23 results about "Terrestrial laser scanning" patented technology

The invention discloses a multi-element historic building maintenance and restoration method based on three-dimensional laser scanning, and relates to the technical field of computer processing, and the method comprises the following steps: S01, obtaining the three-dimensional point cloud data of a historic building foundation through a ground laser scanner, the basic three-dimensional point cloud data comprises geometric information of a building facade, a ground structure and a near-ground decoration component; s02, an unmanned aerial vehicle carries a multi-line laser scanning device to scan the top and the high-altitude structure of the building, supplementary point cloud data is generated, and the millimeter-level precision three-dimensional coordinates of the pitched roof, the tower eave and the cornice bracket are covered with the supplementary point cloud data; and S03, synchronously acquiring surface texture data of the historic building by adopting multispectral imaging equipment. According to the method, the millimeter-level precision of three-dimensional reconstruction of the historic building is realized through multi-source data fusion and a hierarchical optimization algorithm, and the maintenance decision efficiency is improved by combining automatic disease recognition and a multi-modal analysis technology.

This invention is a multi-dimensional ancient building maintenance and restoration method based on 3D laser scanning, relating to the field of computer processing technology. The method comprises the following steps: S01, using a terrestrial laser scanner to acquire basic 3D point cloud data of the ancient building. This basic 3D point cloud data includes geometric information of the building facade, ground structure, and near-ground decorative components; S02, using an unmanned aerial vehicle equipped with a multi-line laser scanner to scan the building roof and high-altitude structures to generate supplementary point cloud data. This supplementary point cloud data covers the millimeter-level precision 3D coordinates of the sloping roof, tower eaves, and eaves brackets; S03, using a multispectral imaging device to simultaneously collect surface texture data of the ancient building. This invention achieves millimeter-level accuracy in 3D reconstruction of ancient buildings through multi-source data fusion and a hierarchical optimization algorithm. Combined with automated defect identification and multimodal analysis techniques, this method improves the efficiency of maintenance decision-making.

The invention discloses a building component surface defect detection method, device and equipment and a storage medium, and the method comprises the steps: obtaining color point cloud data of a to-be-detected building component through a ground laser scanning technology, and generating a three-dimensional grid model based on the color point cloud data; parameterizing the three-dimensional grid model by adopting a UV expansion mode to generate a two-dimensional UV image, and determining a corresponding texture image based on the two-dimensional UV image; and performing defect detection on the texture image by using a preset semantic segmentation model to obtain a defect detection result of the to-be-detected building component. According to the method, the three-dimensional grid model is generated based on the color point cloud data of the building component to be detected, then the three-dimensional grid model is subjected to dimensionality reduction to form the two-dimensional UV graph, and then defect detection is performed, so that compared with the prior art, the method has the advantages that the detection precision of the surface defects of the building component is effectively improved, and the detection cost is reduced.

A method for automatically estimating a leaf inclination angle distribution of a wheat canopy based on a voxel segmentation normal vector algorithm, including the following steps: step 1: obtaining point cloud data of a wheat canopy; step 2: splicing and denoising point clouds; step 3: calculating normal vectors of the point clouds; step 4: performing voxelization on the point clouds; step 5: segmenting the normal vectors by using a voxel; step 6: calculating angles of the voxels; step 7: collecting statistics on the angles of the voxels and performing curve fitting calculation to obtain a leaf inclination angle distribution and an average leaf inclination angle. The average leaf inclination angle estimated by the method is compared with field measured data, and the feasibility of the algorithm is verified by using a three-dimensional radiative transfer model.

