32 results about "Terrestrial laser scanning" patented technology

A method, apparatus, system, and article of manufacture provide object descriptors for objects in point cloud data for an urban environment by segmenting the point cloud data. Point cloud data for an urban environment is obtained using a ground-based laser scanner. Terrain points are filtered out from the point cloud data using ground filtering. The point cloud data is then segmented into two or more blocks. Objects that lie on neighboring adjacent blocks are combined. Object descriptors for the combined objects are then provided (e.g., to the user or a program used by the user).

The invention discloses a method for quickly measuring topography by using a ground laser scanner based on CORS (Continuous Operational Reference System) and ICP (Iterative Closest Point) algorithms. The method comprises the following steps: 1, coaxially connecting a GPS (Global Position System) antenna with the scanner; 2, mounting the scanner at the top of a vehicle; 3, scanning in 360 degrees through the scanner during operating; 4, synchronously measuring a topocentric geodetic coordinate of the scanner by using a GPS carrier phase dynamic real-time difference method on the basis of CORS; 5, partitioning large measuring regions into blocks along roads and rivers; 6, roughly registering measurement blocks by using public planimetric points between measuring stations; 7, carrying out multi-station cloud accurate splicing in the measurement blocks by using an iterative closest point algorithm from a point to a tangent plane; 8, converting point cloud of the measurement blocks to the geodetic coordinate by using the topocentric geodetic coordinate; 9, dividing a three-dimensional point cloud into ground points and non-ground points and generating a contour line by using the ground points; 10, measuring and drawing a terrain landform line graph by using a method of collecting man-machine interaction details and slicing point cloud based on the three-dimensional point cloud; and 11, carrying out field investigation and plotting, mending and measuring, and editing and finishing a terrain line graph to obtain a topography result.

The invention discloses a multi-platform point cloud data fusion method and relates to the fields of mapping and engineering surveying. The method comprises the following steps: data collecting: original data of ground objects and landform in an object region are obtained through data acquisition equipment and fixed type ground laser scanning equipment carried on mobile platforms; data preprocessing: preprocessing work such as engineered organizational management, filtering and noise reduction and the like are respectively performed on the collected original data; data fusion: after filtering and noise reduction to the point cloud data are performed, the point cloud data is accurately analyzed, point cloud data with highest accuracy is used as a basis to carry out accuracy rectification on other data, in addition, coordinate transformation of data obtained by the fixed type ground laser scanning equipment is achieved on the basis of the mobile platforms, and that point coordinate transformation without field control is achieved. The multi-platform point cloud data fusion method has the advantages that point cloud data collected by different platforms at different time are fused, so as to enable respective advantage complementation to be achieved, further comprehensive utilization of date is further improved, and fusion using of the multi-platforms and multi-scale point cloud data can be achieved.

The invention discloses a bilateral filtering de-noising method of point cloud data based on KDTree when a ground laser scanning technology is used to carry out measured object surface reconstruction. In a bilateral filtering algorithm, a scattered point cloud data point field is established through the KDTree so as to obtain a field point set of the data point. An inverse iteration method is used to calculate the field point so as to obtain a normal vector ni. A filtering function parameter and a filtering Gauss parameter are calculated so as to obtain a bilateral filtering power factor lambda. Finally, a new data point after the filtering can be obtained, wherein the new data point p'i=pi+ lambdani. The bilateral filtering method of the invention is used to filter the noise of the point cloud data. The method is simple and effective. An operation speed is fast. A characteristic can be maintained and simultaneously the noise can be removed. The method is suitable for processing the noise in a small scope.

