234 results about "Three dimensional vision" patented technology

The invention discloses an electronic cabin assembly quality detection method based on three-dimensional vision. The method comprises the following steps: firstly, generating data for comparison during detection for a standard electronic cabin; shooting all to-be-detected objects in the to-be-detected electronic cabin to obtain a depth image and a color image of each to-be-detected object; according to the depth image and the color image of the to-be-detected object, combining with the data of the standard electronic cabin for comparison during detection; and whether screws of the electronic cabin to be detected are neglected and not installed in place, whether cable plugs are neglected and not installed in place, whether connectors and cable plugs in the wiring unit are wrongly matched, and whether the bending radius of cables is too small are detected in sequence. Through fusion of depth information, three-dimensional quantitative detection of the assembly quality is realized, and the detection precision, reliability and automation level are significantly improved.

The invention discloses a lithium battery laser welding detection method, and belongs to the technical field of nondestructive testing. The method comprises the following steps: acquiring a three-dimensional path point sequence of a to-be-detected welding seam; controlling the transient thermal excitation unit to apply transient thermal excitation along the path; a high-speed multispectral imaging unit is used for synchronously collecting the thermal radiation attenuation process of the weld joint under at least two different spectral bands, and a space-time spectrum data cube is formed; and finally, inputting the data cube into an artificial intelligence model for defect identification. The system correspondingly comprises a three-dimensional visual sensor, a transient thermal excitation unit, a high-speed multispectral imaging unit and a synchronous control and data processing unit. According to the method, different types of welding defects such as air holes and incomplete fusion can be effectively distinguished by analyzing the heat diffusion characteristic difference under different spectral bands, the interference of uneven surface emissivity is inhibited, and the online detection accuracy and reliability of the three-dimensional complex welding seam are improved.

The invention relates to the technical field of visual recognition, and particularly provides a bottle sorting-oriented three-dimensional visual posture recognition method and related equipment, and the method comprises the steps: obtaining three-dimensional point cloud data and RGB image data of bottles on a conveyor belt, carrying out the preprocessing, feature extraction and geometric analysis of the three-dimensional point cloud data, and obtaining six-degree-of-freedom posture parameters; and the six-degree-of-freedom attitude parameters are projected to RGB image data, the overlap ratio of the projection contour and the actual bottle contour is calculated, and when the overlap ratio is larger than a preset threshold value, it is confirmed that the attitude is effective, and the corresponding attitude parameters are output to a mechanical arm sorting control system. Six-degree-of-freedom posture parameters of the bottle body can be obtained through posture alignment and template point cloud registration, reliability confirmation of a posture recognition result is achieved through overlap ratio verification in an RGB image data domain, and the defect that the posture recognition result is not reliable under the conditions that the posture change of the bottle body is large, the appearance of the bottle body is diversified and the bottle body is inclined or shielded and interfered is overcome. The problems of insufficient recognition precision and incomplete attitude parameters exist.

The invention discloses a three-dimensional vision measurement system error compensation method based on a spatial adaptive weighted RBF residual neural network, and particularly provides a spatial adaptive residual learning framework SAW-RFramework, in the first stage, global rigid body errors are eliminated based on a Kabsch algorithm, and point cloud rigid alignment is realized; in the second stage, a thin-plate spline kernel RBF interpolator is used for modeling a spatial smoothing error field, and position-related systematic deviation is captured; and in the third stage, a partition weighted residual neural network is adopted, and an improved residual compensation network is proposed for residual high-fluctuation nonlinear residual after RBF modeling: a regional importance coefficient is calculated based on a mean value and a variance of the residual in a self-adaptive three-dimensional grid, so that network training focuses on a high-error and high-fluctuation region. According to the method, the problems of insufficient global spatial modeling capability, low boundary region compensation precision, poor generalization capability and the like of an existing method can be solved; high-precision error compensation of the three-dimensional vision measurement system is realized, the measurement result reaches submillimeter precision, and the application reliability in robot control, man-machine interaction and precision manufacturing is improved.

