Digital Twin Image Registration Using Layout Feature Positioning
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Solution Overview
Problem
Existing digital twin generation techniques are costly and complex, requiring specialized hardware and trained personnel, and conventional alignment methods are inaccurate and time-consuming.
Innovation Solution
Utilizing consumer-grade hardware, such as smartphones and panoramic cameras, to capture ultra-wide angle images and automatically register them to layout plans, employing machine learning and feature detection to generate digital twins.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional digital twin generation techniques are used, then the digital twin can be created, but the process is costly and complex requiring specialized hardware and trained personnel
Solution Approach 1:
The patent replaces expensive specialized hardware with consumer-grade devices such as smartphones and panoramic cameras. These inexpensive, widely available devices capture images that are processed through automated algorithms to generate digital twins, eliminating the need for costly specialized equipment while maintaining acceptable quality standards
Solution Approach 2:
The system employs automated machine learning algorithms and feature detection techniques that enable the digital twin generation process to operate without trained personnel. The automated registration system independently aligns captured images with layout plans, performing tasks that previously required expert intervention, thereby making the process self-sufficient
2Measurement precision
If conventional alignment methods are used to register images to layout plans, then alignment can be achieved, but the process is inaccurate and time-consuming
Solution Approach 1:
The patent replaces manual mechanical alignment methods with automated computer vision and machine learning systems. Feature detection algorithms automatically identify key points in images and match them with corresponding features in layout plans, using computational geometry and optimization algorithms to achieve precise alignment without manual intervention
Solution Approach 2:
The system transforms the alignment problem from a manual coordinate-matching task into an automated feature-based registration process. By detecting features such as corners, edges, and distinctive patterns in images and comparing them with layout plan data, the system dynamically adjusts alignment parameters to achieve accurate registration rapidly and automatically
Data Source
AI summary
Examples described herein provide a method that includes capturing, using a camera, a first image of an environment. The method further includes performing, by a processing system, a first positioning to establish a position of the first image in a layout of the environment. The method further includes detecting, by the processing system, a feature in the first image. The method further includes performing, by the processing system, a second positioning based at least in part on the feature to refine the position of the first image in the layout. The method further includes capturing, using the camera, a second image of the environment and automatically registering the second image to the layout. The method further includes generating a digital twin representation of the environment using the first image based at least in part on the refined position of the first image in the layout and using the second image.