The invention relates to the field of construction site surveying and provides a construction site surveying method combining surface laser radar and oblique photography, comprising: S1, acquiring radar data; S2, acquiring oblique photography data; S3, introducing a laser point cloud model and a three-dimensional real scene model into a three-dimensional platform for processing, and allowing the processed information to be used in construction site design; S4, drawing a tomographic map and section of a construction site based on laser point cloud data to provide geographic data for working site design. A construction site surveying system combining surface laser radar and oblique photography is provided, comprising a surface laser scanner, a flight platform, a processing module and a camera mounted on the flight platform. The construction site surveying method and system combining surface laser radar and oblique photography have the advantages that the ground laser radar and oblique photography are combined, technical advantages are mutually complemented, manual measurement can be replaced, fine working site surveying can be achieved under high quality and high precision, field work is greatly decreased, surveying period is greatly shortened, and significant technical and social benefits are provided; rich information is acquired and is recorded via equipment, and data can be back-tracked at any time.

The invention discloses a point cloud and image automatic high-precision registration method based on mutual information. The method comprises the steps of shooting a close-range optical image of a to-be-detected object; collecting ground laser scanning point cloud of the to-be-detected object, and generating an RGB color image and a reflection intensity image; calculating coarse registration parameters by using the close-range optical image and the point cloud RGB color image; extracting Harris operator feature points from the reflection intensity image by grids; a Forstner positioning operator is used for accurate positioning, rough registration parameters are used for calculating the initial positions of corresponding homonymous points of the feature points, the mutual information theory is used for searching for accurate matching point positions of the feature points in the close-range optical images in the initial positions, and accurate homonymous feature point pairs of the two images are obtained; calculating a fine registration parameter; and generating an accurate color point cloud model of the to-be-tested object. The method is not influenced by non-homologous data projection and radiation difference, and meets the requirement for reconstruction of a high-precision three-dimensional color texture model in the cultural relic protection industry.

The invention relates to a tunnel collapse area surrounding rock deformation real-time monitoring method based on video monitoring. The method comprises the following steps: firstly, obtaining a three-dimensional virtual real scene model of a tunnel collapse area by adopting a ground laser scanning technology, wherein the three-dimensional virtual real scene model comprises point cloud data and three-dimensional image data; secondly, according to the position of each camera and the video data of the monitoring collapse area, constructing a transmission projection relationship between the position of each camera and the three-dimensional virtual real scene model of the collapse area; then adopting a trifocal tensor and an antipodal constraint technology to realize positioning of a collapsearea target monitoring point in a multi-view photographic video relative to a reference control point under a spatial three-dimensional coordinate system; and finally calculating a displacement valueof the target monitoring point according to a transmission projection relationship. Non-contact real-time full-time automatic deformation monitoring is realized, the high risk of tunnel collapse areamonitoring is avoided, and the problems that the contact measurement range and cost are limited, and the non-full-time monitoring cannot timely and safely give an early warning are solved.

A method, apparatus, system, and article of manufacture provide object descriptors for objects in point cloud data for an urban environment by segmenting the point cloud data. Point cloud data for an urban environment is obtained using a ground-based laser scanner. Terrain points are filtered out from the point cloud data using ground filtering. The point cloud data is then segmented into two or more blocks. Objects that lie on neighboring adjacent blocks are combined. Object descriptors for the combined objects are then provided (e.g., to the user or a program used by the user).

According to an indoor three-dimensional point cloud automatic registration method for pole extraction, on the premise of avoiding network construction and normal direction estimation of point cloud data, automatic registration of multi-station ground laser scanning point cloud data in an indoor scene of a building is achieved, and the method mainly comprises two parts of pole extraction and point cloud registration. The method is analyzed and verified by using real indoor point cloud data of a building, registration parameter calculation is performed on multiple groups of point cloud data, a registration result is compared with a result calculated through a manual target selection mode, and the precision and reliability of the method are further verified. According to the point cloud automatic registration method based on pole extraction provided by the invention, the reliability and the precision meet the working requirements, the target does not need to be manually arranged, the parameter setting is simple, and the automation degree is high.

