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Merged develop into feature/fixExpressionFactorKeys

release/4.3a0
Frank Dellaert 2016-04-16 10:36:48 -07:00
parent 8149bffcb7 bcf34f0fea
commit 2c05664731
162 changed files with 1120 additions and 1320 deletions
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33
gtsam.h
View File

@ -1798,9 +1798,6 @@ class Values {
void insert(size_t j, Vector t);
void insert(size_t j, Matrix t);
// Fixed size version
void insertFixed(size_t j, Vector t, size_t n);
void update(size_t j, const gtsam::Point2& t);
void update(size_t j, const gtsam::Point3& t);
void update(size_t j, const gtsam::Rot2& t);
@ -1818,12 +1815,6 @@ class Values {
template<T = {gtsam::Point2, gtsam::Point3, gtsam::Rot2, gtsam::Pose2, gtsam::Rot3, gtsam::Pose3, gtsam::Cal3_S2, gtsam::Cal3DS2, gtsam::Cal3Bundler, gtsam::EssentialMatrix, gtsam::imuBias::ConstantBias, Vector, Matrix}>
T at(size_t j);
/// Fixed size versions, for n in 1..9
void insertFixed(size_t j, Vector t, size_t n);
/// Fixed size versions, for n in 1..9
Vector atFixed(size_t j, size_t n);
/// version for double
void insertDouble(size_t j, double c);
double atDouble(size_t j) const;
@ -2489,10 +2480,30 @@ class NavState {
gtsam::Pose3 pose() const;
};
#include <gtsam/navigation/PreintegratedRotation.h>
virtual class PreintegratedRotationParams {
PreintegratedRotationParams();
void setGyroscopeCovariance(Matrix cov);
void setOmegaCoriolis(Vector omega);
void setBodyPSensor(const gtsam::Pose3& pose);
Matrix getGyroscopeCovariance() const;
// TODO(frank): allow optional
// boost::optional<Vector3> getOmegaCoriolis() const;
// boost::optional<Pose3> getBodyPSensor() const;
};
#include <gtsam/navigation/PreintegrationParams.h>
class PreintegrationParams {
virtual class PreintegrationParams : gtsam::PreintegratedRotationParams {
PreintegrationParams(Vector n_gravity);
// TODO(frank): add setters/getters or make this MATLAB wrapper feature
void setAccelerometerCovariance(Matrix cov);
void setIntegrationCovariance(Matrix cov);
void setUse2ndOrderCoriolis(bool flag);
Matrix getAccelerometerCovariance() const;
Matrix getIntegrationCovariance() const;
bool getUse2ndOrderCoriolis() const;
};
#include <gtsam/navigation/PreintegrationBase.h>

82
gtsam/base/Matrix.cpp
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@ -38,26 +38,6 @@ using namespace std;
namespace gtsam {
/* ************************************************************************* */
Matrix zeros( size_t m, size_t n ) {
return Matrix::Zero(m,n);
}
/* ************************************************************************* */
Matrix ones( size_t m, size_t n ) {
return Matrix::Ones(m,n);
}
/* ************************************************************************* */
Matrix eye( size_t m, size_t n) {
return Matrix::Identity(m, n);
}
/* ************************************************************************* */
Matrix diag(const Vector& v) {
return v.asDiagonal();
}
/* ************************************************************************* */
bool assert_equal(const Matrix& expected, const Matrix& actual, double tol) {
@ -146,16 +126,6 @@ bool linear_dependent(const Matrix& A, const Matrix& B, double tol) {
}
}
/* ************************************************************************* */
void multiplyAdd(double alpha, const Matrix& A, const Vector& x, Vector& e) {
e += alpha * A * x;
}
/* ************************************************************************* */
void multiplyAdd(const Matrix& A, const Vector& x, Vector& e) {
e += A * x;
}
/* ************************************************************************* */
Vector operator^(const Matrix& A, const Vector & v) {
