227 results about "Iterative closest point" patented technology

The invention discloses a smart factory management system and method based on MES, and relates to the technical field of smart manufacturing, and the method comprises the steps: calculating a health degree score based on equipment historical maintenance records, and generating a time-space correlation multi-dimensional analysis data set containing an equipment topological structure and health degree parameters in combination with a digital twin network; performing spatial-temporal feature coupling and topological weight dynamic adjustment on the spatial-temporal correlation multi-dimensional analysis data set through a dynamic topological analysis algorithm, and generating a high-risk equipment list and an anomaly control instruction set; based on the high-risk equipment list, constructing a standardized multi-dimensional abnormal feature vector, and generating an aging weighted danger level signal through an entropy weight method; according to the method, space-time alignment of equipment operation state data and physical topology data is realized through a multi-rate Kalman filtering algorithm and an iterative nearest point algorithm, a high-fidelity digital twin network is constructed in combination with a dynamic graph convolutional network, and accurate anomaly detection and health degree evaluation are supported.

The invention discloses a textile production defect detection method and system based on image processing, and belongs to the field of textile defect detection. According to the method, the point cloud data is generated by collecting the surface deformation stripes, and the penetrability reflection signal containing the sound wave propagation time is obtained through the multi-frequency ultrasonic probe. Denoising the point cloud data and then positioning a surface deformation candidate area based on a curvature gradient and a normal vector deflection angle; and an internal abnormal region is determined by separating and extracting a reflection wave amplitude value and a time delay parameter through an ultrasonic frequency band. And matching the surface point cloud coordinates with the ultrasonic propagation time by adopting an iterative nearest point algorithm to establish feature association between the normal vector deflection angle and the reflected wave amplitude. And dynamically distributing fusion weights for the surface features and the internal features according to the curvature gradient change rate and the amplitude attenuation rate to generate a multi-modal defect distribution diagram. And geometric shape and reflection intensity parameters are extracted and matched with a preset defect template to realize type and position judgment, and the method can be suitable for production defect detection of the thick textile with a multi-layer composite material structure.

The invention relates to a point cloud registration method based on improved ISS-TOLDI features in combination with ICP, and belongs to the technical field of laser point cloud application. The method comprises the steps of obtaining a source point cloud and a target point cloud, and performing preprocessing; extracting feature points from the point cloud data by using an internal shape descriptor ISS algorithm; performing feature description on the extracted feature point set by using an improved TOLDI algorithm; performing coarse registration on the point cloud by using a sampling consistency initial registration algorithm; and performing fine registration on the coarsely registered point cloud data by using an iterative closest point ICP algorithm to complete point cloud registration. According to the method, the TOLDI algorithm is improved, so that the calculation complexity is reduced, the feature integrity is ensured, and the point cloud registration precision is higher.

The present invention relates to the field of virtual pre-assembly matching of bridge engineering components based on 3D point clouds, and particularly relates to a method for virtual pre-assembly matching of prefabricated beams based on design-measured point cloud models. An oriented bounding box is computed respectively for 3D point clouds of two prefabricated beams with assembly relationship therebetween, and two point cloud slices and design point cloud are formed; the two point cloud slices are respectively registered with the generated design point cloud by the iterative closest point algorithm; boundary features and corner features of a pre-assembly interface of two components to be assembled are fitted and extracted; and coarse matching and fine matching of the assembly interface are achieved by the Procrustes analysis algorithm and the iterative closest point algorithm in sequence, a matching degree error of the interface is computed, and an assembly result is evaluated. According to the present invention, design point cloud is introduced to achieve the screening of point clouds near features to be extracted, and posture adjustment on components to be assembled is performed by combining a plurality of algorithms, which not only improves the degree of automation and reduces the manual intervention, but also improves the precision of virtual pre-assembly matching and saves the computation time.