The application provides a structure health monitoring and intelligent-prediction method based on TLS (Terrestrial Laser Scanning) technology. The method includes: acquiring point cloud data of each area of an external structure of a component, and preprocessing the obtained point cloud data; further processing the point cloud data to obtain a complete three-dimensional model and deformation and crack data, and then carrying out finite-element analysis on the initial model to obtain material deformation and crack results; reconstructing a finite-element model according to corrected parameters,analyzing structure deformation and crack situations, and comparing the deformation and crack results of the finite-element model with the point cloud data to obtain a numerical-value comparison result; and utilizing the reconstructed finite-element model to intelligently predict future deformation situations of the structure, carrying out risk analysis, and giving a risk analysis report. According to the method, detection time of the external structure of the component can be shortened, and accuracy of a defect change trend of the component can be improved.

The invention relates to accurate positioning of a planar target for ground laser scanning data registration, comprising the following steps: first, using the principal component analysis algorithm to determine a plane where point cloud data is located based on the point cloud data near the bull's eye of an artificial target; second, orthographically projecting the point cloud data to generate an orthographic projection intensity image of the point cloud data; third, in the generated orthographic projection intensity image, determining the position of the bull's eye in the intensity image based on a rotational symmetry pattern recognition and positioning algorithm; and fourth, determining the three-dimensional coordinates of the bull's eye according to the corresponding relationship between laser scanning points and the pixels of the projected image. When there is no laser scanning point corresponding to the bull's eye positioned in the intensity image, the three-dimensional coordinates of the bull's eye are estimated through a nonlinear least squares method. The technical scheme put forward by the invention is of great significance and practical value to ground laser scanning data processing and three-dimensional modeling. By applying the method put forward by the invention to ground laser scanning point cloud data registration, the registration precision can reach the millimeter level.

The invention discloses a point cloud registration method from a point to an optimal plane in data acquisition of a ground laser scanner, which comprises the following steps: how to search the optimalplane corresponding to each point and establish a pair of points to the optimal plane; the constrained weighted total least square method is used to calculate the plane parameters; the constrained weighted total least square method is used to calculate the transformation parameters. The invention has better registration accuracy.

The invention discloses a circular tunnel central axis extraction method based on three-dimensional laser point cloud, and belongs to the technical field of ground laser scanning point cloud data processing. The method comprises the following steps: firstly carrying out the three-dimensional scanning of a tunnel through a three-dimensional laser scanner, and obtaining the three-dimensional laser point cloud of the tunnel; projecting the tunnel point cloud in the horizontal direction, and extracting the point cloud boundary according to the geometrical characteristics of the boundary feature points; rasterizing the boundary of the point cloud for fine extraction, and extracting a horizontal central axis according to boundary points; considering the influence of the conditions of tunnel bending radian, appendages, point cloud missing and the like on the precision of the horizontal central axis, extracting an accurate horizontal central axis by adopting a sliding window robust fitting method; finally, tunnel cross section point clouds are extracted at equal intervals with the accurate horizontal central axis as the benchmark, cross section contour lines are fitted, the circle center is extracted to serve as a tunnel central axis point, the central axis points at all cross sections are sequentially connected, and therefore accurate extraction of the central axis of the circular tunnel is achieved.

The invention discloses an automatic calculation method of the tree breast diameter based on voxel growth and ground laser point cloud. The method is based on three-dimensional point cloud data obtained by ground laser scanning. Firstly, the point cloud above the ground is intercepted and below 3 meters, leaving only the trunk part required for breast diameter measurement, and then the ground point is filtered out. Then, the Z component of the point cloud normal vector is used to filter and voxelize the remaining point cloud, and the trunk is extracted by the top-down growth method. Then, thetrunk is sliced, and the plane perpendicular to the trunk is obtained by the cross multiplication of the trunk point cloud normal vectors. Finally, the point cloud slices are projected onto the planeand the DBH of the tree is obtained by circle fitting. The method has the advantages of fast calculation speed, high accuracy and universality, can be applied to complex and dense forest area environment, and can play a better role in forest resource planning and investigation.

