Camera Pose Estimation Using CAD Line Association
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for determining the position and posture of a camera in augmented reality systems using CAD data face challenges such as difficulty in accurately detecting feature points, limited selection of corresponding pairs due to manual operation, and reduced calculation accuracy, especially when associating images with CAD data or other shape information.
Innovation Solution
An image processing device that extracts candidate lines from shape information, generates association information by linking these lines with feature lines detected from an image, and determines the association result based on errors, automatically selecting corresponding pairs to improve calculation efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual operation is used to select corresponding pairs between image feature points and CAD data, then user control is maintained, but the calculation accuracy is reduced and the process becomes time-consuming
Solution Approach 1:
The system automatically extracts candidate lines from CAD data, detects feature lines from the image, and performs matching without requiring manual user intervention. The calculation unit autonomously determines the camera's position and posture by processing the extracted geometric features, making the system self-sufficient and eliminating time-consuming manual operations while maintaining high calculation accuracy
Solution Approach 2:
The system performs preliminary extraction of candidate lines from CAD data and preliminary detection of feature lines from the image before the actual matching process. This preliminary preparation of geometric features enables faster and more accurate subsequent processing, as the data is pre-processed and organized for optimal matching
2Measurement precision
If the number of corresponding pairs is limited due to manual operation, then the operation remains simple, but the calculation accuracy of camera position and posture is reduced
Solution Approach 1:
The calculation unit automatically generates multiple corresponding pairs by processing all extracted candidate lines and detected feature lines without manual limitation. The system self-determines the optimal number of corresponding pairs based on the available geometric features, thereby increasing calculation accuracy without requiring complex manual configuration or selection processes
3Productivity
If automatic extraction of corresponding pairs is implemented, then calculation efficiency is improved, but the complexity of the processing system increases
Solution Approach 1:
The system segments the processing into distinct functional units: a line extraction unit that processes CAD data, a feature detection unit that processes images, and a calculation unit that performs matching. This segmentation allows each unit to specialize in a specific task, improving overall calculation efficiency while keeping the complexity of each individual unit manageable and well-defined
Solution Approach 2:
The system replaces manual mechanical operations with automated computational processes. Instead of manual selection and matching of corresponding pairs, the system uses algorithmic processing to automatically extract, detect, and match geometric features, thereby dramatically improving calculation efficiency while the modular structure keeps processing complexity controlled
Data Source
AI summary
A computer extracts a plurality of candidate lines that are observed from a position of an imaging device that captures an image of an object from among a plurality of candidate lines included in shape information of the object. The computer generates plural pieces of association information indicating a prescribed number of combinations obtained by respectively associating the prescribed number of candidate lines of the observed plurality of candidate lines with the prescribed number of feature lines of a plurality of feature lines detected from the image. The computer determines an association result according to respective errors of the plural pieces of association information.