Automated Prefabricated Beam Pre-Assembly Matching with 3D Point Clouds
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Solution Overview
Problem
Existing methods struggle to efficiently and accurately perform virtual pre-assembly matching of prefabricated beams using 3D point clouds, particularly in the face of complex geometric features, leading to low computational efficiency and automation.
Innovation Solution
A method utilizing the iterative closest point algorithm, Procrustes analysis algorithm, and feature fitting algorithm for virtual pre-assembly matching, involving oriented bounding box computation, point cloud slicing, registration, denoising, and sequential coarse and fine matching to improve automation and precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional measurement technologies are used for virtual pre-assembly matching, then the process can be performed, but the computational efficiency and automation degree are low
Solution Approach 1:
The patent replaces traditional manual measurement and matching methods with 3D laser scanning technology and automated point cloud processing algorithms. The iterative closest point algorithm and Procrustes analysis automatically perform coordinate calibration, feature extraction, and matching without manual intervention, substituting mechanical measurement processes with computational methods that significantly improve both efficiency and automation degree.
Solution Approach 2:
The patent transforms physical measurement parameters into digital point cloud data parameters through 3D laser scanning. By converting real-world geometric features into coordinate data and applying mathematical transformations through algorithms, the system achieves automated processing with high computational efficiency while maintaining measurement accuracy.
2Productivity
If 3D laser scanning technology is used to obtain point cloud data, then measurement efficiency and data quantity are improved, but the difficulty of identifying required features from massive data increases
Solution Approach 1:
The patent extracts only the necessary assembly interface features from the massive point cloud data through targeted feature extraction algorithms. Instead of processing all point cloud data, the system identifies and extracts key geometric features at the assembly interface, significantly reducing the complexity of feature identification while maintaining high measurement efficiency.
Solution Approach 2:
The patent segments the point cloud data into relevant and irrelevant portions by focusing specifically on the assembly interface region. Through coordinate calibration and selective feature extraction, the system divides the massive dataset into manageable segments, making feature identification easier while preserving the benefits of high-efficiency 3D laser scanning.
3Measurement precision
If complex geometric features are present at the assembly interface, then the matching accuracy can be improved, but the computational complexity and time required increase
Solution Approach 1:
The patent performs preliminary coordinate calibration and feature extraction before the main matching process. By pre-processing the point cloud data to establish accurate coordinate systems and identify key features in advance, the system reduces the computational complexity during the actual matching phase while maintaining high matching accuracy even for complex geometric features.
Solution Approach 2:
The patent introduces the iterative closest point algorithm as an intermediary step between raw point cloud data and final matching results. This intermediate processing stage handles the complexity of geometric features by iteratively finding corresponding points and refining alignments, thereby simplifying the overall computational process while achieving high precision.
4Ease of operation
If manual intervention is used for feature extraction and matching, then flexibility can be maintained, but the automation degree and computational efficiency are reduced
Solution Approach 1:
The patent enables the system to perform feature extraction and matching automatically without manual intervention. The iterative closest point algorithm and Procrustes analysis self-adjust and optimize the matching process autonomously, achieving high computational efficiency and automation degree while maintaining operational flexibility through programmable parameters.
Data Source
AI summary
The present invention relates to the field of virtual pre-assembly matching of bridge engineering components based on 3D point clouds, and particularly relates to a method for virtual pre-assembly matching of prefabricated beams based on design-measured point cloud models. An oriented bounding box is computed respectively for 3D point clouds of two prefabricated beams with assembly relationship therebetween, and two point cloud slices and design point cloud are formed; the two point cloud slices are respectively registered with the generated design point cloud by the iterative closest point algorithm; boundary features and corner features of a pre-assembly interface of two components to be assembled are fitted and extracted; and coarse matching and fine matching of the assembly interface are achieved by the Procrustes analysis algorithm and the iterative closest point algorithm in sequence, a matching degree error of the interface is computed, and an assembly result is evaluated.