- freely available
Sensors 2016, 16(6), 864; https://doi.org/10.3390/s16060864
- We have proposed a new software architecture for sensor fusion driver utilizing the quaternion based indirect Kalman filter in conjunction with additional feedback components for gyro drift correction. These components do not only handle the external feedback loop issue between the device driver and applications, but also cancel the non-gyro signal in the measured state vector.
- The developed sensor fusion driver abstracts underlying sensor hardware to provide an unified framework for mobile applications. The multi-sensing information is facilitated without any requirement of re-programming or modification in the existing applications. It supports backward compatibility for the legacy applications as well.
- The implementation in the device driver layer provides greater performance up to 10 times from 538 to 5,347 samples per second, and lower latency in calculation time from 1.8579 ns to 0.18702 ns. The duplication of sensor fusion process among applications is completely addressed.
2. Related Work
2.1. Systematical Designs
2.2. Theoretical Approaches
3.1. Problem Statement
- External feedback loop: There is a feedback loop between the modules of the filter. If the indirect Kalman filter were directly applied, it would result in a feedback loop signal between the applications and the device driver, which is not a desirable configuration. When the Kalman filter is implemented in the device driver level, it would use Euler angle kinematics in calculation, which is not linear. The orientation value is transformed to gyro value before coming out of the Kalman filter, then it has to be converted to orientation value again inside the applications. The repeated transformations may cause error accumulations because Euler angle kinematics is not linear. Note that the feedback loop is outside of the Kalman filter.
- Non-gyro signal in the measured state vector: Within the original indirect Kalman filter, a non-gyro signal always appears in the state vector after measurement. It is not hard to resolve this problem in the desired applications, but it wastes more time.
3.2. Our Approach
4. Proposed Solution
4.1. Sensor Fusion Driver Architecture
4.2. Gyro Drift Correction
5. Experimental Results and Discussions
5.2. Accuracy Evaluation
5.3. Performance Evaluation
Conflicts of Interest
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|CPU||S5PV210 ARM-CORTEX A8 [1 GHz]|
|Memory||512M DDR SDRAM|
|Kernel||Linux kernel 2.6.32 (Android OS)|
|Accelerometer sensor||3-axis accelerometer sensor|
|Gyroscope sensor||2-axis gyroscope sensor|
|Sampling frequency||100 Hz|
|Our proposed methods||Accelerometer||0.04875||0.0537||0.2537|
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