347 results about "Plane fitting" patented technology

The invention provides a regeneration wafer defect detection method and system based on point cloud, and belongs to the technical field of semiconductor manufacturing detection, and the method comprises the steps: carrying out the registration and fusion processing of the point cloud data of a target wafer, and obtaining the target point cloud data; performing plane fitting on the target point cloud data to determine a reference plane; performing feature extraction on the target point cloud data relative to the reference plane to obtain feature data; positioning a defect area based on the feature data, and calculating geometric parameters of the defect area; correcting the geometric parameters by using a preset error compensation model to obtain target geometric parameters; inputting the position of the defect area, the target geometric parameter and the feature data of the corresponding defect area into a defect classification model to obtain a defect type of the defect area; and generating a defect detection result of the target wafer based on the defect types, the positions, the target geometric parameters and the feature data of all the defect areas of the target wafer. According to the invention, the efficiency and reliability of regenerated wafer defect detection are improved.

The invention discloses a high-precision plane fitting method for a multi-noise point cloud, and the method comprises the steps: carrying out the plane rough fitting of an original point cloud after down-sampling, taking the longest side of a minimum bounding box of a lower sampling point cloud as a distance threshold value, and calculating the distance weight of each point through a weight mathematical model, then, plane fine fitting is carried out on the weighted down-sampling point cloud, the distance weight of each point is updated after each fitting, plane fitting is carried out on the point cloud of which the weight is updated again, and a fine fitting plane of the down-sampling point cloud is calculated in an iterative manner; and finally, calculating the distance weight of each point in the original point cloud based on the fine fitting plane and the iterated distance threshold value, performing plane fitting on the weighted original point cloud in an iteration mode, and finally obtaining the fine fitting plane of the original point cloud. According to the technical scheme, the method is high in stability and low in calculation amount, can obtain a high-precision fitting plane in a weighted iteration mode under the condition of more noise, and is suitable for different types of point cloud data.

The invention relates to the technical field of computer vision detection, and discloses a height feature information generation method, device and equipment based on point cloud and a medium, and the method comprises the steps: obtaining three-dimensional point cloud data of the surface of an object to be detected, carrying out the preprocessing of the data to obtain a target point cloud, and carrying out the reference plane fitting of the target point cloud, and calculating the vertical distance of each point in the target point cloud based on the reference plane obtained by fitting and generating height difference data, and finally extracting and outputting surface height feature information according to the height difference data. According to the method, the three-dimensional point cloud data is subjected to preprocessing noise removal, datum plane accurate fitting and height difference data separation, high-precision extraction of tiny height changes of a complex surface is achieved, the detection efficiency is improved, dependence on high-cost optical equipment is reduced, and the method is compatible with various workpiece shapes.

The invention relates to a workpiece polishing track generation method and equipment, and the method comprises the steps: collecting a current workpiece surface point cloud, carrying out the plane fitting, obtaining an upper surface equation and an upper surface point cloud, projecting the upper surface point cloud to the upper surface equation, obtaining a two-dimensional projection drawing, obtaining the template workpiece contour of each type of template workpiece, and carrying out the polishing of the workpiece. And calculating the polishing track points of each type of template workpiece, matching the two-dimensional projection drawing with the contour of the template workpiece to obtain the corresponding polishing track points, and back-projecting the polishing track points to the three-dimensional space to obtain the three-dimensional polishing track of the current workpiece. The problem that the workpiece surface polishing track cannot be quickly and effectively recognized in the prior art is solved.

