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EKF: fixed some comment typos

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Signed-off-by: CarlOlsson <carlolsson.co@gmail.com>
This commit is contained in:
Carl Olsson
2019-03-18 10:20:33 -04:00
committed by Daniel Agar
parent 6e77b197c7
commit f0889c1760
27 changed files with 133 additions and 134 deletions
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EKF/covariance.cpp
+4 -4
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@@ -165,7 +165,7 @@ void Ekf::predictCovariance()
// convert rate of change of accelerometer bias (m/s**3) as specified by the parameter to an expected change in delta velocity (m/s) since the last update
float d_vel_bias_sig = dt * dt * math::constrain(_params.accel_bias_p_noise, 0.0f, 1.0f);
// inhibit learning of imu acccel bias if the manoeuvre levels are too high to protect against the effect of sensor nonlinearities or bad accel data is detected
// 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
float alpha = math::constrain((dt / _params.acc_bias_learn_tc), 0.0f, 1.0f);
float beta = 1.0f - alpha;
_ang_rate_mag_filt = fmaxf(dt_inv * _imu_sample_delayed.delta_ang.norm(), beta * _ang_rate_mag_filt);
@@ -744,10 +744,10 @@ void Ekf::fixCovarianceErrors()
P[i][i] = math::constrain(P[i][i], 0.0f, P_lim[3]);
}
// force symmetry on the quaternion, velocity and positon state covariances
// force symmetry on the quaternion, velocity and position state covariances
makeSymmetrical(P, 0, 12);
// the following states are optional and are deactivaed when not required
// the following states are optional and are deactivated when not required
// by ensuring the corresponding covariance matrix values are kept at zero
// accelerometer bias states
@@ -807,7 +807,7 @@ void Ekf::fixCovarianceErrors()
down_dvel_bias += _state.accel_bias(axis_index) * _R_to_earth(2, axis_index);
}
// check that the vertical componenent of accel bias is consistent with both the vertical position and velocity innovation
// check that the vertical component of accel bias is consistent with both the vertical position and velocity innovation
bool bad_acc_bias = (fabsf(down_dvel_bias) > dVel_bias_lim
&& down_dvel_bias * _vel_pos_innov[2] < 0.0f
&& down_dvel_bias * _vel_pos_innov[5] < 0.0f);