179 results about "Rigid transformation" patented technology

The invention provides a digital information processing method for a hospital radiology department, which comprises the following steps: S1, multi-modal image collaborative acquisition and standardization: synchronously acquiring anatomical structure images, functional images and metabolic parameter data of a patient through radiology department imaging equipment, and converting the anatomical structure images, the functional images and the metabolic parameter data into space-time aligned three-dimensional digital matrixes; s2, image quality optimization processing: performing nonlinear contrast enhancement and noise suppression on the original image to improve the signal-to-noise ratio of a target area; s3, dynamic self-adaptive registration: according to the biomechanical characteristics of the organ, fusing the rigid transformation model and the elastic deformation model, and according to the digital information processing method for the hospital radiology department, based on the dynamic registration matrix of the biomechanical model, improving the multi-modal image fusion precision; a deep learning segmentation algorithm fused with morphological constraints improves the focus boundary recognition accuracy; a texture mapping three-dimensional reconstruction technology is mixed, and an anatomical structure and metabolism information are presented at the same time; the invention discloses a structured report automatic generation system based on an attention mechanism.

The invention discloses a slope cutting house building deformation monitoring method based on unmanned aerial vehicle remote sensing, and the method comprises the steps: carrying out the periodic observation of an artificial slope through an unmanned aerial vehicle carrying a three-dimensional laser scanner, and obtaining slope three-dimensional point clouds in at least two different periods; performing denoising, down-sampling and coordinate normalization on the point cloud of each period to obtain a preprocessed data set; generating a global feature descriptor of each period by using a PointNet feature extraction unit; on the basis of a Lucas-Kanade iterative optimization algorithm, the descriptors of all periods are matched with the first period as the reference, and optimal rigid body transformation parameters are obtained; completing registration of the cross-period point clouds in a unified coordinate system according to the parameters; calculating displacement by adopting point-by-point difference and forming a differential deformation field; and comparing the displacement with a preset safety threshold, positioning an abnormal area and giving a risk level. According to the method, centimeter order registration and deformation identification of the cross-period point cloud under the unified stable reference are realized, and early warning and hidden danger assessment of a slope cutting and house building scene are supported.

The invention discloses a three-dimensional vision calibration and positioning method and system based on an event camera, and belongs to the field of positioning. The method comprises the steps of constructing an array containing a plurality of unique frequency coding light sources, constructing a binocular event camera system, collecting light source array light signal event streams, extracting pixel positions, frequencies and timestamp information, determining pixel coordinate centroids of the light sources through frequency and pixel clustering analysis, and establishing frequency and centroid mapping. Auxiliary light sources with the same coding rule are arranged on the surface of a target object, and after a binocular system collects light signals of the auxiliary light sources, the center-of-mass coordinates of the light sources are matched according to the mapping relation. And based on event camera calibration parameters and binocular external parameters, correcting and stereoscopically matching centroid coordinates, and calculating three-dimensional coordinates of the auxiliary light source through triangulation. And in combination with the known geometric topological relation of the auxiliary light source on the surface of the object, the spatial pose of the target object is solved by using a rigid transformation algorithm, and high-precision positioning is completed. According to the embodiment, the calibration precision can be improved in a complex application environment.

The invention relates to a transcranial magnetic stimulation high-precision positioning method, device and equipment based on optical navigation. Comprising the steps of selecting non-coplanar reference points based on a three-dimensional brain model of a patient, obtaining image coordinates and physical coordinates of all the reference points, carrying out corresponding point matching through the image coordinates and the physical coordinates, calculating and generating an optimal rigid transformation matrix, carrying out target registration error calculation, and realizing registration of an image coordinate system and a physical coordinate system. And according to the registered brain tissue conductivity parameters, calculating electric field space distribution induced by the transcranial magnetic stimulation coil in a target brain area, dynamically correcting stimulation target point coordinates of the virtual coil, and according to a completed coordinate system registration result, dynamically displaying the position and direction of the virtual coil corresponding to the transcranial magnetic stimulation coil on the three-dimensional brain model. And synchronously updating the brain slice view based on the corrected target point coordinates, and highlighting the actually stimulated brain region boundary. According to the method, adaptive neural navigation is driven through electric field remapping, and compensation target point correction is achieved.

