Tracking System Calibration via Inertial-Visual Data Reconciliation
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Solution Overview
Problem
Conventional gaming systems require a controlled and precise calibration process for tracking systems, which is time-consuming and inconvenient, as they cannot perform calibration without measuring properties like camera tilt and distance from the user.
Innovation Solution
A method and apparatus for calibrating a tracking system using positional data from a sensor and inertial data, where the object is moved through a rich motion path to compute acceleration and reconcile inertial data with positional data, allowing for automatic calibration of the tracking system's orientation and field of view.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional calibration process is used, then measurement precision is achieved, but ease of operation deteriorates due to requiring controlled and precise manual measurements
Solution Approach 1:
The system performs self-calibration by automatically comparing inertial sensor measurements with visual tracking data. The calibration process does not require manual intervention for measuring camera tilt, distance, or other properties - the system autonomously computes calibration parameters by reconciling data from multiple sensors during normal operation.
Solution Approach 2:
Manual mechanical measurement processes are replaced with automated sensor-based measurement. Instead of requiring users to physically measure and input calibration parameters, the system uses inertial sensors and visual tracking to automatically determine calibration values through data reconciliation.
2Measurement precision
If conventional calibration process is used, then measurement precision is achieved, but loss of time increases due to time-consuming manual measurements
Solution Approach 1:
The system performs calibration preparations in advance by continuously collecting and processing data from inertial sensors and visual tracking systems. Calibration parameters are pre-computed and stored, allowing rapid deployment without requiring time-consuming manual measurements at the time of use.
Solution Approach 2:
The calibration process is integrated into continuous system operation rather than being a separate discrete step. The system continuously reconciles sensor data to maintain and update calibration parameters, eliminating idle calibration time and ensuring accuracy throughout operation.
3Ease of operation
If automatic calibration is implemented, then ease of operation improves, but measurement precision may deteriorate without controlled measurement processes
Solution Approach 1:
The system uses feedback from multiple independent sensors (inertial sensors and visual tracking system) to verify and refine calibration parameters. By continuously comparing measurements from different sensing modalities, the system ensures accuracy while maintaining automated operation.
Solution Approach 2:
The calibration system uses multiple sensing functions simultaneously - combining inertial measurement unit data with visual tracking data from cameras. This multi-functional approach provides redundant verification of calibration parameters, ensuring precision while keeping the process automated and user-friendly.
Data Source
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AI summary
To calibrate an positional sensor, a plurality of image locations and image sizes of a tracked object are received as the tracked object is moved through a rich motion path. Inertial data is received from the tracked object as the tracked object is moved through the rich motion path. Each of the plurality of image locations is converted to a three-dimensional coordinate system of the positional sensor based on the corresponding image sizes and a field of view of the positional sensor. An acceleration of the tracked object is computed in the three-dimensional coordinate system of the positional sensor. The inertial data is reconciled with the computed acceleration, calibrating the positional sensor.