Hand-Tracked AR Device Alignment for Shared Coordinate Systems
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Solution Overview
Problem
Sharing augmented reality experiences between multiple AR devices is challenging due to the different reference coordinate systems of each device, leading to discrepancies in the display of virtual objects.
Innovation Solution
A method for aligning the coordinate systems of handheld AR devices by tracking the hand of one user holding another device, eliminating the need for environmental mapping, fiduciary markers, or specific hand gestures, and reducing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional environmental mapping and fiduciary markers are used to align coordinate systems, then alignment accuracy is improved, but device complexity and resource consumption increase
Solution Approach 1:
The patent extracts and eliminates the need for environmental mapping and fiduciary markers from the coordinate alignment process. By using direct hand tracking between devices, the system removes these intermediate components while maintaining alignment accuracy through direct device-to-device visual observation of hand positions.
Solution Approach 2:
The patent introduces hand tracking data as an intermediary element to establish coordinate system relationships. Instead of mapping the entire environment or using fiduciary markers, the system uses the tracked hand position and orientation as a mediator to compute transformation matrices between device coordinate systems.
2Measurement precision
If environmental mapping and fiduciary markers are used for coordinate alignment, then alignment precision is improved, but computing resource consumption increases
Solution Approach 1:
The patent removes the computationally intensive environmental mapping process and fiduciary marker detection from the system. By directly tracking hand positions between devices, the system eliminates these resource-consuming steps while maintaining alignment precision through simplified visual hand tracking.
3Use of energy by moving object
If hand tracking is used for coordinate alignment, then resource consumption is reduced, but alignment precision may be affected
Solution Approach 1:
The patent replaces complex mechanical and computational systems (environmental mapping, fiduciary markers) with a simpler visual tracking system. By using camera-based hand tracking and computational geometry to calculate transformation matrices, the system achieves accurate alignment with reduced computational overhead.
Solution Approach 2:
The patent changes the approach from mapping entire environments to tracking specific hand parameters (position, orientation). By focusing computational resources on tracking hand landmarks and calculating transformation matrices from these parameters, the system achieves efficient and accurate coordinate alignment.
4Measurement precision
If traditional coordinate alignment methods are used, then alignment accuracy is improved, but ease of operation deteriorates due to required gestures and markers
Solution Approach 1:
The patent removes the requirement for fiduciary markers and specific hand gestures from the alignment process. Users simply need to hold their devices and allow the system to track their hands, eliminating the need to learn or perform specific gestures or place markers in the environment.
Solution Approach 2:
The system performs automatic hand tracking and coordinate alignment without requiring user intervention beyond holding the device. The hand tracking system automatically detects and tracks the user's hand, computes transformation matrices, and aligns coordinate systems without needing explicit user commands or gestures.
Data Source
AI summary
A method for aligning coordinate systems between devices is described. The method comprises accessing pose data from a first device that defines a first coordinate system and receiving pose data from a second device that defines a second coordinate system. The method further includes identifying a coordinate transformation that relates the first device, a hand joint of a user holding the second device, and the second device, and then aligning the first coordinate system with the second based on the pose data and the identified transformation.