The invention relates to a ship berthing position detection method and system based on shore end laser radars, and the method comprises the steps: deploying a plurality of laser radars at a shore end, and collecting original point clouds in parallel through an asynchronous thread model; constructing a rigid transformation matrix based on the radar installation attitude parameters, and further generating fusion point cloud data under a unified world coordinate system; then extracting 3D geometric edge points in a preset range of the front edge of the wharf as near-shore point cloud data, and further calculating the minimum ship-shore distance; based on the ship bow direction and a preset segmentation threshold value, the coastal point cloud data are segmented into ship bow area point cloud and ship stern area point cloud in the x-axis direction, the coordinate mean value of the ship bow area point cloud and the ship stern area point cloud is calculated and compared with an expected position in a wharf operation system, and berthing accuracy is judged; and the drift distance is calculated in real time, the drift distance, the attitude angle and the change rate of the attitude angle are compared with corresponding preset threshold values respectively for graded early warning and drift alarm, and high-precision ship berthing position dynamic detection is achieved.

A reference point is arranged on the ground to calibrate a ground world coordinate system, a robot base coordinate system is established based on robot base features, and a rigid transformation matrix between the two is calculated to achieve spatial alignment. A multi-sensor network is constructed, a visual sensor and a laser radar are associated to a double-reference coordinate system, and data are synchronously collected and unified to the same coordinate system. And according to the spatial consistency error of the data, dynamically evaluating the credibility weight of the sensor, constructing a joint error equation, and based on least square method optimization, weighting fusion data and outputting calibration parameters. According to the invention, the problems of dependence on single sensor precision, lack of a system-level calibration scheme and complex calibration process are solved, and the calibration precision and efficiency of the robot system are improved.

The invention relates to the technical field of production process control, in particular to an MES system management and control method and system based on precision mold production and manufacturing, and the method comprises the following steps: obtaining a measurement point cloud and a theoretical model, calculating rigid body transformation matrix registration to generate a registration point cloud, searching a nearest projection point, measuring geometric deviation, and constructing a three-dimensional deviation vector; and calculating the actual residual material thickness, screening and generating margin insufficient risk points, analyzing a numerical control code to extract a coordinate instruction, retrieving a nearest risk point, calculating a compensation distance, and adjusting coordinates to generate a self-adaptive finish machining program. According to the method, the three-dimensional deviation vector is constructed to represent the entity state, the risk area is screened in combination with the machining allowance, the compensation distance is calculated according to the actual residual thickness, and the instruction is dynamically adjusted to generate the self-adaptive program, so that the path is automatically corrected according to the shape of the workpiece, and the overcut risk caused by insufficient allowance is effectively avoided; and accurate matching between the machining process and the workpiece state is ensured.

The invention discloses an AR glasses blogger live video anti-shake processing method and processing system, and the method specifically comprises the steps: carrying out the calculation based on an original video stream and equipment motion data, and generating a preliminary stable frame sequence; based on the preliminary stable frame sequence, marking a key semantic region as a strong stability demand region, and segmenting a foreground main body and a background region; performing fine stabilization on the strong stability demand area and the foreground main body by applying non-rigid transformation, performing stabilization on the background area by applying rigid transformation and retaining the depth of the scene, and obtaining a frame sequence after partition correction; predicting a future multi-frame equipment motion track based on the frame sequence after partition correction, the equipment motion data and the segmentation information of the foreground main body / background region; and calculating and executing prospective canvas motion compensation in advance based on the future multi-frame equipment motion trail and the segmentation information of the foreground main body / background region. According to the invention, efficient anti-shake processing of the live video of the AR glasses is realized, and the stability of the live picture and the user experience are effectively improved.

