When performing the initial in-flight mag yaw reset for RW vehicle, do not reset the quaternion states and corresponding variances unless there has been a change in yaw angle large enough to cause problems with navigation.
This is because the state estimates after a reset are more vulnerable to transient sensor errors, so a reset should be avoided if possible.
When performing the initial in-flight magnetic field reset for fixed wing vehicles, resetting the quaternion states and their corresponding covariances should be avoided unless yaw errors are large, because state estimates are vulnerable to transient sensor errors immediately following a reset.
Ensures that a complete reset of velocity and position states will always be performed if yaw has had to be reset using GPS velocity.
Ensures that the yaw_align status cannot be set to false once the filter has aligned.
Reduces susceptibility to incorrect estimation of acceleration bias during sustained yaw rate.
Requires an increase in RAM allocation of 837 Bytes to allow for the longer IMU and output predictor buffers that can be created.
Use of air data to navigate should be classified as dead-reckoning because neither ground relative velocity or position is observed directly and errors grow faster.
* Unfortunately, due to the SWIG dependency, we need sudo to install on
Travis (conflicts when adding with debian-sid source prevent using addons)
which means we cannot use the container-based infrastructure anymore.
* Building the Python bindings requires g++5 (at least with -Werr set).
* When building the Python bindings on Travis, the numpy includes are not found
by cmake, so they have to be added separately by running a Python process with
`numpy.get_include()`
* The build script now (somewhat clumsily) depends on the RUN_PYTEST environment
variable. If it is set to anything other than "", it will make the tests and
run tests and benchmarks
* Add requirements.txt file with required Python packages
* Read requirements.txt from CMakeLists.txt to check dependencies and alert the
user if necessary.
* Add SWIG interface definition (and external numpy interface) to ecl classes
* Add section in CMakeLists.txt to build Python bindings and execute
Python-based tests
* Write (property-based) tests that show the basic functionality of the Python
bindings and the EKF (using pytest and hypothesis libraries)
* Write minimal benchmark for the EKF update (using benchmark plugin for pytest)
* Add plotting utilities to analyze tests
* Add lint script to keep the Python scripts clean
* This is a sane choice (and should arguably always be done for classes with
virtual methods to avoid undefined behavior)
* It is required for wrapping the EstimatorInterface with SWIG (without virtual
destructor, deriving from the EstimatorInterface leads to
-Werror=delete-non-virtual-dtor).
When starting aiding using EV only and commencing GPS aiding later, this change means that the GPS origin is set to the local position 0,0 point rather than the current vehicle position. This avoids large changes in local position when GPs aiding starts.
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.
Moves calculation only required for mag heading fusion into the if (_control_status.flags.mag_hdg) branch
When using EV yaw, the observed yaw angle is calculated directly from the EV quaternions using derived expressions from references in code comments.
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.
- when switching to range finder use the current terrain estimate as
height sensor offset, otherwise spikes in the range measurements could lead
to a wrong offset
Signed-off-by: Roman <bapstroman@gmail.com>
- do not subtract the height sensor offset variable when computing the
baro offset from the local origin. The baro height offset is calculated
when baro is not fused and so the height sensor offset used in that case
is associated to another sensor and has nothing to do with the baro.
Signed-off-by: Roman <bapstroman@gmail.com>
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
Paul Riseborough
Paul Riseborough
ChristophTobler
CarlOlsson
Daniel Agar
ChristophTobler
Peter Dürr
Isaac
Roman
Julien Lecoeur