if (A.rows()!=v.size()) throw std::invalid_argument(
@ -166,21 +136,6 @@ Vector operator^(const Matrix& A, const Vector & v) {
return A.transpose() * v;
}
/* ************************************************************************* */
void transposeMultiplyAdd(double alpha, const Matrix& A, const Vector& e, Vector& x) {
x += alpha * A.transpose() * e;
}
/* ************************************************************************* */
void transposeMultiplyAdd(const Matrix& A, const Vector& e, Vector& x) {
x += A.transpose() * e;
}
/* ************************************************************************* */
void transposeMultiplyAdd(double alpha, const Matrix& A, const Vector& e, SubVector x) {
x += alpha * A.transpose() * e;
}
/* ************************************************************************* */
//3 argument call
void print(const Matrix& A, const string &s, ostream& stream) {
@ -250,7 +205,7 @@ Matrix diag(const std::vector<Matrix>& Hs) {
rows+= Hs[i].rows();
cols+= Hs[i].cols();
}
Matrix results = zeros(rows,cols);
Matrix results = Matrix::Zero(rows,cols);
size_t r = 0, c = 0;
for (size_t i = 0; i<Hs.size(); ++i) {
insertSub(results, Hs[i], r, c);
@ -260,16 +215,6 @@ Matrix diag(const std::vector<Matrix>& Hs) {
return results;
}
/* ************************************************************************* */
void insertColumn(Matrix& A, const Vector& col, size_t j) {
A.col(j) = col;
}
/* ************************************************************************* */
void insertColumn(Matrix& A, const Vector& col, size_t i, size_t j) {
A.col(j).segment(i, col.size()) = col;
}
/* ************************************************************************* */
Vector columnNormSquare(const Matrix &A) {
Vector v (A.cols()) ;
@ -279,24 +224,13 @@ Vector columnNormSquare(const Matrix &A) {
return v ;
}
/* ************************************************************************* */
void solve(Matrix& A, Matrix& B) {
// Eigen version - untested
B = A.fullPivLu().solve(B);
}
/* ************************************************************************* */
Matrix inverse(const Matrix& A) {
return A.inverse();
}
/* ************************************************************************* */
/** Householder QR factorization, Golub & Van Loan p 224, explicit version */
/* ************************************************************************* */
pair<Matrix,Matrix> qr(const Matrix& A) {
const size_t m = A.rows(), n = A.cols(), kprime = min(m,n);
Matrix Q=eye(m,m),R(A);
Matrix Q=Matrix::Identity(m,m),R(A);
Vector v(m);
// loop over the kprime first columns
@ -319,7 +253,7 @@ pair<Matrix,Matrix> qr(const Matrix& A) {
v(k) = k<j ? 0.0 : vjm(k-j);
// create Householder reflection matrix Qj = I-beta*v*v'
Matrix Qj = eye(m) - beta * v * v.transpose();
Matrix Qj = Matrix::Identity(m,m) - beta * v * v.transpose();
R = Qj * R; // update R
Q = Q * Qj; // update Q
@ -339,7 +273,7 @@ weighted_eliminate(Matrix& A, Vector& b, const Vector& sigmas) {
list<boost::tuple<Vector, double, double> > results;
Vector pseudo(m); // allocate storage for pseudo-inverse
Vector weights = reciprocal(emul(sigmas,sigmas)); // calculate weights once
Vector weights = sigmas.array().square().inverse(); // calculate weights once
// We loop over all columns, because the columns that can be eliminated
// are not necessarily contiguous. For each one, estimate the corresponding
@ -356,7 +290,7 @@ weighted_eliminate(Matrix& A, Vector& b, const Vector& sigmas) {
if (precision < 1e-8) continue;
// create solution and copy into r
Vector r(basis(n, j));
Vector r(Vector::Unit(n,j));
for (size_t j2=j+1; j2<n; ++j2)
r(j2) = pseudo.dot(A.col(j2));