The invention relates to the field of spatial information processing, and discloses a live-action three-dimensional scene construction method, which comprises the following steps: S1, collecting multi-source heterogeneous data of a scene to be constructed, including image data, laser radar point cloud data and geographic information data; according to the method, high-precision rigid registration between the laser radar point cloud data and the image and geographic information data is realized by introducing an improved iterative nearest point algorithm, and on the basis of a traditional ICP algorithm, a dynamic weight adjustment mechanism is adopted, a weighting matrix is introduced into a target function for minimizing registration residual errors, so that the registration precision of the laser radar point cloud data is improved. According to the method, the registration contribution degree can be adaptively adjusted according to the quality, density and spatial distribution characteristics of different data sources, the spatial fusion precision between heterogeneous data is effectively improved, and the risk of model offset or distortion caused by sensor errors is reduced.

The invention discloses a high-reflective surface defect detection method based on structured light and phase deflection, which belongs to the technical field of visual inspection, and comprises the following steps: generating a multi-frequency sinusoidal grating pattern by using a structured light module, and projecting the multi-frequency sinusoidal grating pattern to the surface of a detected object; multi-frequency phase shift coding is introduced, an absolute phase is calculated through a pilot frequency unwrapping method, a depth map obtained by the structured light is calculated, and a structured light point cloud is reconstructed; a unit normal vector is calculated, a Poisson reconstruction model is adopted to obtain a depth map obtained by phase deflection, and deflection point cloud is obtained; the structured light point cloud data and the deflection point cloud data are registered through an iterative nearest point algorithm and then fused to form a fused depth map; and determining a defect area by adopting the maximum principal curvature and the minimum principal curvature combined mask. By means of the mode, the limitation of a traditional three-dimensional measurement means in high-reflection and complex-curvature surface defect recognition is solved, and submicron-level three-dimensional deformation detection precision is achieved.

The invention discloses a narrow space spraying trajectory planning method based on reinforcement learning, and the method comprises the steps: obtaining environment data through a LiDAR camera and a TOF camera, and obtaining a LiDAR point cloud and a TOF point cloud; carrying out registration fusion on the LiDAR point cloud and the TOF point cloud, and carrying out normal vector unification processing on each point of the fused point cloud; reconstructing by introducing a Poisson equation improved by shielding perception weight, and eliminating noise through Laplacian smoothing processing to obtain a target point cloud set; designing a state space, an action space and a reward function of reinforcement learning according to the planning of the spraying path trajectory, and determining a spraying path trajectory planning strategy; and based on the target point cloud set, adopting a spraying path trajectory planning strategy to carry out spraying trajectory planning. Sensor data fusion is realized through an improved iterative nearest point algorithm, and a shielding area is repaired in cooperation with a Poisson reconstruction algorithm, so that accurate correspondence between input parameters and output film thickness can be realized, and the control precision of the spraying thickness is remarkably improved.

The invention discloses an environment modeling method, system and equipment based on laser radar and vision fusion, and belongs to the technical field of mobile robot environment modeling. Through synchronous acquisition of three-dimensional point cloud data and image data, feature extraction and distortion correction are completed on the data acquired through the two different ways; accurate modeling features can be obtained, a geometric pose state quantity and a luminosity pose state quantity are calculated through an implicit moving least square iterative nearest point algorithm, the calculation precision of a curved surface in an environment is improved, and through point cloud splicing, a two-dimensional grid map and a three-dimensional point cloud map are generated. The two-dimensional grid map and the three-dimensional point cloud map are fused to complete environment modeling, the modeling speed is increased, the environment object information is aligned, information dislocation is avoided, and the environment modeling efficiency and accuracy are improved.

The invention relates to a laser radar camera calibration method and device, and a medium. The method comprises the steps: collecting multi-frame laser radar point cloud data and synchronous corresponding image data in a construction scene; carrying out multi-frame point cloud fusion and dense reconstruction to generate a laser dense point cloud; visual sparse point cloud reconstruction is carried out, and cross-modal scale unification and space alignment are carried out on an initial visual point cloud obtained through reconstruction and the laser dense point cloud; generating a visual dense point cloud through three-dimensional Gaussian splashing; and performing registration on the laser dense point cloud and the visual dense point cloud by adopting a point-to-line iterative nearest point algorithm, and performing calculation to obtain an external parameter calibration matrix of the camera and the laser radar. Compared with the prior art, the method has the advantages of high precision, low cost, high stability and the like.

