See discussion here:
https://github.com/ArduPilot/ardupilot/issues/7331
we were getting some uninitialised variables. While it only showed up in
AP_SbusOut, it means we can't be sure it won't happen on other objects,
so safest to remove the approach
Thanks to assistance from Lucas, Peter and Francisco
Fix rounding error bug preventing state from updating after initial convergence.
Decouple GPS reference height from published EKf origin height.
Add bitmask parameter to control update and publishing of GPS reference height.
this changes the stragegy for load levelling between EKF cores so it
works between EK2 and EK3, and with future estimators as well.
It allows us to run EK3 and EK2 at the same time with good scheduling
performance
Revert "AP_NavEKF2: Fix bug in published yaw reset value found during code review"
commit 175faf1e41.
Revert "AP_NavEKF2: use a struct for all yaw step class variables"
commit 77fad065d1.
Partially revert "AP_NavEKF2: Handle yaw jumps due to core switches"
commit 885bfd1b4e.
The EK2_RNG_USE_HGT parameter sets the height (expressed as a percentage of the maximum range of the range finder as set by the RNGFND_MAX_CM parameter) below which the range finder will be used as the primary height source when the vehicle is moving slowly.
When using a height reference other than GPS, the height datum can drift due to air pressure changes if using baro, or due to terrain height changes if using range finder as the primary height source. To ensure that a consistent height datum is available when switching between altitude sources, the WGS-84 height estimate of the EKF's local positi norigin is updated using a
single state Bayes estimator,
If rngfinder or gps height data is lost whilst being used, there will be a fall-back to baro data.
Allow different process noise to be set for body (sensor bias) and earth field states.
This allows a stable magnetometer bias estimate to be available at end of flight whilst still allowing for external magnetic anomalies during landing.
Adjust default values to give stable mag bias learning and fast learning of external anomalies.
Automatically use the highest gain consistent with a 5% overshoot to minimise RMS tracking errors.
Provide an alternative correction method for the position and velocity states that allows the user to specify the time-constant. This can be used to fine tune the output observer for for platform specific sensor errors and control loop sensitivity estimation noise.
Michael du Breuil
Peter Barker
Paul Riseborough
Andrew Tridgell
Lucas De Marchi
Dr.-Ing. Amilcar Do Carmo Lucas
Randy Mackay
Jacob Walser
Mathieu OTHACEHE
Francisco Ferreira