@ -600,7 +534,7 @@ Matrix RtR(const Matrix &A)
Matrix cholesky_inverse(const Matrix &A)
{
Eigen::LLT<Matrix> llt(A);
Matrix inv = eye(A.rows());
Matrix inv = Matrix::Identity(A.rows(),A.rows());
llt.matrixU().solveInPlace<Eigen::OnTheRight>(inv);
return inv*inv.transpose();
}
@ -612,7 +546,7 @@ Matrix cholesky_inverse(const Matrix &A)
// inv(B' * B) == A
Matrix inverse_square_root(const Matrix& A) {
Eigen::LLT<Matrix> llt(A);
Matrix inv = eye(A.rows());
Matrix inv = Matrix::Identity(A.rows(),A.rows());
llt.matrixU().solveInPlace<Eigen::OnTheRight>(inv);
return inv.transpose();
}
@ -648,7 +582,7 @@ boost::tuple<int, double, Vector> DLT(const Matrix& A, double rank_tol) {
/* ************************************************************************* */
Matrix expm(const Matrix& A, size_t K) {
Matrix E = eye(A.rows()), A_k = eye(A.rows());
Matrix E = Matrix::Identity(A.rows(),A.rows()), A_k = Matrix::Identity(A.rows(),A.rows());
for(size_t k=1;k<=K;k++) {
A_k = A_k*A/double(k);
E = E + A_k;

118
gtsam/base/Matrix.h
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@ -16,6 +16,7 @@
* @author Kai Ni
* @author Frank Dellaert
* @author Alex Cunningham
* @author Alex Hagiopol
*/
// \callgraph
@ -23,17 +24,17 @@
#pragma once
#include <gtsam/base/OptionalJacobian.h>
#include <gtsam/base/Vector.h>
#include <gtsam/config.h> // Configuration from CMake
#include <gtsam/config.h>
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
#include <Eigen/Core>
#include <Eigen/Cholesky>
#include <Eigen/LU>
#endif
#include <boost/format.hpp>
#include <boost/function.hpp>
#include <boost/tuple/tuple.hpp>
#include <boost/math/special_functions/fpclassify.hpp>
/**
* Matrix is a typedef in the gtsam namespace
* TODO: make a version to work with matlab wrapping
@ -74,40 +75,8 @@ GTSAM_MAKE_MATRIX_DEFS(9);
typedef Eigen::Block<Matrix> SubMatrix;
typedef Eigen::Block<const Matrix> ConstSubMatrix;
// Matlab-like syntax
/**
* Creates an zeros matrix, with matlab-like syntax
*
* Note: if assigning a block (created from an Eigen block() function) of a matrix to zeros,
* don't use this function, instead use ".setZero(m,n)" to avoid an Eigen error.
*/
GTSAM_EXPORT Matrix zeros(size_t m, size_t n);
/**
* Creates an ones matrix, with matlab-like syntax
*/
GTSAM_EXPORT Matrix ones(size_t m, size_t n);
/**
* Creates an identity matrix, with matlab-like syntax
*
* Note: if assigning a block (created from an Eigen block() function) of a matrix to identity,
* don't use this function, instead use ".setIdentity(m,n)" to avoid an Eigen error.
*/
GTSAM_EXPORT Matrix eye(size_t m, size_t n);
/**
* Creates a square identity matrix, with matlab-like syntax
*
* Note: if assigning a block (created from an Eigen block() function) of a matrix to identity,
* don't use this function, instead use ".setIdentity(m)" to avoid an Eigen error.
*/
inline Matrix eye( size_t m ) { return eye(m,m); }
GTSAM_EXPORT Matrix diag(const Vector& v);
/**
* equals with an tolerance
* equals with a tolerance
*/
template <class MATRIX>
bool equal_with_abs_tol(const Eigen::DenseBase<MATRIX>& A, const Eigen::DenseBase<MATRIX>& B, double tol = 1e-9) {
@ -166,37 +135,12 @@ GTSAM_EXPORT bool linear_independent(const Matrix& A, const Matrix& B, double to
*/
GTSAM_EXPORT bool linear_dependent(const Matrix& A, const Matrix& B, double tol = 1e-9);
/**
* BLAS Level-2 style e <- e + alpha*A*x
*/
GTSAM_EXPORT void multiplyAdd(double alpha, const Matrix& A, const Vector& x, Vector& e);
/**
* BLAS Level-2 style e <- e + A*x
*/
GTSAM_EXPORT void multiplyAdd(const Matrix& A, const Vector& x, Vector& e);
/**
* overload ^ for trans(A)*v
* We transpose the vectors for speed.
