* ekf_control: Inhibit mag fusion when field magnitude is large
Move mag inhibition check in separate function
* ekf_control: pull out of functionalities out of controlMagFusion
- yaw abd mag bias observability checks
- mag 3D conditions
- load mag covariances
- set and clear mag control modes
* ekf_control: refactor mag heading/3D start/stop.
Move mag declination, mag 3d and mag heading fusion out of the main function
* ekf_control: extract mag yaw reset and mag declination fusion requirements
* ekf_control: use WMM in isStronMagneticField for mag fusion inhibition
- Correct units of WMM strength table
* ekf_control: extract mag_state_only functionality of AUTOFW (VTOL custom)
Also split inAirYawReset from onGroundYawReset
* ekf_control: extract mag automatic selection
- transform if-else into switch-case for parameter fusion type selection
* ekf_control: extract run3DMagAndDeclFusion, reorganize functions, fix
flag naming in Test script
* ekf_control: do not run mag fusion if tilt is not aligned.
Reset some variables on ground even if mag fusion is not running yet. It
could be that it runs later so we need to make sure that those variables
are properly set.
* ekf_control: move controlMagFusion and related functions to mag_control.cpp
* ekf control: check for validity of mag strength from WMM and falls back
to average earth mag field with larger gate if not valid
* ekf control: remove evyaw check for mag inhibition
* ekf control: change nested ternary operator into if-else if
* Ekf: create AlphaFilter template class for simple low-pass filtering
0.1/0.9 type low-pass filters are commonly used to smooth data, this
class is meant to abstract the computation of this filter
* ekf control: reset heading using mag_lpf data to avoid resetting on an outlier
fixes ecl issue #525
* ekf control: replace mag_states_only flag with mag_field_disturbed and
add parameter to enable or disable mag field strength check
* ekf control: remove AUTOFW mag fusion type as not needed This was implemented for VTOL but did not solve the problem and should not be used anymore
* ekf control: use start/stop mag functions everywhere instead of setting the flag
* ekf control: Run mag fusion depending on yaw_align instead of tilt_align
as there is no reason to fuse mag when the ekf isn't aligned
* AlphaFilter: add test for float and Vector3f
The quaternion to inverse rotation matrix function has been updated so that the rotation it produces is the inverse to that produced by the matrix library and the the inverse of the quaternion is uses. This function is now used to directly calculate an inverse rotation matrix rather than calculating the forward rotation and then transposing it.
clearance. This is the best estimate as we should not rely on a distance
sensor while on the ground. This also helps when the drone is carried
over as it avoids starting with a crazy downward distance for optical
flow scaling.
- calculate a theoretical value based on the knowledge of the direction
and strength of the magnetic field vector and X/Y sensor measurements
- needs knowledge about location on earth to work
This removes the check for _rng_hgt_faulty in the decision of publishing
a fake range measurement. The reason for this is that some distance sensors
don't populate the quality flag, even if they are saturated. Hence, if we
are on the ground and not moving, it is safe to publish a fake measurement
of the distance sensor and overwrite the actual sensor data.
This commit introduces a quality measure in the range data. It is
used to properly decide whether to initialize the HAGL estimate on
sensor data or MIN_HGT parameter, as well as in the decision of
whether a 'fake' measurement should be published on the ground to
allow for optical flow take-offs.
position/velocity aiding sensor.
Until now, it was alway resetting if the vehicle does not have gps or
external vision. This caused a reset/glitch at every stop (when range data gets
valid)
angle- and delta velocity bias variance
- the contribution of process noise per iteration for these states can be so
small that it gets lost if using standard floating point summation
Signed-off-by: Roman <bapstroman@gmail.com>
This adds support for the paparazzi autopilot to use this library as EKF.
The header order change has to be done in order to have std.h included to get certain defines at the start of the estimator_interface.h.
Fusion with large initial magnetometer biases errors can result in the the NE earth field states reducing in magnitude and effectively flipping sign.
EKF: Move declination state limiting into a separate function
EKF: Limit NE mag states after each 3-axis mag fusion
EKF: Fix bug in mag field strength look-up scale factor
Simplify calling so that it is only called in two ways:
1) Immediately before 3-axis mag fusion if not called since the last earth field covariance reset so that the earth field declination information can be formed.
2) Immediately after 3-axis mag fusion otherwise.
In the case where the EKF is switching between 3-axis and heading fusion, off-diagonal elements containing the correlation between N,E components of the earth field were being lost on each switch event. These elements contained information about the declination uncertainty and should be preserved.
Ensures that each time the earth field covariance and variance data is reset, that the off-diagonal elements containing earth field declination angle certainty is restored.
Use a new method that preserves the roll and pitch information and adds the uncertainty for yaw only.
Ensure that correlation information to non-quaternion states is removed when a reset occurs to prevent fusion of subsequent observations (e.g. GPS) causing incorrect yaw.
EKF waits 10s after GPS signal is lost before setting GPS control status flag to false. As the position information given by the alternative position sources drifts from the last GPS position, the controller over corrects.
With this update, the time horizon until GPS control flag set to false is reduced and only alternative position source is used for estimation.
tested with optical flow as position souce
log from as is https://review.px4.io/plot_app?log=d624af5e-dde4-40ab-ba5b-a693a49f5a36
log with update https://review.px4.io/plot_app?log=13ed6dc3-22dd-43f8-b898-4add41d60439
Previously, the reset of the yaw when climbing above 1.5m was not performed until 3-axis fusion was enabled. This could result in loss of navigation depending on the value of EKF2_MAG_TYPE and the flight profile.
