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Overview
Frequently-asked Questions
However, at the beginning of each FIRST FRC match, the robot is turned on for about a minute
before the match begins. During this time period, the motors are not energized and thus do not add
magnetic interference that would disturb the magnetometer readings. Once the magnetometer is
calibrated, navX2-MXP will return either an accurate magnetometer reading, or an indication that
its measurement of the earth's magnetic field has been disturbed.
Magnetometer readings taken at the beginning of a match, when combined with the navX2-MXP
yaw measurements, enable a robot's pose and absolute heading to be maintained throughout the
match. This feature of the navX2-MXP is referred to as a "9-axis" heading.
Why do the Yaw angles provided by the navX2-MXP drift over time?
The short answer is that the yaw angle is calculated by integrating reading from a gyroscope
which measures changes in rotation, rather than absolute angles. Over time, small errors in the
rotation measurements build up over time. The navX2-MXP features sophisticated digital motion
processing and calibration algorithms that limit this error in the yaw angle of ~.5 degree per
minute when moving, and ~.2 degree per hour when still. For further details, please see the
Drift
page.
Can the navX2-MXP "Displacement" estimates be used for tracking a FRC or FTC robot's change in
position (dead-reckoning) during autonomous?
Accelerometer data from the navX-MXP's onboard MPU-9250 are double-integrated by the navX-
MXP firmware to estimate displacement, and are accurate to approximately .1 meter of error
during a 15 second period.
To track a FRC or FTC robot's position during autonomous requires an accuracy of about 1 cm of
error per 15 seconds. While the accuracy of the navX2-MXP displacement estimates might be
good enough to track the position of an automobile on a road, it is typically too low for use in
tracking a FRC or FTC robot's position during the 15 second autonomous period, and employing
a sensor such as a quadrature encoder on the robot drive wheels is recommended.
The root cause of the displacement estimate error rate is accelerometer noise. Estimating
displacement requires first that each acceleration sample be multiple by itself twice (cubed), and
then integrated over time. Practically, if a noisy signal is cubed, the result is very noisy, and when
this very noisy value is integrated over time, the total amount of error grows very quickly.
The current noise levels (approximately 60 micro-g per square-root-hertz) would need to be
reduced by approaximately a factor of 10 (one order of magnitude) before displacement estimates
with 1 cm of error per 15 seconds can be achieved by double-integration of accelerometers.
Yaw
7
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