloses a city-level live-action three-dimensional modeling method based on air-ground multi-source data, and the method comprises the following steps: 1) respectively constructing a ground point cloud and an air point cloud, respectively extracting semantic tags, local geometric features and color features of a ground image and an air image, and carrying out the back projection of the semantic tags, local geometric features and color features to the corresponding ground point cloud and air point cloud; the method comprises the steps of (1) obtaining a ground point cloud and an aerial point cloud, (2) respectively calculating fusion multi-modal features of the ground point cloud and the aerial point cloud, (3) realizing cross-point-cloud feature interaction through a Transform point cloud registration model, and (4) selecting a three-dimensional reconstruction area. According to the invention, accurate registration of different-source point clouds with large air-ground view angle difference and low overlapping degree can be realized; the problems that an existing oblique photography urban three-dimensional model is lack of details and insufficient in precision in ground and building facade areas, and a live-action three-dimensional model is complex in updating process, long in period and low in automation degree are solved.

Three-dimensional Gaussian splatting mechanisms that initialize a set of 3D Gaussian distributions, un-project pixels from two-dimensional (2D) planes to 3D space by applying queries to the 3D Gaussians at expected un-projected ray depth positions, and splat the 3D Gaussian distributions on the 2D planes based on the expected un-projected ray depth positions.

The invention relates to the technical field of intelligent robot path planning based on visual identification, and discloses an intelligent robot path planning method based on visual identification. According to the scheme, the problems of inconsistency of environment sensing data and lack of accurate geometric information are solved by utilizing data acquisition and calibration processing of the camera and the laser radar. The camera collects images, and parameters are determined by hardware; the internal reference matrix of the camera ensures clear correspondence between pixels and actual coordinates. The laser radar collects point cloud data, records three-dimensional coordinates of the environment, and is high in precision and real-time. Through data fusion and calibration, point cloud and image alignment by the system, camera internal reference matrix establishment and external reference calibration, accurate mapping and back projection are realized, and grassland area and obstacle information is extracted. According to the method, the sensing precision is improved, support is provided for path planning and navigation, and the robot has accurate positioning and efficient path planning capabilities.

The invention discloses a vertical rail scanning remote sensing satellite natural multi-coverage image resolution improving method and system, and relates to the field of remote sensing image processing. The method solves the problems that due to the fact that the distance between an existing vertical rail scanning imaging sensor and a ground target is increased, image pixels cover a wider ground range, the actually-measured spatial resolution is reduced, and fine detection and recognition of ships, aircrafts and the ground target cannot be supported. SIFT feature point extraction is carried out, and feature points extracted from different images are matched by using a Euclidean nearest distance matching strategy; taking one registered image as a reference, and constructing an initial high-resolution image through up-sampling; establishing an imaging degradation model from a high-resolution image to a low-resolution observation image; calculating a residual image of the simulation image and the real observation image; and back-projecting the residual error back to the high-resolution image space, and updating high-resolution image estimation.

The invention discloses a voice compression method and system based on multi-scale back projection feature fusion, and belongs to the technical field of voice synthesis, and the method comprises the steps: calling a plurality of multi-scale back projection feature fusion layers in an encoder to encode a to-be-synthesized voice signal, and obtaining voice features; calling a plurality of multi-scale back projection feature fusion layers in a decoder to decode the voice features to obtain synthetic voice; wherein the process of encoding or decoding the input features by the multi-scale back projection feature fusion layer comprises the following steps: carrying out cross learning on the input features by using convolution kernels of different scales to obtain multi-scale features, carrying out back projection on the multi-scale features respectively to obtain back projection features, the back projection features and the multi-scale features are fused and then fused with features input into the multi-scale back projection feature fusion layer, and output features of the multi-scale back projection feature fusion layer are obtained. The speech synthesis quality is improved, and the technical problem that the speech synthesis quality of a current method is limited is solved.

The invention relates to the technical field of rubber performance evaluation, in particular to a performance data analysis and evaluation method for a rubber material. The method comprises the following steps: acquiring X-ray scanning data of a rubber sample; performing phase contrast imaging on the X-ray scanning data of the rubber sample to obtain projection data of the rubber sample; performing back projection reconstruction on the projection data of the rubber sample to obtain three-dimensional structural data of the rubber material; carrying out multi-parameter environmental simulation loading on the rubber sample to obtain environmental response data of the rubber material; dynamically collecting the environmental response data of the rubber material to obtain deformation process data of the rubber material; synchronously recording and analyzing the deformation process data of the rubber material to obtain a stress-strain characteristic pattern of the rubber material; and performing polarization spectrum excitation on the rubber sample based on the rubber material stress-strain characteristic pattern to obtain rubber molecular chain fluorescence data. According to the invention, the reliability and accuracy of rubber material performance evaluation are improved.