According to one embodiment, a three-dimensional (3D) measuring device is provided. The 3D measuring device includes a processor system that is configured to generate a point cloud representing multiple surfaces. The point cloud includes multiple scan points. Generating the point cloud includes receiving spherical coordinates for a scan point, the spherical coordinates comprising a distance (r), a polar angle (θ), and an azimuth angle (φ). Generating the point cloud further includes homogenizing a scan point density of the surfaces by filtering the scan points. The homogenizing includes computing a value (p) for the scan point based on the spherical coordinates. Based on the value exceeding a predetermined threshold, storing the scan point as part of the point cloud, and based on the value not exceeding the predetermined threshold, discarding the scan point.

The invention discloses an LOD (level of detail) model quick constructing method for scanning mass scattered point cloud by ground laser in a single station. The method comprises the following steps: projecting laser scanned point cloud of an original single station to a spherical surface, constructing a regular spherical grid according to a certain expected resolution, and resampling project data points and color values in the grid to obtain regular point cloud; establishing a topological relationship among the re-sampled points to generate a triangular mesh; and coloring the vertex of the triangle, and deleting unreasonable triangles to obtain a matrix stored true color triangular grid. The LOD model generated by the method has high compression rate, has important influence in the fields of surveying and mapping and reverse engineering, and can be used in simulation roaming.

The invention relates to a method for extracting tree heights of high-canopy forest plots combined with UAV images and TLS point clouds, and a method for extracting tree heights of forest plots combined with UAV images and ground lidar point clouds: simultaneously obtaining high canopy density The UAV (UAV: Unmanned aerial vehicle) image point cloud of the forest plot and the ground lidar (TLS: Terrestrial Laser Scanning) point cloud data of the plot. The two kinds of data matching and fusion are realized through the scale-invariant feature transform (SIFT) feature point matching algorithm. Based on the fused point cloud data, the improved local maximum algorithm was used to extract the height of individual trees in the plot. The invention can realize the measurement of the parameters of the forest plot from two directions of understory and canopy at the same time, and provides a new approach for more comprehensive and accurate estimation of the tree height of the subtropical high canopy density forest plot.

The invention discloses a laser point cloud and dense matching point cloud fusion method, which aims at solving the problems of quality degradation and layering existing in fusion of ground laser scanning point cloud and aerial photography dense matching point cloud, carries out data redundancy removal and quality improvement on mixed point cloud on the basis of air-ground heterogenous point cloud registration, and discloses the laser point cloud and dense matching point cloud fusion method. The method comprises the following steps: firstly, constructing an energy function data item according to a distance between a dense point cloud and a laser point cloud and a normal vector included angle, constructing a smooth item by utilizing a geometric neighborhood relationship and color difference in the dense point cloud, and optimizing by adopting a graph cut algorithm to obtain a dichotomy label set of the dense point cloud; removing an overlapped redundant region between the two types of point clouds according to the labels; and finally, according to a neighborhood point selection strategy, carrying out surface curvature weighted guide point cloud filtering on dense matching points near the boundary, and combining different-source point clouds to obtain a fused point cloud.

The invention discloses an automatic high-precision registration method for point clouds and images based on mutual information, comprising: shooting close-range optical images of objects to be measured; collecting ground laser scanning point clouds of objects to be measured, and generating RGB color images and reflection intensity images; Use close-range optical images and point cloud RGB color images to calculate rough registration parameters; divide the reflection intensity image into grids to extract Harris operator feature points, and apply Forstner positioning operators for precise positioning, and use coarse registration parameters to calculate feature points. Corresponding to the initial position of the point with the same name, use the mutual information theory to search for the exact matching point of the feature point in the close-range optical image within the initial position, and obtain the exact feature point pair of the same name in the two images; then calculate the fine registration parameters; generate Accurate color point cloud model of the object to be measured. The invention is not affected by non-homologous data projection and radiation difference, and meets the demand for high-precision three-dimensional color texture model reconstruction in the cultural relics protection industry.