- the velocity of the optical flow sensor relative to the IMU expressed in
body frame is the cross product of the angular velocity with the vector
from IMU to the sensor. If we use the angular velocity stored in the flow
sample struct we need to use a negative sign as that angular velocity
follows the opposite sign convention.
Signed-off-by: Roman <bapstroman@gmail.com>
- _flt_mag_align_complete was not set when choosing pure 3D mag fusion.
one effect of this was that the declination used in the filter was not
the one calculated from the magnetic field states.
Signed-off-by: Roman <bapstroman@gmail.com>
The handling of invalid flow data when on ground is performed in controlOpticalFlowFusion() where it is able to handle flow sensors that don't publish gyro data.
Heading data is assumed to be from a dual antenna array at a specified yaw angle offset in body frame, but with the heading data already corrected for antenna offset. The offset is required to apply the correct compensation for combined rotations and to determine when the yaw observation has become badly conditioned.
The parameter used to control the maximum dead reckoning time had 'gps' in the parameter name which was confusing because it was used for all measurement types capable of constraining horizontal velocity error growth. The parameter variable has been renamed and the documentation for it improved.
The parameter used to control the maximum time since fusing a measurement before the measurement is considered to be not contributing to aiding had misleading documentation which has been updated.
* terrain_estimator : guard against case where latest range sample is newer than IMU sample
* EKF : control : correct detection of no optical flow fusion over a time period
Motion compensated optical flow rates are supposed to be zeroed if reported flow quality is below the minimum threshold value when on ground.
The comments and logic have been amended to be consistent and make the design intent clearer.
Terrain validity is determined solely by successful range finder fusion and terrain state initialisation.
A range finder that has been declared faulty requires continuous range finder data fusion requires data to be continuous before the fault status _rng_hgt_faulty can be cleared. This will enforce the requirement for continuous data before fusion can commence.
Eliminate race condition caused by checking for data freshness using time stamps from buffer push instead than buffer pop events.
Consistent use of range data ready and range data fault flags. This achieved by ensuring that _rng_hgt_faulty is set to true for all range data faults, not just data freshness.
Include range data validity requirement in rangeAidConditionsMet() check.
* optical flow: fixed calculation of velocity of the flow sensor relative to
the IMU
- gyroXYZ holds a delta angle and first needs to converted to a gyro rate
Signed-off-by: Roman <bapstroman@gmail.com>
* optical flow: calculate height above the ground with respect to the flow
camera
- the flow camera can be offset from the IMU which needs to be considered
Signed-off-by: Roman <bapstroman@gmail.com>
* estimator interface: fixed comment regarding optical flow sample timestamp
- the timestamp on an optical flow sample corresponds to the trailing
edge of the flow integration period
Signed-off-by: Roman <bapstroman@gmail.com>
This brings all the range finder data checks (excluding innovation consistency checks) into one place and eliminates the need to perform range checking external to the library.
The hard coded optical flow tilt limit is changed to use the same value as the range finder.
Variable names are changed to make a clear distinction between the max/min values calculated by the stuck range check and the max/min valid values for the sensor.
* EKF angle constants in degrees for readability
* EKF make FILTER_UPDATE_PERIOD_MS static constexpr and add FILTER_UPDATE_PERIOD_S
* EKF controlOpticalFlowFusion() use constants and update comments
* EKF controlMagFusion() use angle in degrees
* EKF move earth spin rate to geo and update usage
* EKF: Fix numerical constant error and clean up comments
Comments do not need to contain numerical values when the code makes these clear.
* EKF collect_imu take const imu sample and populate buffer
* EKF calculateOutputStates cleanup
* EKF add calculate_quaternion output predictor method
* EKF: update documentation
* EKF: remove unnecessary getter function
* EKF calculateOutputStates only apply dt correction to bias
* EKF pytest assert attitude validity, not update() return
* EKF: correct documentation
* EKF: Do not make attitude validity dependent on yaw alignment status
Yaw alignment could fail in flight due to temporary loss of data and yet the quaternions would still usable for stabilisation even though the absolute earth yaw angle wrt true north was uncertain.
This fixes a error condition that occurs if _time_last_gps is greater than _time_last_imu.
By checking time stamps at the fusion time horizon, we guarantee that this cannot happen because all observations must have a time stamp smaller or equal to _imu_sample_delayed.time_us before they are retrieved from the buffers.
The gyro data accumulation needs to be across the same integration period as the flow sensor. The previous code didn't sample the accumulation until the midpoint of the flow data had fallen behind the fusion time horizon.
This PR changes the optical flow time stamp definition so that flow data is retrieved when the leading edge of the flow accumulation period falls behind the fusion time horizon. This enables the accumulated gyro data to be sampled at the correct time. Fusion is then delayed until the mid sample time has fallen behind the fusion time horizon.
High rate optical flow data could make flow fusion to run every major update cycle, resulting in the calculation of bias errors in the body rates used to compensate flow data failing time validity checks and not running. This resulted in a slow drift of the nav solution if bias errors were present in the in the gyro data used for flow sensor motion compensation.
kamilritz
bresch
RomanBapst
Martina Rivizzigno
Paul Riseborough
Jannik Beyerstedt
Matthias Grob
Julian Kent
Nico van Duijn
Andreas Antener
Jan Brehmer
CarlOlsson
Robert de Vries
Beat Küng
Mohammed Kabir
Nuno Marques
Daniel Agar
Freek van Tienen
Anna Dai
Paul Riseborough
Hamish Willee
jie.zhang
Dion Gonano
Kabir Mohammed
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