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.
CarlOlsson
ChristophTobler
Paul Riseborough
Paul Riseborough
ChristophTobler
Daniel Agar
Roman
devbharat
Beat Küng
Youssef Demitri