Precast Beam Flatness Detection via 3D Point Cloud Calibration
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Solution Overview
Problem
Current methods for detecting the flatness of precast beams, such as contact measurement using feeler gauges and profilometers, are inefficient and lack accuracy, making it difficult to manage detection results digitally and automate the process effectively.
Innovation Solution
A method utilizing a three-dimensional point cloud model for precast beams involves coarse and fine calibration, determining normal vectors, and iteratively finding an optimal reference plane to calculate surface flatness, enhancing detection efficiency and automation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If contact measurement methods such as feeler gauges and profilometers are used, then the detection process is simple to operate, but the detection efficiency is low and accuracy is insufficient
Solution Approach 1:
The patent replaces contact-based mechanical measurement methods (feeler gauges and profilometers) with a non-contact three-dimensional laser scanning system. The laser scanner captures point cloud data of the precast beam surface, which is then processed through coordinate calibration, surface extraction, and flatness calculation algorithms to achieve both high accuracy and efficient automated detection
Solution Approach 2:
The patent creates a digital three-dimensional point cloud model that serves as a virtual copy of the physical precast beam surface. This digital model allows for repeated analysis, automated processing, and precise flatness measurement without physically contacting the actual beam, thereby improving both accuracy and detection efficiency
2Loss of information
If contact measurement methods are used, then the equipment is simple, but the detection results are not easy to manage digitally
Solution Approach 1:
The patent creates a digital three-dimensional point cloud model that serves as a virtual copy of the physical precast beam surface. This digital model allows for repeated analysis, automated processing, and precise flatness measurement without physically contacting the actual beam, thereby improving both accuracy and detection efficiency
Solution Approach 2:
The patent transforms physical surface characteristics into digital parameters through laser scanning. The point cloud data contains three-dimensional coordinates that can be processed through coordinate calibration, surface extraction, and flatness calculation algorithms to generate digital detection results that are easy to store, manage, and analyze
3Productivity
If three-dimensional point cloud model is used, then the sampling rate is high and automation degree is high, but it is difficult to quickly extract target surface and implement automatic calculation
Solution Approach 1:
The patent segments the complex point cloud data processing into distinct sequential steps: coordinate calibration (coarse and fine), surface extraction through normal vector calculation, reference plane determination, and flatness calculation. This segmentation makes the automated processing of large point cloud datasets manageable and efficient
Solution Approach 2:
The patent performs preliminary coordinate calibration (both coarse and fine) and surface extraction before the actual flatness measurement. By pre-processing the point cloud data to establish accurate coordinate systems and extract relevant surface points, the subsequent flatness calculation becomes more efficient and accurate
Data Source
AI summary
The present invention discloses a method for detecting surface flatness of a precast beam based on a three-dimensional point cloud model, including the following steps: (1) performing, according to a specific geometry of a three-dimensional point cloud model of a target component in a three-dimensional coordinate system, coarse calibration and fine calibration on the model sequentially to determine a spatial rotation matrix and perform point cloud coordinate calibration; (2) determining normal vectors at positions of points of the three-dimensional point cloud model of the component according to a principal component analysis method and a K-nearest-neighbor principle, so that a to-be-detected surface is segmented and extracted by defining a normal vector direction and a coordinate interval; and (3) iteratively searching for an optimal reference plane according to a form relationship between the to-be-detected surface and the three-dimensional coordinate system and calculating flatness of the surface.