The invention discloses a multi-modal image registration processing method and system based on deep learning, and relates to the technical field of medical image processing, and the method comprises the steps: calculating a mutual information loss value after registration through a mutual information loss method, optimizing a CNN model and UNet model parameters in combination with the total loss generated by the fusion of global and local deformation fields, and obtaining a registered sCT image. A CNN model and a UNet model are combined to generate global and local deformation fields, overall rigid transformation and local nonlinear deformation are effectively captured, the spatial alignment precision of sCT and reference CT is improved, model parameters are optimized through mutual information loss and total loss, the intensity distribution consistency is ensured, feature weights and treatment plan parameters are automatically adjusted through registration quality feedback, and the accuracy of the treatment plan is improved. The coverage precision and efficiency of the radiotherapy plan are remarkably improved, automatic and high-precision image registration and treatment plan optimization are realized, and the reliability and practicability of clinical image processing are enhanced.

The invention discloses a visual alignment method and device for printing draft intelligent deflection and spraying vehicle cooperative control, and belongs to the technical field of machine visual localization, and the method specifically comprises the steps: firstly, arranging a calibration target in an ink-jet printing area, employing an image collection device to collect an image, and calculating a rigid transformation matrix of a visual coordinate system and a printer spraying vehicle coordinate system; establishing high-precision coordinate mapping; the control terminal reads the reference position and attitude information of the original draft in the spraying vehicle coordinate system from the production management system; the method comprises the following steps: after primary inkjet of a paperboard, shooting the whole paperboard by an image acquisition device, and extracting actual contour features and coordinates of a draft through image processing; the coordinates are mapped to a spraying vehicle coordinate system through a transformation matrix, and a rotation deviation angle and a translation deviation vector are calculated; analyzing to obtain angle and translation compensation values, packaging the angle and translation compensation values into a control instruction, and sending the control instruction to ink-jet printing control software through a real-time bus; finally, software drives a servo executing mechanism, the position and the posture of the spraying vehicle are adjusted according to a new target, and accurate alignment with the paperboard is achieved.

The invention discloses an intelligent detection method and system for a flexible circuit board based on machine vision, and relates to the field of circuit board detection.The method comprises the steps that firstly, through a global coarse registration step, rigid transformation such as translation, rotation and zooming of the whole to-be-detected FPC image is rapidly corrected, and preliminary alignment with a standard template is achieved; then, local fine registration is introduced for nonlinear deformation such as local stretching and wrinkles caused by the characteristics of the FPC material, so that the fine and non-uniform local dislocation is accurately sensed and compensated, and nearly pixel-level accurate matching between the to-be-detected image and the template is realized. On the basis of high-precision alignment, differential attention deficit segmentation is carried out, so that false differences caused by inaccurate deformation registration can be effectively eliminated, real defects can be accurately identified, the false alarm rate is remarkably reduced, and the reliability and practicability of a detection scheme are greatly improved.

The invention discloses a color point cloud registration method and system based on feature fusion, and the method comprises the steps: obtaining a source point cloud and a target point cloud; extracting first geometric key points and first texture key points of the source point cloud, and fusing the first geometric key points and the first texture key points to obtain a first key point set; a second geometric key point and a second texture key point of the target point cloud are fused to obtain a second key point set; respectively extracting a first feature descriptor of each point in the first key point set and a second feature descriptor of each point in the second key point set; according to the first feature descriptor and the second feature descriptor, performing coarse registration by adopting a random sampling consistency algorithm to obtain an initial rigid transformation matrix; constructing a loss function according to geometric distances and color distances of corresponding points between the source point cloud and the target point cloud, and performing iterative optimization on the initial rigid transformation matrix to obtain a final rigid transformation matrix; and performing registration splicing on the source point cloud and the target point cloud according to the final rigid transformation matrix. And the registration precision is improved.