The invention discloses a real-time monitoring and early warning method for deformation of a foundation pit support based on oblique photography, and relates to the technical field of photogrammetry and deformation measurement, high-resolution images of a support structure are acquired in a multi-angle manner through a multi-stage route, and a high-density three-dimensional point cloud reference model is constructed after preprocessing; then periodically acquiring a monitoring point cloud, and realizing high-precision space alignment with the reference model through control point coarse registration and iterative nearest point fine registration; and finally, calculating the three-dimensional coordinate deviation of each point through point cloud matching, extracting the full-surface deformation, and generating a deformation cloud picture and a statistical report. According to the method, the technical spanning from discrete point monitoring to full-field continuous monitoring and from low-dimensional data to true three-dimensional vectorization deformation analysis is realized, and the comprehensiveness and accuracy of deformation monitoring are improved.

The invention discloses an external damage hidden danger identification method and system based on AI image and radar dual verification, and relates to the technical field of artificial intelligence and multi-source perception fusion, and the method comprises the following steps: extracting the motion trail features of perception data in a target region, solving a coordinate transformation matrix through an iterative nearest point algorithm, carrying out the time sequence alignment, and carrying out the recognition of the motion trail features of the perception data; obtaining the calibrated sensing data; taking the calibrated image data and the calibrated radar data as input, and outputting a radar detection result and a visual identification result; projecting radar coordinates to an image coordinate system according to a coordinate transformation matrix based on a visual identification result and a radar detection result, outputting a space overlapping degree, and generating a matching result set; carrying out confidence fusion on the matching result set through a Bayesian probability model, and judging an external damage hidden danger level in combination with a radar detection result; according to the invention, through the AI vision and radar dual verification fusion technology, the problems of inconsistent multi-source perception and low external damage hidden danger identification precision are solved.

The invention relates to the technical field of defect detection, in particular to a steel pipe curved surface defect detection method and system based on three-dimensional data fitting, and the method comprises the steps: obtaining three-dimensional contour data of a steel pipe, and numbering the obtained three-dimensional contour data; setting a reference contour based on the obtained three-dimensional contour data; searching a single contour defect position according to a reference contour fitting curve; counting the number of multi-contour defect position recognition by using the single contour defect position; counting all contour data of the whole detection area; according to the method, the reference contour and the to-be-detected contour are aligned in a nearest point iteration mode, meanwhile, the adjusting threshold value is set to adjust the translation vector to update the contour position so as to obtain the contour correction data, data collection displacement deviation caused by factors such as production line vibration can be effectively compensated, the defect misjudgment and missed judgment situations caused by data deviation are reduced, and the detection accuracy is improved. And the reliability of the detection result is improved.

The invention discloses a digestive endoscopic esophageal trajectory optimization method based on Kalman filtering, and belongs to the technical field of digestive endoscopic surgical navigation, and the method comprises the following steps: S1, based on an esophageal model of preoperative CT three-dimensional reconstruction, extracting a geometric center point sequence of an esophageal cavity, generating an esophageal center line, and extracting a geometric center point sequence of the esophageal cavity; the method comprises the following steps of S1, acquiring six-degree-of-freedom pose data of the front end of an endoscope in real time through an electromagnetic positioning system, and generating an original motion track, and S3, performing rigid body registration on the original motion track and an esophagus center line by utilizing an iterative closest point (ICP) algorithm, so as to realize coordinate system coarse alignment. The real-time data of an electromagnetic positioning system and a three-dimensional model reconstructed by CT are combined, the navigation robustness is improved, the pose is adaptively corrected through Kalman filtering, the influence of environmental interference is reduced, the space relation between the endoscope and surrounding organs is visually displayed through an optimized track, and accurate operation of a doctor is assisted.