The invention belongs to the technical field of plate shape monitoring and visual analysis, and discloses a stainless steel medium-thickness plate double-face contour measurement method and system, and the method comprises the steps: S1, completing the calibration of a double-line-scan digital camera, and obtaining the upper and lower surface images of a medium-thickness plate; s2, carrying out binocular stereoscopic vision three-dimensional reconstruction to generate a point cloud picture, and converting the point cloud picture into a point cloud picture under the same coordinate system for display; s3, eliminating transverse rotation and deviation according to point cloud center line fitting and plane fitting of the upper surface and the lower surface of the medium-thickness plate; s4, comparing the geometrical relationship between the point clouds of the upper and lower surfaces to quantify local vibration and thickness change, correcting vibration errors, and obtaining the thickness of the medium-thickness plate; and S5, extracting an edge point set from the point cloud image to obtain the overall contour of the medium-thickness plate, and obtaining the position of a shear line based on the width and edge linearity detection of the medium-thickness plate. By the adoption of the technical scheme, the overall contour, overall thickness distribution and accurate shearing position of the medium-thickness plate can be obtained.

The invention discloses a laser radar visual joint calibration method based on lane line extraction, and the method comprises the steps: S1, obtaining environment point cloud data from a laser radar sensor, carrying out the plane fitting through an RANSAC algorithm, carrying out the lane line point cloud extraction, and carrying out the clustering of the extracted lane line point cloud, and forming a line feature point cloud; s2, acquiring environment image data from a camera, performing image semantic segmentation on the environment image data, extracting a pixel region of a lane line in the environment image data by using a DeepLab-V3 network, and then performing feature mask binarization processing; s3, finding out line features in the environment point cloud data and the environment image data, then carrying out consistency matching, solving a three-line perspective problem, and obtaining preliminary external parameter estimation by establishing a corresponding relation of three line features; and S4, performing refined calibration on the preliminary matching result by using an optimization algorithm, and outputting external parameter calibration results of the laser radar and the camera through multiple measurements and data fusion, including a rotation matrix and a translation vector.

The invention discloses a building roof elevation extraction method and system based on city modeling, and the method comprises the steps: S1, point cloud data and image data fusion registration: extracting data features, and carrying out alignment registration; s2, roof denoising and boundary optimization: identifying interference information, extracting a roof boundary, and generating a roof digital surface model through interpolation; s3, roof elevation extraction: for a flat roof, extracting the top elevation of the flat roof through a plane fitting method; for the slope top roof, extracting the top elevation of the slope top roof through an extreme point detection method; and extracting the top elevation of the roofs except the flat roofs and the slope roofs through a point cloud clustering method. According to the scheme, the roof elevation can be rapidly and accurately extracted, and high-precision three-dimensional data support is provided for urban planning, energy assessment and disaster emergency; the problems that in the prior art, roof elevation extraction efficiency is low, complex roof structure recognition precision is insufficient, and multi-source data fusion is difficult are solved.

The invention relates to the technical field of three-dimensional modeling, in particular to a three-dimensional modeling system and method based on a laser radar, and the system comprises a point cloud boundary extraction module, a boundary error analysis module, a self-adaptive cutting repair module and a stitching structure optimization module. According to the method, three-dimensional coordinates, normal vectors, reflection intensity, distances and indexes of boundary points are called layer by layer, a Sobel operator and a Canny algorithm are combined to cooperatively recognize a boundary and an error region, then local region repair is completed based on least square plane fitting and spatial residual analysis, a tension vector is analyzed through dynamic planning, and stitching path adjustment is executed. On the cooperative basis of multi-type point cloud features, a spatial topological structure and boundary continuity dynamic modeling, the problems of local damage, connection errors and instable stitching paths of point cloud boundaries in complex scenes are solved, and the integrity of boundary structures, the coherence of stitching transition and the rationality of spatial geometric expression are remarkably improved.

The invention relates to the technical field of building structure health monitoring, in particular to an unmanned aerial vehicle laser radar-based building point cloud deformation quantitative extraction method, which comprises the following steps of: based on building corner structure characteristics in point cloud data, carrying out quantitative evaluation on the overall inclination state of a building through point cloud segmentation, straight line fitting and angle calculation; based on an ideal plane hypothesis, performing plane fitting and point-to-plane distance analysis on the building facade point cloud in the point cloud data, identifying a local concave-convex deformation area of the building facade, and performing quantitative evaluation; and roof plane detection and wall surface normal vector analysis are carried out based on the point cloud data, and accurate quantification is carried out on parapet wall inclination based on region growth segmentation and PCA principal direction analysis. Through automatic processing and quantitative analysis of an algorithm, subjective errors and inconsistency possibly occurring in a traditional manual evaluation method are avoided, and objectivity and repeatability of an evaluation result are enhanced.