The invention belongs to the technical field of industrial robot motion control, and discloses an industrial robot precise path generation system based on three-dimensional visual reconstruction, comprising an environment sensing device which is composed of a multi-baseline structured light projector and a multi-view camera array and obtains point cloud data through light interference suppression and spectrum correction; the three-dimensional reconstruction processing unit generates a three-dimensional model through rigid transformation and deep learning correction; the geometric feature analysis unit performs curvature analysis and boundary recognition to form region description; the trajectory planning device adopts a weighted B spline to generate a trajectory under curvature constraint and energy optimization; the dynamic correction unit is switched between vision feedback and force feedback according to an error threshold value; the control conversion processor combines inverse kinematics and dynamics compensation and predicts deviation to output a control instruction; and the execution driving mechanism utilizes a servo motor and closed loop feedback to execute actions, so that high-precision generation and stable execution of a track under a complex working condition are realized.

The invention discloses a surgical navigation point cloud registration system based on global optimization, and the system comprises the steps: reconstructing a target point cloud based on a preoperative medical image, collecting an intraoperative anatomical surface source point cloud, and converting the point cloud to a patient reference coordinate system; the method comprises the following steps of: selecting a registration point pair with anatomical representativeness to carry out initial coarse registration; on the basis of the coarse registration, executing fine registration, including establishing a point pair relationship, calculating weighting based on a distance residual error and a neighborhood structure, filtering out abnormal point pairs by adopting a bidirectional pruning mechanism based on statistical analysis, jointly estimating rotation and translation parameters through a robust optimization algorithm, and iteratively updating to convergence; and finally outputting a rigid transformation matrix for surgical navigation. According to the method, stable and accurate point cloud registration can be realized under the conditions of sparse point cloud, noise and low overlapping rate in an operation, reliable space mapping support is provided for operation navigation under image guidance, and the method has relatively high robustness and clinical adaptability.

The invention discloses a point cloud splicing method based on three-dimensional centroid matching of optical mark points, which comprises the following steps of: reconstructing the three-dimensional shape of an object through a monocular system, randomly generating three-dimensional mark points in a standard geometrical shape in a target flat area, reversely resolving the three-dimensional mark points to a two-dimensional projector to generate a mark point pattern, and projecting the mark point pattern to the object; constraint of a public area under multiple view angles is realized; the method comprises the following steps: reconstructing a three-dimensional point cloud and calculating a geometric centroid of a cursor in a public area as a multi-view feature matching point by using data collected by a binocular system at different view angles, constructing feature point pairs based on spatial topology invariance, solving a rigid transformation matrix RT through SVD, and realizing high-precision point cloud splicing. Mark points do not need to be manually pasted, and the splicing efficiency is greatly improved; through three-dimensional centroid constraint, the problem that weak texture splicing errors are large is solved, robustness is high, and high-precision multi-view three-dimensional splicing is achieved.

The invention discloses an ultrahigh-voltage power transmission line tower gradient calculation method and device and a medium, and the method comprises the steps: fusing a high-definition camera image with a laser ranging eyepiece image, achieving the quick alignment of a heterogenous coordinate system through a rigid transformation matrix, and precisely capturing the depth information of the feature points of a tower target; a global-local detection model is constructed through cooperative work of a high-definition camera and a laser distance measuring sensor, and calculation complexity is reduced by adopting an SE-R3det network; the device and the medium can realize rapid and accurate tower inclination automatic measurement based on the method. The measurement efficiency is improved, laser ranging is adopted, feedback control is introduced, and automation of the whole process is achieved while the measurement precision is improved.

The invention discloses a hand-object interaction data set expansion method and device based on Gaussian splashing, and belongs to the field of computer graphics. In order to solve the problems of scarcity of existing hand-object interaction data, high labeling cost and limited data quality, a Gaussian kernel modeling method is mainly adopted to construct a hand-object interaction model, postures are optimized through rigid transformation and skeleton transformation, and meanwhile, a super-resolution module and a generative adversarial network are combined to improve the sense of reality of the data. According to the method, high-quality and diversified hand-object interaction data can be efficiently generated, rich prior information is provided for deep learning model training, and the learning effect of interaction tasks is improved.

