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Fixed compile errors due to new SharedGaussian definition

release/4.3a0
Stephen Williams 2011-08-27 13:50:35 +00:00
parent 82fdb0a5f8
commit 27d062a0f0
4 changed files with 6 additions and 6 deletions
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4
examples/easyPoint2KalmanFilter.cpp
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@ -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] // 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]. // 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); 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 // This simple motion can be modeled with a BetweenFactor
// Create Keys // 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 // 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 // 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]. // 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 // This simple measurement can be modeled with a PriorFactor
Point2 z1(1.0, 0.0); Point2 z1(1.0, 0.0);

2
gtsam/nonlinear/ExtendedKalmanFilter-inl.h
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@ -64,7 +64,7 @@ typename ExtendedKalmanFilter<VALUES, KEY>::T ExtendedKalmanFilter<VALUES, KEY>:
/* ************************************************************************* */ /* ************************************************************************* */
template<class VALUES, class KEY> template<class VALUES, class KEY>
ExtendedKalmanFilter<VALUES, KEY>::ExtendedKalmanFilter(T x_initial, SharedGaussian P_initial) { ExtendedKalmanFilter<VALUES, KEY>::ExtendedKalmanFilter(T x_initial, noiseModel::Gaussian::shared_ptr P_initial) {
// Set the initial linearization point to the provided mean // Set the initial linearization point to the provided mean
x_ = x_initial; x_ = x_initial;

2
gtsam/nonlinear/ExtendedKalmanFilter.h
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@ -60,7 +60,7 @@ protected:
JacobianFactor::shared_ptr& newPrior) const; JacobianFactor::shared_ptr& newPrior) const;
public: public:
ExtendedKalmanFilter(T x_initial, SharedGaussian P_initial); ExtendedKalmanFilter(T x_initial, noiseModel::Gaussian::shared_ptr P_initial);
T predict(const MotionFactor& motionFactor); T predict(const MotionFactor& motionFactor);
T update(const MeasurementFactor& measurementFactor); T update(const MeasurementFactor& measurementFactor);

4
tests/testExtendedKalmanFilter.cpp
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@ -50,11 +50,11 @@ TEST( ExtendedKalmanFilter, linear ) {
double dt = 1.0; double dt = 1.0;
Vector u = Vector_(2, 1.0, 0.0); Vector u = Vector_(2, 1.0, 0.0);
Point2 difference(u*dt); 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 z1(1.0, 0.0);
Point2 z2(2.0, 0.0); Point2 z2(2.0, 0.0);
Point2 z3(3.0, 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 // Create the set of expected output values
Point2 expected1(1.0, 0.0); Point2 expected1(1.0, 0.0);