The invention discloses a landslide emergency monitoring system, and the system comprises a ground-based interference synthetic aperture radar device which is used for monitoring the deformation condition of a landslide region in real time; the ground laser scanning equipment is used for acquiring three-dimensional space data of a landslide area; the unmanned aerial vehicle carries a high-resolution camera and is used for collecting high-resolution image information of the landslide area; and the data processing center is used for receiving data collected by the ground-based interferometric synthetic aperture radar, the ground laser scanning and the unmanned aerial vehicle and carrying out data fusion and analysis and early warning information generation. According to the emergency monitoring method, data collected by the ground-based interferometric synthetic aperture radar device, the ground laser scanning device and the unmanned aerial vehicle are transmitted to the data processing center, and multi-source data are matched and integrated through a data fusion method; and analyzing the fused data, evaluating the landslide risk, and generating early warning information. The method is suitable for geographic information monitoring in emergency.

The invention discloses an intelligent observation station planning method based on a ground laser scanner in a complex forest area environment, and the method comprises the following steps: S1, employing a spatial analysis method based on multi-source geometric constraints, and achieving the separation of a single tree hierarchical structure; s2, evaluating the visual range of the candidate points according to occlusion judgment; s3, performing visibility analysis to select efficient candidate points; and S4, selecting a final frame site based on fusion of heuristic search and integer linear programming. According to the intelligent observation station planning method based on the ground laser scanner in the complex forest area environment, firstly, efficient candidate point identification is carried out by adopting multi-source geometric constraint, single tree and candidate point distribution condition identification, shielding judgment and visibility analysis, and then heuristic search and an integer linear programming algorithm are fused; and the final position of the ground laser scanner stand is obtained. Compared with a traditional algorithm, the optimization algorithm based on the visibility constraint can greatly reduce the number of stand stations and improve the calculation efficiency under the condition that the scanning integrity is guaranteed.

The invention relates to a laser scanner with an image derivation scan setting function, in particular to a stationary ground laser scanner and an image processing method, which process a 2D image of a scanning sphere captured by a camera of the stationary laser scanner prior to measuring a scanning point on the basis of a machine learning algorithm, the image is divided into a plurality of clusters having different predefined measurement importance categories, and at least one adaptive scan parameter is set depending on the presence and / or absence of a cluster having an importance category in the first 2D image.

This invention discloses an intensity-based autonomous calibration method for terrestrial laser scanners. This method requires no additional deployment costs and can simultaneously calibrate both bifacially sensitive and non-bifacially sensitive parameters in the calibration model, achieving millimeter / submillimeter and arc-second accuracy for offset and angle parameters, respectively. The method comprises Step S1: acquiring point cloud data at two measuring stations and roughly calculating the relative pose between the measuring stations using a point cloud registration method; Step S2: uniformly selecting a number of core points from the point cloud and generating an intensity image based on their nearby points; Step S3: extracting and matching feature points from the intensity image; and Step S4: constructing a system of linear equations to solve the calibration model parameters.

The invention discloses a Chinese classical garden surface model reconstruction, rainfall runoff simulation and risk identification method based on three-dimensional point cloud, and the method comprises the steps: (1) obtaining Chinese classical garden surface total factor millimeter-level three-dimensional point cloud data through a ground laser scanner, and carrying out the data preprocessing; step (2), selecting a differentiated point cloud modeling method according to ground feature features by using the preprocessed three-dimensional point cloud data, and integrating a ground surface three-dimensional model of the Chinese classical garden; step (3), loading rainfall parameters by using hydrological simulation software, generating visual runoff paths of the Chinese classical garden in different rainfall scenes, and identifying hydrological risk points by using a grid division accumulation method; and step (4), verifying the reliability of rainfall runoff simulation and the accuracy of hydrological risk point identification by comparing the field observation condition with the simulation result of the earth surface model. According to the invention, the efficiency and accuracy of Chinese classical garden hydrological risk point identification with complex surface features can be improved.