The invention discloses a point cloud anomaly detection method based on registration guide rotation invariant feature learning, which comprises the following steps: acquiring a normal point cloud, applying random rigid transformation to generate a point cloud template, and performing multi-scale voxel downsampling; a feature extraction model is built and trained, a loss function is optimized based on a block matching pair and a point matching pair, and an optimal feature extraction model is obtained through training; registering and aligning the normal point cloud with the template, integrating the local rotation invariant features and the registered coordinates to obtain the final features of the normal point cloud, and establishing a normal feature memory bank; during detection, the test point cloud and the template are registered and aligned, the final features of the test point cloud are obtained and then are compared with the features in the normal feature memory bank, and abnormal scores are calculated to judge abnormal points. According to the method, the problem of detection deviation caused by rotation change and insufficient feature representation capability in the prior art can be solved, the accuracy and robustness of point cloud anomaly detection in industrial quality inspection are remarkably improved, and the method is suitable for automatic defect detection of a high-precision three-dimensional scanning piece.

The invention provides a calibration method, a calibration device, a tracking type scanning system and a storage medium, and the method comprises the steps: scanning a reference object on the calibration device through a scanner, and obtaining first scanning data corresponding to the reference object in a scanning coordinate system; acquiring the pose of the marker on the scanner and the pose of the calibration device by using a tracker; calibrating equipment parameters of the scanner based on the true value corresponding to the reference object and the first scanning data; based on the pose of the marker and the pose of the calibration device, converting the first scanning data to a tracking coordinate system of a tracker; and based on the true value corresponding to the reference object and the first scanning data under the tracking coordinate system, completing rigid body transformation relation calibration between the local coordinate system corresponding to the marker and the scanning coordinate system. Through the method, calibration errors can be reduced to improve measurement precision.

The invention discloses an air-ground multi-source point cloud robust registration method based on structure entropy sampling, and the method comprises the steps: S1, reading a source point cloud and a target point cloud, and obtaining the space coordinates, normal vectors and curvatures of the source point cloud and the target point cloud; s2, performing standardized preprocessing on the source point cloud and the target point cloud; s3, performing structure entropy sampling on the source point cloud and the target point cloud after standardization processing, and constructing a sampling point set for registration; s4, adopting a point-to-point registration method with robustness to calculate an optimal rigid transformation matrix between the source point cloud and the target point cloud obtained through structure entropy sampling in the step S3, and realizing point cloud registration; s5, performing spatial restoration on a registration result in the step S4, and constructing a rigid transformation matrix under an original coordinate; and S6, outputting the transformation matrix and the registration point cloud after spatial restoration. The method has the advantages that the key points are optimized through structural entropy driving sampling, the anti-noise capability is high, the registration precision is high, and the registration requirement of small-error space-ground fusion data can be met.

The invention provides a laser point cloud inter-frame matching method and system, and the method comprises the steps: reading a source point cloud file from a designated path or a data source; performing down-sampling on the point cloud by using a voxel filtering method, and reserving a representative point in each voxel by dividing a point cloud space into voxel grids; removing outliers in the point cloud by using statistical filtering; constructing an ikd-tree for the target point cloud, searching a nearest neighbor point in the target point cloud through the ikd-tree for each point in the transformed source point cloud, and screening out a matching point pair meeting a preset distance threshold; calculating an optimal rigid transformation matrix from the source point cloud to the target point cloud by using an iterative nearest point algorithm in combination with singular value decomposition according to the screened matching point pairs; and transforming the source point cloud by using the optimal rigid transformation matrix to complete point cloud inter-frame matching. The method not only improves the precision and efficiency of point cloud registration, but also has good real-time performance and robustness.

