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ekf2: covariance update use IMU sample dt (#22032)

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- usually the delta angle and delta velocity dt is the same, but they can be slightly different
This commit is contained in:
Daniel Agar
2023-09-13 09:58:56 -04:00
committed by GitHub
parent 9bcfd1a7f7
commit 016db84d69
5 changed files with 672 additions and 663 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/modules/ekf2/EKF/covariance.cpp
+10 -8
View File
@@ -98,15 +98,14 @@ void Ekf::predictCovariance(const imuSample &imu_delayed)
{
// Use average update interval to reduce accumulated covariance prediction errors due to small single frame dt values
const float dt = _dt_ekf_avg;
const float dt_inv = 1.f / dt;
// inhibit learning of imu accel bias if the manoeuvre levels are too high to protect against the effect of sensor nonlinearities or bad accel data is detected
// xy accel bias learning is also disabled on ground as those states are poorly observable when perpendicular to the gravity vector
const float alpha = math::constrain((dt / _params.acc_bias_learn_tc), 0.0f, 1.0f);
const float beta = 1.0f - alpha;
_ang_rate_magnitude_filt = fmaxf(dt_inv * imu_delayed.delta_ang.norm(), beta * _ang_rate_magnitude_filt);
_accel_magnitude_filt = fmaxf(dt_inv * imu_delayed.delta_vel.norm(), beta * _accel_magnitude_filt);
_accel_vec_filt = alpha * dt_inv * imu_delayed.delta_vel + beta * _accel_vec_filt;
_ang_rate_magnitude_filt = fmaxf(imu_delayed.delta_ang.norm() / imu_delayed.delta_ang_dt, beta * _ang_rate_magnitude_filt);
_accel_magnitude_filt = fmaxf(imu_delayed.delta_vel.norm() / imu_delayed.delta_vel_dt, beta * _accel_magnitude_filt);
_accel_vec_filt = alpha * imu_delayed.delta_vel / imu_delayed.delta_vel_dt + beta * _accel_vec_filt;
const bool is_manoeuvre_level_high = _ang_rate_magnitude_filt > _params.acc_bias_learn_gyr_lim
|| _accel_magnitude_filt > _params.acc_bias_learn_acc_lim;
@@ -216,7 +215,7 @@ void Ekf::predictCovariance(const imuSample &imu_delayed)
// assign IMU noise variances
// inputs to the system are 3 delta angles and 3 delta velocities
float gyro_noise = math::constrain(_params.gyro_noise, 0.0f, 1.0f);
const float d_ang_var = sq(dt * gyro_noise);
const float d_ang_var = sq(imu_delayed.delta_ang_dt * gyro_noise);
float accel_noise = math::constrain(_params.accel_noise, 0.0f, 1.0f);
@@ -225,10 +224,10 @@ void Ekf::predictCovariance(const imuSample &imu_delayed)
for (int i = 0; i <= 2; i++) {
if (_fault_status.flags.bad_acc_vertical || imu_delayed.delta_vel_clipping[i]) {
// Increase accelerometer process noise if bad accel data is detected
d_vel_var(i) = sq(dt * BADACC_BIAS_PNOISE);
d_vel_var(i) = sq(imu_delayed.delta_vel_dt * BADACC_BIAS_PNOISE);
} else {
d_vel_var(i) = sq(dt * accel_noise);
d_vel_var(i) = sq(imu_delayed.delta_vel_dt * accel_noise);
}
}
@@ -236,7 +235,10 @@ void Ekf::predictCovariance(const imuSample &imu_delayed)
SquareMatrix24f nextP;
// calculate variances and upper diagonal covariances for quaternion, velocity, position and gyro bias states
sym::PredictCovariance(getStateAtFusionHorizonAsVector(), P, imu_delayed.delta_vel, d_vel_var, imu_delayed.delta_ang, d_ang_var, dt, &nextP);
sym::PredictCovariance(getStateAtFusionHorizonAsVector(), P,
imu_delayed.delta_vel, imu_delayed.delta_vel_dt, d_vel_var,
imu_delayed.delta_ang, imu_delayed.delta_ang_dt, d_ang_var,
&nextP);
// compute noise variance for stationary processes
Vector24f process_noise;
src/modules/ekf2/EKF/python/ekf_derivation/derivation.py
+8 -7
View File
@@ -1,4 +1,4 @@
#!/usr/bin/env python
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Copyright (c) 2022 PX4 Development Team
@@ -82,19 +82,20 @@ def predict_covariance(
state: VState,
P: MState,
d_vel: sf.V3,
d_vel_dt: sf.Scalar,
d_vel_var: sf.V3,
d_ang: sf.V3,
d_ang_var: sf.Scalar,
dt: sf.Scalar
d_ang_dt: sf.Scalar,
d_ang_var: sf.Scalar
):
g = sf.Symbol("g") # does not appear in the jacobians
accel_b = sf.V3(state[State.accel_bx], state[State.accel_by], state[State.accel_bz])
d_vel_b = accel_b * dt
d_vel_b = accel_b * d_vel_dt
d_vel_true = d_vel - d_vel_b