The invention relates to the technical field of artificial intelligence, and discloses a disordered workpiece grabbing method based on three-dimensional visual perception and intelligent decision, and the method comprises the steps: collecting the color image data and depth image data of a scene, and generating the three-dimensional point cloud data of the scene based on the internal reference of a camera; performing target detection and instance segmentation processing based on the color image data to generate target segmentation mask data, calculating an outlier degree score and a top degree score of a target in combination with depth data, and selecting an optimal capture target according to a comprehensive score; through an intelligent target selection strategy based on YOLOv11-SAMV3 fusion, in combination with an outlier degree score and a top degree score, a grabbing target is comprehensively evaluated, an isolated workpiece located at the top of a stack is preferentially selected, the shielding and collision risks are reduced, intelligent and automatic grabbing target selection is guaranteed, and the grabbing efficiency and the system adaptability are improved.

The invention relates to the technical field of robot automatic polishing, in particular to a vision-guided robot polishing path planning method and system, and the method comprises the steps: obtaining a workpiece surface point cloud in real time through a three-dimensional vision sensor, generating a trajectory seed point with adaptive density in combination with curvature, a normal vector and a defect semantic tag, and carrying out the positioning of a workpiece surface point; and a polishing quality evaluation function is constructed based on force sense feedback and a visual reconstruction result, so that path online correction is realized. According to the method, the polishing path and the tool posture can be dynamically adjusted, high-precision differentiation processing is achieved on the free-form surface, the edge and the defect area, and the polishing consistency and efficiency are improved.

The invention discloses a vehicle body gluing method and device based on three-dimensional visual guidance, and the method comprises the steps: obtaining a to-be-glued image of a to-be-glued region of a vehicle body through a three-dimensional visual image collection device in response to the arrival of the to-be-glued vehicle body at a gluing station, carrying out the image recognition of the to-be-glued image, obtaining the first coordinate information of a positioning hole, and obtaining the first coordinate information of the positioning hole; based on the first coordinate information, second coordinate information is generated, and according to the preset positioning hole coordinate information and the second coordinate information, the first vehicle body deviation of the to-be-glued vehicle body is calculated; and performing deviation correction on the preset gluing track according to the first vehicle body deviation to obtain a target gluing track. And the gluing robot is used for gluing the to-be-glued vehicle body based on the target gluing track. According to the method, the to-be-glued image is collected through the three-dimensional visual image collecting device, the vehicle body deviation is obtained based on the actual positioning hole coordinate information and the preset positioning hole coordinate information, then the gluing track is corrected, and the accuracy of the gluing track is improved.

The invention relates to the technical field of automatic welding, and discloses a special-shaped curved surface steel structure self-adaptive welding control system which comprises a three-dimensional visual scanning unit, a welding robot, an acoustic feedforward sensing unit, a molten pool state diagnosis unit and a central control and calculation unit. The acoustic feed-forward sensing unit collects sound wave field data of a to-be-welded area in front of a welding gun, the molten pool state diagnosis unit obtains real-time temperature and component information of a molten pool through spectral analysis, a model correction module in the central control and calculation unit dynamically corrects an acoustic elastic coefficient according to the real-time temperature and component information, and a stress field reconstruction module calculates a stress field of the to-be-welded area based on the coefficient and the wave field data. The method comprises the steps that a feedforward stress field distribution diagram is reconstructed, an instruction generation module analyzes the gradient and amplitude of a stress field, a welding path avoiding a high stress gradient area and a compensation stress amplitude and a parameter regulation and control instruction are generated, and feedforward self-adaptive control is achieved. The welding joint quality is improved.