The invention discloses a dual-stage CBCT and oral scanning tooth registration method and system, and relates to the field of tooth three-dimensional digital model registration, and the method comprises the following steps: extracting a tooth voxel structure in a CBCT image and a dental crown grid structure in an oral scanning image through a segmentation network; a coarse-to-fine depth registration network is introduced, geometric feature coding and feature matching are carried out on super-points obtained through multi-resolution down-sampling, local matching is carried out on a dense point set in a super-point neighborhood, a rigid transformation matrix is calculated in parallel, and an optimal registration result is screened out from the rigid transformation matrix; and dividing a registration object into a plurality of three-tooth groups containing three adjacent teeth, and performing local iteration alignment based on an initial registration result. The method still has higher robustness and accuracy under the complex conditions of large cross-modal difference, high input point cloud density and the like, is shorter in time consumption, and provides reliable data basis and automatic support for clinical scenes such as orthodontics and implantation.

The invention provides a mechanical arm tail end positioning method, and relates to the technical field of visual localization, the method comprises the following steps: using a tail end flange, an imaging device and a base as original points to construct a three-dimensional coordinate system, and obtaining a flange coordinate system, an imaging coordinate system and a base coordinate system; off-line calibration is conducted on the mechanical arm operation system, and calibration parameters are obtained; a mechanical arm operation system is started, and a second identification code pose matrix at the moment is obtained; and determining a first operation point pose matrix based on the first rigid deviation matrix, the first actuator pose matrix, the first identification code pose matrix, the coordinate system rigid transformation matrix and the second identification code pose matrix, and determining the operation position of the end effector based on the first operation point pose matrix. According to the method, the operation position of the mechanical arm can be corrected only according to the rigidity deviation obtained through one-time off-line calibration, and it is ensured that the operation position of the mechanical arm is obtained with high efficiency and high accuracy.

The invention provides an equipment calibration method, a tracking type scanning system and a storage medium, the equipment calibration method is applied to the tracking type scanning system, the tracking type scanning system comprises a tracker and a scanner, and the method comprises the following steps: scanning a reference object by using the scanner to obtain scanning data corresponding to the reference object; the tracker is used for tracking a marker on the scanner, the pose of the marker is obtained, and the relative position between the marker and the scanner is fixed; converting scanning data under a scanning coordinate system of the scanner into a tracking coordinate system of the tracker based on the pose of the marker; and completing rigid body transformation relation calibration between the local coordinate system corresponding to the marker and the scanning coordinate system based on the true value corresponding to the reference object and the scanning data under the tracking coordinate system. According to the method, the equipment calibration efficiency can be improved.

The invention discloses an IOS image and CBCT image registration method based on particle swarm and single-tooth optimization, and belongs to the field of image data processing, and the method comprises the following steps: respectively obtaining tooth semantic segmentation results and point cloud data of an IOS image and a CBCT image through preprocessing and segmentation steps; introducing a particle swarm optimization algorithm, and performing iterative search and optimization on six-degree-of-freedom rigid transformation parameters between the IOS image data and the CBCT image data so as to complete global initial registration of the dental arch scale; and on the basis of a result after global registration, a registration object is divided into a plurality of local units based on a single tooth, and local iteration alignment is performed on each unit, so that tiny dislocation on the single tooth is eliminated, and the overall registration effect is optimized. According to the method, on the basis of processing cross-modal difference and realizing global registration, the registration precision of a single tooth level is further ensured through a local optimization mechanism, and reliable automatic registration support is provided for clinical scenes such as orthodontics and implantation.

The invention relates to the technical field of image processing, in particular to a positioning method for lamination of multi-layer flexible materials based on machine vision, which comprises the following steps: acquiring images of a first flexible material to be laminated and a second flexible material serving as a reference; contour geometry and surface texture features are extracted in parallel for the image, and a first feature map and a second feature map with the features as nodes and the spatial topological relation and gradient mutual information as weighted edges are constructed; calculating an initial nonlinear deformation field of the first flexible material; utilizing the initial nonlinear deformation field guide graph matching algorithm to obtain rough matching feature point pairs; and further solving to obtain a final non-rigid transformation model, and generating positioning parameters including non-uniform scaling and local distortion to guide fitting operation. According to the invention, the quality and efficiency of the flexible material laminating process can be improved.

