127 results about "Iterative closest point" patented technology

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 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 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.

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 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 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 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.

According to the automatic measurement system and method for the double-view-field structured light turbine blade, the double-view-field structured light measurement system is constructed, automatic viewpoint planning is carried out based on a turbine blade CAD model, discretization processing is carried out on the turbine blade CAD model to obtain point cloud data, a normal vector clustering algorithm is used for carrying out region segmentation on point cloud, and the point cloud data are subjected to region segmentation; estimating a scanning visual angle direction and a viewpoint position based on a clustering result, calculating displacement of each axis by combining an inverse kinematics model of the three-axis motion platform, and controlling the three-axis motion platform to move to a preset pose; double-view-field structured light hybrid measurement and coordinate system unification are executed, point cloud data obtained through multiple times of measurement are subjected to precise registration, point clouds are unified into an approximate global coordinate system through pose information of a three-axis motion platform, precise registration is conducted on the point clouds through an iterative nearest point algorithm, the splicing precision is improved, and accumulative errors are reduced.

The invention discloses a novel symmetric iteration nearest point cloud registration calculation method for quality detection, and the calculation method comprises the steps: setting a point set and a point set in a three-dimensional space, carrying out the registration of a point set P and a point set Q, so as to solve a six-degree-of-freedom rigid body transformation matrix, and carrying out the registration of the point set P and the point set Q; the six-degree-of-freedom rigid body transformation matrix comprises a three-degree-of-freedom rotation matrix R and a three-degree-of-freedom translation vector t; a weighted point-surface model is constructed, and a symmetric iteration nearest point objective function based on weighted point-surface measurement and an adaptive robust loss function is constructed based on the weighted point-surface model; the calculation of the symmetric iteration nearest point objective function based on the weighted point-surface measurement and the adaptive robust loss function comprises the step of alternately executing a corresponding point updating step and a registration step until a convergence condition is met.

The invention relates to the technical field of augmented reality image processing, and discloses a traditional Chinese medicine acupuncture auxiliary positioning system based on an AR technology, which comprises a body surface anatomical feature intelligent extraction module, a personalized meridian model adaptive deformation module, a dynamic virtual-real registration tracking module and an AR visual rendering and quality closed loop module, according to the method, a thin-plate spline transformation algorithm is adopted to elastically deform a standard meridian model to the body surface of an individual, the individualized cun unit length is dynamically calculated in combination with a bone degree cun method, virtual-real registration is optimized through an iterative nearest point algorithm, movement and breathing deviation of a patient are compensated, the positioning quality is evaluated through a confidence coefficient thermodynamic diagram, closed-loop reacquisition optimization is driven, and the positioning accuracy is improved. Accurate AR-assisted positioning of acupuncture points of the whole body is realized.

The invention discloses an air-ground combined post-disaster building three-dimensional structure damage fine detection method, and belongs to the technical field of post-disaster emergency building damage assessment, and the method comprises the steps: collecting an air-ground image of a post-disaster building, and carrying out the preprocessing; registration and fusion are carried out by combining fast point feature histogram (FPFH) features and point clouds of iterative closest points (ICP); carrying out data enhancement by using the CT-3DGAN; and carrying out fine segmentation on the three-dimensional damage of the building based on CD-PointNet + +. On the basis of ground and aviation data, an improved iterative closest point ICP algorithm is provided to ensure high-precision registration, especially in a scene with large point cloud noise and low overlapping rate; according to the CT-3DGAN model, three-dimensional convolution and three-dimensional Transform are integrated in a generative adversarial network GAN framework, a three-dimensional building damage data set is expanded, representativeness is enhanced, and training of a deep learning model is promoted; a CD-PointNet + + model effectively identifies complex three-dimensional damage features, and accurate segmentation of building facade and roof damage after disasters is achieved.