The embodiment of the invention discloses a robust extraction method for three-dimensional geometric features of a precast beam based on point cloud edge topology recombination. According to the technical scheme provided by the embodiment of the invention, the three-dimensional point cloud data of the precast beam is subjected to the surface domain separation to obtain the surface domain separation result, the plane fitting is performed according to the surface domain separation result to obtain the fitting plane, and the three-dimensional point cloud data is projected to the fitting plane to obtain the two-dimensional projection point cloud data; edge point detection is performed on the two-dimensional projection point cloud data to obtain a precast beam edge point set, ordered recombination is performed on the precast beam edge point set to obtain an ordered edge point sequence, and feature corner point extraction is performed according to the ordered edge point sequence to obtain precast beam corner points, so that the precast beam corner point extraction efficiency, accuracy and robustness are improved; the precast beam feature extraction efficiency can be improved, and the precast beam feature extraction precision can be effectively improved.

The invention relates to the technical field of large-size part three-dimensional measurement, and discloses a large-size part three-dimensional size measurement method and device based on machine vision, and the method comprises the steps: obtaining a material reflection characteristic reference data set and original point cloud data; according to the reference data set, laser power and gain are adjusted in a self-adaptive mode, and optimized scanning parameters are obtained; and performing denoising, feature extraction, region segmentation and plane fitting according to the optimized parameters and the original point cloud to obtain a high-precision geometric dimensioning result. According to the invention, high-precision, high-stability and high-efficiency three-dimensional size measurement can be realized on a large-size part made of a complex material and having a curved surface, and the three-dimensional size measurement method is obviously superior to a traditional scheme.

The invention discloses a LiDAR-IMU-camera tight coupling positioning and mapping method and device for a mobile platform, and belongs to the technical field of robot SLAM. Synchronously triggering the color camera, the laser radar and the IMU through hardware, and unifying timestamps; performing compensation and distortion removal on the laser point cloud motion by the IMU data; based on curvature, intensity and density self-adaptive downsampling, local plane fitting errors are used for distributing observation weights; the IMU pre-integration pose is used as an initial value, point-to-surface registration of the weighted point cloud and the local map is carried out, and a laser-IMU tight coupling odometer factor is obtained; the color image and the laser intensity graph are fused into a multi-mode loopback descriptor, and loopback factors are generated through global retrieval and geometric verification; and inputting an odometer, an IMU, a laser loopback factor and a visual loopback factor into an increment factor graph optimizer, jointly solving a global optimal key frame pose, and outputting a dense laser point cloud map and a color visual point cloud map. The method can be operated in real time on an embedded platform, effectively inhibits drifting, and realizes centimeter-level global consistent positioning and mapping.

The invention provides a crack three-way measurement research method and system based on multi-coordinate system fusion, and relates to the technical field of earth and rockfill dam monitoring. The method comprises the following steps: shooting concentric circle target images at multiple angles through a monocular camera, extracting feature points through preprocessing, and correcting projection distortion of non-parallel shooting; establishing a mapping relation between pixels and a three-dimensional coordinate system, and optimizing camera pose parameters; reconstructing a three-dimensional coordinate point cloud based on the multi-view data, screening outliers and fitting plane optimization data; and registering the three-dimensional point cloud to a world coordinate system through a coordinate system conversion algorithm, and comparing two measurement results to calculate the three-way displacement of the crack. According to the method, concentric circle target constraint feature precision is utilized, projection correction and plane fitting are combined to compensate monocular vision depth deficiency, high-precision displacement detection is achieved, and engineering monitoring requirements are met.