Provided is a forest resource measurement system that calculates the tree height and trunk diameter for each tree with high precision. 【Solution means】 A system having a ground laser scanner 2 and a forest resource measurement device 10 that calculates the tree height and trunk diameter for each tree based on the three-dimensional point cloud data obtained from the ground laser scanner 2. The forest resource measurement device 10 segments the three-dimensional point cloud data for each tree included in the three-dimensional point cloud data obtained by irradiating the tree with laser light by the ground laser scanner 2, and after performing a filtering process of extracting only the portion corresponding to the trunk from the segmented three-dimensional point cloud data for each tree, from the three-dimensional point cloud data corresponding to the trunk, the diameter of the trunk is calculated by performing circle fitting using at least two or more three-dimensional point cloud data at a predetermined height from the ground, and by obtaining a conical shape passing through the two circles, the diameter or tree height of the trunk at an arbitrary height is calculated.

The invention relates to the technical field of laser instrument scanning calibration, and discloses a double-target-ball cooperative laser scanner calibration device which comprises a base, a ground laser scanner and a laser tracker, a supporting assembly is installed in the middle of the upper end of the base, and the upper end of the supporting assembly is rotationally connected with a fixing assembly; the device comprises a fixing assembly, a scanner target ball with a notch is arranged on the left side of the fixing assembly, one end of the scanner target ball is in threaded connection with the fixing assembly through a connecting assembly, and an adjusting seat mechanism is installed on the outer side of the fixing assembly. According to the ground laser scanner coordinate measuring and calibrating device, the working efficiency of calibration is improved, the device can be conveniently moved, supported and fixed, the supporting height and angle can be conveniently adjusted, multiple sets of different data can be conveniently measured, and through comparison and analysis of the multiple sets of data, the coordinate measuring and calibrating precision of the ground laser scanner is improved.

The invention discloses a ground laser scanner precision detection method, and belongs to the technical field of measurement, and the method comprises the following steps: taking a tripod, placing the tripod on a flat ground, and then taking a ground laser scanner and installing the ground laser scanner on the tripod; carrying out standard deviation detection in the horizontal direction; placing at least six targets around the ground laser scanner, wherein each target and the ground laser scanner are positioned on the same horizontal plane; a ground laser scanner is started to obtain point cloud data of each target, three groups of measurement are carried out, and each group of measurement comprises four measurement loops; processing data in the horizontal direction; standard deviation detection in the vertical direction is carried out; geometric centers of at least six targets are collinearly arranged in the vertical direction; starting a ground laser scanner to measure data in the vertical direction; and processing data in the vertical direction. Through precision detection in the horizontal direction and the vertical direction, the scanning result can be effectively connected with existing civil engineering measurement data, and data support is provided for engineering evaluation and decision making.

The invention discloses an ancient building component refined modeling method and system based on multi-scale scanning, and relates to the technical field of live-action three-dimensional construction.The method comprises the steps of 1, multi-scale data collection, 2, multi-source data fusion processing, 3, refined modeling and state evaluation, and 4, modeling and state evaluation. The method comprises the following steps: respectively acquiring macroscopic point cloud, mesoscopic geometry and microscopic texture data of an ancient building through ground laser scanning, close-range photogrammetry and high-resolution scanning, segmenting after unifying data coordinates and registering and identifying key parts of components, adding function types and material attribute labels for the components, modeling according to the component attribute labels, and establishing a model; the component storage state is analyzed in combination with multi-dimensional features, targeted repair suggestions are generated, refined modeling and scientific protection of the component are achieved, and the precision and efficiency of digital protection of the ancient building are improved.

The invention relates to the technical field of water conservancy and hydropower engineering construction, and discloses a three-dimensional live-action model construction method applied to an inclined wall dam construction site, and the method comprises the steps: collecting a site aerial survey image and key node three-dimensional point cloud data through unmanned aerial vehicle aerial survey and ground laser scanning, and generating an initial three-dimensional live-action model through fusion processing; performing lightweight optimization on the model to obtain a lightweight model, and deploying the lightweight model to a mobile terminal; loading the model at the mobile terminal and displaying the deviation between the design and the actual construction in real time to assist in decision making; and performing iterative data acquisition and comparison according to a preset period, dynamically updating the model, and re-executing lightweight and deployment to realize synchronous updating of the model. According to the method, the high-precision model is constructed through multi-source data fusion, real-time, accurate and visual monitoring and dynamic management of the construction progress and quality are realized in combination with lightweight processing and mobile terminal application, and the management and control efficiency and decision-making level of inclined wall dam construction are improved.

