11 results about "Terrestrial laser scanning" patented technology

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.

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.

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 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.

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 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.