The invention discloses an indoor scene three-dimensional reconstruction method based on deep fusion and confidence modeling, and belongs to the technical field of computer vision. According to the method, composite data frames such as a color image, a depth map, an IMU (Inertial Measurement Unit) and a camera attitude are comprehensively utilized to carry out regional three-dimensional reconstruction on an indoor scene: firstly, the scene is divided into a smooth region (such as a wall, a ground, a ceiling, a glass plane, a mirror surface, a blackboard and other planes) and a complex curved surface region; aiming at the smooth area, adopting geometric prior guide plane fitting provided by a visual large model, and combining sensor attitude information to quickly reconstruct a regular plane model; for a complex curved surface area, a multi-frame point cloud fusion strategy is adopted to accumulate different view angle information, and a deep residual error refining network is utilized to recover curved surface details, so that a high-precision curved surface model is obtained. According to the method, the three-dimensional structure of the indoor scene can be efficiently reconstructed, the global framework of the smooth area is reserved, and the details of the surface of a complex object are depicted in detail.

The invention relates to the technical field of building design, in particular to a BIM building model construction method and system.The BIM building model construction method comprises the steps that firstly, based on the plane continuity characteristic of a wall body structure in a building construction environment, rough segmentation is conducted on point cloud through an RANSAC multi-plane fitting algorithm, and a wall area and outer points of a complex structure are preliminarily separated; further, for complex structure areas such as curved surfaces and arc connection areas, model contribution factors are dynamically generated by calculating relative distances and geometric coherence between outer points and a fitting plane, and higher weights are given to point clouds in a high-curvature area; according to the method, the registration process is optimized in combination with the weighted ICP algorithm, and the key structure area is aligned preferentially, so that the damage to the integrity and continuity of a complex structure is reduced, the registered building point cloud data after registration is more accurate, and the accuracy of the BIM model constructed according to the registered building point cloud data is improved.

The invention provides an urban special-shaped building monomer reconstruction method based on inclination model data, and the method comprises the steps: carrying out the segmentation and recognition of a monomer building based on deep learning, European clustering and a graph cutting method, fitting a feature plane of the monomer building based on a point cloud plane fitting algorithm, recognizing a complex special-shaped building according to the normal vector gradient distribution of the feature plane, and carrying out the reconstruction of the complex special-shaped building. According to the method, the three-dimensional Mesh model of the special-shaped building is simplified, textures are filled and the like, reconstruction of a complex special-shaped building model is achieved, and the problems that an existing mainstream urban building monomer reconstruction algorithm is only suitable for common regular buildings, and automatic monomer segmentation and three-dimensional structured reconstruction of the special-shaped building cannot be achieved are solved. And the automation rate and the efficiency of reconstruction of the special-shaped building in the urban scene are obviously improved.

The invention discloses a target sheet identification method based on point cloud data, and belongs to the field of target sheet identification methods, and the method comprises the following steps: 1, collecting original point cloud data of a target range environment through a laser radar; step 2, anti-rain and anti-fog interference processing; step 3, carrying out gale shake compensation; 4, reconstructing a basic curved surface, and executing random sampling consistency plane fitting; step 5, enhancing and reconstructing a wrinkle region, specifically, detecting a region with a curvature change rate of greater than 0.05 mm <-2 > on the basic curved surface; marking as a to-be-enhanced area and transmitting to a sub-curved surface processing flow; step 6, bullet hole identification: calculating the local curvature of the point cloud, and screening abrupt change points of which the curvature values are greater than 0.25 mm <-1 >; fitting the three-dimensional coordinates of the bullet hole by adopting a least square method; and step 7, outputting target sheet attitude parameters and bullet hole ring values. According to the invention, interference of rain, fog and strong wind can be reduced, and identification precision is improved.