The application discloses a kind of glass curtain wall intelligent detection system and detection method, it is related to building curtain wall detection technical field.System includes ground laser scanning and modeling unit, wall surface crawling robot unit, cloud data analysis and processing unit, ground monitoring and visualization unit;Detection method obtains building facade point cloud data by ground laser scanning, constructs three-dimensional digital twin basic model with glass unique ID, robot full coverage operation path is planned based on model, wall surface crawling robot executes standardization three times knock collection vibration signal, and hidden danger such as structural adhesive aging, fixed part loosening, glass cracking is intelligently identified by cloud deep learning model, and diagnostic result is returned to three-dimensional model to realize accurate visualization identification.The application realizes curtain wall detection full-process automation closed loop, can complete full coverage, standardization detection, accurately position defect, improve detection efficiency and intelligent level, reduce high-altitude work safety risk.

The application provides a positioning network determination method for a ground laser scanner of a pre-assembled structure. A plane model of a target structure and a surrounding area is generated in a NetLogo platform, a restricted area and a blocked area are marked in the surrounding area, and a multi-agent model is obtained; peripheries of assembling interfaces and non-assembling interfaces of the target structure are discretized according to different densities, a visible area of each discrete point is obtained according to a TLS constraint parameter, and a global visible area of the periphery, i.e., a global TLS site area, is obtained by superimposing the visible areas of all the discrete points on the periphery; an initial site position set is calculated by using a genetic algorithm according to the global site area, and then a TLS site network is established, and the site network is optimized by using a registration evaluation method to obtain a TLS positioning network. In the establishment of the global TLS site area, the assembling interfaces are considered, the quality of point cloud data obtained by the TLS positioning network is improved, and the accuracy of structure assembling is ensured, which is fast and efficient.

This invention discloses a method for reconstructing a surface model of classical Chinese gardens, simulating rainwater runoff, and identifying risks based on 3D point clouds. The method includes the following steps: Step (1) acquiring millimeter-level 3D point cloud data of all surface elements of classical Chinese gardens using a ground laser scanner and performing data preprocessing; Step (2) using the preprocessed 3D point cloud data, selecting a differentiated point cloud modeling method based on land features, and integrating the 3D surface model of the classical Chinese gardens; Step (3) loading rainfall parameters using hydrological simulation software to generate visualized runoff paths of classical Chinese gardens under different rainfall scenarios, and identifying hydrological risk points using a grid-based cumulative method; Step (4) verifying the reliability of rainwater runoff simulation and the accuracy of hydrological risk point identification by comparing on-site observations and surface model simulation results. This invention can improve the efficiency and accuracy of identifying hydrological risk points in classical Chinese gardens with complex surface features.

The invention discloses a steel arch bridge construction linear analysis method based on three-dimensional laser scanning, and belongs to the technical field of bridge linear monitoring. A laser scanner is used for conducting ground laser scanning on a steel arch bridge in an arch bridge rib closure stage, a concrete pouring stage, a bridge floor construction completion stage and a bridge floor traffic stage; at least six groups of three-dimensional point cloud data are obtained in each stage; performing registration, noise reduction and down-sampling processing on the multi-site point cloud to generate a structured point cloud with consistent normal vectors; carrying out iterative fitting operation through an RANSAC (Random Sample Consensus) algorithm to separate linear features in the structured point cloud, and clustering feature vectors of each group of linear features by utilizing a DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm; and based on a clustering result, comparing key point coordinates of all stages to calculate a vertical deformation amount. According to the improved DBSCAN algorithm fused with the RANSAC, discrete points and point clouds of bridge floor and cross bar parts can be well deleted, the segmentation accuracy is improved, the actual line shape is smooth and free of abrupt change points, and the parameter fluctuation amplitude in each stage is small.

