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EKF: test new derivation
Use direct attitude parameterisation Discard scale factors Add accel bias
This commit is contained in:
Paul Riseborough
+46
-41
@@ -108,11 +108,11 @@ void Ekf::resetHeight()
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_state.pos(2) = new_pos_down;
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// reset the associated covariance values
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zeroRows(P, 8, 8);
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zeroCols(P, 8, 8);
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zeroRows(P, 9, 9);
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zeroCols(P, 9, 9);
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// the state variance is the same as the observation
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P[8][8] = sq(_params.range_noise);
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P[9][9] = sq(_params.range_noise);
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vert_pos_reset = true;
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@@ -132,11 +132,11 @@ void Ekf::resetHeight()
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_state.pos(2) = _hgt_sensor_offset - baro_newest.hgt + _baro_hgt_offset;
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// reset the associated covariance values
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zeroRows(P, 8, 8);
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zeroCols(P, 8, 8);
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zeroRows(P, 9, 9);
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zeroCols(P, 9, 9);
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// the state variance is th esame as the observation
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P[8][8] = sq(_params.baro_noise);
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P[9][9] = sq(_params.baro_noise);
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vert_pos_reset = true;
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@@ -150,11 +150,11 @@ void Ekf::resetHeight()
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_state.pos(2) = _hgt_sensor_offset - gps_newest.hgt + _gps_alt_ref;
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// reset the associated covarince values
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zeroRows(P, 8, 8);
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zeroCols(P, 8, 8);
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zeroRows(P, 9, 9);
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zeroCols(P, 9, 9);
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// the state variance is the same as the observation
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P[8][8] = sq(gps_newest.hacc);
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P[9][9] = sq(gps_newest.hacc);
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vert_pos_reset = true;
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@@ -168,8 +168,8 @@ void Ekf::resetHeight()
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}
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// reset the vertical velocity covariance values
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zeroRows(P, 5, 5);
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zeroCols(P, 5, 5);
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zeroRows(P, 6, 6);
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zeroCols(P, 6, 6);
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// reset the vertical velocity state
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if (_control_status.flags.gps && (_time_last_imu - gps_newest.time_us < 2 * GPS_MAX_INTERVAL)) {
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@@ -177,7 +177,7 @@ void Ekf::resetHeight()
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_state.vel(2) = gps_newest.vel(2);
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// the state variance is the same as the observation
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P[5][5] = sq(1.5f * gps_newest.sacc);
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P[6][6] = sq(1.5f * gps_newest.sacc);
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} else {
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// we don't know what the vertical velocity is, so set it to zero
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@@ -185,7 +185,7 @@ void Ekf::resetHeight()
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// Set the variance to a value large enough to allow the state to converge quickly
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// that does not destabilise the filter
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P[5][5] = fminf(sq(_state.vel(2)),100.0f);
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P[6][6] = fminf(sq(_state.vel(2)),100.0f);
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}
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vert_vel_reset = true;
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@@ -248,11 +248,11 @@ bool Ekf::resetMagHeading(Vector3f &mag_init)
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// we don't change the output attitude to avoid jumps
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_state.quat_nominal = Quaternion(euler_init);
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// reset the angle error variances because the yaw angle could have changed by a significant amount
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// reset the quaternion variances because the yaw angle could have changed by a significant amount
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// by setting them to zero we avoid 'kicks' in angle when 3-D fusion starts and the imu process noise
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// will grow them again.