The invention discloses an attitude detection method and system for a photovoltaic panel. The method comprises the following steps: S1, scanning through a laser radar to obtain light point cloud data; s2, segmenting the point cloud data through a clustering algorithm, and determining and storing clustering point cloud data to which the photovoltaic panel belongs; s3, performing plane fitting on the clustering point cloud data to which the photovoltaic panel belongs to obtain a plane equation, and projecting the point cloud data to the plane equation; s4, performing edge extraction on the point cloud of the photovoltaic panel to obtain a top edge linear equation; s5, postures are combined according to the vectors. According to the invention, the laser radar is used for data acquisition, the stability of equipment is improved, the operation is simple, other sensors are not depended on, a training set is not needed to train a model, the use stability and reliability are high, the system can be used in extreme environments such as night, heavy fog and rainy days, and the real-time analysis effect is achieved through algorithm optimization of point cloud processing. And the calculation efficiency of the attitude of the photovoltaic panel is improved.

The invention provides a pallet pose estimation method and system, and the method comprises the steps: obtaining a color image which comprises a pallet region and at least part of pixels of which are aligned, and point cloud data, and cutting the color image to obtain a target image which only comprises the pallet region; obtaining a point cloud of an area corresponding to the target image from the point cloud data according to an alignment relationship between the color image and the point cloud data, and obtaining a to-be-fitted point cloud; performing plane fitting on the to-be-fitted point cloud to obtain a plane fitting model; angle information and center coordinates of the pallet are calculated based on the plane fitting model and a preset reference coordinate system, and the angle information and the center coordinates of the pallet are utilized to construct a pose matrix of the pallet to obtain pose information of the pallet. The pallet pose estimation method provided by the invention has higher robustness and higher accuracy.

The invention discloses a reinforcing steel bar surface binding point identification and positioning method based on point cloud and pixel mapping. The method comprises the following steps: obtaining original three-dimensional point cloud data to carry out outlier noise point elimination; performing plane fitting on the denoised point clouds, identifying template surface point clouds and removing the template surface point clouds; mapping the spatial physical coordinate attribute of the steel bar surface point cloud into a three-channel coding image; separating the three-channel coded image into three single-channel images: a length image, a width image and a depth image; processing the depth image to extract an initial reinforcing steel bar straight line set; screening out a parallel steel bar straight line set from the initial steel bar straight line set according to a set reference direction and a parallel angle tolerance threshold value; and calculating straight line intersection points in the parallel steel bar straight line set to obtain candidate binding point pixel coordinates, determining representative binding point pixel positions through clustering, querying coordinate information of the three single-channel images, and outputting three-dimensional space coordinates of the steel bar binding points. According to the invention, robust and accurate steel bar binding point identification and positioning are realized.

The invention relates to a method for detecting the gradient of a plane and optimizing the machining precision, and the method comprises the following steps: (1), carrying out the preliminary path collection processing of the plane at the current position before the actual machining; (2) carrying out optimal feature point selection based on the acquired data set; (3) carrying out plane fitting processing based on the selected optimal feature point; (4) calculating a normal vector of the current position plane and a corresponding base vector based on the selected optimal feature point; (5) constructing a local coordinate system based on the current position, and calculating transformation between the local coordinate system and the workpiece global coordinate system; and (6) optimizing the machining path of the cutting head according to the local coordinate system. By adopting the method for detecting the inclination of the plane and optimizing the machining precision, the machining path is optimized according to the local coordinate system, so that the machining precision is effectively improved, and errors caused by inclination of the workpiece are reduced.

The invention provides a dimple quality online detection method and system based on 3D scanning and virtual assembly, and the method comprises the steps: S1, obtaining dimple hole point cloud data, segmenting the point cloud data, and obtaining conical surface point cloud data; s2, performing conical surface fitting on the conical surface point cloud data through a fitting algorithm to obtain a local point set and a cone parameter; and S3, according to the local point set and the cone parameters, pit diameter contour plane fitting is carried out, the pit diameter contour plane and the nail head are assembled in a simulated mode, and a dimple depth error value is obtained. On the basis of 3D scanning and virtual assembly, pit hole surface point cloud analysis, nail rod interference analysis and compensation functions are achieved, pit depth measurement is achieved, and compared with a monocular or binocular vision scheme, the measurement precision is higher.

