Method for detecting parallelism and flatness of truss track based on three-dimensional laser scanner

Abstract

The invention relates to a method for detecting parallelism and flatness of a truss track based on a three-dimensional laser scanner. The method comprises the following steps: taking point-cloud data of two different tracks, obtained by the laser scanner as a processing object; performing cross-section cutting on the point-cloud data on the same track, and extracting section data according with conditions by use of cross section data filtering and cross section data matching methods so as to fit a center point of each cross section; and then through reference conversion, converting the track and the center point of each cross section into a specified coordinate system so as to calculate track linearity, a track span, a track elevation difference and a relative elevation difference. Compared to the prior art, the method solves the problems of difficult data acquisition and tedious data post processing and realizes such advantages of data acquisition and processing integration, automation and the like.

Description

Technical field [0001] The invention relates to a method for detecting the parallelism and flatness of a truss track, in particular to a method for detecting the parallelism and flatness of a truss track based on a three-dimensional laser scanner. Background technique [0002] Deformation monitoring during operation of large structures is an important research content of industrial measurement. As a large structure, trusses will inevitably produce large wear and deformation during long-term operation, which affects construction safety and quality. Therefore, there is an urgent need to find a technology and method that can safely, quickly and accurately monitor its deformation. [0003] The research on the whole set of detection methods from data collection, data processing to result output and storage has been in-depth. After summarizing the specific detection process and the use of manual detection data processing software, it is found that the current detection system has the fo...

AI Technical Summary

Problems solved by technology

[0005] (1) Since the truss is generally suspended in mid-air, it is almost impossible to place reflective prisms or reflectors on its observation target point, and the use of total station observation will seriously affect the accuracy of observation data

[0006] (2) The observation needs to be carried out point by point, the observation efficiency is low, it takes a lot of time, and it is difficult to ensure the rapid detection of deformation

[0007] (3) Each observation requires monitoring personnel to enter the workshop for a long time to work, and the internal environment of the workshop is complex, which will seriously threaten the health and safety of monitoring personnel

[0009] (1) Using manual processing of the collected point cloud data, the operation is very complicated, wastes manpower, and the repeatability is poor. For the same data model, the results of each processing are quite different

[0010] (2) The manual operation error is relatively large, and the center point of the orbit is determined visually by the eyes, which is relatively random, resulting in low quality and poor reliability of the measurement results

[0011] (3) There is no supporting data post-processing software for the point cloud data collected by 3D laser scanning, and manual data processing is also prone to errors, low efficiency, and poor continuity between internal and external operations

[0012] (4) The output of the achievement report takes a long time, and the forms are diverse and inconsistent, which is not conducive to preservation and management

[0013] (5) There are many types of systems and the degree of professionalism is not enough, which is not conducive to operation, maintenance and management

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