forked from Archive/PX4-Autopilot
ekf2: delete orientation_covariances_euler()
- usage replaced with now public calcRotVecVariances()
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@ -204,8 +204,6 @@ public:
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// get the orientation (quaterion) covariances
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matrix::SquareMatrix<float, 4> orientation_covariances() const { return P.slice<4, 4>(0, 0); }
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matrix::SquareMatrix<float, 3> orientation_covariances_euler() const;
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// get the linear velocity covariances
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matrix::SquareMatrix<float, 3> velocity_covariances() const { return P.slice<3, 3>(4, 4); }
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@ -391,6 +389,9 @@ public:
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// use the latest IMU data at the current time horizon.
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Quatf calculate_quaternion() const;
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// rotate quaternion covariances into variances for an equivalent rotation vector
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Vector3f calcRotVecVariances() const;
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// set minimum continuous period without GPS fail required to mark a healthy GPS status
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void set_min_required_gps_health_time(uint32_t time_us) { _min_gps_health_time_us = time_us; }
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@ -967,9 +968,6 @@ private:
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// calculate the measurement variance for the optical flow sensor
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float calcOptFlowMeasVar(const flowSample &flow_sample);
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// rotate quaternion covariances into variances for an equivalent rotation vector
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Vector3f calcRotVecVariances();
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// initialise the quaternion covariances using rotation vector variances
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// do not call before quaternion states are initialised
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void initialiseQuatCovariances(Vector3f &rot_vec_var);
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@ -966,7 +966,7 @@ void Ekf::updateVerticalDeadReckoningStatus()
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}
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// calculate the variances for the rotation vector equivalent
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Vector3f Ekf::calcRotVecVariances()
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Vector3f Ekf::calcRotVecVariances() const
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{
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Vector3f rot_var_vec;
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float q0, q1, q2, q3;
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@ -1521,60 +1521,3 @@ void Ekf::resetGpsDriftCheckFilters()
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_gps_vertical_position_drift_rate_m_s = NAN;
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_gps_filtered_horizontal_velocity_m_s = NAN;
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}
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matrix::SquareMatrix<float, 3> Ekf::orientation_covariances_euler() const
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{
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// Jacobian matrix (3x4) containing the partial derivatives of the
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// Euler angle equations with respect to the quaternions
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matrix::Matrix<float, 3, 4> G;
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// quaternion components
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float q1 = _state.quat_nominal(0);
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float q2 = _state.quat_nominal(1);
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float q3 = _state.quat_nominal(2);
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float q4 = _state.quat_nominal(3);
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// numerator components
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float n1 = 2 * q1 * q2 + 2 * q2 * q4;
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float n2 = -2 * q2 * q2 - 2 * q3 * q3 + 1;
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float n3 = 2 * q1 * q4 + 2 * q2 * q3;
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float n4 = -2 * q3 * q3 - 2 * q4 * q4 + 1;
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float n5 = 2 * q1 * q3 + 2 * q2 * q4;
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float n6 = -2 * q1 * q2 - 2 * q2 * q4;
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float n7 = -2 * q1 * q4 - 2 * q2 * q3;
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// Protect against division by 0
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float d1 = n1 * n1 + n2 * n2;
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float d2 = n3 * n3 + n4 * n4;
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if (fabsf(d1) < FLT_EPSILON) {
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d1 = FLT_EPSILON;
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}
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if (fabsf(d2) < FLT_EPSILON) {
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d2 = FLT_EPSILON;
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}
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// Protect against square root of negative numbers
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float x = math::max(-n5 * n5 + 1, 0.0f);
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// compute G matrix
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float sqrt_x = sqrtf(x);
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float g00_03 = 2 * q2 * n2 / d1;
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G(0, 0) = g00_03;
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G(0, 1) = -4 * q2 * n6 / d1 + (2 * q1 + 2 * q4) * n2 / d1;
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G(0, 2) = -4 * q3 * n6 / d1;
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G(0, 3) = g00_03;
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G(1, 0) = 2 * q3 / sqrt_x;
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G(1, 1) = 2 * q4 / sqrt_x;
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G(1, 2) = 2 * q1 / sqrt_x;
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G(1, 3) = 2 * q2 / sqrt_x;
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G(2, 0) = 2 * q4 * n4 / d2;
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G(2, 1) = 2 * q3 * n4 / d2;
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G(2, 2) = 2 * q2 * n4 / d2 - 4 * q3 * n7 / d2;
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G(2, 3) = 2 * q1 * n4 / d2 - 4 * q4 * n7 / d2;
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const matrix::SquareMatrix<float, 4> quat_covariances = P.slice<4, 4>(0, 0);
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return G * quat_covariances * G.transpose();
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}
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@ -1196,7 +1196,7 @@ void EKF2::PublishOdometry(const hrt_abstime ×tamp)
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_ekf.position_covariances().diag().copyTo(odom.position_variance);
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// orientation covariance
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_ekf.orientation_covariances_euler().diag().copyTo(odom.orientation_variance);
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_ekf.calcRotVecVariances().copyTo(odom.orientation_variance);
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odom.reset_counter = _ekf.get_quat_reset_count()
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+ _ekf.get_velNE_reset_count() + _ekf.get_velD_reset_count()
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