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ekf2: refactor output predictor to class

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- refactor all EKF backend output predictor pieces into new OutputPredictor class
 - output states are now calculated immediately with new high rate IMU rather than after EKF update
 - IMU delayed sample is passed as around as control data to avoid storing an extra copy and make the requirement clear
This commit is contained in:
Daniel Agar
2023-01-18 10:59:34 -05:00
committed by GitHub
parent c054ca20cc
commit 95300d5637
39 changed files with 855 additions and 618 deletions
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src/modules/ekf2/EKF/gps_yaw_fusion.cpp
+3 -3
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@@ -137,8 +137,8 @@ void Ekf::fuseGpsYaw()
gnss_yaw.fused = is_fused;
if (is_fused) {
_time_last_heading_fuse = _imu_sample_delayed.time_us;
gnss_yaw.time_last_fuse = _imu_sample_delayed.time_us;
_time_last_heading_fuse = _time_delayed_us;
gnss_yaw.time_last_fuse = _time_delayed_us;
}
}
@@ -159,7 +159,7 @@ bool Ekf::resetYawToGps(const float gnss_yaw)
const float yaw_variance = sq(fmaxf(_params.gps_heading_noise, 1.e-2f));
resetQuatStateYaw(measured_yaw, yaw_variance);
_aid_src_gnss_yaw.time_last_fuse = _imu_sample_delayed.time_us;
_aid_src_gnss_yaw.time_last_fuse = _time_delayed_us;
_gnss_yaw_signed_test_ratio_lpf.reset(0.f);
return true;