Marker Position and Orientation Calculation in Mixed Reality
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Solution Overview
Problem
Existing methods for calculating the position and orientation of indices in mixed reality registration are cumbersome, particularly when dealing with a large number of markers, and suffer from issues like marker ID overlapping and recognition errors, leading to inefficient calibration and registration processes.
Innovation Solution
An image processing system that automatically or semi-automatically selects indices for calibration and registration, using a graphical user interface to manage marker definition information and exclude overlapping or incorrectly recognized markers, thereby reducing operator load and improving processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a large number of markers are allocated to broaden the registration space range, then the registration coverage is improved, but the time and complexity for measuring and inputting marker positions and orientations increases
Solution Approach 1:
The system automatically detects markers in captured images and calculates their positions and orientations without requiring manual measurement and input. The image processing apparatus performs self-service by autonomously identifying marker locations, extracting feature points, and computing spatial parameters, thereby eliminating the time-consuming manual operation while supporting a large number of markers across broad registration spaces
Solution Approach 2:
The patent replaces the manual mechanical process of measuring and inputting marker positions with an automated image processing system. The system uses computer vision algorithms to detect markers in images, automatically calculate their 3D positions and orientations through coordinate transformations, and store the results, substituting human manual operations with automated computational processes
2Measurement precision
If all captured markers are calibrated and used in registration processing, then the registration precision is improved, but the processing load increases
Solution Approach 1:
The system extracts only the necessary markers from the captured image for calibration and registration processing. By automatically detecting which markers are visible and relevant in the current field of view, the system extracts a subset of markers that suffices for accurate registration, thereby reducing the processing load while maintaining registration precision. Not all detected markers need to be processed if they fall outside the required registration volume or are redundant
Solution Approach 2:
The system performs partial calibration by processing only the markers that are currently visible and relevant in the captured image, rather than calibrating all markers in the entire space. This partial action approach reduces computational complexity while achieving sufficient registration precision for the current viewing angle and registration requirements
3Productivity
If markers are manually selected and carefully captured to reduce the number of markers, then the processing load is reduced, but the operation requires operator experience and skill
Solution Approach 1:
The image processing apparatus performs self-service by automatically detecting markers in captured images and determining which markers should be used for calibration. The system autonomously identifies marker positions, extracts feature points, and selects appropriate markers for processing without requiring operator intervention or expertise in marker selection, thereby improving processing efficiency while eliminating the need for specialized operator skills
Solution Approach 2:
The patent replaces the manual operator-based marker selection process with an automated image processing system. The system uses computer vision algorithms to automatically detect markers, identify their positions and orientations, and select which markers to use for calibration based on image analysis, substituting human judgment and skill with automated computational methods
4Measurement precision
If the calibration is re-executed after excluding overlapping markers, then the accuracy is improved, but the time and operational complexity increases
Solution Approach 1:
The system implements feedback by automatically detecting marker overlapping in captured images and using this information to exclude overlapping markers from calibration processing. The image processing apparatus analyzes marker positions, identifies overlaps, and adjusts the calibration process accordingly without requiring manual intervention or re-imaging, thereby maintaining calibration accuracy while eliminating the time loss associated with repeated calibration attempts
Solution Approach 2:
The system performs preliminary analysis of captured images to identify and exclude overlapping markers before executing the calibration process. By detecting marker overlaps in advance and removing them from the calibration set, the system prevents calibration errors that would require re-execution, thereby improving accuracy while saving the time that would be spent on repeated calibration attempts
Data Source
AI summary
Indices in an image of a physical space on which a plurality of indices are allocated are identified, and the positions and orientations, on the physical space, of all or some identified indices are calculated.