The invention relates to the field of mechanical engineering, discloses a quick-connection welding control method and system for aluminum alloy pipe fittings, and aims to solve the problems that in the prior art, heat input control is not accurate, the positioning and centering efficiency is low, parameter adjustment lacks self-adaptability, and the multivariable cooperative control capacity is insufficient. The method comprises the following steps: acquiring point cloud data of the end surface of a pipe fitting through three-dimensional scanning, and calculating a six-degree-of-freedom butt joint deviation by adopting an improved iterative nearest point algorithm; a compensation track is generated based on multi-objective optimization, and the six-degree-of-freedom parallel robot is driven to achieve fine adjustment; and after the butt joint deviation is stable, welding is started according to a sectional type current increasing strategy, and the wire feeding and welding speed is dynamically adjusted in combination with fusion depth feedback and visual detection. According to the scheme, high-precision automatic butt joint, stable molten pool control and online defect closed-loop treatment are achieved, the welding quality consistency and the production efficiency are remarkably improved, and the method is suitable for efficient and high-quality connection of various aluminum alloy pipe fittings.

The invention relates to the technical field of image recognition, and particularly provides a multi-view point cloud registration method, which comprises the following steps of: performing coarse registration on a source point cloud and a target point cloud based on multi-dimensional features by using an RANSAC (Random Sample Consensus) algorithm in a coarse registration stage to obtain a coarse registration transformation matrix; in the fine registration stage, the coarse registration transformation matrix is used as an initial value, point cloud registration is carried out in at least two resolution spaces, segmented iterative optimization is carried out by using an error loss function set in each resolution space, rapid convergence is carried out in a low-resolution space through an iterative nearest point algorithm, and the point cloud registration is realized. And local geometric alignment optimization is carried out in other resolution spaces through a generalized iterative nearest point algorithm, and fine registration of the source point cloud and the target point cloud is completed after multi-resolution space progressive optimization registration. According to the method, the robustness of feature matching of the low-overlap region is remarkably improved, the registration speed and precision are balanced, and the limitation of a traditional point cloud registration method under the low-overlap and non-ideal point cloud condition is solved.

The invention discloses a grinding error compensation method and system based on workpiece surface defect detection, and the method comprises the steps: carrying out the three-dimensional modeling of a ground workpiece through 3D scanning equipment, and obtaining an initial point cloud; noise reduction is carried out on the initial point cloud through a random sampling consensus algorithm introducing a sampling weight; performing difference comparison on the noise reduction point cloud and the ideal point cloud through structural difference analysis, and marking a feature region according to a result; scanning equipment is used for carrying out high-resolution scanning on the feature region to obtain feature point cloud; fusing the feature point cloud into the noise reduction point cloud by using an iterative nearest point algorithm of a dynamic weight distribution strategy and weighted fusion to obtain a final scanning point cloud; and performing difference comparison on the final scanning point cloud and the ideal point cloud through texture difference analysis to judge defects, and performing grinding error compensation on the defect part through a particle swarm optimization algorithm added with local sensitive particle initialization.

The invention relates to a three-dimensional point cloud reconstruction method and device using a single-line laser radar and an inertial measurement unit (IMU). According to the method, a stepping motor drives a single-line laser radar to periodically pitch and swing, and the scanning dimension of the single-line laser radar is expanded to a three-dimensional space; the method comprises the following steps: synchronously acquiring point cloud data and IMU (Inertial Measurement Unit) data of a laser radar, performing intra-frame motion distortion compensation on each frame of point cloud data by using the IMU data, and eliminating distortion generated by carrier motion; projecting the compensated three-dimensional point cloud data to a two-dimensional aerial view (BEV) plane to generate a two-dimensional point set, optimizing and solving inter-frame two-dimensional rigid body transformation by adopting an iterative closest point (ICP) algorithm based on the BEV point set of adjacent frames, then lifting the inter-frame two-dimensional rigid body transformation into three-dimensional transformation, and updating the global three-dimensional pose of a carrier; according to the method, low-cost and high-precision three-dimensional point cloud reconstruction is realized, the hardware cost is reduced, the operation efficiency is improved, and the method is suitable for various devices such as a mobile robot, a sweeper and a simple scanner.