The invention discloses a deep learning point cloud registration method for low-overlapping-rate point clouds. The method comprises the following steps: (1) carrying out multi-scale down-sampling on an input point cloud, calculating a weighted geometric centroid and a normal vector based on an adaptive neighborhood, and constructing a stable local reference point; (2) proposing a centroid geometric structure code, and embedding an attention mechanism by taking an inter-centroid Euclidean distance, a normal included angle and a neighborhood radius ratio as geometric priori to realize feature matching of structure constraint; (3) in a training stage, optimizing overlapping region positioning and point pair matching consistency by adopting a joint loss function formed by circular ring feature loss, overlapping perception loss and matchability loss; and (4) rigid body transformation is estimated by combining a coarse-to-fine matching strategy with a random sampling consistency algorithm, and global high-precision alignment is realized. According to the method, the registration precision and robustness under the conditions of low overlapping rate, noise and shielding are effectively improved, and the method is superior to an existing method in the aspects of a standard data set and real measurement data.

The invention provides a core image registration method based on multi-modal image segmentation and feature matching. The method is characterized in that the imaging difference between a rock core laser confocal image and a CT image is reduced through preprocessing color conversion and a normalization strategy, a marker is segmented by using deep learning, an algorithm target area is optimized in combination with morphological operation, feature points are detected under the guidance of an ROI area by using an ORB algorithm, and a target image is obtained. Matching of feature points is achieved by combining an FLANN feature point matching algorithm and a random K-d tree acceleration algorithm, image alignment is completed through a rigid transformation model, and registration precision is quantified through Dice. According to the method provided by the invention, the registration problem of the multi-modal core image caused by imaging difference is solved, the average Dice reaches 0.9210, and the registration efficiency and accuracy are remarkably improved.

The invention discloses a robotized autonomous assembly system and method for large aircraft components, and the system comprises a sensing module, a decision module and an execution module, the sensing module comprises a depth camera and a point cloud registration unit which is used for estimating the relative rigid transformation of a target point cloud of the current large aircraft component relative to a source point cloud of a known assembly pose; the decision-making module is used for converting relative rigidity into a tail end target pose under a base coordinate system of the mechanical arm based on a coordinate system chain type transmission principle and rigid body pose invariance constraint; the execution module is used for receiving the target pose of the mechanical arm and planning a collision-free, smooth and efficient motion track from the current pose to the target pose; the mechanical arm executes the assembling action according to the motion trail, meanwhile, the depth camera feeds back pose data in real time, and closed-loop control is formed till assembling is completed; according to the invention, the technical problems of insufficient sensing precision, poor system flexibility, limited measurement real-time performance and splitting of each link in the existing aircraft large component assembly technology can be effectively solved.

The invention relates to the technical field of medical image processing, in particular to a multi-time-point neural image comparative analysis method and system.The method comprises the following steps that a first three-dimensional neural image matrix and a second three-dimensional neural image matrix are obtained, rigid transformation matrix registration is calculated, and a third three-dimensional neural image matrix is generated; constructing a three-dimensional elastic grid model and adjusting node coordinates based on a mutual information criterion; generating a three-dimensional displacement vector field; constructing a Jacobian matrix and calculating determinant values; executing natural logarithmic transformation to generate a microscopic volume change rate map; and calculating a logarithm Jacobian mean value and determining a volume change state. According to the method, pose deviation is eliminated by calculating a rigid transformation matrix, a vector field reflecting fine displacement is constructed by utilizing a mutual information criterion, voxel-level quantitative analysis is realized in combination with a Jacobian matrix and logarithmic transformation, and minimal lesion recognition precision and diagnosis efficiency are improved in cooperation with a standard brain anatomy template.