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.
Use vertical velocity and position innovation failure to detect bad accelerometer data caused by clipping or aliasing which can cause large vertical acceleration errors and loss of height estimation. When bad accel data is detected:
1) Inhibit accelerometer bias learning
2) Force fusion of vertical velocity and height data
3) Increase accelerometer process noise
This was incorrectly using the IMU (1/250 sec) timestamp instead of the EKF (1/100 sec) value.
The corresponding accelerometer limit has been made a parameter and adjusted to match previous behaviour.
Paul Riseborough
Daniel Agar
Paul Riseborough
Carl Olsson
ChristophTobler
Roman