The invention relates to the technical field of shield tunnel section measurement, in particular to a shield tunnel section extraction method based on ground laser scanning point clouds, which comprises the following steps: scanning a tunnel through ground laser to obtain a shield tunnel section position, and calculating and extracting a center coordinate of the shield tunnel section position; determining a tunnel section equation based on the center coordinate; determining a ray equation in the tunnel section based on the tunnel section equation; determining section points based on the ray equation; according to the method, section fitting is carried out based on the section points, then resampling is carried out, a final tunnel section is obtained, the tunnel is scanned through ground laser, a worker does not need to enter the tunnel for measurement in the whole process, resampling is carried out after section fitting, it is ensured that the finally extracted section points are complete and continuous, and therefore the measurement efficiency and precision are improved. The problems that an existing tunnel section measuring method is long in measuring time and low in measuring efficiency are solved.

The invention provides a carbonate rock dolomite content simulation method based on ground laser scanning. The method comprises the following steps: acquiring field carbonate rock reservoir geologicalprofile data; establishing a relation model between the laser intensity value of the carbonate rock sample and the dolomite content, and taking the relation model as a prediction model of the dolomite content of the carbonate rock sample; establishing a carbonate reservoir digital geological section surface layer model according to the field carbonate reservoir geological section data; and carrying out dolomite content prediction representation on all nodes of the carbonate reservoir digital geological section surface layer model by utilizing the prediction model of the dolomite content of the carbonate rock sample to obtain planar distribution of the dolomite content of the carbonate reservoir geological section. The method has the advantages of rapid and accurate prediction and the like, provides a new thought for fine and quantitative characterization of dolomite content of the carbonate reservoir profile, and has a good application prospect in oil and gas exploration and development and geological application.

The invention relates to a real-time monitoring method for surrounding rock deformation in a tunnel collapse area based on video monitoring. Firstly, the ground laser scanning technology is used to obtain the 3D virtual reality model of the tunnel collapse area, including point cloud data and 3D image data. Secondly, according to the video data of each camera position and monitoring the collapse area, the camera position and the 3D virtual reality scene of the collapse area are constructed. The transmission and projection relationship between the models, and then using the trifocal tensor and epipolar constraint method, realized the positioning of the target monitoring point in the landslide area in the multi-view photography video relative to the reference control point in the three-dimensional coordinate system, and finally based on the transmission projection relationship Calculate the displacement value of the target monitoring point. The invention realizes non-contact real-time full-time automatic deformation monitoring, avoids the high-risk and high-risk monitoring of tunnel collapse areas, and solves the dilemma of contact measurement range and cost limitations, and non-full-time monitoring that cannot provide timely safety warnings.

The invention discloses a laser ranging type dynamic belt deviation detection system, which comprises a belt conveyor, a laser scanner, a serial port processor, a speed measurement sensor, a single chip microcomputer and an upper computer, wherein the laser scanner is fixedly installed on the side edge of the edge of a conveying belt, the installation distance meets the error range of belt deviation, the scanning face of laser scanning distance measuring equipment is perpendicular to the horizontal plane of the belt and the ground, the laser scanning data is sent to the Ethernet by the serialport processor to realize remote transmission, the speed measurement sensor is fixed on the central shaft of the ascending carrier roller of the belt conveyor and coaxially rotates with the carrier roller, belt speed data are collected by the single chip microcomputer and transmitted to the remote upper computer through a GSM / GPRS module, and the upper computer carries out real-time calculation according to measurement data of the detection system. According to the invention, belt deviation can be dynamically detected, the initial position of belt deviation can be conveniently found, and the system is simple in structure, high in automation degree and high in real-time performance and is not affected by the external environment on material distribution measurement.

The invention relates to a network configuration method for single-station ground laser scanning point cloud, which includes the following steps: defining a projection sphere and projecting the single-station laser scanning point cloud onto the projection sphere; constructing an initial triangulation; in turn inserting the data points obtained after the projection of point cloud, which includes three steps; searching a triangle containing the data points to be inserted; locally reconstructing a triangulation, that is ,after finding the triangle containing the data points to be inserted ,connecting the points with the three sides of the triangle to form three new triangles; carrying out sphere LOP local optimization for the new triangulation obtained by local reconstruction; deleting six intersecting points constructing the initial triangulation to obtain a Delaunay triangulation of the projection-point sphere; projecting back the data points in the Delaunay triangulation of the projection-point sphere to the initial positions to obtain a target triangulation of the single-station laser scanning point cloud. The technology scheme provided by the invention, which can greatly improvethe efficiency of reconstructing object surface with ground laser scanning technique, is of great significance in reverse engineering technology.

