167 results about "Plane fitting" patented technology

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 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 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 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 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.

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 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.

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 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.

This application provides a method, apparatus, device, and medium for determining joint calibration parameters. In this method, a calibration board is placed in different poses. For each pose, based on the edge line of the calibration board in the image acquired by the camera at that pose, the first coordinates of a preset position on the calibration board in the image are determined. Then, the point cloud data at that pose is processed using a plane fitting algorithm to obtain a first plane corresponding to the calibration board in the point cloud data acquired by the LiDAR. Based on the laser point located on the edge line of the calibration board and the determined first plane, the second coordinates of the target laser point at the preset position on the calibration board are determined. Based on the determined first and second coordinates of each pose and a preset data registration algorithm, the parameters for joint calibration of the camera and LiDAR are comprehensively determined, mitigating the problem of low accuracy in determining calibration parameters caused by error accumulation.

The application discloses a multi-point focusing fitting microscopic imaging plane leveling method, system and device, which is used for high-precision automatic leveling of an imaging plane. The method comprises the following steps: establishing a two-dimensional sampling grid based on a measured plane, and determining grid node coordinates; establishing a space coordinate system, and calling calibration data to obtain a calibration image plane; continuously collecting an image sequence covering the two-dimensional sampling grid; dividing the image sequence according to the grid node coordinates to obtain a grid image sequence; calculating the definition of the grid image sequence by using a no-reference definition evaluation method to obtain a definition sequence; establishing an index relationship between the grid node coordinates and the maximum definition in the corresponding definition sequence to obtain a definition point set; performing plane fitting based on the definition point set to obtain a global focal plane model; calculating parallel parameters of the global focal plane relative to the calibration image plane according to the global focal plane model; and adjusting the posture of a placement platform of the measured plane through the parallel parameters.

The invention relates to the field of laser radar mapping positioning, in particular to a mapping positioning method and device based on a solid-state laser radar. The method comprises the steps of performing pose prediction based on measurement data of an inertial measurement unit to obtain an initial pose of a laser radar, performing distortion correction on original point cloud data acquired by the laser radar, performing neighbor matching and plane fitting by combining a multi-resolution surface element map, and performing positioning degradation detection based on plane features to output a degradation level. And performing hierarchical response according to the degradation level to obtain the final pose of the laser radar and updating the multi-resolution surface element map. The invention further comprises a device for realizing the method. The method can adapt to degradation conditions of different scenes, and effectively improves the accuracy and reliability of laser radar mapping positioning.

The invention provides a floor template accurate measurement method based on unmanned aerial vehicle photography. The method comprises the following steps: step 1, floor template scene image acquisition and image preprocessing; 2, sparse point clouds and dense point clouds of the floor template scene are generated; 3, extracting a building contour boundary, and segmenting and extracting a single template point cloud; and 4, calculating the three-dimensional size of the template based on plane fitting optimized by a sparrow search algorithm. According to the method, the 3D GS technology is adopted to overcome the reconstruction defect of a traditional MVS in a weak texture area, the problem of complex background interference is solved through multi-feature collaborative segmentation, adaptive adjustment of plane fitting parameters is achieved through an intelligent optimization algorithm, accurate measurement of the plane size and depth of the template is achieved, and the method is suitable for large-scale popularization and application. And the automation degree of floor template measurement is obviously improved.

This invention relates to the field of flatness measurement technology, and discloses a Chebyshev flatness measurement method and system based on convex hull. The method includes: inputting a depth image, extracting a point array from the Region of Interest (ROI), and performing convex hull detection on the point array to obtain a convex hull containing the point array; determining whether the number of convex hull faces is greater than 0; if so, constructing candidate planes based on the convex hull, and using a candidate plane filtering algorithm to obtain the optimal Chebyshev plane; if not, performing plane fitting using the least squares method based on the point array to obtain the optimal Chebyshev plane; calculating the absolute value of the distance from the point array to the optimal Chebyshev plane, sorting the points in descending order of absolute value, and removing noise points according to a set rule; performing convex hull detection again on the remaining point array until the optimal Chebyshev plane is obtained, and calculating the flatness. This method simplifies the computation and enables fast and accurate calculation of Chebyshev flatness.

The invention provides a vehicle-mounted steel coil center position detection method based on a three-dimensional laser radar, and relates to the technical field of automatic control. According to the method, a three-line laser radar is used for collecting environment and calibrated point cloud data, and position calibration between the three-dimensional laser radar and a world coordinate system is completed; the laser radar obtains three-dimensional point cloud data of a to-be-detected vehicle, sends the data to the information processing server, extracts a vehicle plate corner point position through data processing modes such as plane fitting and straight-through filtering, and finally obtains the position of a vehicle plate plane in a world coordinate system and coordinate information of the center of a to-be-detected vehicle-mounted steel coil in a three-dimensional space. The method has the advantages of being simple, low in manufacturing cost, easy to maintain, high in target recognition speed and high in recognition precision, and can be applied to unmanned crown block hoisting operation on an actual site.