The invention discloses a three-dimensional visual positioning method based on uncertainty relation perception Transform, and the method comprises the steps: inputting three-dimensional point cloud data, extracting point cloud features through a Point Net + + backbone network, and generating seed points; generating voting points by adopting an uncertainty perception voting strategy, and obtaining candidate areas in combination with a probability offset refinement strategy; the prompt statement is input into a prompt word learning module for semantic enhancement, text features are extracted, and corresponding text categories are generated; constructing a relationship perception Transform module, modeling a multi-dimensional spatial relationship between the candidate regions, realizing cross-modal fusion of the candidate regions of the visual end and text features of the semantic end, and outputting fusion features; and predicting the confidence of the candidate region and the position of the bounding box based on the feedforward neural network, and outputting a target positioning region which is most matched with the semantics. According to the method provided by the invention, the accuracy and robustness of three-dimensional visual positioning in a multi-element complex scene can be effectively improved.

The invention discloses a semantic-guided lightweight three-dimensional reconstruction method based on three-dimensional Gaussian, and the method comprises the steps: inputting a multi-view two-dimensional image, and extracting two-dimensional semantic features; projecting the two-dimensional semantic features to the three-dimensional Gaussian primitives; learning three-dimensional semantic distribution in a knowledge distillation mode, and predicting three-dimensional semantic embedding features; dividing a large scene into a plurality of sub-regions, and performing divide-and-conquer three-dimensional Gaussian optimization and semantic learning; performing semantic-guided lightweight modeling on the three-dimensional Gaussian primitives in each sub-region; executing sub-scene fusion, and performing quantitative compression on the three-dimensional data in the fused scene; and a rasterization renderer is used to carry out efficient real-time rendering under any visual angle, and a lightweight large-scene three-dimensional reconstruction result is output. According to the method provided by the invention, the real-time rendering performance of a complex scene can be remarkably improved, the model volume is effectively compressed, meanwhile, the model training process is accelerated, and the method is suitable for efficient three-dimensional visual reconstruction tasks.

The invention discloses a pronunciation recognition method based on multi-modal fusion and related equipment, and the method comprises the steps: obtaining a to-be-processed millimeter-wave radar signal and three-dimensional visual data, carrying out the preprocessing of the obtained millimeter-wave radar signal, and determining a radar data set; preprocessing the three-dimensional visual data to obtain a three-dimensional data set; performing feature extraction on the radar data set to obtain radar spatial-temporal features; performing feature extraction on the three-dimensional data set to determine three-dimensional visual features; performing feature fusion on the radar spatial-temporal features and the three-dimensional visual features according to a cross attention mechanism to obtain a feature fusion vector; inputting the obtained feature fusion vector into a trained feature decoupling network for identification, and determining pathological features and pronunciation features; and generating a corresponding pronunciation result according to the pathological features and the pronunciation features. According to the embodiment of the invention, the recognition accuracy can be improved. The method can be widely applied to the technical field of speech recognition.

The invention relates to the technical field of mine disaster prevention and control, in particular to a fault water disaster sensing and danger relieving operation method and system, and the method comprises the steps: setting a monitoring region, carrying out the three-dimensional space grid segmentation, calculating the coupling coefficient of data in each grid, setting a multi-level risk threshold value, and carrying out the risk level judgment of the coupling coefficient of each grid; based on the determined grid risk level, constructing a knowledge reasoning mode, an acoustic alarm mode and a three-dimensional visual mode, performing weighted fusion on a normalization result according to a preset reliability weight to obtain a final risk value, comparing the final risk value with a preset early warning threshold, and triggering early warning of a corresponding level; based on the judged grids of the corresponding levels of early warning, the grids of high-risk early warning are judged, and the drilling verification and grouting plugging danger relieving process is started; the technical problem of poor intelligent cooperation of mine disaster multi-source data cooperative sensing, early warning and danger solving is solved.