The invention provides an ancient building simulation restoration method based on aviation oblique photography and three-dimensional laser scanning, and relates to the technical field of building measurement, and the method comprises the following steps: S1, carrying out three-dimensional laser scanning; s2, carrying out aerial oblique photography; s3, multi-point data fusion is carried out; step S4, carrying out data modeling; s5, simulating and restoring the ancient building; the aerial oblique photography technology and the three-dimensional laser scanning technology are combined and applied to the cultural relic ancient building, unmanned aerial vehicle oblique photography measurement image conversion data and ground laser scanning point data are subjected to registration fusion, then ground three-dimensional laser scanning dead angles are supplemented, and the integrity of point cloud data of the cultural relic ancient building is guaranteed. As the three-dimensional laser is good in penetrability and high in data precision, the manufacturing cost is reduced, the method is not limited by the height of the ancient building component, the requirements for rapid surveying and mapping and simulation restoration of batch ancient buildings can be met, a simulation restoration finished product can show texture features, and the problems of poor data quality and local garland are solved.

The invention discloses an automatic calculation method of the tree breast diameter based on voxel growth and ground laser point cloud. The method is based on three-dimensional point cloud data obtained by ground laser scanning. Firstly, the point cloud above the ground is intercepted and below 3 meters, leaving only the trunk part required for breast diameter measurement, and then the ground point is filtered out. Then, the Z component of the point cloud normal vector is used to filter and voxelize the remaining point cloud, and the trunk is extracted by the top-down growth method. Then, thetrunk is sliced, and the plane perpendicular to the trunk is obtained by the cross multiplication of the trunk point cloud normal vectors. Finally, the point cloud slices are projected onto the planeand the DBH of the tree is obtained by circle fitting. The method has the advantages of fast calculation speed, high accuracy and universality, can be applied to complex and dense forest area environment, and can play a better role in forest resource planning and investigation.

The invention discloses a bilateral filtering de-noising method of point cloud data based on KDTree when a ground laser scanning technology is used to carry out measured object surface reconstruction. In a bilateral filtering algorithm, a scattered point cloud data point field is established through the KDTree so as to obtain a field point set of the data point. An inverse iteration method is used to calculate the field point so as to obtain a normal vector ni. A filtering function parameter and a filtering Gauss parameter are calculated so as to obtain a bilateral filtering power factor lambda. Finally, a new data point after the filtering can be obtained, wherein the new data point p'i=pi+ lambdani. The bilateral filtering method of the invention is used to filter the noise of the point cloud data. The method is simple and effective. An operation speed is fast. A characteristic can be maintained and simultaneously the noise can be removed. The method is suitable for processing the noise in a small scope.

The invention discloses a multi-platform point cloud data fusion method and relates to the fields of mapping and engineering surveying. The method comprises the following steps: data collecting: original data of ground objects and landform in an object region are obtained through data acquisition equipment and fixed type ground laser scanning equipment carried on mobile platforms; data preprocessing: preprocessing work such as engineered organizational management, filtering and noise reduction and the like are respectively performed on the collected original data; data fusion: after filtering and noise reduction to the point cloud data are performed, the point cloud data is accurately analyzed, point cloud data with highest accuracy is used as a basis to carry out accuracy rectification on other data, in addition, coordinate transformation of data obtained by the fixed type ground laser scanning equipment is achieved on the basis of the mobile platforms, and that point coordinate transformation without field control is achieved. The multi-platform point cloud data fusion method has the advantages that point cloud data collected by different platforms at different time are fused, so as to enable respective advantage complementation to be achieved, further comprehensive utilization of date is further improved, and fusion using of the multi-platforms and multi-scale point cloud data can be achieved.