The invention provides a three-dimensional modeling method and system based on multi-source heterogeneous data, and relates to the technical field of three-dimensional reconstruction. Multi-view point clouds, images and environment data in the same time state are obtained, and point cloud alignment precision is enhanced through feature detection and descriptor matching; an iterative nearest point algorithm is utilized to complete initial point cloud fusion to generate a three-dimensional model, position calibration is realized in combination with geometric features of environmental data, triangular meshes are divided by adopting a triangulation algorithm, surface defects of the model are repaired by adopting a hole filling algorithm, camera calibration is performed on image data, the image data are projected to the corresponding triangular meshes, and the three-dimensional model is obtained. And setting a pixel boundary threshold value to control a projection position, realizing accurate texture mapping, and defining a degradation coefficient to evaluate a local deformation condition of the three-dimensional model. According to the method, by fusing multi-source heterogeneous data and combining feature matching, position calibration and degradation coefficient evaluation, high-precision and complete three-dimensional model construction is achieved.

The invention provides a radar system error estimation method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining radar data and GPS data; performing time registration processing on the radar data and the GPS data to obtain time-aligned GPS data; performing filtering preprocessing on the radar data and the time alignment GPS data to obtain filtered radar data and filtered GPS data; according to the filtered GPS data, scaling the filtered radar data through anisotropic scaling based on scale transformation to obtain scaled radar data; carrying out coordinate conversion on the filtered GPS data, and converting the filtered GPS data into a radar polar coordinate system; and in a radar polar coordinate system, registration is carried out on the scaled radar data and the filtered GPS data through an improved iterative nearest point algorithm, and a radar system error is estimated. According to the method, the estimation precision of the radar system error is remarkably improved, and the problem of non-rigid deformation and rigid error coupling is effectively solved.

The invention relates to the technical field of three-dimensional modeling, in particular to an augmented reality-oriented three-dimensional scene reconstruction method and system, and is used for solving the technical problem that an iterative nearest point algorithm cannot meet high real-time performance and high-precision reconstruction requirements required by augmented reality at the same time. The method comprises the following steps: acquiring a pose transformation relationship between multiple frames of point cloud data and adjacent frames of point cloud data; extracting a plurality of feature points in each frame of point cloud data, and determining a feature descriptor of each feature point; according to the feature descriptors of the feature points, calculating a matching weight between feature point pairs in two adjacent frames of point cloud data, and calculating a local matching error based on the matching weight and a pose transformation relationship; performing global matching optimization based on the local matching error of the plurality of feature point pairs and the distribution density of each feature point in the respective point cloud frame, and determining an optimal matching point pair set which minimizes the global matching error; and according to the optimal matching point pair set, fusing the multi-frame point cloud data, and reconstructing a three-dimensional scene model.

The invention belongs to the technical field of three-dimensional geometric measurement, and particularly relates to a three-coordinate detection data processing method and system for automobile parts, and the method comprises the steps: obtaining an actual point cloud and a standard point cloud; by calculating the maximum angle gap of the data points, screening to obtain actual and standard feature point sets; anti-noise geometric descriptors based on tangential weights are constructed for the feature points in the two sets of feature point sets respectively; matching the two groups of feature points based on the descriptors and generating an initial matching set; solving global optimal transformation from the initial matching set by adopting a random sampling consistency algorithm; and the transformation is applied to the actual point cloud, fine registration is carried out by using an iterative nearest point algorithm, and finally deviation analysis is completed. The technical problem that traditional registration depends on initial alignment and is prone to falling into local optimum is solved, and the robustness and accuracy of registration are improved.

The invention discloses an existing bridge high-precision three-dimensional reconstruction method based on laser point cloud data, and relates to the technical field of bridge engineering digital reconstruction. Multi-source laser point cloud cooperatively collects detail point cloud of key components of an existing bridge, point cloud of a bridge floor and an upper structure and point cloud of a shielding part; multi-source point cloud coarse registration is completed based on a target ball of a preset specification, and then fine registration is carried out by adopting an improved iterative nearest point algorithm of integrated normal vector constraint; and adopting a layered denoising strategy to carry out statistical filtering denoising on the flat area, and carrying out adaptive radius filtering on the vulnerable area to reserve cracks and peel off disease points. Through collaborative design of multi-source acquisition, precise processing, intelligent segmentation, parameterized modeling and hierarchical verification, data integrity improvement, consideration of point cloud processing precision and disease retention, semantic segmentation and instance identification precision optimization, BIM model engineering value improvement and achievement adaptability enhancement are realized.