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zeroRows(P, 0, 2);
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zeroCols(P, 0, 2);
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zeroRows(P, 0, 3);
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zeroCols(P, 0, 3);
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// calculate initial earth magnetic field states
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matrix::Dcm<float> R_to_earth(euler_init);
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@@ -305,8 +305,8 @@ void Ekf::makeSymmetrical()
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void Ekf::constrainStates()
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{
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for (int i = 0; i < 3; i++) {
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_state.ang_error(i) = math::constrain(_state.ang_error(i), -1.0f, 1.0f);
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for (int i = 0; i < 4; i++) {
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_state.quat_nominal(i) = math::constrain(_state.quat_nominal(i), -1.0f, 1.0f);
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}
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for (int i = 0; i < 3; i++) {
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@@ -322,11 +322,9 @@ void Ekf::constrainStates()
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}
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for (int i = 0; i < 3; i++) {
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_state.gyro_scale(i) = math::constrain(_state.gyro_scale(i), 0.95f, 1.05f);
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_state.accel_bias(i) = math::constrain(_state.accel_bias(i), -1.0f * _dt_imu_avg, 1.0f * _dt_imu_avg);
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}
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_state.accel_z_bias = math::constrain(_state.accel_z_bias, -1.0f * _dt_imu_avg, 1.0f * _dt_imu_avg);
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for (int i = 0; i < 3; i++) {
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_state.mag_I(i) = math::constrain(_state.mag_I(i), -1.0f, 1.0f);
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}
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@@ -407,28 +405,26 @@ void Ekf::get_gps_check_status(uint16_t *val)
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// get the state vector at the delayed time horizon
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void Ekf::get_state_delayed(float *state)
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{
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for (int i = 0; i < 3; i++) {
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state[i] = _state.ang_error(i);
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for (int i = 0; i < 4; i++) {
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state[i] = _state.quat_nominal(i);
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}
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for (int i = 0; i < 3; i++) {
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state[i + 3] = _state.vel(i);
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state[i + 4] = _state.vel(i);
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}
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for (int i = 0; i < 3; i++) {
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state[i + 6] = _state.pos(i);
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state[i + 7] = _state.pos(i);
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}
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for (int i = 0; i < 3; i++) {
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state[i + 9] = _state.gyro_bias(i);
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state[i + 10] = _state.gyro_bias(i);
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}
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for (int i = 0; i < 3; i++) {
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state[i + 12] = _state.gyro_scale(i);
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state[i + 13] = _state.accel_bias(i);
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}
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state[15] = _state.accel_z_bias;
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for (int i = 0; i < 3; i++) {
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state[i + 16] = _state.mag_I(i);
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}
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@@ -442,6 +438,16 @@ void Ekf::get_state_delayed(float *state)
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}
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}
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// get the accelerometer bias
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void Ekf::get_accel_bias(float bias[3])
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{
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float temp[3];
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temp[0] = _state.accel_bias(0) /_dt_ekf_avg;
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temp[1] = _state.accel_bias(1) /_dt_ekf_avg;
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temp[2] = _state.accel_bias(2) /_dt_ekf_avg;
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memcpy(bias, temp, 3 * sizeof(float));
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}
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// get the diagonal elements of the covariance matrix
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void Ekf::get_covariances(float *covariances)
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{
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@@ -463,8 +469,8 @@ void Ekf::get_ekf_accuracy(float *ekf_eph, float *ekf_epv, bool *dead_reckoning)
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{
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// report absolute accuracy taking into account the uncertainty in location of the origin
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// TODO we a need a way to allow for baro drift error
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float temp1 = sqrtf(P[6][6] + P[7][7] + sq(_gps_origin_eph));
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float temp2 = sqrtf(P[8][8] + sq(_gps_origin_epv));
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float temp1 = sqrtf(P[7][7] + P[8][8] + sq(_gps_origin_eph));
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float temp2 = sqrtf(P[9][9] + sq(_gps_origin_epv));
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memcpy(ekf_eph, &temp1, sizeof(float));
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memcpy(ekf_epv, &temp2, sizeof(float));
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@@ -476,28 +482,27 @@ void Ekf::get_ekf_accuracy(float *ekf_eph, float *ekf_epv, bool *dead_reckoning)
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// fuse measurement
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void Ekf::fuse(float *K, float innovation)
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{
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for (unsigned i = 0; i < 4; i++) {
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_state.quat_nominal(i) = _state.quat_nominal(i) - K[i] * innovation;
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}
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_state.quat_nominal.normalize();
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for (unsigned i = 0; i < 3; i++) {
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_state.ang_error(i) = _state.ang_error(i) - K[i] * innovation;
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_state.vel(i) = _state.vel(i) - K[i + 4] * innovation;
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}
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for (unsigned i = 0; i < 3; i++) {
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_state.vel(i) = _state.vel(i) - K[i + 3] * innovation;
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_state.pos(i) = _state.pos(i) - K[i + 7] * innovation;
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}
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for (unsigned i = 0; i < 3; i++) {
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_state.pos(i) = _state.pos(i) - K[i + 6] * innovation;
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_state.gyro_bias(i) = _state.gyro_bias(i) - K[i + 10] * innovation;
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}
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for (unsigned i = 0; i < 3; i++) {
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_state.gyro_bias(i) = _state.gyro_bias(i) - K[i + 9] * innovation;
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_state.accel_bias(i) = _state.accel_bias(i) - K[i + 13] * innovation;
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}
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for (unsigned i = 0; i < 3; i++) {
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_state.gyro_scale(i) = _state.gyro_scale(i) - K[i + 12] * innovation;
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}
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_state.accel_z_bias -= K[15] * innovation;
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for (unsigned i = 0; i < 3; i++) {
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_state.mag_I(i) = _state.mag_I(i) - K[i + 16] * innovation;
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}
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