gyro_b = sf.V3(state[State.gyro_bx], state[State.gyro_by], state[State.gyro_bz])
d_ang_b = gyro_b * dt
d_ang_b = gyro_b * d_ang_dt
d_ang_true = d_ang - d_ang_b
q = state_to_quat(state)
@@ -103,8 +104,8 @@ def predict_covariance(
p = sf.V3(state[State.px], state[State.py], state[State.pz])
q_new = q * sf.Quaternion(sf.V3(0.5 * d_ang_true[0], 0.5 * d_ang_true[1], 0.5 * d_ang_true[2]), 1)
v_new = v + R_to_earth * d_vel_true + sf.V3(0, 0, g) * dt
p_new = p + v * dt
v_new = v + R_to_earth * d_vel_true + sf.V3(0, 0, g) * d_vel_dt
p_new = p + v * d_vel_dt
# Predicted state vector at time t + dt
state_new = VState.block_matrix([[sf.V4(q_new.w, q_new.x, q_new.y, q_new.z)], [v_new], [p_new], [sf.Matrix(state[State.gyro_bx:State.n_states])]])
src/modules/ekf2/EKF/python/ekf_derivation/generated/predict_covariance.h
+643 -637
View File
File diff suppressed because it is too large Load Diff
src/modules/ekf2/test/change_indication/ekf_gsf_reset.csv
+7 -7
View File
@@ -215,9 +215,9 @@ Timestamp,state[0],state[1],state[2],state[3],state[4],state[5],state[6],state[7
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@@ -242,7 +242,7 @@ Timestamp,state[0],state[1],state[2],state[3],state[4],state[5],state[6],state[7
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@@ -290,7 +290,7 @@ Timestamp,state[0],state[1],state[2],state[3],state[4],state[5],state[6],state[7
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@@ -322,7 +322,7 @@ Timestamp,state[0],state[1],state[2],state[3],state[4],state[5],state[6],state[7
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1 Timestamp state[0] state[1] state[2] state[3] state[4] state[5] state[6] state[7] state[8] state[9] state[10] state[11] state[12] state[13] state[14] state[15] state[16] state[17] state[18] state[19] state[20] state[21] state[22] state[23] variance[0] variance[1] variance[2] variance[3] variance[4] variance[5] variance[6] variance[7] variance[8] variance[9] variance[10] variance[11] variance[12] variance[13] variance[14] variance[15] variance[16] variance[17] variance[18] variance[19] variance[20] variance[21] variance[22] variance[23]
215 21290000 0.71 0.00047 -0.012 0.71 -0.004 -0.018 0.016 0.0034 -0.0055 -3.7e+02 -0.0014 -0.006 4.1e-05 -0.00073 0.007 -0.13 0.37 0.0037 0.025 0 0 0 0 0 0.0024 9.9e-05 9.8e-05 0.0023 0.024 0.024 0.0086 0.048 0.048 0.039 1.5e-06 1.5e-06 3.1e-06 0.029 0.029 0.0005 0 0 0 0 0 0 0 0
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340 33790000 -0.067 -0.0044 -0.0073 1 -1.1 -0.72 0.78 -16 -7.5 -3.7e+02 -0.0015 -0.0057 -8.9e-05 -0.019 0.0056 -0.092 0.37 0.0037 0.025 0 0 0 0 0 0.0011 7.3e-05 8e-05 2e-05 0.028 0.034 0.0074 0.36 0.4 0.036 2.7e-07 2.8e-07 3e-06 0.027 0.025 0.0005 0.0025 8.3e-05 0.0025 0.0025 0.0025 0.0025 0 0
src/modules/ekf2/test/change_indication/iris_gps.csv
+4 -4
View File
@@ -214,7 +214,7 @@ Timestamp,state[0],state[1],state[2],state[3],state[4],state[5],state[6],state[7
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1 Timestamp state[0] state[1] state[2] state[3] state[4] state[5] state[6] state[7] state[8] state[9] state[10] state[11] state[12] state[13] state[14] state[15] state[16] state[17] state[18] state[19] state[20] state[21] state[22] state[23] variance[0] variance[1] variance[2] variance[3] variance[4] variance[5] variance[6] variance[7] variance[8] variance[9] variance[10] variance[11] variance[12] variance[13] variance[14] variance[15] variance[16] variance[17] variance[18] variance[19] variance[20] variance[21] variance[22] variance[23]
214 21190000 0.98 0.0017 -0.0059 0.19 -0.049 0.05 -0.5 -0.0013 0.0035 -0.078 -0.0014 -0.0059 8.3e-06 0.015 0.0012 -0.13 0.37 0.0037 0.025 0 0 0 0 0 0.00017 9.6e-05 9.4e-05 0.0045 0.023 0.023 0.0083 0.043 0.043 0.036 1.6e-06 1.6e-06 3.1e-06 0.029 0.029 0.0005 0 0 0 0 0 0 0 0
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218 21590000 0.98 -0.0033 -0.0083 0.19 -0.032 0.043 -1 -0.008 0.016 -0.38 -0.0014 -0.0059 8.2e-06 0.015 0.00063 -0.13 0.37 0.0037 0.025 0 0 0 0 0 0.00017 9.2e-05 9.1e-05 0.0045 0.023 0.023 0.0082 0.043 0.043 0.036 1.3e-06 1.3e-06 3.1e-06 0.029 0.029 0.0005 0 0 0 0 0 0 0 0
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