234 results about "Rigid transformation" patented technology

The invention belongs to the technical field of computer vision, and discloses a sparse point cloud completion method based on multi-view fusion, which comprises the following steps: (1) inputting point cloud data, extracting point cloud features, and obtaining an overlapping region of the point cloud data; (2) calculating a similarity matrix of inter-frame point clouds in the overlapping region, and solving inter-frame rigid transformation by using a weighted singular value decomposition algorithm; (3) realizing accurate registration of multi-view point clouds through an accumulative registration strategy; (4) performing coarse-grained completion on the point cloud by using a 3D convolutional mesh residual network module, and further fusing 3D voxel features by using a multi-layer perceptron and completing fine-grained accurate point cloud completion; and (5) optimizing the weight of the model by calculating a loss function, and improving the overall effect and precision of completion. According to the invention, the accuracy and robustness of vehicle detection are improved by fusing a multi-view registration technology and a coarse-to-fine modular point cloud completion method.

The invention discloses an unsupervised registration method and system for pelvic reduction surgical navigation, and the method comprises the steps: constructing a three-dimensional reconstruction network model, and completing the training based on a generative adversarial loss function; inputting the intra-operative two-dimensional X-ray image into the three-dimensional reconstruction network model to obtain a three-dimensional intra-operative CT image; respectively segmenting the preoperative CT image and the intraoperative CT image by adopting a MedSAM segmentation model to obtain a preoperative segmented image and an intraoperative segmented image of the pelvis region; key points of the preoperative segmented image and the intra-operative segmented image are determined through a convolutional neural network, the distance between the key points is subjected to minimization calculation according to a closed-form function, and a rigid transformation matrix is obtained through derivation; and registering the intraoperative CT image according to the rigid transformation matrix and the preoperative CT image. Through the technical scheme of the invention, the registration precision and generalization ability are improved, the prediction range of the pose is wider, and good technical support is provided for the rapidness and convenience of surgical navigation.

The invention provides a rainfall flood resource retaining structure displacement intelligent monitoring method, and relates to the technical field of data processing, and the method comprises the steps: obtaining a second source point cloud and a second target point cloud of a clay region, and building a spatial distribution map; calculating a global rigid transformation matrix based on the second source point cloud and the second target point cloud; performing rigid-non-rigid mixed registration according to the spatial distribution map and the global rigid transformation matrix to obtain a registered point cloud; calculating a corrected displacement field sequence based on the registered point cloud; and carrying out time-frequency domain analysis on the displacement correction factor and the corrected displacement field sequence, extracting a characteristic frequency and a damping ratio to construct a space-time incidence matrix, and generating a displacement data sequence containing geological parameter space-time variation characteristics. According to the invention, the precision and reliability of displacement monitoring are improved.

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.

The invention discloses a multi-modal three-dimensional point cloud registration method and system, a terminal and a storage medium, and the method comprises the steps: obtaining initial point cloud data, carrying out the point cloud feature extraction processing and point cloud image feature extraction processing, and obtaining point cloud features and point cloud image features; performing feature enhancement processing and feature fusion processing on the point cloud features and the point cloud image features to obtain mixed features; performing feature splicing processing and significance score calculation according to the mixed features to obtain significance scores, and performing feature selection processing according to the significance scores to obtain key points and key point features; and performing matching matrix construction processing and related weight calculation according to the key points and the key point features to obtain target rigid transformation parameters, and aligning the initial point cloud data according to the target rigid transformation parameters. According to the method, the optimal rigid transformation parameter is calculated, and the point cloud data is registered according to the optimal rigid transformation parameter, so that accurate alignment of the point cloud data can be quickly realized.

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 relates to an electromagnetic environment measurement method and device for a complex terrain crossing area. The method comprises the following steps: acquiring a source point cloud and a target point cloud of a to-be-detected area, and acquiring a filtering point cloud set corresponding to the source point cloud; performing region growth segmentation on the filtering point cloud set, and determining region geometric information corresponding to each filtering point cloud in the filtering point cloud set; obtaining a matching pair between the first feature descriptor and the second feature descriptor to construct a feature matching relation set; determining a target rigid transformation matrix according to the feature matching relation set and the regional geometric information corresponding to the filtering point cloud, and performing point cloud registration on the filtering point cloud through the target rigid transformation matrix; and combining the filtered point clouds after point cloud registration to generate a three-dimensional point cloud database of the to-be-detected area, and modeling an electromagnetic environment of the to-be-detected area according to the three-dimensional point cloud database. By adopting the method, the measurement accuracy of the electromagnetic environment of the crossing area of the power transmission line can be improved.

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 a self-adaptive unmanned aerial vehicle remote sensing image intelligent registration method. The method comprises the steps of data collection, basic enhancement processing, registration precision optimization and self-adaptive intelligent registration. The invention relates to the technical field of image registration, in particular to a self-adaptive unmanned aerial vehicle remote sensing image intelligent registration method, which adopts two aspects of basic enhancement and self-adaptive registration optimization to enhance self-adaptive performance, and optimizes data quality through self-adaptive basic attribute enhancement. The intelligence and the adaptivity of rigid transformation parameters are improved by combining a registration precision optimization model trained by multiple parameter data; a self-adaptive image registration optimization method based on combination of teaching learning and reinforcement learning is adopted, through combination of reinforcement learning and teaching learning, a parameter optimal solution can be intelligently optimized, and self-adaptive parameter adjustment can be performed according to provided image flight parameter information, so that the registration precision of an intelligent registration result can be optimized.

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 feature extraction section extracts source domain structural features from input source domain image data and, extracting target domain structural features from input target domain image data. The rigid transformation section generates transformed structural features by rigid transforming the structural features with reference to conversion parameters. The relighting section generates new view features with reference to the transformed structural features and the conversion parameters in a way that new view features approximate the structural features which are extracted from input image data at the views indicated by the conversion parameters. The class prediction section predicts source domain class predictions from the source domain structural features and the source domain new view features, and predicting target domain class predictions from the target domain structural features and the target domain new view features. The updating section updates at least one of the feature extraction section, the relighting extraction section, and the class prediction extraction section.

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.

To provide a system and a method for optimizing streaming of a dynamic point clouds sequence.SOLUTION: An example disclosed method includes (i) transmitting a first point cloud to a client, wherein the first point cloud corresponds to a reference point cloud, (ii) receiving a second point cloud, and (iii) hierarchically determining changes in the second point cloud from the reference point cloud, wherein hierarchically determining the changes includes (a) identifying first areas in the second point cloud that have changed from the reference point cloud, and (b) for a first area having a highest priority, determining a first rigid 3D transformation that approximates a first change from the reference point cloud, and if the first rigid 3D transformation cannot be determined, further determining first points to be used to modify the reference point cloud, wherein the first points are representative of the first change.SELECTED DRAWING: Figure 5

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 relates to industrial printing defect detection, in particular to a curved surface printing defect detection method based on rigid and flexible image matching, which comprises the following steps of: calibrating a curved surface material, determining a mapping relation between a curved surface image and a plane image, and performing curved surface correction on an image to be detected and a template image; performing rigid image matching on the corrected to-be-detected image and the template image so as to perform optimal rigid transformation on the to-be-detected image and realize primary coarse alignment; inputting the to-be-detected image after rigid transformation and the template image into a pre-trained curved surface printing defect detection model, performing flexible image matching, and detecting curved surface printing defects; according to the technical scheme provided by the invention, the defect of relatively low curved surface printing defect detection precision caused by nonlinear deformation generated by material surface bending and surface characteristic interference of a printing material in the prior art can be effectively overcome.

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.