This invention provides a method and system for monitoring surrounding rock deformation in underground coal mines based on three-dimensional point clouds, relating to the field of coal mine safety monitoring. The invention includes the following steps: installing a reference target in the monitoring area and collecting point cloud data; preprocessing the point cloud data, performing coarse registration using the reference target and fine registration using the iterative nearest point algorithm, and generating a triangular mesh model using the Poisson surface reconstruction algorithm; parameterizing the triangular mesh model of the monitoring area into a two-dimensional analysis mesh, calculating the three-dimensional displacement vector of each node, and establishing a displacement field matrix; calculating the displacement gradient tensor based on the displacement vector, deriving the Green-Lagrange strain tensor, and obtaining the full-field strain distribution; recording the strain value of each mesh node, and determining whether an instability warning is triggered based on the relationship between the strain value and the warning threshold; this method can achieve high-precision monitoring of surrounding rock deformation in underground coal mines.

The invention discloses a mine car obstacle recognition and distance measurement method and device and a storage medium. The method comprises the steps of constructing a segmentation template, marking image feature points, extracting an obstacle contour line, performing coarse and fine segmentation on a target obstacle based on the intersection point proportion of a background segmentation surface and the obstacle contour line, and determining a contour segmentation area of the target obstacle; the method comprises the following steps: marking point cloud feature points, constructing an obstacle template, carrying out matching mapping on a target obstacle and the obstacle template by adopting a non-rigid iterative nearest point algorithm, correcting feature point offset in a matching process in combination with a KD-Tree method, and calculating to obtain point cloud parameters of the target obstacle. And identifying the type of the target obstacle and measuring the distance according to the contour segmentation region and the point cloud parameters. According to the method, the target obstacle image and the point cloud information are combined, the target obstacle is segmented according to the intersection point proportion, the fuzzy area is smoothly segmented, the point cloud parameters are measured while the target obstacle is segmented, and the purpose of identifying the type and the distance of the target obstacle is achieved.

The invention discloses a Lidar point cloud registration optimization method based on geometric feature reliability, and the method comprises the steps: carrying out the preprocessing and initial alignment of a laser radar scanning point cloud, extracting the features of a maximum principal curvature and a minimum principal curvature based on the local curvature of the point cloud, dividing a point region into two types of geometric features of angular points and plane points according to the threshold value of the maximum principal curvature, and carrying out the registration of the angular points and the plane points. On the basis, fitting quality factors including fitting errors, local curvatures and spectral entropies of the linear features and the plane features are calculated respectively, then the three factors are fused into a unified reliability weight based on a Bayesian probability model, and finally the feature reliability weight is introduced into an optimization objective function of iterative nearest point registration to execute weighted ICP registration. And outputting positioning and attitude determination results. According to the method, the reliability of geometric features is quantitatively evaluated, and a weighted optimization framework is constructed, so that the point cloud registration precision and robustness are remarkably improved, and the problem that a traditional ICP algorithm is sensitive to unreliable features in feature degradation or high-dynamic scenes is effectively solved.

The invention discloses a transformer substation civil engineering surveying and mapping data processing method and system based on multi-source data, and the method comprises the steps: obtaining three-dimensional point cloud data of a transformer substation, importing beam column coordinates and equipment basic parameters in a BIM design model, and building an initial space reference coordinate system; performing coarse registration of the actually measured point cloud and the BIM model by adopting an iterative nearest point algorithm, and correcting a registration error by introducing a ground control point coordinate; constructing a three-dimensional difference analysis layer, automatically marking a deviation region, exceeding a preset deviation value, between the actually measured point cloud and the BIM model, and generating alarm data including coordinate positions and the deviation value; performing deformation trend verification on the deviation area based on monitoring data of the optical fiber sensor, and triggering model reconstruction when the change rate of continuous three times of sampling data is greater than 1mm / h; and outputting a three-dimensional visualization report integrating the BIM model, the actual measurement point cloud and the deviation area. The problem that early deformation of a civil engineering structure cannot be captured according to civil engineering surveying and mapping data of a transformer substation in the prior art is solved.

