260 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 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 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 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 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 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 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.

The invention discloses a vehicle-mounted multi-laser radar automatic calibration method and system based on ground fitting, and relates to unmanned driving. The method comprises the steps that original point cloud data of vehicle-mounted multi-laser radars are collected, one laser radar is selected from the multi-laser radars to serve as a reference laser radar, and the rest laser radars serve as to-be-calibrated laser radars; dividing the original point cloud data of each laser radar into a ground point set and a non-ground point set; performing plane fitting by adopting an RANSAC (Random Sample Consensus) algorithm, and selecting a plane equation with the maximum number of inner points as a ground model through iterative calculation; respectively constructing a ground loss function Lg and a non-ground loss function Lng; according to the ground loss function Lg and the non-ground loss function Lng, a total optimization function Ltotal is constructed through weighted fusion; solving the total optimization function Ltotal by adopting a numerical optimization algorithm to obtain an optimal transformation matrix T; and performing multi-laser radar calibration according to the optimal transformation matrix T. According to the method, drift of the yaw angle and the horizontal displacement in the optimization process is avoided, and the multi-laser calibration stability is improved.

The invention discloses a berthing state estimation method based on shipborne laser radar target detection, and the method comprises the steps: constructing a ship three-dimensional target detection network model through an improved PointPill coding network, and introducing an average pool layer and an attention pooling layer into the coding network to carry out the fusion of the global perception information and the local perception information of a target, and performing model training according to the constructed model loss function, detecting and confirming whether an anchored ship exists in a preset berth through an optimal detection network model, and respectively acquiring whether the anchored ship exists or does not exist in the preset berth by introducing an RANSAC plane fitting algorithm. A berthing speed used for ship berthing state estimation is obtained by combining the required berthing front edge plane or the outer contour surface of the target ship, namely a berthing berthing boundary surface and the position of the shipborne laser radar; the defect that the berthing state estimation accuracy is poor due to the fact that a berthing line model obtained through a traditional method based on linear fitting serves as the berthing state estimation reference is solved.

The application discloses the technical field of image data processing, and provides a method, device and equipment for risk assessment of an underground cavern and a medium, which comprises the following steps: adopting a geometric segmentation algorithm to process point cloud data of a target cavern acquired in advance, combining a plane fitting technology, identifying a structural surface and extracting structural surface parameters; constructing a geological topology model according to the structural surface parameters, identifying key blocks of the geological topology model based on a preset block identification standard, and randomly disturbing the structural surface parameters to obtain multiple groups of sample parameters; determining the instability probability of each key block based on the sample parameters, performing risk assessment on the key blocks based on the instability probability, and obtaining a risk assessment result of the underground cavern. The application significantly improves the timeliness and efficiency of risk identification of underground engineering, and effectively solves the problems of accurate identification of a structural surface and risk quantization under complex geological conditions.

This invention discloses a 3D reconstruction method and apparatus based on line laser scanning, solving the problems of poor 3D reconstruction accuracy of existing binocular cameras and the difficulty of 3D reconstruction using line laser scanning. It utilizes a line laser scanner to achieve accurate and rapid 3D reconstruction of a calibration object. First, a high-precision turntable and a laser scanning module are mounted on the same plane, and a standard calibration cube is mounted on the turntable. Then, the high-precision turntable encoder is controlled to collect depth data of the calibration object around one revolution. The influence of turntable acceleration and deceleration is eliminated by removing overlapping point cloud portions at the beginning and end, and outliers are eliminated using a filtering algorithm. Next, the average depth data of the point cloud, the scanning rotation angle, and the geometric features of the calibration block are used to perform 3D reconstruction of the calibration block, obtaining the reconstructed model point cloud. Finally, plane fitting is performed on the reconstructed point cloud, and the orthogonality of the normal vectors of the four faces is used to determine whether the reconstruction is successful. This invention improves both the accuracy and efficiency of 3D reconstruction by eliminating overlapping point cloud portions to reduce the influence of turntable acceleration and deceleration, and by using the geometric features of the reconstructed model to determine reconstruction accuracy.

