Kauai Labs navX2-MXP User Manual page 77

Guidance
Yaw Drift
With the navX-sensor, Quantization error is minimized due to internal signal conditioning, high-
resolution 16-bit Analog-to-Digital Converters (ADC), and extremely fast internal sampling (416Hz).
Scale factor error is easily corrected for by factory calibration, which the navX-sensor provides. So these
two noise sources are not significant in a navX-sensor.
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 those used in a navX-sensor.
Temperature instability can cause major amounts of error, and should be managed to get the best
result. To address this, navX-sensors automatically re-calibrate the gyro biases whenever it is still
for several seconds, which helps manages temperature instability.
Errors in the navX-sensor Pitch and Roll values are small – these angles are 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-sensor 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 understand, making it
typically beyond the capabilities of many robotics engineers. The "Generation 2" navX-sensors
implement an Extended Kalman Filter that runs at 416 Khz and yields extremely accurate results. The
page for links to more information on these algorithms.
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