The invention relates to the technical field of agent deep learning, and particularly discloses a single-agent three-dimensional visual positioning method and system based on deep learning, and the method comprises the steps: generating an optical flow field through a deep learning optical flow prediction model, judging the prediction accuracy in combination with analysis process parameters, guaranteeing the input quality, and providing a reliable basis for subsequent three-dimensional visual positioning; an essential matrix is constructed and decomposed, an initial three-dimensional visual positioning result is obtained in combination with constraints, positioning precision is improved, when the initial three-dimensional visual positioning result is unreliable, inertial measurement unit data is fused for space-time alignment, fusion reliability is verified through process parameters, and positioning robustness in a complex scene is enhanced. And the accuracy and the stability are considered, and the accuracy and the reliability of single-agent three-dimensional visual positioning are effectively improved.

The invention relates to the technical field of computer graphics, and discloses a reflection prompt and self-correction-based large model three-dimensional vision generation method, which comprises a data acquisition stage, a data preprocessing stage, a coarse generation stage and a refinement stage, in the coarse generation stage, regularization is performed on a pre-trained 3D generation model through a symmetric guide diffusion mechanism, and the regularization is performed on the pre-trained 3D generation model; an initial neural radiation field with a consistent geometric structure is generated, in a refinement stage, the NeRF is refined by using a multi-loss optimization framework including reconstruction loss, diffusion prior loss and depth loss, and in the refinement stage, a DPF strategy is introduced, the strategy adaptively updates text prompts for SDS loss by using LLM, and the SDS loss is improved. And the signal is converted into a targeted correction signal. According to the method, a symmetric guide diffusion mechanism is introduced, symmetric geometric priori is applied in the reverse process of a 3D diffusion model, a geometric coherent and balanced basic shape is forcibly generated, and the ambiguity of a single view is effectively relieved.

The invention relates to a three-dimensional visual synthesis system based on heterogeneous collaboration and screen alignment fusion, which is characterized in that a master control system generates complete viewpoint configuration information of a current scene acquired by an image acquisition device, and the complete viewpoint configuration information is used as a single data source and is simultaneously distributed to a graphic processing unit and a neural rendering coprocessor of the master control system; synchronous starting and parallel execution of the two heterogeneous rendering pipelines are achieved. And in combination with a subsequent screen alignment fusion mechanism, an end-to-end collaboration framework with low delay, low coupling and high coordination is constructed, and the key problem of real-time integration of high-fidelity contents is effectively solved. On the premise of not depending on a traditional deep buffer area or geometric alignment, high-fidelity visual content generated by special hardware acceleration and a dynamic environment of a main control system are efficiently fused, and complex effects such as lightweight asynchronous communication and real-time shadow are supported. Therefore, multiple bottlenecks in the aspects of real-time performance, compatibility and engineering deployment in the prior art are broken through.

The invention discloses a mechanical arm double-shaft-hole bolt inserting method based on three-dimensional visual positioning and admittance force control, and belongs to the technical field of mechanical arm intelligent assembly.The mechanical arm double-shaft-hole bolt inserting method comprises the steps that bolt inserting position estimation is conducted through an image recognition algorithm, and a shaft hole recognition strategy combining a YOLOv8 neural network model and Hough circle detection is adopted; extracting a shaft hole normal vector by using principal component analysis, and constructing a spatial constraint to solve a tail end target attitude; formulating a two-step visual guidance strategy to avoid an observation blind area, and controlling the six-degree-of-freedom mechanical arm to move to a bolt preparation point position in stages; and finally, switching an admittance force control mode to realize fine and smooth bolt operation. According to the method, high-robustness assembly of the metal shaft hole in the unstructured environment is realized, and the problem of positioning failure caused by specular reflection interference and a depth observation blind area is effectively solved.

The invention relates to the technical field of rice phenotype extraction, and discloses a rice fine phenotype extraction method based on three-dimensional vision, a terminal and a storage medium. The method comprises the following steps: acquiring a multi-view image of a rice plant by using a depth camera, and performing camera attitude estimation on the multi-view enhanced image to obtain camera internal and external parameters corresponding to each view; constructing a neural radiation field network, training the neural radiation field network by taking the enhanced image, the contour mask and internal and external parameters of the camera as input, and learning color and density distribution of a scene by utilizing a volume rendering technology; extracting surface points based on a mixed threshold strategy by using the trained neural radiation field network, and generating a rice three-dimensional point cloud; inputting the rice three-dimensional point cloud into a point cloud instance segmentation network, and outputting an instance mask containing each organ of the rice and a segmentation result of a three-dimensional bounding box; and counting phenotypic parameters of the rice according to a segmentation result. According to the method, the fine instance segmentation of the high-density point cloud is realized while the reconstruction precision is ensured, and the phenotypic analysis of the spike fraction is supported.