The invention provides a method for indoor three-dimensional mapping of an unmanned aerial vehicle, which comprises the following steps: converting polar coordinate data into Cartesian coordinate point cloud under a body coordinate system, and performing motion distortion correction on the point cloud based on high-frequency pose data of an inertial measurement unit to obtain corrected point cloud; intercepting the region of interest as a candidate ground point, performing plane fitting on the candidate ground point, and separating the ground point from a non-ground point based on a fitting plane; taking the pre-integration pose of the inertial measurement unit as initial estimation, and performing iterative nearest point matching on a current scanning frame formed by non-ground points and the established local subgraph to obtain an optimized pose of the unmanned aerial vehicle at the current moment; inserting a non-ground point of the current scanning frame into the local sub-graph based on the optimized pose, performing global pose graph optimization when a loop is detected, and outputting a three-dimensional point cloud map and a two-dimensional grid map generated by projection of the non-ground point. And the mapping precision, robustness and practicability of the unmanned aerial vehicle in a complex indoor environment are comprehensively improved.

The invention provides a multi-view panoramic image indoor layout prediction method fusing semantics and planarity. The method comprises the following steps: step 1, carrying out three-dimensional semantic segmentation combined with three-dimensional point cloud reconstruction and a multi-view geometry and two-dimensional segmentation large model; step 2, obtaining a pure structured point cloud; step 3, multi-plane detection and classification; 4, extracting an initial contour; 5, generating a wall line based on plane fitting; step 6, performing multi-label graph segmentation guided by the two-dimensional contour and the wall surface; step 7, extracting a non-Manhattan ceiling line; and step 8, stretching the two-dimensional wall line along the vertical direction on the basis of the contour of the dimensional plane graph in combination with the vertical height of the room, generating an intersection line of the wall surface and the ceiling and an intersection line of the wall surface and the ground, and finally outputting a complete indoor three-dimensional vectorization layout model. According to the method, the layout integrity of the complex scene is improved, the boundary recovery precision of the occlusion area is improved compared with that of a traditional projection method, and the detection accuracy is improved by matching with multi-model RANSAC plane detection.

The invention discloses a luggage height calculation method, device and equipment based on point cloud, and the method comprises the steps: obtaining a scene image of a current scene and a depth image corresponding to the scene image through a 3D camera, and generating point cloud data; performing target luggage detection on the scene image to obtain at least one luggage area corresponding to the target luggage; performing plane fitting processing on the upper surface of the luggage in each luggage area to obtain a fitting plane, projecting the point cloud data to the fitting plane, and calculating a circumscribed polygon of projection point cloud; and selecting a plurality of vertexes of the circumscribed polygon, selecting a thin strip point cloud with a preset width in the luggage area by taking each vertex as a center for calculation to obtain an initial luggage height, and calculating an average value of the initial luggage height to obtain the luggage height. The luggage measurement precision can be effectively improved.

The invention belongs to the technical field of data processing, and provides a roadway contour extraction method, system, equipment and medium based on three-dimensional point cloud in order to solve the problems of inaccuracy, low efficiency and the like in existing coal mine roadway feature extraction. Combined noise filtering is carried out on a space formed by connecting angular points of fitting contours on adjacent slices, so that scattered noise and useless shielding are effectively reduced, and the data definition and reliability are improved; based on neighborhood calculation perpendicularity features, a top plate, the ground and the side wall of the roadway are recognized, and non-roadway structure data such as pipelines are accurately removed in combination with a cylinder fitting method and a plane fitting method, so that the accuracy of roadway contour extraction is ensured; and fitting the outermost plane based on the farthest points of the point clouds on the upper, lower, left and right surfaces by adopting a segmentation-projection method, and extracting irregular roadway contours from the top surface, the ground and the side surfaces to realize refined roadway contour extraction.