The invention provides a multi-mode remote sensing target tracking and positioning and intention discrimination method and device. The method comprises the following steps: acquiring a plurality of visible light image frames and a plurality of infrared light image frames, and carrying out frame alignment operation on each visible light image frame and each infrared light image frame to obtain a plurality of groups of effective image frame pairs; for each group of effective image frame pairs, determining tracking identification information of each detection object in the effective image frame pairs based on the effective image frame pairs and a pre-trained multi-modal detection tracking model; for each detection object, determining longitude and latitude tracks of the detection object based on the tracking identification information and a back projection mapping function; and determining the behavior intention of each detection object based on a behavior recognition model and the longitude and latitude tracks of each detection object. The accuracy of target tracking and behavior intention recognition in the remote sensing video can be improved.

The invention belongs to the technical field of magnetic particle imaging, particularly relates to a magnetic particle imaging system and method based on a flexible receiving coil array, and aims to solve the problems that an existing rigid receiving coil is poor in biological surface adaptability and low in signal-to-noise ratio for micro geometric texture imaging. According to the structure, a receiving coil array adheres to the surface of an object to be measured, the receiving coil array and a compensation coil array are distributed on the two sides of a flexible substrate, the receiving coil array is used for receiving magnetic nanoparticle signals, and the partitioned small compensation coil array is used for conducting feed-through interference and electromagnetic interference compensation on a receiving coil; the reconstruction method comprises the following steps: sequentially carrying out Fourier transform on the collected receiving signals, extracting in-phase third harmonics and superposing to obtain processed signals, interpolating to construct a sinogram for Cartesian coordinate distribution, and reconstructing the three-dimensional particle concentration distribution of a measured object by a filtering back projection method. A flexible receiving coil array strategy is adopted, an adaptive reconstruction method is provided, and the imaging signal-to-noise ratio is improved.

The invention relates to an image three-dimensional reconstruction method, in particular to a multi-view three-dimensional reconstruction method based on frequency perception feature enhancement and cost aggregation. The objective of the invention is to overcome the defects of lack of frequency sensing capability and limited processing capability for problems of weak texture, noise, illumination variation, color distortion and the like in an existing learning-based multi-view three-dimensional reconstruction method. According to the method, multi-view three-dimensional reconstruction is realized through the steps of acquiring a multi-view image, calculating a multi-scale frequency sensing feature, calculating an initial cost body, embedding frequency information into the initial cost body, calculating depth estimation, performing back projection and the like in sequence; when multi-scale frequency sensing features are calculated, a double-branch frequency component enhancement module is arranged to process wavelet transform layer decomposition to obtain lossless approximate low-frequency components and high-frequency components of an input image, so that global consistency and local detail expression of depth estimation are improved.

The present invention relates to the technical field of medical images. Disclosed are a deep learning-based CT artifact removal method and system. The method comprises: acquiring a CT image, and separately performing sine transform and wavelet transform processing on the CT image; constructing an image enhancement model, and performing image optimization on the processed image separately by means of the image enhancement model and a random inversion layer which are connected in sequence; coupling the optimized image and the original image and then inputting the coupled image into the image enhancement model for reprocessing; and performing element-wise addition on the reprocessed image and the optimized image to obtain an artifact-removed CT image. In the present invention, wavelet transform is introduced to process the CT image to extract the context and spatial information of the CT image, effectively extracting feature information in an artifact removal process and improving the performance of image enhancement; and a CT image resolution enhancement model based on a VMamba model is established, enhancing the long-term dependencies in network training, effectively recognizing and removing radioactive artifacts, and improving the network training efficiency.

The invention discloses a road corrugated plate integrity detection method based on a vehicle-mounted three-dimensional camera, and relates to the technical field of traffic infrastructure intelligent management and maintenance, and the method comprises the steps: generating a system configuration file through camera internal geometric parameters and multi-line light plane calibration; collecting an image sequence in real time, and extracting a light strip center pixel coordinate; based on a system configuration file, carrying out back projection on the pixel coordinates to solve a three-dimensional intersection point to generate three-dimensional point cloud data of the waveform plate; extracting the three-dimensional point cloud data to obtain the three-dimensional point cloud data of the segmented waveform plate; fitting a reference surface to calculate a height deviation value from a point to a plane; sampling according to multiple layers and multiple intervals and forming a feature string; comparing and identifying defect types and degrees bit by bit; and analyzing the aging trend of the corrugated plate based on historical data. According to the method, the multi-level three-dimensional geometrical characteristics of the surface of the corrugated plate can be efficiently obtained, and automatic identification and positioning of defects are realized.