The invention discloses a bone defect repair material suitability prediction method based on deep learning, and the method comprises the steps: carrying out the overlapping and uniform partitioning of a bone defect region and a host bone region in three-dimensional CT image data, carrying out the position coding and feature extraction of a plurality of sub-blocks through a three-dimensional visual Transform model of multi-scale position coding, and obtaining the anatomical and functional features; calculating a bone metabolism activity score and a systemic inflammation score based on blood detection data, and then constructing pathophysiological features; performing multi-round interaction on the anatomical and functional features, the pathophysiological features, the parameter data and the multidirectional multi-head cross attention layer, in each round, selecting one modal feature as a query vector, and selecting any modal feature in the rest modal features as a key vector and a value vector; and fusing the interaction features of each round, and determining the suitability score and the risk level of the bone repair material by combining the attention weight and the bone metabolism activity score in the multidirectional multi-head cross attention layer.

The invention belongs to the technical field of three-dimensional modeling, and particularly relates to a three-dimensional visual detection method for motorcycle tires. According to the method, a tire is fixed, multi-view point cloud scanning is carried out, and an improved SAC-IA algorithm is adopted for registration and fusion to construct a three-dimensional point cloud model; the method comprises the following steps: designing adaptive voxel grid filtering and denoising based on tire geometric prior, and after improved bilateral filtering smoothing, fitting and unifying a coordinate system through principal component analysis and least square to obtain a standardized point cloud model; and extracting four core parameters of thickness uniformity and sidewall contour curvature, inputting the four core parameters into the hybrid dynamic weight scoring network to output quality scores, and meeting the requirements of high-precision and high-efficiency quality inspection of motorcycle tires in an industrial environment.

The invention discloses an immersive off-road mode switching mode, and relates to the field of vehicle electronic technology and intelligent cabin man-machine interaction, and the method comprises the steps: responding to a switching instruction, and controlling a virtual camera to move towards an off-road scene along a planned path while preloading resources. The system divides a control state according to a transition progress: in a pre-interaction state that resources are ready but transition is not completed, the off-road instrument assembly is controlled to fade in display and open input, a user pre-operation instruction is temporarily stored in an instruction buffer queue through de-duplication logic, and a physical signal is temporarily not issued through a vehicle bus interface; and when the transition time is over and a fully activated state is entered, the queue is locked, and an instruction is converted into a standard protocol data unit to be sent to a chassis electronic control unit. Through the staged state machine and instruction buffer logic, three-dimensional visual rendering and a bottom layer control time sequence are decoupled, and continuity of interactive operation and deterministic transmission of a control instruction are realized while scene switching smoothness is ensured.

The invention provides an intelligent cutting device for an underwater canal slope lining plate and a construction method. The device comprises an underwater self-adaptive walking platform, a three-dimensional visual recognition module, a cutting execution module, a buoyancy and pressure adjusting module and a remote control module. The intelligent cutting device for the underwater canal slope lining plate integrates three-dimensional laser scanning and algorithm recognition, the cutting range can be automatically recognized, manual judgment is not needed, the limitation of manual control of a simple robot is broken through, and the construction efficiency is improved; a triangular crawler belt of the underwater self-adaptive walking platform is matched with buoyancy adjustment of a buoyancy and pressure adjusting module, the underwater self-adaptive walking platform can be matched with a 1: 1.5-1: 3 variable-gradient channel slope and the water depth of 1-3 m, and the problem that a traditional robot slips and rolls over is solved. Closure pumping drainage is not needed, underwater cutting is directly carried out, the technical problem that water supply is affected by dry field repair is solved, and the pumping drainage cost is saved; no person enters the whole process, and safety accidents of divers are eliminated.

