in air bias estimation is usually really accurate and should be weighted
more heavily compared to the calibration parameters that are often
more approximate given the worse magnetic environment near the ground.
- since last_us is set to 0 every time the bias is not observable, the
total time was also reset -> needed 30 consecutive seconds in mag 3D
to be declared "stable"
- after landing, the mag_aligned_in_flight flag is reset. Using this for
bias validity makes it invalid before we have a chance to save it to
the calibration.
- Do not pull in PWM parameters when DISABLE_PARAMS_MODULE_SCOPING is TRUE since VOXL2 has no PWM nor any of the required timer_config files that go along with that
- Replace non-standard M_PI constants with PX4 defined M_PI_F constants
- Include missing header file for function hrt_absolute_time declaration
- Add new PX4_SOC_ARCH_ID for the VOXL2 board
Instead of always starting with instance 0 (potentially an internal
mag), first take the current sensor as reference to compare the other
ones against it.
The issue is that otherwise we can end up in a
situation where a switch occurs because the currently used sensor isn't
much better than a sensor with a lower index: because the selected one
isn't much better, we cannot "fail-over" to it and get stuck on another
sensor, triggering a fail-over.
- WelfordMeanVector now computes covariance
- use Kahan summation for Welford mean (but continue using float32 for actual mean, etc)
- WelfordMean and WelfordMeanVector handle initial value and count roll over
- Welford mean count rollover at 16 bit max to prevent numerical issues and shift weight to newer samples
- sensors/vehicle_imu: update Welford mean usage (now simplified with resets removed)
- fix vehicle_imu_status accel var, now properly rotated with full covariance matrix
- gyro_calibration: update Welford mean usage
- vehicle_angular_velocity and vehicle_angular_acceleration are produced together from the same input data, consumed together, and share the the same metadata (timestamps)
- individually these topics each have 16 bytes of metadata (2 timestamps) for 12 bytes of data (x,y,z float32)
- preflight mag bias estimate is in the vehicle body frame and removed from the raw mag data after it's rotated, calibrated, etc
- in flight mag bias (from ekf2) is in the vehicle body frame, but stored as a sensor frame offset immediately
- vehicle_angular_velocity: ESC RPM notch filters minimize filter resets
- only allow one filter init per axis per cycle
- "park" ESC notch filters at min frequency instead of full disable
- relax timeout before a notch filter is disabled
- add new parameter IMU_GYRO_DNF_MIN for configuring the minimum notch filter frequency
- in the worst case scenario printing the error message can take longer than the next gyro publication, so combined with an infinite loop you could get stuck here
- vehicle_imu_status can publish immediately on any measured sample
rate change or sensor error increment, but the windowed mean/variance
shouldn't necessarily reset that often
- much simpler direct interface using Ignition Transport
- in tree models and worlds
- control allocation output configuration, no more magic actuator mapping to mavlink and back
- currently requires no custom Gazebo plugins (keeping things as lightweight and simple as possible)
Co-authored-by: Jaeyoung-Lim <jalim@ethz.ch>
- if using multi-EKF across all magnetometers then an instance of
vehicle_magnetometer is advertised immediately for every sensor_mag
instance
- this can become problematic if EKF2 multi-mag is enabled, but with
only 1 IMU (EKF2_MULTI_MAG) because you will be stuck with no magnetometer data
- all sources of optical flow publish sensor_optical_flow
- sensor_optical_flow is aggregated by the sensors module, aligned with integrated gyro, and published as vehicle_optical_flow
Co-authored-by: alexklimaj <alex@arkelectron.com>
Sergei Grichine