The invention discloses a method for optimizing micro-crack segmentation based on deep learning and super-resolution reconstruction, and the method comprises the steps: cutting an image in real time, obtaining an image block which takes a component as a target main body, and synchronously recording a homography matrix for geometric mapping; selecting an amplification strategy to improve the resolution, and recording a scale mapping relation; inputting the enhanced image block into a double-flow network; adaptive fusion and reconstruction are carried out on the two branch features, and a high-resolution texture image is output; generating a geometrically corrected ortho-image, fusing the geometrically corrected ortho-image with original illumination information, and outputting a corrected image with a known pixel size; identifying cracks, spalling and honeycomb diseases in parallel; generating a unified defect confidence map; calculating real geometric parameters of the BIM in a BIM global coordinate system through coordinate back projection; and generating quantitative defect reports and maintenance suggestions. The method has the advantage that seamless connection between the detection result and the BIM global coordinates is realized.

The invention discloses a vision-driven multi-mode fusion lightweight semantic map construction method and system. The method comprises the following steps: synchronously acquiring a binocular image pair sequence, IMU data and GNSS data of a target area; based on the acquired multi-modal data, performing multi-sensor joint state estimation through a differential weighted fusion strategy, and outputting camera global pose and scene depth information; based on a current frame and a historical frame in the binocular image pair sequence, combining a camera global pose, extracting geometric prior auxiliary time sequence cross-frame semantic feature fusion through stereoscopic vision, and outputting a two-dimensional semantic segmentation result of the current frame; and back-projecting the two-dimensional semantic segmentation result into a lightweight global three-dimensional semantic map based on camera pose and scene depth information, and carrying out maintenance and updating through a voxelization statistical mechanism. Compared with a traditional dense point cloud map, the method has the light weight effect that the storage space is greatly reduced.

The invention discloses an AGV-oriented tray dynamic identification and positioning method, and the method comprises the steps: firstly collecting a tray image training model, deploying a weight, and laying an image identification foundation; the RGB-D camera synchronously collects multi-modal images in real time and stores the multi-modal images in a buffer area, so that the data timeliness is guaranteed. And calling a CUDA acceleration model to extract a tray ROI region from the latest RGB image. The depth map is aligned to an RGB coordinate system and a local point cloud is generated. And segmenting the point cloud of the front plane of the tray through RANSAC and projecting to generate a two-dimensional image. And extracting the pixel coordinates of the key points of the contour of the tray based on Canny and Hough transform. And back-projecting the pixel coordinates of the key points to a three-dimensional space to calculate the pose. And the pose information is transmitted to the AGV motion control module. According to the method, the tray pose is accurately obtained, and efficient track adjustment of the AGV is assisted. And carrying out dynamic tracking and accurate positioning on a tray target. Moreover, the AGV can obtain the real-time positioning data of the target in the movement process, thereby dynamically adjusting the track, and effectively solving the problem of accumulative errors of a conventional static positioning mode.

The invention discloses an industrial art material detection method based on multi-modal deep learning, particularly relates to the field of material analysis, and is used for solving the problems that cross-modal data alignment of curved surface utensils in highlight and multi-layer coating scenes is difficult, and material boundaries are easy to drift along with shooting batches and visual directions. The method comprises the following steps of: positioning a hyperspectral line scanning fragment; constructing a local reference grid to realize initial pairing; generating pixel-level residual image quantization dislocation; estimating luminosity mapping to form consistent bimodal data pairs after eliminating a high-reflectivity region; iteratively supplementing anchor points or adjusting grid rigidity, generating a stable material graph, updating an index table and a consistency record, ensuring cross-batch reusability and traceability, and improving the material detection precision.

The invention provides a multi-source perception method suitable for an unstructured scene of engineering machinery, and relates to the technical field of engineering machinery environment perception, and the method comprises the steps: carrying out the preprocessing of LiDAR point cloud data, generating a 2D perspective view aligned with an RGB image, and providing a basis for the subsequent feature extraction; semantic segmentation is carried out on image branches by using an improved deep learning network, and spatial depth features are extracted by point cloud branches through an optimized network structure. And the two are subjected to feature complementation through an FFAM module, so that the ability of understanding a complex scene is remarkably enhanced. Furthermore, a refinement module is introduced, an advanced 3D sparse convolution technology is adopted, and optimization processing is performed on point cloud features after back projection, so that the problem of fuzzy target boundary segmentation is effectively solved.