This invention discloses a method, device, and medium for correcting human motion posture based on AI vision, relating to the field of AI vision technology. The method includes: acquiring multi-channel image sequences of human posture using multiple cameras; performing temporal alignment and normalization on the multi-channel image sequences; obtaining a human probability map using a pre-trained human semantic segmentation network; performing morphological cleanup to obtain a human silhouette sequence; performing back-projection according to camera intrinsic and extrinsic parameters to obtain a 3D joint sequence from a 3D AI visual volume; extracting the human motion manifold to perform contact-sensing neural motion priors and outputting prior signals; and using posture feasibility scores and joint importance weights to weightedly fuse the geometric difference vector and the center of gravity correction component to obtain a comprehensive joint correction vector. This invention generates a 3D AI visual volume through back-projection and extracts the central skeleton, using constant bone segment lengths for constraint and correction, thus enhancing the reliability of posture stability analysis and alignment with standard action templates.

The utility model discloses a three-dimensional visual identification device for auxiliary material warehousing detection, which belongs to the technical field of auxiliary material warehousing identification and comprises a 3D camera, a visual support frame and a 2D camera. The visual supporting frame comprises a 3D camera support and a 2D camera support. The 3D camera is installed on the 3D camera support, and the 2D camera is installed on the 2D camera support. The 3D camera is used for carrying out high-angle shooting on the incoming materials, the two 2D cameras are used for carrying out auxiliary shooting on the incoming materials from the two sides, the types and the number of the incoming materials can be recognized more accurately and rapidly, the incoming materials at any time can be processed in time, dependence on personnel is small, and checking operation can be continuously carried out for a long time.

The application belongs to the field of three-dimensional vision and digitization, and relates to a three-dimensional human body reconstruction method and system based on three-dimensional Gaussian splashing. The method steps are as follows: based on a layered hash coding parameter field, the center position of each Gaussian primitive in the constructed three-dimensional Gaussian primitive set is corrected, and the color of the Gaussian primitive under the current observation angle is predicted; based on the human body posture parameters and shape parameters corresponding to the monocular video sequence, linear mixed skin transformation is performed on the corrected Gaussian primitive to map to the posture space; based on the Gaussian primitive parameters mapped to the posture space, three-dimensional Gaussian differentiable rendering is performed on the Gaussian primitive mapped to the posture space to obtain a rendering image consistent with the corresponding view angle of the monocular video; based on the constructed joint loss function, the Gaussian primitive parameters and the layered hash coding parameter field are optimized to obtain a three-dimensional human body model. The application can quickly reconstruct an animatable three-dimensional human body model with stable contours and clear textures from a monocular video.

The invention provides a gate system self-calibration method and a gate system self-calibration system capable of realizing multi-source interference shielding, which are applied to the technical field of passage detection, and the method comprises the following steps: acquiring images through three-dimensional vision equipment, detecting gate fan blades, clustering and grouping fan blade space distribution, calculating geometric calibration parameters, and realizing channel self-calibration; background modeling and updating are carried out based on the depth image, a static structure is included into the background to form a static shielding area, a fan blade moving area is determined according to geometric calibration parameters, dynamic shielding is carried out on a foreground in the moving area when it is detected that the fan blade is in a moving state, and static appearance interference and fan blade opening and closing interference are restrained. The system comprises an image acquisition module, a fan blade detection and clustering module, a geometric parameter calculation module, a background modeling and updating module, a static appearance shielding module, a fan blade motion shielding module, a real-time detection optimization module and the like. According to the invention, the deployment and maintenance cost is reduced, false detection and missing detection are reduced, and the stability and robustness of detection and measurement in a complex environment are improved.