The invention relates to the technical field of stratified rock mass numerical simulation, and discloses a stratified rock mass numerical simulation method based on a hybrid progressive failure model, and the method comprises the steps: obtaining an engineering rock mass structural plane of a research area, and grading geological structural planes such as faults, rock veins and fractures of surrounding rock of an underground cavity based on structural plane features; through three-dimensional laser scanning, obtaining the spatial three-dimensional coordinates of each sampling point on the surface of the footrill, obtaining the point cloud of a measured object, and connecting adjacent discrete points; carrying out secondary sampling on the spliced point cloud data, carrying out plane fitting to obtain a structural plane occurrence, and constructing a footrill simulation model; analyzing the occurrence of the structural surface by the footrill simulation model to obtain a stratified rock mass value, and dividing the structural surface in the rock mass into a rigid structural surface and a weak structural surface according to the character, weathering degree, tightness degree and filler condition; and outputting the data to a cloud platform. According to the invention, manual intervention is reduced, and the safety, accuracy and efficiency of measurement are improved.

This invention discloses a multi-vision method for measuring the dimensions of small workpieces based on a binocular vision system, belonging to the field of workpiece dimension measurement technology. Addressing the difficulty in achieving accurate three-dimensional dimension measurement using existing methods, the following solution is proposed, comprising the following steps: S1: Chessboard corner point extraction; S2: Calibration plate removal; S3: Plane fitting; S4: Vertex extraction; S5: Stereo matching algorithm; S6: Dimension calculation. The average absolute error of this invention at different angles is 0.33 mm, the total relative error is 0.90%, and the variance is less than 0.01 mm. 2 This enables relatively accurate measurement of the three-dimensional dimensions of small workpieces from multiple perspectives.

The invention relates to the field of optical detection, in particular to a large-aperture quasi-plane optical element in-situ contour detection method based on three measuring heads, which comprises the following steps: S1, planning a detection point and a detection path of a large-aperture quasi-plane optical element; s2, carrying out point-by-point scanning on detection points distributed on the large-aperture quasi-plane optical element according to the planned detection path by adopting a three-probe detection device; wherein lasers emitted by three laser measuring heads of the three-measuring-head detection device are obliquely incident to the surface of the large-aperture quasi-plane optical element respectively to form three measuring points, and distance values from the three laser measuring heads to the measuring points are obtained at the same time; s3, calculating space coordinates of the three measurement points by using the three distance values, and performing plane fitting to obtain two-dimensional slopes of the detection points; and S4, reconstructing the surface shape of the large-aperture quasi-plane optical element according to the two-dimensional slope of each detection point. According to the invention, large-range mirror surface contour detection can be realized, and the mirror surface contour detection precision can be improved.

The invention discloses a shield segment splicing elliptic change detection method, relates to the technical field of shield segments, and breaks through the limitation that in traditional detection, elliptic change and slab staggering are detected separately, and judgment depends on artificial experience. An integrated design of circular seam point cloud accurate screening-double circular seam plane fitting correction-least square circle fitting and ellipsometry parameter linkage calculation is adopted, secondary correction is performed through a second circular seam plane equation, the problem that single circular seam plane fitting is easily influenced by model reconstruction errors is solved, the fitting precision is improved, and the fitting precision is improved. The technical closed loop of section geometric feature extraction-ellipse parameter quantization-slab staggering reference calibration is achieved, meanwhile, least square circle fitting of YZ section point clouds is combined with ellipse long axis, ellipse short axis, ellipse rate and deformation deviation calculation depth, ellipse judgment is achieved through double threshold values of ellipse rate grading and deformation deviation threshold values, the risk of misjudgment of a single parameter is avoided, and the method has the advantages of being high in practicability and high in practicability. And a high-precision reference surface is provided for subsequent slab staggering quantity calculation.