Fuse external vision data using a relative position odometry method when GPS data is also being used and enable both GPOS and EV data to be fused on the same time step.
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>
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.
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
The previous practice of relying on the off-diagonals being zero caused problems with conditioning of the magnetometer fusion on one flight. By storing the variances when the learning inhibit becomes active and ensuring that the rows and columns in the covariance matrix for the inhibited states are always zero, the observed numerical conditioning error has been eliminated for replay of the problem flight log .
Make the target EKF rate an integer multiple of the IMU rate. This slightly increases the average prediction time step for the EKF from just over 10msec to 12msec, but the variation reduces significantly which makes filter tuning more deterministic.
Improve the algorithm used to adjust the collection time criteria to reduce jitter in the correction.
This is a functionally equivalent. It moves all of the code for the terrain estimator into a single function call from the main filter update, making it clear that it is independent of the main filter.
Everywhere where KHP is used, it is first completely reset, thus making
it unnecessary to keep it as a class member.
This saves 2.3KB RAM.
Stack sizes don't need changing, since there is already a function
Ekf::predictCovariance(), which needs around 3KB of stack and is called
close to where the fuse* functions are called.
All the decision for a sensor are made within a specific function for that sensor and when there is data to process at the fusion time horizon.
Information and warning messages are improved.
Wait until enough height has been gained to be clear of ground based magnetic anomalies. Failure to do so can result in incorrect earth field initialisation.
Convert uncertainty in initial rotate vector into quaternion covariances using symbolic toolbox derived expressions.
Enable setting of initial angle uncertainty via a parameter
Convert quaternion covariances into an angular alignment variance vector and discard the z component so that yaw uncertainty does not affect the result.
Replace the delayed time feedback mechanism used by the translational states with a direct feedback method.
Time constants for velocity and position convergence can be separately adjusted with tunable parameters
The method is more computationally more expensive because it requires modification of the output buffer history but is acceptable because it only requires 6 FLOP per buffer index for a total of 30*6 = 180 FLOP
The method was not applied to the attitude states because the quaternion operations required at each buffer index would have been computationally prohibitive.
Combines the forced symmetry, variance limiting and zeroing of covariances for unwanted states in the one function.
This ensures a consistent correction is applied after every covariance prediction or correction.
With the EKF, the average update rate is more important than the instantaneous value as it affects tuning. This patch ensures that the EKF prediction cycle will be performed early if the previous one was late in an attempt to maintain the target update rate.
This method is more suitable than a raw heading measurement because it works across a full range of pitch angles.
It has been made the default for ground operation.
ChristophTobler
Paul Riseborough
Paul Riseborough
Daniel Agar
Roman
devbharat
CarlOlsson
Beat Küng
Youssef Demitri
Roman Bapst
waltjohnson
Lorenz Meier
mcsauder
Roman Bapst