This invention proposes a method and system for detecting missing weld points on vehicle bodies based on 3D vision and global matching, belonging to the field of image recognition technology. The method includes: determining the transformation relationship between the camera coordinate system and the vehicle body coordinate system through calibration; at each observation position, a 3D camera acquires an image I containing a frame of 2D texture and the corresponding 3D data; inputting the acquired image I into a pre-trained deep learning object detection model, outputting the bounding box information (u, v, w, h) for each weld point; for each detected weld point, calculating the set S of the weld point's 3D coordinates in the vehicle body coordinate system. det For the theoretical solder joint library S cad Each theoretical solder joint P in cad,i In set S det The invention employs a "detect first, match later" strategy, which does not rely on the precise projection of the theoretical position, thus reducing the requirements for the positioning accuracy of the production line tooling fixtures and conveying system.

The invention relates to the technical field of flight control, in particular to an unmanned aerial vehicle real-time obstacle avoidance method based on space-time probability grids, and the method comprises the steps: collecting a three-dimensional visual data flow, and mapping the three-dimensional visual data flow to a probability grid unit of a space-time coordinate system; defining a current moment and a future prediction period; modeling occupancy states of the grid units based on a sparse Gaussian process, and approximating posteriori distribution by using a random variational inference method; acquiring occupancy probability distribution, and constructing and online updating a space-time probability grid; generating a space-time visual cost grid representing a future collision risk; and outputting the cost grid to a flight control system to execute trajectory planning. By adopting the sparse Gaussian process and the random variational inference method, modeling and online updating are carried out on the grid occupancy state covering the current and future periods, the calculation complexity is reduced, the space-time visual cost grid can be generated to evaluate the future collision risk, and real-time obstacle avoidance is supported.

The invention discloses a three-dimensional image display device and a vehicle, and is applied to the technical field of vehicle-mounted display. The three-dimensional image display device comprises a first image generation module and a second image generation module, the first image generation module is used for generating and displaying a first image, the second image generation module is used for generating and displaying a second image, and the second image generation module comprises a second image generation unit with transmittance; the positions of the first image and the second image in the first direction do not coincide, and a three-dimensional visual image which can be presented through the second image generation unit is formed. According to the invention, the human eyes can watch the second image and the first image which are not overlapped through the second image generation unit along the first direction, and the second image and the first image are real images which are not overlapped and distorted, so that a real three-dimensional visual image can be presented when the human eyes watch the image, and no vertigo feeling is generated.

This invention provides a three-dimensional measurement method for the surface of a moving object on a production line based on phase-shifting profilometry. Employing a three-dimensional vision measurement system composed of a projector and a camera, and based on the measurement principle of phase-shifting profilometry, the method analyzes the phase error caused by the motion of the measured object and proposes a three-dimensional measurement method for the surface of a moving object that does not require phase error correction. By setting the installation position and angle of the projector and camera, the phase encoding direction in the projected stripe pattern is perpendicular to the direction of motion of the measured object, thereby eliminating the phase error caused by motion. This allows the three-dimensional surface measurement results of the measured object under motion conditions to essentially achieve the measurement accuracy of the measured object under static conditions, realizing high-precision online real-time measurement of the surface three-dimensional morphology of moving objects on actual production lines.

The invention discloses a manual teleoperation robot based on a magnetic control handle. Comprising an execution end which comprises a lifting vehicle, a mechanical arm, a tail end operation tool and a feedback module; the operation end comprises a magnetic control handle and a three-dimensional visual display module; the three-dimensional visual display module is used for displaying the three-dimensional visual image of the operation area acquired by the feedback module; the magnetic control handle is used for an operator to output an action instruction; and the control module is in signal connection with the execution end and the operation end through the communication module and is used for generating and issuing a control instruction according to the action instruction of the magnetic control handle and controlling the execution end to work. The device has the beneficial effects that a three-dimensional visual image of an operation area can be collected through the feedback module, an action instruction is output through the magnetic control handle, operation is easy and flexible, operation efficiency is high, and the device is suitable for coating operation in the high-altitude and complex environment.