- when switching to range finder use the current terrain estimate as
height sensor offset, otherwise spikes in the range measurements could lead
to a wrong offset
Signed-off-by: Roman <bapstroman@gmail.com>
- do not subtract the height sensor offset variable when computing the
baro offset from the local origin. The baro height offset is calculated
when baro is not fused and so the height sensor offset used in that case
is associated to another sensor and has nothing to do with the baro.
Signed-off-by: Roman <bapstroman@gmail.com>
the primary height source
- moved height control into single function in order to decide which sensor
should be used for estimating height
- under certain conditions allow to use the range finder to estimate height
even if it's not the primary height source
- fixed a bug where the delta time for the baro offset calculation was always
zero
- use methods to set height control flags to reduce code duplication and
to prevent bugs
Signed-off-by: Roman <bapstroman@gmail.com>
Add calculation of a vertical position derivative to the output predictor. This will have degraded tracking relative to the EKF states, but the velocity will be closer to the first derivative of the position and reduce the effect inertial prediction errors on control loops that are operating in a pure velocity feedback mode.
Move calculation of IMU offset angular rate correction out of velocity accessor and into output predictor.
Provide separate accessor for vertical position derivative.
Use horizontal acceleration to check if yaw is observable independent of the magnetometer.
Use rotation about the vertical to check if mag raises are observable.
If neither yaw of mag biases are observable, save the magnetic field variances and switch to magnetic yaw fusion.
Use the last learned declination when using magnetic yaw fusion so that the yaw reference remains consistent.
When yaw or biases become observable, reinstate the saved variances and switch back to 3D mag fusion.
Add a bitmask parameter to control bias learning for individual axes. This is achieved by setting the disabled states to zero together with their corresponding covariances.
Minor cleanup of the covariance prediction comments.
Removal of unnecessary variable copy operations.
Replace index operations to initialise covariance to zero with the more efficient memset.
Use vertical velocity and position innovation failure to detect bad accelerometer data caused by clipping or aliasing which can cause large vertical acceleration errors and loss of height estimation. When bad accel data is detected:
1) Inhibit accelerometer bias learning
2) Force fusion of vertical velocity and height data
3) Increase accelerometer process noise
The previous practice of relying on the off-diagonals being zero caused problems with conditioning of the magnetometer fusion on one flight. By storing the variances when the learning inhibit becomes active and ensuring that the rows and columns in the covariance matrix for the inhibited states are always zero, the observed numerical conditioning error has been eliminated for replay of the problem flight log .
Doing so can casue large jumps in GPS position and innovation check errors after landing and also reduces the effectiveness of pre-flight innovation consistency checks.
Previously GPS quality checks were only run until the EKF origin was set. This meant that they could not be used by other pre-flight checks.
This change ensures that checks will always be run when the vehicle on-ground or not using GPS to enable use by external preflight checks.
ChristophTobler