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use prior naming scheme for bias jacobians

release/4.3a0
Varun Agrawal 2022-05-22 16:39:06 -04:00
parent a17134dd6a
commit cb75d92bf6
1 changed files with 14 additions and 14 deletions
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28
gtsam/navigation/CombinedImuFactor.cpp
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@ -110,21 +110,21 @@ void PreintegratedCombinedMeasurements::integrateMeasurement(
// and preintegrated measurements // and preintegrated measurements
// Single Jacobians to propagate covariance // Single Jacobians to propagate covariance
Matrix3 theta_H_omega = C.topRows<3>(); Matrix3 theta_H_biasOmega = C.topRows<3>();
Matrix3 pos_H_acc = B.middleRows<3>(3); Matrix3 pos_H_biasAcc = B.middleRows<3>(3);
Matrix3 vel_H_acc = B.bottomRows<3>(); Matrix3 vel_H_biasAcc = B.bottomRows<3>();
Matrix3 theta_H_biasOmegaInit = -theta_H_omega; Matrix3 theta_H_biasOmegaInit = -theta_H_biasOmega;
Matrix3 pos_H_biasAccInit = -pos_H_acc; Matrix3 pos_H_biasAccInit = -pos_H_biasAcc;
Matrix3 vel_H_biasAccInit = -vel_H_acc; Matrix3 vel_H_biasAccInit = -vel_H_biasAcc;
// overall Jacobian wrt preintegrated measurements (df/dx) // overall Jacobian wrt preintegrated measurements (df/dx)
Eigen::Matrix<double, 15, 15> F; Eigen::Matrix<double, 15, 15> F;
F.setZero(); F.setZero();
F.block<9, 9>(0, 0) = A; F.block<9, 9>(0, 0) = A;
F.block<3, 3>(0, 12) = theta_H_omega; F.block<3, 3>(0, 12) = theta_H_biasOmega;
F.block<3, 3>(3, 9) = pos_H_acc; F.block<3, 3>(3, 9) = pos_H_biasAcc;
F.block<3, 3>(6, 9) = vel_H_acc; F.block<3, 3>(6, 9) = vel_H_biasAcc;
F.block<6, 6>(9, 9) = I_6x6; F.block<6, 6>(9, 9) = I_6x6;
// Update the uncertainty on the state (matrix F in [4]). // Update the uncertainty on the state (matrix F in [4]).
@ -149,17 +149,17 @@ void PreintegratedCombinedMeasurements::integrateMeasurement(
// BLOCK DIAGONAL TERMS // BLOCK DIAGONAL TERMS
D_R_R(&G_measCov_Gt) = D_R_R(&G_measCov_Gt) =
(theta_H_omega * (wCov / dt) * theta_H_omega.transpose()) // (theta_H_biasOmega * (wCov / dt) * theta_H_biasOmega.transpose()) //
+ +
(theta_H_biasOmegaInit * bInitCov22 * theta_H_biasOmegaInit.transpose()); (theta_H_biasOmegaInit * bInitCov22 * theta_H_biasOmegaInit.transpose());
D_t_t(&G_measCov_Gt) = D_t_t(&G_measCov_Gt) =
(pos_H_acc * (aCov / dt) * pos_H_acc.transpose()) // (pos_H_biasAcc * (aCov / dt) * pos_H_biasAcc.transpose()) //
+ (pos_H_biasAccInit * bInitCov11 * pos_H_biasAccInit.transpose()) // + (pos_H_biasAccInit * bInitCov11 * pos_H_biasAccInit.transpose()) //
+ (dt * iCov); + (dt * iCov);
D_v_v(&G_measCov_Gt) = D_v_v(&G_measCov_Gt) =
(vel_H_acc * (aCov / dt) * vel_H_acc.transpose()) // (vel_H_biasAcc * (aCov / dt) * vel_H_biasAcc.transpose()) //
+ (vel_H_biasAccInit * bInitCov11 * vel_H_biasAccInit.transpose()); + (vel_H_biasAccInit * bInitCov11 * vel_H_biasAccInit.transpose());
D_a_a(&G_measCov_Gt) = dt * p().biasAccCovariance; D_a_a(&G_measCov_Gt) = dt * p().biasAccCovariance;
@ -173,12 +173,12 @@ void PreintegratedCombinedMeasurements::integrateMeasurement(
D_t_R(&G_measCov_Gt) = D_t_R(&G_measCov_Gt) =
pos_H_biasAccInit * bInitCov12 * theta_H_biasOmegaInit.transpose(); pos_H_biasAccInit * bInitCov12 * theta_H_biasOmegaInit.transpose();
D_t_v(&G_measCov_Gt) = D_t_v(&G_measCov_Gt) =
(pos_H_acc * (aCov / dt) * vel_H_acc.transpose()) + (pos_H_biasAcc * (aCov / dt) * vel_H_biasAcc.transpose()) +
(pos_H_biasAccInit * bInitCov11 * vel_H_biasAccInit.transpose()); (pos_H_biasAccInit * bInitCov11 * vel_H_biasAccInit.transpose());
D_v_R(&G_measCov_Gt) = D_v_R(&G_measCov_Gt) =
vel_H_biasAccInit * bInitCov12 * theta_H_biasOmegaInit.transpose(); vel_H_biasAccInit * bInitCov12 * theta_H_biasOmegaInit.transpose();
D_v_t(&G_measCov_Gt) = D_v_t(&G_measCov_Gt) =
(vel_H_acc * (aCov / dt) * pos_H_acc.transpose()) + (vel_H_biasAcc * (aCov / dt) * pos_H_biasAcc.transpose()) +
(vel_H_biasAccInit * bInitCov11 * pos_H_biasAccInit.transpose()); (vel_H_biasAccInit * bInitCov11 * pos_H_biasAccInit.transpose());
preintMeasCov_.noalias() += G_measCov_Gt; preintMeasCov_.noalias() += G_measCov_Gt;