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.
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 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.
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.
Remove duplicate checking for dead reckoning and consolidate into a single function.
Use separate timers to check for start of dead reckoning and check when dead reckoning has been performed for too long for the nav solution to be valid.
Allow the timeout for validity reporting to be adjusted externally.
Separate external reporting of dead reckoning from internal checks.
* EKF: Move optical flow specific state reset to helper functions
* EKF: Ensure loss of optical flow aiding is handled correctly
If data is only source of aiding and has been rejected for too long - reset using flow data as a velocity reference.
If flow data is unavailable for too long - declare optical flow use stopped.
Use consistent time periods for all resets
* EKF: Ensure loss of external vision aiding is handled correctly
If data is only source of aiding and has been rejected for too long - reset using data as a position.
Don't reset velocity if there is another source of aiding constraining it.
If data is unavailable for too long, declare external vision use stopped.
Use consistent time periods for all resets.
* EKF: Update parameter documentation
Make the distinction between the no_gps_timeout_max and no_aid_timeout_max parameters clearer
* EKF: make class variable units consistent with documentation
* EKF: Don't reset states when optical flow use commences if using external vision
* EKF: Stop optical flow fusion when on ground if excessive movement is detected.
* EKF: fix terrain estimator vulnerabilities
Reset estimate to sensor value if rejected for 10 seconds
Protect against user motion when on ground.
Fix unnecessary duplication of terrain validity check and separate validity update and reporting.
* EKF: remove unnecessary Info console prints
Optical flow use information can be obtained from the estimator_status.control_mode_flags message
* EKF: fix inaccurate comment
* EKF: remove duplicate calculation from terrain validity accessor function
This enables the EKF to use an additional NE velocity measurement. This can be used to improve position hold stability when landing using a beacon system for positioning by fusing the beacon velocity estimates.
Apply a dead-zone to the vertical position innovation if using baro for height and if in the ground effect region during and just after takeoff.
Method needs to be activated externally.
Turns off automatically after 10 seconds or if specified height gained.
This enables the initial uncertainty to be set based on application and also ensures that the max allowed growth in wind state variance is consistent with the initial uncertainty specified.
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.
Paul Riseborough
Dion Gonano
Daniel Agar
Mohammed Kabir
Paul Riseborough
Carl Olsson
ChristophTobler
Roman