The invention provides a pavement pit detection method and device, and the method comprises the steps: carrying out the plane fitting of obtained pit point cloud data, and determining a target fitting plane and a target normal vector corresponding to the target fitting plane; according to the target normal vector and the included angle in the z-axis direction of the original space coordinate system, the target fitting plane is rotationally projected to the original space horizontal plane, and a corrected point cloud set is obtained; obtaining geometric features between the target point and the neighbor point clouds, determining a weight corresponding to each neighbor point cloud by using an elevation difference between the target point and the neighbor point clouds, obtaining a semantic feature value of the target point through weighted aggregation, screening out a pit point cloud set from the corrected point cloud set based on the semantic feature value, and obtaining a pit point cloud set; and determining a depth parameter and a volume parameter of the pit based on the pit point cloud set. By using the geometric features between the target points and the neighbor points, the semantic attributes of the pit points, the ground points and the noise points are differentiated, and the accuracy of pavement pit detection is improved.

The invention provides a high and steep slope dangerous rock mass instability mode judgment method and system, and belongs to the field of rock mass engineering, dangerous rock mass point cloud data of a target area is obtained, neighborhood point searching is performed on point clouds at different positions of a slope according to coordinate information in the dangerous rock mass point cloud data, plane fitting and coplanarity testing are performed on neighborhood points, and a dangerous rock mass instability mode judgment result is obtained. And merging the coplanar point sets, and calculating to obtain the inclination and the inclination angle of the structural plane according to the point sets. And clustering and grouping the structural surfaces, fitting the structural surfaces and performing intersection calculation. Point-surface calculation and analysis are carried out by combining topographic features of the high and steep slope, geometric morphology of the rock mass and development conditions of structural surfaces, the dangerous rock mass is positioned, the damage mode of the dangerous rock mass is identified, the judgment accuracy is improved, the damage mode of the slope rock mass is finely divided, and disaster prevention and reduction work of rockfall disasters of the high and steep slope is facilitated.

The invention provides a wafer Bump height and coplanarity automatic calculation method based on K-Means clustering segmentation, and belongs to the field of semiconductor morphology detection. By combining a plane fitting method such as principal component analysis (PCA) and a K-Means clustering segmentation technology, the height and coplanarity parameters of the Bump can be efficiently and accurately extracted from three-dimensional point cloud data, and the measurement automation degree of the wafer Bump is remarkably improved. According to the method, accurate extraction of the Bump position of the wafer is achieved through two times of segmentation, and the method can adapt to Bumps of wafers with different densities, shapes and sizes in Die (the general method represents chips) and has high universality and adaptability. By accurately detecting the height of the wafer Bump and evaluating the coplanarity, defects in the manufacturing process can be found in time, and the quality control level of the wafer Bump in the semiconductor manufacturing process is effectively improved.

The invention relates to the technical field of visual positioning, and provides a 66kV transformer substation drainage wire clamp visual guidance dismounting method and device and a medium, through identifying two to-be-dismounted assemblies longitudinally arranged in a drainage wire clamp image and assembly types, the to-be-dismounted assemblies can be conveniently and subsequently processed in a targeted manner according to the assembly types; plane fitting is carried out through the point cloud information, the data dimension is effectively reduced, the data processing efficiency is improved, and accurate target positioning can be assisted. A pitch angle, a yaw angle, a yaw angle and a central point between two to-be-assembled and disassembled components are calculated according to plane parameters, and calculation is further simplified in combination with conversion from an Euler rotation angle to quaternion, so that the positioning efficiency is improved; the rotation matrix is corrected according to the coordinate system of the mechanical arm, the output matrix is adjusted according to the center point and the corrected rotation matrix, the matrix finally output to the mechanical arm is more accurate, and therefore the mechanical arm can be assisted in efficiently and accurately conducting pose adjustment, and live-line disassembly and assembly are conducted on the drainage wire clamp.