This invention relates to the field of hydropower station construction, specifically disclosing an intelligent splicing method for elbow pipes based on digital pre-assembly. The method includes: S1: Establishing an absolute coordinate measurement control network in the pre-assembly area; S2: Setting a checkerboard target for positioning on the surface of the elbow pipe block and scanning the block using a 3D laser scanner; S3: Performing multi-site point cloud splicing based on the center coordinates of the target sphere to reconstruct a complete block point cloud model; S4: Processing the complete block point cloud model using a voxel grid centroid sampling method; S5: Establishing a theoretical BIM model and placing it in the coordinate system of the control network to obtain the ideal installation posture; S6: Using an iterative nearest-point algorithm, matching the simplified block point cloud model with the theoretical model in spatial pose to obtain the final installation posture of the elbow pipe block; S7: Based on the final installation posture, calculating the theoretical installation coordinates of the checkerboard target center, generating adjustment commands, and positioning the elbow pipe block according to the commands. This method solves the technical problems of low efficiency and poor accuracy in traditional elbow pipe component assembly, as well as difficulties in segment alignment due to accumulated manufacturing errors.

The invention relates to the technical field of intelligent driving, in particular to a sensing element and map data fused lane modeling method, system and device and a medium, and the method comprises the steps: obtaining sensing point cloud data of lane elements around a vehicle and map point cloud data around the current position of the vehicle in real time; performing coordinate system conversion and time synchronization on the sensing point cloud data and the map point cloud data; performing registration by using an iteration nearest point, and calculating a registration confidence coefficient; calculating a perception confidence coefficient and an initial map confidence coefficient, correcting the initial map confidence coefficient based on the registration confidence coefficient to obtain a corrected map confidence coefficient, and distributing a fusion weight based on the perception confidence coefficient and the corrected map confidence coefficient; and based on the fusion weight, fitting a parameterized curve through a weighted fitting algorithm, and generating a fusion lane model. According to the method, adaptive optimization fusion of the perception data and the map data can be realized, and the precision, stability and environmental adaptability of lane modeling are improved.

The application belongs to the technical field of medical image processing, and particularly relates to a kind of bone fracture block automatic reduction methods based on medical image;Including: obtaining the three-dimensional medical image data and three-dimensional label data after fracture of the object to be processed, and pre-processing the three-dimensional medical image data after fracture;The three-dimensional medical image data after fracture is input into the fracture surface segmentation model trained, and the main bone surface area and fracture block surface area are obtained;Three-dimensional coordinates are extracted and main bone surface point cloud and fracture block surface point cloud are constructed;Iterative closest point registration is carried out on the two kinds, and the cumulative rigid body transformation parameter is obtained;The voxel coordinate set corresponding to the fracture block is determined, and the voxel coordinate set is subjected to spatial transformation using the cumulative rigid body transformation parameter, to obtain the three-dimensional label result after reduction;The application can simultaneously complete the problems of automatic identification of fracture section and automatic solution of fracture block reduction posture, improve the degree of automation, result stability and interpretability of fracture block reduction process.

The invention relates to a low-wire-harness multi-laser-radar automatic calibration method and device, electronic equipment and a storage medium. The method comprises the steps that after static point cloud data collected by a preset number of low-wire-harness radars are preprocessed, ground point cloud extraction is carried out based on an improved RANSAC algorithm, and a ground plane is generated; based on a ground plane extracted from the point cloud data of the preset number of low-wire-harness radars, plane matching is carried out to solve an external parameter initial value; and setting a region of interest (ROI), carrying out iterative closest point (ICP) algorithm optimization by a hierarchical search strategy to generate an external parameter matrix, and completing automatic calibration of the low-wire-harness multi-laser radar. According to the target-free on-line calibration method based on the natural scene features, no pre-arranged facility is needed, full-scene self-adaptive calibration is achieved, and the flexibility of a calibration scheme and the convenience of long-term maintenance are remarkably improved.

