Kauai Labs navX-MXP User Manual page 76

Guidance
Yaw Drift
Over time, these errors accumulate leading to greater and greater amounts of error.
With the navX-MXP, Quantization error is minimized due to the MPU-9250's internal signal
conditioning, high-resolution 16-bit Analog-to-Digital Converters (ADC), and extremely fast
internal sampling (200Hz). Scale factor error is easily corrected for by factory calibration, which
the navX-MXP provides. So these two noise sources are not significant in the navX-MXP.
The remaining sources of error – temperature instability and bias error – are more challenging to
overcome:
Gyro bias error is a major contributor to yaw drift error, but is inherent in modern MEMS-
based gyroscopes like the MPU-9250.
Temperature instability can cause major amounts of error, and should be managed to
get the best result. To address this, the MPU-9250 automatically re-calibrates the gyro
biases whenever it is still for 8 seconds, which helps manages temperature instability.
Errors in the navX-MXP Pitch and Roll values to be extremely accurate over time since
gyroscope values in the pitch/roll axes can be compared to the corresponding values from the
accelerometer. This is because when navX-MXP is still, the accelerometer data reflects only the
linear acceleration due to gravity.
Correcting for integration error in the Yaw axis is more complicated, since the accelerometer
values in this axis are the same no matter how much yaw rotation exists.
To deal with this, several different "data fusion" algorithms have been developed, including:
Complementary filter
Extended Kalman filter (EKF)
Direction Cosine Matrix filter (DCM)
Note: See the References
These algorithms combine the acceleromter and gyroscope data together to reduce errors.
The Complementary and EKF filter algorithms are designed to process 3-axis accelerometer
and 3-axis gyroscope values and yield yaw/pitch/roll values. The Complementary filter is a
simple approach, and works rather well, however the response time is somewhat slower than
the EKF, and the accuracy is somewhat lower.
The DCM filtering approach is similarly accurate and responsive as the EKF, however it requires
information from a 3-axis magnetometer as well to work correctly. Since the magnetometer on a
FIRST FRC robot typically experiences significant amounts of magnetic disturbance, the DCM
algorithm is not well suited for use in a Robotics Navigation Sensor.
For these reasons, the EKF is the preferred filtering algorithm to provide the highest
performance IMU on a FIRST FRC robot. However, the EKF algorithm is complex and difficult to
page for links to more information on these algorithms.
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