The invention discloses a corrugated plate depth detection method based on a three-dimensional point cloud of laser scanning. The method comprises the following steps: forming a depth detection device by using a mechanical arm, a sliding rail and linear structure laser, and carrying out parallel calibration; calculating the noise level according to the 2D point cloud obtained by the line laser sensor, and respectively searching the optimal scanning poses in the X and Y directions; according to the obtained corrugated plate point clouds, extracting the curvature of the point clouds and setting a threshold value, and taking the point clouds greater than the threshold value as wave crests and wave troughs; according to a DBSCAN point cloud clustering algorithm, the obtained wave crests and wave troughs are clustered, and then the clusters are sorted; fitting adjacent clusters and optimizing planes according to an RANSC plane fitting algorithm and a plane optimization algorithm; and according to a point-to-plane distance formula, calculating the distance between the wave crest and wave trough points and the corresponding plane. According to the method, through the designed scanning device, the optimal scanning pose can be obtained through automatic adjustment, and the distance between the wave crest and the wave trough is measured more accurately by adopting a mode of firstly fitting and then optimizing.

The invention discloses a method for generating a cutting track of a convex riser on the surface of a casting based on machine vision, and the method comprises the steps: carrying out the self-adaptive anisotropic downsampling of an original point cloud, so as to reduce the data volume while keeping key geometric features; performing robust principal component analysis plane fitting on a point cloud downsampling result to obtain a reference plane parameter of the casting body under a preset coordinate system; differential geometry-based riser region segmentation is carried out on the point cloud downsampling result according to the reference plane parameters, and convex riser region point clouds are obtained; according to the point cloud of the convex riser region, constructing a triangular mesh curved surface about the surface of the convex riser; performing plane cutting on the triangular mesh curved surface to obtain a cutting contour line; based on a beam projection method, an initial cutting path is obtained according to the cutting contour line; and constructing a cutting path optimization energy functional so as to optimize the initial cutting path to generate an optimal cutting track of the convex riser on the surface of the casting. The problems that a traditional manual cutting mode is low in cutting efficiency, poor in precision, poor in consistency and large in potential safety hazard are solved.

The invention discloses a steel truss extraction method based on point cloud segmentation and feature analysis, and belongs to the technical field of three-dimensional point cloud processing. The method mainly comprises the following steps: firstly, carrying out multi-scale filtering denoising on an original point cloud and constructing an octree index; local geometric features such as normal vectors, curvatures and first-order plane fitting errors of all the points are calculated; based on the adaptively selected seed points, carrying out region growth by combining a normal vector included angle, a curvature difference and an adaptive edge criterion to realize preliminary segmentation; and finally, a coarse-to-fine multi-layer cascade classifier is adopted to screen the segmented areas, and the steel truss point cloud is accurately extracted through geometric dimension filtering, shape feature screening and multi-dimensional feature matching in sequence. The method does not need pre-training, has the advantages of strong anti-interference capability, high extraction precision and high calculation efficiency, and is suitable for engineering scenes of bridge detection, building surveying and mapping and the like.

The invention discloses an element identification and installation system based on machine vision, relates to the technical field of intelligent manufacturing and computer vision, and constructs a multi-modal vision original data set of an element assembly area, performs alignment and feature cleaning on original image data, establishes a digital twin vision model and generates a baseline feature set; according to the method, the digital twinborn model is constructed, the attitude estimation model is established, the PnP algorithm, the RANSAC plane fitting and the L-M optimization are combined to realize high-precision attitude solution, and the method has the advantages of high precision, high precision and high precision. Self-adaptive assembly is realized through path planning, attitude correction and predictive control, quality evaluation is performed by using Mask R-CNN model detection and deviation analysis, continuous self-learning and performance optimization are realized through model recharging, and the recognition precision, the assembly stability and the long-term reliability are improved.