- saves a small amount of work for the ekf2 selector in multi-EKF mode (visual_odometry_aligned now ignored)
- helps to distinguish the origin/purpose from vehicle_odometry and vehicle_visual_odometry
- ekf2 can now run in multi-instance mode (currently up to 9 instances)
- in multi mode all estimates are published to alternate topics (eg estimator_attitude instead of vehicle_attitude)
- new ekf2 selector runs in multi-instance mode to monitor and compare all instances, selecting a primary (eg N x estimator_attitude => vehicle_attitude)
- sensors module accel & gyro inconsistency checks are now relative to the mean of all instances, rather than the current primary (when active ekf2 selector is responsible for choosing primary accel & gyro)
- existing consumers of estimator_status must check estimator_selector_status to select current primary instance status
- ekf2 single instance mode is still fully supported and the default
Co-authored-by: Paul Riseborough <gncsolns@gmail.com>
- new sensors work item that subscribes to N x sensor_gps and publishes vehicle_gps_position
- blending is now configurable with SENS_GPS_MASK and SENS_GPS_TAU
Co-authored-by: Jacob Crabill <jacob@volans-i.com>
Co-authored-by: Jacob Dahl <dahl.jakejacob@gmail.com>
120 seconds of learning time in 3D fusion mode was too long for most normal flights. The learned bias is usually really good after a shorter period and was not used to update the parameters. 30s seems to be a good compromise.
Within ekf2, optical flow messages (amongst others) are fused to the state estimates. It might occur that optical flow sensors report unreliable and unrealistic spikes. In that case, the state estimator went crazy so far and just ignored optical flow values from that moment on.
The common thread for all these spikes seems to be a too high integration time span. Therefore, this fix adds a simple logic that ignores unrealistically high integration time spans. As a threshold, 1 second was chosen.
Reported-by: Dominik Natter <dominik.natter@gmail.com>
- avoids the need for ekf2_timestamp publications by q and lpe
- adds logger to the lockstep cycle and makes it poll on ekf2_timestamps
or vehicle_attitude. This avoids dropped samples (required for replay).
- IMU integration move from drivers (PX4Accelerometer/PX4Gyroscope) to sensors/vehicle_imu
- sensors: voted_sensors_update now consumes vehicle_imu
- delete sensor_accel_integrated, sensor_gyro_integrated
- merge sensor_accel_status/sensor_gyro_status into vehicle_imu_status
- sensors status output minor improvements (ordering, whitespace, show selected sensor device id and instance)
* msg: Add EKF-GSF yaw estimator logging data
* ecl: update to version with EKF-GSF yaw estimator
* ekf2: Add param control and logging for EKF-GSF yaw estimator
* logger: Add logging for EKF-GSF yaw esimtator
Daniel Agar