The present invention relates to the field of three-dimensional modeling technology, and in particular to a method and system for three-dimensional modeling of slopes in difficult mountainous areas based on multi-source data fusion. The method comprises the following steps: using a cluster of unmanned aerial vehicles to perform multi-angle acquisition of the mountain slope to obtain multi-angle image data of the slope; performing ground laser scanning on the mountain slope to obtain a slope point cloud dataset; performing seismic reflection wave detection on the mountain slope to obtain slope seismic reflection data; performing electromagnetic tomography detection on the mountain slope to obtain slope electromagnetic tomography data; fusing and inverting the slope seismic reflection data and the slope electromagnetic tomography data to obtain slope internal structure data; and performing coarse registration of the slope multi-angle image data with the slope point cloud dataset to obtain initial image-point cloud registration data. The present invention can achieve fine reconstruction of a complete three-dimensional slope model.

The present invention provides a scanning planning method for a ground laser scanner at a construction site based on oblique photography, including the steps of: S1: using a drone to photograph the site and importing it into modeling software for three-dimensional reconstruction and point cloud data acquisition; S2: preliminarily processing the point cloud data; S3: classifying the point cloud data; S4: setting the maximum scanning distance and maximum scanning angle according to the scanner parameters and scanning accuracy requirements; S5: visibility judgment and scanning range statistics; S6: using a genetic algorithm to perform scanning planning for the construction site; S7: setting the scanning resolution. The scanning planning method for a ground laser scanner at a construction site based on oblique photography of the present invention uses a genetic algorithm to perform scanning planning for the construction site. By performing binary coding on the preselected measurement sites, setting relevant screening criteria, fitness criteria, iteration methods, and termination criteria, the optimal combination of scanning measurement sites is finally obtained.

The utility model discloses a portable throwing type ground laser scanner target, which relates to the field of three-dimensional laser scanning devices, and comprises a circular plate assembly, a positioning rod seat assembly and a lifting support tip, and the circular plate assembly comprises a circular plate body, a first buffer damping ring and a first cloth-based adhesive tape layer. The first cloth-based adhesive tape layer is adhered to the upper surface of the circular plate body, the first buffer damping ring is arranged on the outer edge of the circular plate body in a sleeving manner, the multiple lifting supporting tips are circularly and uniformly distributed at the bottom of the circular plate body, and the positioning rod seat assembly penetrates through the circular plate body and is in threaded fastening connection to the center position of the circular plate body. The ground laser scanner is simple in structure, convenient to assemble and capable of adapting to the terrain environment of the soft soil slope, and therefore it is guaranteed that the ground laser scanner achieves high-precision data collection.

The application discloses a modeling method for timber volume of Taiwania flousiana based on ground laser scanning, and comprises the following modeling steps: S1, aerial photography of Taiwania flousiana forest panorama, selection of a research area and a test area, S2, collection of research data and test data of Taiwania flousiana respectively, S3, extraction of standing timber parameters, calculation of standing timber volume, S4, fitting of a volume equation, evaluation of fitting effect, and test of applicability of the volume table, wherein the Taiwania flousiana forest panorama is photographed before data collection, and according to the distribution of Taiwania flousiana, a peripheral area, a random middle area and a middle extension line corresponding area are selected as the research area, and trees in two extension line areas are reserved as the test area, so that the selected data is uniformly distributed, the inner and outer data are involved, the data collection efficiency is higher, the data is more abundant, the test data is selected through the extension line, subsequent test is facilitated, and subsequent modeling is more smooth.

A stationary terrestrial laser scanner and method with image processing, based on a machine learning algorithm, of a 2D-image of a scan sphere, captured with a camera of the stationed laser scanner before measurement of scan points, in such a way that the image is partitioned in multiple clusters of different predefined categories of measurement significance and setting at least one adaptable scan parameter according to the presence and / or absence of clusters of a significance category in the first 2D-image.