Fixed compile errors due to new SharedGaussian definition
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82fdb0a5f8
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27d062a0f0
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@ -75,7 +75,7 @@ int main() {
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// the controls are driving the point to the right at 1 m/s. Then, F = [1 0 ; 0 1], B = [1 0 ; 0 1]
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// and u = [1 ; 0]. Let us also assume that the process noise Q = [0.1 0 ; 0 0.1].
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Vector u = Vector_(2, 1.0, 0.0);
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SharedGaussian Q = noiseModel::Diagonal::Sigmas(Vector_(2, 0.1, 0.1), true);
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SharedDiagonal Q = noiseModel::Diagonal::Sigmas(Vector_(2, 0.1, 0.1), true);
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// This simple motion can be modeled with a BetweenFactor
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// Create Keys
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@ -100,7 +100,7 @@ int main() {
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// For the purposes of this example, let us assume we have something like a GPS that returns
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// the current position of the robot. Then H = [1 0 ; 0 1]. Let us also assume that the measurement noise
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// R = [0.25 0 ; 0 0.25].
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SharedGaussian R = noiseModel::Diagonal::Sigmas(Vector_(2, 0.25, 0.25), true);
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SharedDiagonal R = noiseModel::Diagonal::Sigmas(Vector_(2, 0.25, 0.25), true);
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// This simple measurement can be modeled with a PriorFactor
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Point2 z1(1.0, 0.0);
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@ -64,7 +64,7 @@ typename ExtendedKalmanFilter<VALUES, KEY>::T ExtendedKalmanFilter<VALUES, KEY>:
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/* ************************************************************************* */
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template<class VALUES, class KEY>
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ExtendedKalmanFilter<VALUES, KEY>::ExtendedKalmanFilter(T x_initial, SharedGaussian P_initial) {
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ExtendedKalmanFilter<VALUES, KEY>::ExtendedKalmanFilter(T x_initial, noiseModel::Gaussian::shared_ptr P_initial) {
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// Set the initial linearization point to the provided mean
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x_ = x_initial;
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@ -60,7 +60,7 @@ protected:
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JacobianFactor::shared_ptr& newPrior) const;
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public:
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ExtendedKalmanFilter(T x_initial, SharedGaussian P_initial);
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ExtendedKalmanFilter(T x_initial, noiseModel::Gaussian::shared_ptr P_initial);
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T predict(const MotionFactor& motionFactor);
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T update(const MeasurementFactor& measurementFactor);
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@ -50,11 +50,11 @@ TEST( ExtendedKalmanFilter, linear ) {
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double dt = 1.0;
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Vector u = Vector_(2, 1.0, 0.0);
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Point2 difference(u*dt);
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SharedGaussian Q = noiseModel::Diagonal::Sigmas(Vector_(2, 0.1, 0.1), true);
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SharedDiagonal Q = noiseModel::Diagonal::Sigmas(Vector_(2, 0.1, 0.1), true);
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Point2 z1(1.0, 0.0);
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Point2 z2(2.0, 0.0);
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Point2 z3(3.0, 0.0);
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SharedGaussian R = noiseModel::Diagonal::Sigmas(Vector_(2, 0.25, 0.25), true);
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SharedDiagonal R = noiseModel::Diagonal::Sigmas(Vector_(2, 0.25, 0.25), true);
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// Create the set of expected output values
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Point2 expected1(1.0, 0.0);
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