Tracking System Angle Error Correction via Sensor Fusion
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Solution Overview
Problem
Tracking systems in gaming environments face errors in angle calibration due to integration drift and magnetic interference, leading to inaccurate user movement tracking.
Innovation Solution
A method and apparatus for correcting angle errors by receiving acceleration data from inertial sensors and positional sensors, transforming data into a common reference frame, and updating the rotation estimate based on error determination to align inertial and positional sensor data, thereby compensating for drift and interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If angle values are updated frequently by integrating angular rate measurements from inertial sensors, then the tracking system can respond to user movements in real-time, but angle error accumulates over time due to integration drift
Solution Approach 1:
The system continuously compares the inertial sensor-derived angles with magnetometer-derived absolute angles and uses feedback control to correct the inertial angles. The correction amount is calculated based on the angle error and applied to update the inertial angles, preventing drift accumulation while maintaining real-time responsiveness.
Solution Approach 2:
The system changes the parameter update frequency dynamically - using high-frequency inertial sensor data for real-time tracking while periodically incorporating low-frequency magnetometer data for absolute reference. This multi-rate parameter updating resolves the contradiction between fast response and long-term accuracy.
2Measurement precision
If magnetometers are used to determine absolute yaw angle, then angle drift is corrected, but the system becomes sensitive to metal and magnetic interference
Solution Approach 1:
The system applies different quality characteristics to different angular measurements - using magnetometers for yaw angle determination where absolute reference is critical, while relying on inertial sensors for pitch and roll angles where magnetic interference would be problematic. This localized application of measurement methods optimizes accuracy while minimizing interference sensitivity.
Solution Approach 2:
The system creates a composite angle measurement approach by fusing data from multiple sensor types (inertial sensors and magnetometers) with different characteristics. The complementary filtering combines the strengths of both sensor types to produce accurate orientation estimates while mitigating their respective weaknesses.
3Measurement precision
If calibration is performed each time the gaming system is used, then initial angle values are accurate, but the calibration process adds time and complexity to system setup
Solution Approach 1:
The system performs self-calibration by automatically determining initial angle values through sensor fusion algorithms without requiring manual user calibration. The microprocessor continuously processes sensor data and autonomously establishes accurate reference frames, eliminating the need for time-consuming manual calibration procedures.
Solution Approach 2:
The system performs preliminary angle estimation using inertial sensor data during the initial phase before full sensor fusion is established. This preliminary action provides immediate accurate angle values while the system gradually converges to the optimal fused solution, reducing perceived calibration time.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the accuracy and reliability of user movement tracking by continuously correcting angle errors, reducing drift and interference-related inaccuracies, and providing a more precise gaming experience.
Implementation Method 1
acceleration data is received corresponding to a tracked object in a reference frame of the tracked object from inertial sensors on the tracked object
Implementation Method 2
positional data of the tracked object is received from a positional sensor, and positional sensor acceleration data is computed from the received positional data
Implementation Method 3
The acceleration data is transformed into a positional sensor reference frame using a rotation estimate. An amount of error between the transformed acceleration data and the positional sensor acceleration data is determined. The rotation estimate is updated responsive to the determined amount of error.
Data Source
Figure 1
Figure 2A~2B
Figure 2C
AI summary
To correct an angle error, acceleration data is received corresponding to a tracked object in a reference frame of the tracked object. Positional data of the tracked object is received from a positional sensor, and positional sensor acceleration data is computed from the received positional data. The acceleration data is transformed into a positional sensor reference frame using a rotation estimate. An amount of error between the transformed acceleration data and the positional sensor acceleration data is determined. The rotation estimate is updated responsive to the determined amount of error.