- for accel/gyro/mag estimated bias only consider them stable (valid
for calibration updates) if the value isn't changing (10% of limit) over
the validity period
Also remove the legacy "range aid" than can be achieved by setting the
height reference to range finder and the range finder control parameter
to "conditional".
Conditional range aiding cal also be set when the height reference isn't
the range finder. This prevents the ratchetting effect due to switching
between references.
Instead of having a single height source fused into the EKF and the
other ones "waiting" for a failure or the primary sensor, fuse all
sources in EKF2 at the same time. To prevent the sources from fighting against each
other, the "primary" source is set as reference and the other ones are
running a bias estimator in order to make all the secondary height
sources converge to the primary one.
If the reference isn't available, another one is automatically selected
from a priority list. This secondary reference keeps its current bias
estimate but stops updating it in order to be the new reference as close
as possible to the primary one.
Using the control status flags isn't robust as this part of the code
runs at the EKF update rate while the in_air transition is don at the
prediction rate. It was then likely to miss the transition
- replace float32[21] URT covariances with smaller dedicated position/velocity/orientation variances (the crossterms are unused, awkward, and relatively costly)
- these are easier to casually inspect and more representative of what's actually being used currently and reduces the size of vehicle_odometry_s quite a bit
- ekf2: add new helper to get roll/pitch/yaw covariances
- mavlink: receiver ODOMETRY handle more frame types for both pose (MAV_FRAME_LOCAL_NED, MAV_FRAME_LOCAL_ENU, MAV_FRAME_LOCAL_FRD, MAV_FRAME_LOCAL_FLU) and velocity (MAV_FRAME_LOCAL_NED, MAV_FRAME_LOCAL_ENU, MAV_FRAME_LOCAL_FRD, MAV_FRAME_LOCAL_FLU, MAV_FRAME_BODY_FRD)
- mavlink: delete unused ATT_POS_MOCAP stream (this is just a passthrough)
Co-authored-by: Mathieu Bresciani <brescianimathieu@gmail.com>
- all sources of optical flow publish sensor_optical_flow
- sensor_optical_flow is aggregated by the sensors module, aligned with integrated gyro, and published as vehicle_optical_flow
Co-authored-by: alexklimaj <alex@arkelectron.com>
The noise spectral density, NSD, (square root of power spectral density) is a
continuous-time parameter that makes the tuning independent from the EKF
prediction rate.
NSD corresponds to the rate at which the state uncertainty increases
when no measurements are fused into the filter.
Given that the current prediction rate of EKF2 is 100Hz, the
same tuning is obtained by dividing the std_dev legacy parameter by 10:
nsd = sqrt(std_dev^2 / 100Hz)
Daniel Agar