This application provides a method for reconstructing an adaptive machining process model for a linear friction welded integral bladed disk, comprising: dividing the blades of the integral bladed disk along the stacking axis into an actual surface region B1, a transition region B2, and a theoretical surface region B3, wherein the upper boundary of the transition region B2 is denoted as S_up, and the lower boundary is denoted as S_down; based on the actual surface region B1, the theoretical blade M_NOM is registered using an iterative nearest-point algorithm, and the blade is moved according to the registration result to obtain the registered and moved blade model M_FIT; based on the upper boundary S_up and the lower boundary S_down of the transition region B2, the adaptive machining transition surface RE_S2 is reconstructed; the actual surface region B1 on M_FIT, the adaptive machining transition surface RE_S2, and the theoretical region B3 on the theoretical blade M_NOM are combined to form an adaptive machining process model for the integral bladed disk blades; each blade is adaptively processed sequentially until all blades on the bladed disk are processed to form the final adaptive machining process model for the integral bladed disk.

The application discloses an electrical pipeline intelligent collaborative construction method based on BIM and the Internet of Things, and relates to the technical field of intelligent building construction and computer data processing technology. Building information model data related to electrical pipelines is collected, pipeline design parameters are extracted, and three-dimensional space point cloud data of a construction site is collected in real time through Internet of Things space sensors arranged on site. An iterative nearest point registration algorithm is used to unify and register and fuse the three-dimensional space point cloud data and the building information model data in a space coordinate system, and dynamic digital twin space data is generated. The application has the advantages that dynamic obstacle avoidance and re-planning are performed using a deep reinforcement learning algorithm, an optimal path that meets both physical avoidance requirements and electrical specifications can be automatically searched in a very short time, the tedious process of manually modifying drawings on site is greatly reduced, and the efficiency and quality of electrical collaborative construction are greatly improved.

This invention discloses a method, apparatus, and device for simultaneous localization and mapping (SLAM) of a mobile robot. The method involves acquiring depth information, LiDAR scanning information, and odometry information of the robot's environment; performing 3D projection on sparse point cloud data to obtain 2D information of the robot's environment; performing spatiotemporal synchronization processing on the 2D information, LiDAR scanning information, and odometry information; establishing pose nodes using the spatiotemporally synchronized 2D information, LiDAR scanning information, and odometry information; performing point cloud registration on the spatiotemporally synchronized 2D information and LiDAR scanning information using an iterative nearest point method; optimizing the pose nodes in real time using a second iterative nearest point method; and drawing a map based on the optimized pose nodes and pose transformation matrix; and completing loop closure screening using a third iterative nearest point method, with the selected loops used for loop closure detection. This invention can simultaneously leverage the advantages of LiDAR and visual sensors in unknown and complex environments.

The invention belongs to the technical field of navigation and shape perception, and relates to a navigation method and system based on an optical fiber shape sensor. The method comprises the following steps: firstly, reconstructing a three-dimensional model of a target channel or space structure by using medical images, three-dimensional scanning or a computer-aided design model, and constructing a reference point cloud; and constructing a navigation object point cloud. Then extracting fast point feature histograms, curvatures and normal vector features of the two types of point clouds, and screening candidate insertion segments; and performing rigid registration on the candidate sub-segments, and performing local weighted correction by introducing an iterative nearest point algorithm of an elastic weight to obtain optimal registration transformation. And calculating matching confidence according to the registration error, determining a tip zero point of the navigation object, performing dynamic updating by adopting a sliding window mechanism, and finally calculating the insertion length according to the insertion section point cloud obtained by matching. According to the invention, accurate matching of the insertion section of the object carried by the optical fiber shape sensor in the pre-constructed three-dimensional channel or space model and real-time estimation of the insertion length are realized.