The application relates to a depth image denoising method and device, computer equipment and a storage medium. The method comprises the following steps: obtaining a plurality of regions of interest in a to-be-processed depth image, performing plane fitting on the plurality of regions of interest to obtain a reference fitting plane; determining the vertical distance from each pixel point in each region of interest to the reference fitting plane; for each region of interest, based on the vertical distances corresponding to the pixel points in the region of interest, determining the distance interval corresponding to the region of interest, and determining the pixel points with the vertical distances in the distance interval as the to-be-fitted pixel points corresponding to the region of interest; performing plane fitting on the to-be-fitted pixel points corresponding to the plurality of regions of interest to obtain a target fitting plane; and based on the target fitting plane and the distance intervals corresponding to the regions of interest, performing denoising processing on the to-be-processed depth image to obtain a target image. The method can improve the denoising precision of the depth image.

The invention provides a point cloud normal estimation method and device based on local subspace clustering, electronic equipment and a storage medium, and the method comprises the steps: introducing a feature point weight evaluation mechanism based on covariance features and geometric structure indexes into a local neighborhood, and screening out feature points with high confidence; carrying out multi-subclass division and plane fitting on the local point cloud by adopting low-rank subspace clustering with priori weight constraint in the neighborhood of the feature points, so as to re-estimate the normal direction of the feature points on the subclass level; meanwhile, noise point filtering and a normal diffusion strategy based on adjacent non-noise points are combined, so that the method can more accurately describe a local geometric structure in a complex scene with noise, multi-structure aliasing and non-uniform sampling, the precision and stability of point cloud normal estimation are remarkably improved, and the method is suitable for popularization and application. And sharp features and edge details of the model can be better maintained.

This invention provides a wave height measurement method and system based on binocular vision, comprising the following steps: acquiring water area images and generating a depth map using a semi-global stereo matching algorithm; converting the 2D pixel coordinates corresponding to the depth map into a 3D water area point cloud in the world coordinate system; processing the 3D water area point cloud using a weighted RANSAC algorithm to determine the average water area plane reference; calculating the instantaneous wave height of the target measurement point based on the processed 3D water area point cloud; performing sliding window filtering on the instantaneous wave height; performing distance-related nonlinear error correction; and outputting the final wave height. The beneficial effects of this invention are: effectively eliminating system bias through dynamic error correction technology; improving the accuracy of the reference plane by combining a weighted plane fitting method; and retaining true signal characteristics using intelligent filtering technology.

The embodiment of the invention relates to the field of intelligent transportation, and discloses a ground segmentation method and device based on a roadside laser radar, a medium and a product. The method comprises the steps of obtaining point cloud data collected by a laser radar, performing rasterization processing on the point cloud data, and selecting a lowest point in each grid as an initial seed point; calculating a statistical characteristic value of the height of each initial seed point, and determining an effective seed point based on the statistical characteristic value; determining a weight value of each effective seed point according to the distance of the effective seed point relative to the roadside laser radar; and based on the effective seed points and the weight values corresponding to the effective seed points, plane fitting processing is executed, a ground model is constructed, and ground segmentation is completed, so that the accuracy of a ground segmentation algorithm in a long-distance and large-scale scene is remarkably improved.

The invention discloses a steering oar flange three-dimensional laser point cloud data denoising method, device and equipment, a medium and a product, and relates to the field of surveying and mapping, and the method comprises the steps: obtaining the three-dimensional laser point cloud data of a steering oar flange through three-dimensional laser scanning equipment; constructing a binary tree based on the three-dimensional laser point cloud data; denoising the three-dimensional laser point cloud data based on the binary tree; performing plane fitting and cylindrical surface fitting on the denoised three-dimensional laser point cloud data to obtain a plurality of planes and cylindrical surfaces; and de-noising the de-noised three-dimensional laser point cloud data again based on the plane and the cylindrical surface. According to the method, the steering oar flange point cloud denoising speed and precision can be improved.

The invention is suitable for the technical field of building material mechanical property testing, and provides an all-solid-state laser radar-based concrete test block performance integrated measurement method, which comprises the following steps of: putting a test block on a chassis, and triggering a system by an induction plate; the first radar collects the point cloud of the pressed surface, and the flatness is calculated through plane fitting; the chassis rotates, the second radar collects point clouds of adjacent surfaces, and the relative degree between the surfaces and the overall distortion are analyzed; combining point cloud to calculate the size; and the system summarizes data and visually outputs the data. The method is automatic, non-contact and high in precision, realizes multi-parameter integrated measurement, improves the detection efficiency, and is suitable for batch test block detection.

