- 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.
Add ability to start before GPS checks pass.
Add ability to turn GPS fusion off and on during replay.
Add ability to turn Optical Flow fusion off and on during replay.
Add ability to turn Visual Odometry fusion off and on during replay.
Convert miscellaneous constants to parameters
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.
Paul Riseborough