Point Cloud Registration for Metrology-to-Build Coordinate Alignment
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Solution Overview
Problem
Different types of three-dimensional data, such as computer-aided design (CAD) and metrology data, have their own native coordinate systems, making it difficult to align and compare them directly, and there is a need to determine the location of a portion of an article represented by a data set in a non-native coordinate system.
Innovation Solution
A computer-implemented method and system that registers data to a coordinate system by generating point clouds from metrology and build data subsets, pre-aligning them, and applying an iterative closest point (ICP) algorithm to calculate a transform, allowing conversion of the metrology data set to the build coordinate system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data from different coordinate systems are used directly without transformation, then data processing is simpler, but accurate alignment and comparison between data types cannot be achieved
Solution Approach 1:
The system performs preliminary coordinate system identification and selection before actual data alignment. The computer identifies the first coordinate system from the first data set and the second coordinate system from the second data set, then selects an appropriate coordinate transformation algorithm based on these identified systems. This preliminary action prepares the data in advance, making the subsequent alignment process more efficient and accurate.
Solution Approach 2:
The system changes the coordinate system parameters by applying mathematical transformations to convert data from one coordinate system to another. The coordinate transformation algorithm modifies the spatial parameters (x, y, z coordinates) of the data points, enabling accurate comparison between metrology and build data by expressing both in the same coordinate framework.
2Measurement precision
If coordinate transformation algorithms are applied to all data, then accurate registration is achieved, but processing time and computational resources increase
Solution Approach 1:
The system segments the data processing by applying coordinate transformations only to the necessary portions of the data sets. Instead of transforming all data points, the system identifies and processes only the relevant data subsets that require alignment, reducing computational overhead while maintaining registration accuracy for the critical features.
Solution Approach 2:
The system applies partial transformation by selecting specific coordinate transformation algorithms based on the identified coordinate systems. Rather than applying a comprehensive transformation to all possible data scenarios, the system chooses the most appropriate and efficient algorithm for each specific case, avoiding unnecessary computational steps.
3Adaptability or versatility
If manual coordinate system alignment is performed, then flexibility in handling different data types is maintained, but the process becomes more complex and error-prone
Solution Approach 1:
The system performs self-service by automatically identifying the coordinate systems from the input data sets and selecting the appropriate transformation algorithm without requiring manual intervention. The computer autonomously determines the first and second coordinate systems and executes the coordinate transformation, reducing human error and simplifying the user interface while maintaining adaptability to different data types.
Solution Approach 2:
The system achieves universality by implementing a coordinated approach that can handle multiple coordinate system types through a single integrated process. The coordinate transformation algorithm is designed to work with various coordinate systems (e.g., STL, ASC, PTC, PROE, IGES, DXF, STEP), providing a multi-functional solution that adapts to different data formats without requiring separate manual procedures for each type.
Data Source
AI summary
A method includes steps of: (1) obtaining a metrology data set and a build data set representing an article; (2) determining a surface geometry of the article in the metrology data set and the build data set; (4) selecting a metrology data subset from the metrology data set representing the surface geometry; (5) selecting a build data subset from the build data set representing the surface geometry; (6) generating a metrology point cloud in a metrology coordinate system; (7) generating a build point cloud in a build coordinate system; (8) registering the metrology point cloud and the build point cloud; (9) calculating a transform between the metrology coordinate system and the build coordinate system; and (10) applying the transform to the metrology data set to convert the metrology data set to the build coordinate system.