From 27d062a0f0fa3787d305f2a8669c0ef661f55065 Mon Sep 17 00:00:00 2001 From: Stephen Williams Date: Sat, 27 Aug 2011 13:50:35 +0000 Subject: [PATCH] Fixed compile errors due to new SharedGaussian definition --- examples/easyPoint2KalmanFilter.cpp | 4 ++-- gtsam/nonlinear/ExtendedKalmanFilter-inl.h | 2 +- gtsam/nonlinear/ExtendedKalmanFilter.h | 2 +- tests/testExtendedKalmanFilter.cpp | 4 ++-- 4 files changed, 6 insertions(+), 6 deletions(-) diff --git a/examples/easyPoint2KalmanFilter.cpp b/examples/easyPoint2KalmanFilter.cpp index 98de6094e..2dbd3da1b 100644 --- a/examples/easyPoint2KalmanFilter.cpp +++ b/examples/easyPoint2KalmanFilter.cpp @@ -75,7 +75,7 @@ int main() { // the controls are driving the point to the right at 1 m/s. Then, F = [1 0 ; 0 1], B = [1 0 ; 0 1] // and u = [1 ; 0]. Let us also assume that the process noise Q = [0.1 0 ; 0 0.1]. Vector u = Vector_(2, 1.0, 0.0); - SharedGaussian Q = noiseModel::Diagonal::Sigmas(Vector_(2, 0.1, 0.1), true); + SharedDiagonal Q = noiseModel::Diagonal::Sigmas(Vector_(2, 0.1, 0.1), true); // This simple motion can be modeled with a BetweenFactor // Create Keys @@ -100,7 +100,7 @@ int main() { // For the purposes of this example, let us assume we have something like a GPS that returns // the current position of the robot. Then H = [1 0 ; 0 1]. Let us also assume that the measurement noise // R = [0.25 0 ; 0 0.25]. - SharedGaussian R = noiseModel::Diagonal::Sigmas(Vector_(2, 0.25, 0.25), true); + SharedDiagonal R = noiseModel::Diagonal::Sigmas(Vector_(2, 0.25, 0.25), true); // This simple measurement can be modeled with a PriorFactor Point2 z1(1.0, 0.0); diff --git a/gtsam/nonlinear/ExtendedKalmanFilter-inl.h b/gtsam/nonlinear/ExtendedKalmanFilter-inl.h index aa2c9b507..cbb7a0af7 100644 --- a/gtsam/nonlinear/ExtendedKalmanFilter-inl.h +++ b/gtsam/nonlinear/ExtendedKalmanFilter-inl.h @@ -64,7 +64,7 @@ typename ExtendedKalmanFilter::T ExtendedKalmanFilter: /* ************************************************************************* */ template -ExtendedKalmanFilter::ExtendedKalmanFilter(T x_initial, SharedGaussian P_initial) { +ExtendedKalmanFilter::ExtendedKalmanFilter(T x_initial, noiseModel::Gaussian::shared_ptr P_initial) { // Set the initial linearization point to the provided mean x_ = x_initial; diff --git a/gtsam/nonlinear/ExtendedKalmanFilter.h b/gtsam/nonlinear/ExtendedKalmanFilter.h index b08b66233..494cd6d73 100644 --- a/gtsam/nonlinear/ExtendedKalmanFilter.h +++ b/gtsam/nonlinear/ExtendedKalmanFilter.h @@ -60,7 +60,7 @@ protected: JacobianFactor::shared_ptr& newPrior) const; public: - ExtendedKalmanFilter(T x_initial, SharedGaussian P_initial); + ExtendedKalmanFilter(T x_initial, noiseModel::Gaussian::shared_ptr P_initial); T predict(const MotionFactor& motionFactor); T update(const MeasurementFactor& measurementFactor); diff --git a/tests/testExtendedKalmanFilter.cpp b/tests/testExtendedKalmanFilter.cpp index 0f8666bff..f36ad8fe8 100644 --- a/tests/testExtendedKalmanFilter.cpp +++ b/tests/testExtendedKalmanFilter.cpp @@ -50,11 +50,11 @@ TEST( ExtendedKalmanFilter, linear ) { double dt = 1.0; Vector u = Vector_(2, 1.0, 0.0); Point2 difference(u*dt); - SharedGaussian Q = noiseModel::Diagonal::Sigmas(Vector_(2, 0.1, 0.1), true); + SharedDiagonal Q = noiseModel::Diagonal::Sigmas(Vector_(2, 0.1, 0.1), true); Point2 z1(1.0, 0.0); Point2 z2(2.0, 0.0); Point2 z3(3.0, 0.0); - SharedGaussian R = noiseModel::Diagonal::Sigmas(Vector_(2, 0.25, 0.25), true); + SharedDiagonal R = noiseModel::Diagonal::Sigmas(Vector_(2, 0.25, 0.25), true); // Create the set of expected output values Point2 expected1(1.0, 0.0);