The invention relates to the technical field of spacecraft navigation, and discloses a method for estimating the relative attitude of a failed spacecraft under the constraint of computing resources, and the method comprises the steps: carrying out the preprocessing of measurement data, obtaining the observation data of the relative attitude of a camera based on an ellipse recognition algorithm, and obtaining the measurement data of the relative attitude of a laser radar based on an iterative nearest point algorithm. Pseudo measurement factors are designed for the camera and the laser radar, and variational integral prediction factors are designed for attitude increments at adjacent moments of the failed spacecraft; factor graph optimization based on a moving window is carried out, and the relative attitude of the failed spacecraft is estimated; the momentum conservation characteristics of the failed spacecraft can be mined through a designed variational integral predictive factor, the constraint relation of attitude increments at adjacent moments is constructed, and the problem that the estimation precision of an existing scheme depends on high-frequency measurement input is solved; and low-frequency measurement data of the camera and the laser radar are fused through a built failure spacecraft full-state factor graph model, so that the relative pose estimation of the non-cooperative target under the low computing power load is realized.

The invention discloses a multi-sensor fusion navigation system and method of an electric power inspection unmanned aerial vehicle and the unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle navigation, the multi-sensor fusion navigation system comprises a laser radar unit and an inertia measurement unit which are coupled to form a laser radar-inertia subsystem, and a vision unit and the inertia measurement unit which are coupled to form a vision-inertia subsystem; the laser radar-inertial subsystem is used for calculating an iterative nearest point error between the current frame and the global map; using the iterative nearest point error as an observed value of an error state iterative Kalman filter to execute a filtering updating process, and constructing and updating a global map; and the vision-inertia subsystem is used for constructing a hybrid reprojection error based on the projection points and the image feature points of the recovery depth, and performing state estimation by taking the hybrid reprojection error as an observation value of an error state iteration Kalman filter. The technical problem that in the prior art, stable and high-precision navigation cannot be achieved in an electric power scene is solved.

The invention provides an unmanned aerial vehicle complex environment positioning system and method based on double laser radars, and the system comprises a positioning data frame construction module which is used for constructing a multi-sensor fusion positioning data frame based on the double laser radars installed on an unmanned aerial vehicle in combination with an inertial measurement unit (IMU), a global positioning system (GPS) and a visual sensor; the degradation environment detection module is used for carrying out degradation environment detection on a preset complex environment based on a degradation environment detection algorithm and optimizing a positioning data framework based on a degradation environment detection result; the data processing module is used for filtering the positioning data in the optimized positioning data framework based on a point cloud filtering algorithm and an ICP algorithm and performing fusion registration and pose estimation to obtain the pose of the unmanned aerial vehicle and an environment map; and the positioning module is used for obtaining a positioning result of the unmanned aerial vehicle in a preset complex environment based on the pose of the unmanned aerial vehicle and the environment map. According to the invention, high-precision positioning and map construction in a complex environment can be realized.

The invention relates to the technical field of medical images, in particular to a multi-modal medical image registration and fusion analysis method, which comprises the following steps of: eliminating image noise and artifacts based on a modal adaptive filtering strategy; constructing a pyramid type feature extraction network to realize multi-scale feature extraction, calculating feature matching degrees among different modal images, and dynamically adjusting matching weights by combining feature differences among modals; a focus area attention mask is constructed, targeted enhancement of registration image features is realized, and a hierarchical fusion strategy is adopted to evaluate the quality of a fused image; and constructing a multi-task deep learning model to complete focus automatic detection, segmentation and benign and malignant preliminary judgment on the fused image. According to the multi-modal medical image registration and fusion analysis method, a three-layer feature pyramid is constructed, a multi-feature fusion matching cost function is introduced, and cross-modal feature matching is optimized through an adaptive weight iteration nearest point ICP algorithm, so that the information richness, marginal definition and focus discrimination of a fused image reach the standard.

The invention discloses a virtual assembly method and system based on improved point cloud registration and precision feature extraction, relates to the technical field of virtual assembly, and aims to solve the technical problem that a conventional ICP algorithm is insufficient in robustness and precision in a point cloud registration link in a current virtual assembly technology based on point cloud data. Comprising a file processing module, a preprocessing module, a point cloud registration module and a many-to-many component matching scheme solving module. According to the method, the RICP algorithm is designed, and a general adaptive robust function is introduced, so that the problem that the traditional ICP algorithm is sensitive to noise and outliers is effectively solved. The robust function can dynamically give a weight according to a point pair distance, stable and high-precision registration can still be realized even in a scene of large point cloud initial pose difference, low overlapping degree or unobvious surface features, and the problem of insufficient robustness and precision of a traditional ICP algorithm in a point cloud registration link of a current virtual assembly technology based on point cloud data is solved.

