Augmented Reality Alignment for Moving Physical Objects
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Solution Overview
Problem
Current methods for aligning augmented reality (AR) objects with physical objects suffer from latency issues when physical objects move, leading to misalignment and a poor user experience.
Innovation Solution
A system utilizing IoT and edge-based computing to identify and classify the movement characteristics of physical objects, determine the needed precision, and precisely align AR objects by analyzing data from AR devices and connected computing devices, recommending additional devices if necessary to minimize latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AR objects are aligned with physical objects using predefined locations, then the alignment is simple to implement, but latency occurs when physical objects move causing misalignment
Solution Approach 1:
The system performs preliminary classification of physical objects into movable and non-movable categories. For movable objects, the system pre-establishes monitoring mechanisms and data analysis pipelines that activate immediately when movement is detected, eliminating the need to process from scratch during alignment updates.
Solution Approach 2:
The system dynamically adjusts its alignment approach based on object characteristics. For movable objects, it uses continuous monitoring and real-time data analysis to update positions. For non-movable objects, it relies on predefined locations. This dynamic adaptation allows precise alignment while minimizing unnecessary processing latency.
2Measurement precision
If continuous monitoring of physical object positions is implemented, then alignment precision is improved, but device complexity increases
Solution Approach 1:
The system segments physical objects into distinct categories (movable and non-movable) and applies different monitoring strategies to each segment. Only movable objects require continuous monitoring with multiple computing devices, while non-movable objects use simpler predefined location methods. This segmentation reduces overall system complexity while maintaining high alignment precision where needed.
Solution Approach 2:
The system introduces an intermediary classification layer that sits between object identification and alignment execution. This intermediary analyzes object characteristics and determines the appropriate monitoring strategy, preventing direct complex monitoring of all objects and thereby reducing unnecessary system complexity.
3Measurement precision
If multiple computing devices are used to monitor and analyze object data, then alignment precision improves, but the quantity of devices increases
Solution Approach 1:
The system applies different levels of monitoring quality to different objects based on their movement characteristics. Movable objects receive high-quality continuous monitoring with multiple devices, while non-movable objects receive minimal or no monitoring. This local differentiation of quality ensures high alignment precision for critical objects while minimizing the total number of devices required.
Data Source
AI summary
According to one embodiment, a method, computer system, and computer program product for precise alignment of one or more augmented reality objects is provided. The present invention may include identifying one or more physical objects in a physical environment that will be overlaid with the one or more AR objects; monitoring positions of the one or more identified physical objects with an AR device and one or more connected computing devices; analyzing data of the one or more identified physical objects; and aligning the one or more AR objects precisely to the one or more identified physical objects based on the analyzed data of the one or more physical objects.