The application discloses a non-line-of-sight building layout relocation method and device in array interferometric synthetic aperture radar three-dimensional imaging, a storage medium and a program product, and is applied to the technical field of array interferometric synthetic aperture radar three-dimensional imaging. The method is aimed at the problem that the prior art cannot realize non-line-of-sight building layout relocation when building layout parameters are unknown. The method comprises the following steps: firstly, an array interferometric synthetic aperture radar system is used to obtain an initial three-dimensional point cloud of an observation scene; then, an Alpha Shape algorithm is applied to surface reconstruction of the point cloud to construct a boundary model for reflection path analysis; then, whether a line connecting the point cloud and a radar antenna intersects with a surface model is analyzed to identify non-line-of-sight points and associated reflection points; finally, a density clustering and plane fitting algorithm is used to correct and relocate the non-line-of-sight building layout in combination with a mirror symmetry geometric relationship. The method does not need prior structural information, can effectively identify a multipath reflection path, restore the real spatial position of a building in a non-line-of-sight area, significantly improves three-dimensional imaging precision, and provides strong support for SAR applications such as city perception and structure reconstruction.

The application provides an angle measurement method, device, equipment, computer program and storage medium. The method comprises: obtaining initial fitting plane information by performing plane detection on a point cloud diagram collected by an image collection device, the initial fitting plane information representing an initial fitting plane corresponding to a reference plane in the point cloud diagram, the reference plane representing a reference object plane in a real image collection scene; calculating an initial distance between a coordinate point in the point cloud diagram and the initial fitting plane according to the initial fitting plane information; performing iterative point set screening and plane fitting on the point cloud diagram based on the initial distance, until the plane fitting result meets a preset fitting condition, to obtain a target distance corresponding to target fitting plane information; the target distance representing a distance from an imaging center of the image collection device to the reference plane; and calculating an image collection angle of the image collection device according to the target distance. Through the application, the convenience and accuracy of angle measurement can be improved.

The invention discloses a quadruped robot three-dimensional map adaptive compression method under a complex terrain, and the method comprises the steps: extracting Z-axis height information of each key frame in a three-dimensional point cloud map, and obtaining an original height sequence; filtering and smoothing the original height sequence; dividing the key frame into a plurality of compression units based on the smooth height sequence; for each compression unit, identifying an initial ground point through an RANSAC plane fitting method, removing non-ground points in combination with normal vector constraint and a height threshold, and determining a reference ground height according to optimized ground points; setting a compression height range based on the reference ground height, and taking the three-dimensional point cloud in the compression height range as a to-be-projected point set; performing orthographic projection on the to-be-projected point set to generate a two-dimensional local sub-graph; and splicing the two-dimensional local sub-graphs to generate a global two-dimensional occupied grid map. According to the method, on the premise that the local topological relation and navigation availability are kept, the complex three-dimensional space is flattened into the two-dimensional map in a segmented mode.

The invention relates to the technical field of slope engineering modeling, and discloses a slope structural surface unmanned aerial vehicle point cloud fusion refined modeling method, which comprises the following steps: acquiring slope global point cloud data and optical images through an unmanned aerial vehicle carried high-precision positioning system, combining local point cloud data acquired by a handheld laser scanner, calibrating and pre-processing, and obtaining a slope global point cloud model; a point cloud normal vector is determined based on a local plane fitting technology, structural boundary information is extracted through a normal vector difference boundary detection algorithm, structural plane clustering segmentation is completed by adopting a region growing algorithm, finally, a triangular mesh model is generated, texture mapping and hierarchical simplification are performed, and refined modeling of a slope structural plane is realized. According to the method, the modeling precision and efficiency are improved, and reliable model support is provided for stability analysis and risk assessment of slope engineering.

The application discloses a kind of big target accurate positioning method based on multi-line laser radar, comprising the following steps: S1, with irregular three-dimensional target as an example, using two multi-line laser radar, vertical installation is different, make up the problem of multi-line laser radar in vertical direction is sparse, realize the accurate positioning of target in three-dimensional space coordinate, scanning target obtains the point cloud data of target.The application is used in the calculation target actual position and correct position deviation, standard shape template is made, only the position information of real-time point cloud is kept, and the accuracy and stability of the calculation coordinate and deviation are improved by using standard template instead of real-time point cloud.RANSAC plane fitting, ICP point cloud registration and other methods are used in the point cloud processing process, such methods are adaptable to target point clouds of different qualities, improve the stability in the process of point cloud processing, and the final positioning accuracy can be controlled within 20mm.