The invention relates to the technical field of robot positioning and mapping, and discloses a positioning and mapping method and device and a storage medium. The method comprises the following steps: performing motion distortion correction on point cloud data of a current frame collected by a laser radar to obtain corrected point cloud data of the current frame; constructing a local sub-map of the current frame, calculating a degradation probability of a unit direction vector of the local sub-map, correcting a Hessian matrix of an iterative nearest point algorithm according to the degradation probability, and determining a pose estimation result of the corrected point cloud data relative to the local sub-map according to the corrected Hessian matrix; and fusing the pose estimation result and an IMU predicted value through a Kalman filter, calculating to obtain optimal pose estimation, and inserting the corrected point cloud data into a global map based on the optimal pose estimation. The problems of point cloud distortion and scene degradation in a dynamic scene are solved, and high-precision and robust positioning and mapping are realized.

The invention relates to a multi-target pose estimation and sorting method based on an optimization template, and the method comprises the steps: importing a model of a target workpiece, and making a model point cloud library; collecting a scene point cloud of the target workpiece by using a depth camera; extracting model point cloud and scene features; loading the scene point cloud; removing scene background information through an RANSAC (Random Sample Consensus) algorithm; sparsification is carried out on the scene point cloud after the background is removed; segmenting the sparsified scene point clouds through clustering, analyzing point cloud features in each cluster according to PCA, dividing the point clouds with the same features into the same category, and obtaining point cloud information of each object in each category; further adopting a point pair feature PPF voting algorithm to carry out coarse registration on the object point cloud and the model point cloud to obtain a pose cluster; and on the basis of the coarse registration, performing fine registration on the object point cloud and the template by adopting an iterative closest point (ICP) algorithm. The method has the advantages that all target objects in a scattered state can be recognized and distinguished through one-time shooting, the poses of all the targets are obtained, the robot is controlled to sequentially execute sorting according to the estimated poses, and the operation efficiency is high.

This invention relates to the field of image recognition technology, specifically providing a multi-view point cloud registration method. In the coarse registration stage, the RANSAC algorithm is used to perform coarse registration of the source and target point clouds based on multi-dimensional features, obtaining a coarse registration transformation matrix. In the fine registration stage, the coarse registration transformation matrix is ​​used as the initial value, and point cloud registration is performed in at least two resolution spaces. Segmented iterative optimization is performed using an error loss function set for each resolution space. In the low-resolution space, an iterative nearest-point algorithm is used for rapid convergence, while in the remaining resolution spaces, a generalized iterative nearest-point algorithm is used for local geometric alignment optimization. After progressive optimization registration in multiple resolution spaces, the fine registration of the source and target point clouds is completed. This invention significantly improves the robustness of feature matching in low-overlap regions, balances registration speed and accuracy, and overcomes the limitations of traditional point cloud registration methods under low-overlap and non-ideal point cloud conditions.

The invention discloses a three-dimensional scene reconstruction method and system based on improved SuperPoint feature points, and solves the problems of low precision and consistency of three-dimensional scene reconstruction in the prior art, and the method comprises the steps: collecting image information, and obtaining an RGB image and a depth image; a SuperPoint-MSFF network is utilized to carry out feature point detection on the RGB image of the current frame and the RGB image of the next frame, and corresponding SuperPoint-MSFF feature descriptors are generated; feature point matching is carried out, camera attitude estimation is carried out, and coarse registration is carried out on the source point cloud; based on the current frame depth map and the next frame depth map, generating a dense point set containing each pixel space position; and using an improved ICP iterative nearest point algorithm to carry out fine registration of the point cloud based on the dense point set to obtain a reconstructed three-dimensional point cloud. The global context information and the local detailed information of the image are effectively captured, and the feature point detection process is further optimized, so that the robustness of feature point matching and the overall performance of three-dimensional reconstruction are improved, and the precision of three-dimensional reconstruction is improved.