Augmented Reality Content Positioning via Spatial Coordinates
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Solution Overview
Problem
Traditional augmented reality technologies are limited by the requirement of proximity to a real-world location, restricting the number of people who can experience AR content, as they primarily provide a first-person view and are site-specific, making it difficult to accurately position and display persistent AR content across different viewpoints.
Innovation Solution
The LockAR system uses sensors and trackable features to measure and estimate six-degree-of-freedom vectors, tethering AR content to physical locations, allowing for accurate positioning and orientation across various viewpoints, even remotely, by creating and updating spatial coordinates and improving positioning accuracy over time through sensor fusion and COFIG groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional augmented reality technologies are used to provide a first-person view at a specific location, then the AR content can be displayed in real-time at that location, but the number of people who can experience the AR content is limited and the positioning accuracy across different viewpoints is poor
Solution Approach 1:
The patent transitions from 2D image-based positioning to 3D spatial coordinate systems. By implementing a three-dimensional coordinate system with X, Y, and Z axes that correspond to real-world spatial relationships, the system enables accurate positioning from multiple viewpoints while maintaining precision across different perspectives and locations.
Solution Approach 2:
The patent creates a universal coordinate system that works across multiple devices and viewpoints. The spatial coordinates and transformation matrices enable any user from any location to view AR content with consistent positioning, making the system universally accessible while maintaining accurate spatial relationships regardless of the observer's position.
2Measurement precision
If multiple sensors are used to measure six-degree-of-freedom vectors between trackable features, then the positioning accuracy of AR content is improved, but the device complexity and computational requirements increase
Solution Approach 1:
The patent divides the complex positioning task into manageable segments by using multiple trackable features independently. Each feature provides partial spatial information, and the system combines these segmented measurements through coordinate transformations to achieve complete and accurate positioning without requiring a single complex sensor system.
Solution Approach 2:
The patent introduces spatial coordinate systems and transformation matrices as intermediaries between raw sensor data and final AR content positioning. These mathematical intermediaries facilitate the conversion of measurements from multiple sensors and viewpoints into unified, accurate spatial coordinates, simplifying the overall system architecture.
3Reliability
If AR content is tethered to physical locations using spatial coordinates, then the content can be accurately positioned across different viewpoints, but the system requires complex coordinate transformations and updates
Solution Approach 1:
The patent establishes spatial coordinate systems and relationships between trackable features in advance, before AR content is displayed. By pre-defining the three-dimensional coordinate framework and transformation matrices, the system ensures consistent positioning across different viewpoints without requiring complex real-time calculations during content delivery.
Data Source
AI summary
A system for accurately positioning augmented reality (AR) content within a coordinate system such as the World Geodetic System (WGS) may include AR content tethered to trackable physical features. As the system is used by mobile computing devices, each mobile device may calculate and compare relative positioning data between the trackable features. The system may connect and group the trackable features hierarchically, as measurements are obtained. As additional measurements are made of the trackable features in a group, the relative position data may be improved, e.g., using statistical methods.