*/
GTSAM_EXPORT Vector operator^(const Matrix& A, const Vector & v);
/**
* BLAS Level-2 style x <- x + alpha*A'*e
*/
GTSAM_EXPORT void transposeMultiplyAdd(double alpha, const Matrix& A, const Vector& e, Vector& x);
/**
* BLAS Level-2 style x <- x + A'*e
*/
GTSAM_EXPORT void transposeMultiplyAdd(const Matrix& A, const Vector& e, Vector& x);
/**
* BLAS Level-2 style x <- x + alpha*A'*e
*/
GTSAM_EXPORT void transposeMultiplyAdd(double alpha, const Matrix& A, const Vector& e, SubVector x);
/** products using old-style format to improve compatibility */
template<class MATRIX>
inline MATRIX prod(const MATRIX& A, const MATRIX&B) {
@ -281,19 +225,6 @@ const typename MATRIX::ConstRowXpr row(const MATRIX& A, size_t j) {
return A.row(j);
}
/**
* inserts a column into a matrix IN PLACE
* NOTE: there is no size checking
* Alternate form allows for vectors smaller than the whole column to be inserted
* @param A matrix to be modified in place
* @param col is the vector to be inserted
* @param j is the index to insert the column
*/
GTSAM_EXPORT void insertColumn(Matrix& A, const Vector& col, size_t j);
GTSAM_EXPORT void insertColumn(Matrix& A, const Vector& col, size_t i, size_t j);
GTSAM_EXPORT Vector columnNormSquare(const Matrix &A);
/**
* Zeros all of the elements below the diagonal of a matrix, in place
* @param A is a matrix, to be modified in place
@ -355,17 +286,6 @@ inline typename Reshape<OutM, OutN, OutOptions, InM, InN, InOptions>::ReshapedTy
return Reshape<OutM, OutN, OutOptions, InM, InN, InOptions>::reshape(m);
}
/**
* solve AX=B via in-place Lu factorization and backsubstitution
* After calling, A contains LU, B the solved RHS vectors
*/
GTSAM_EXPORT void solve(Matrix& A, Matrix& B);
/**
* invert A
*/
GTSAM_EXPORT Matrix inverse(const Matrix& A);
/**
* QR factorization, inefficient, best use imperative householder below
* m*n matrix -> m*m Q, m*n R
@ -492,12 +412,6 @@ inline Matrix3 skewSymmetric(const Eigen::MatrixBase<Derived>& w) {
/** Use Cholesky to calculate inverse square root of a matrix */
GTSAM_EXPORT Matrix inverse_square_root(const Matrix& A);
/** Calculate the LL^t decomposition of a S.P.D matrix */
GTSAM_EXPORT Matrix LLt(const Matrix& A);
/** Calculate the R^tR decomposition of a S.P.D matrix */
GTSAM_EXPORT Matrix RtR(const Matrix& A);
/** Return the inverse of a S.P.D. matrix. Inversion is done via Cholesky decomposition. */
GTSAM_EXPORT Matrix cholesky_inverse(const Matrix &A);
@ -603,6 +517,28 @@ struct MultiplyWithInverseFunction {
const Operator phi_;
};
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
inline Matrix zeros( size_t m, size_t n ) { return Matrix::Zero(m,n); }
inline Matrix ones( size_t m, size_t n ) { return Matrix::Ones(m,n); }
inline Matrix eye( size_t m, size_t n) { return Matrix::Identity(m, n); }
inline Matrix eye( size_t m ) { return eye(m,m); }
inline Matrix diag(const Vector& v) { return v.asDiagonal(); }
inline void multiplyAdd(double alpha, const Matrix& A, const Vector& x, Vector& e) { e += alpha * A * x; }
inline void multiplyAdd(const Matrix& A, const Vector& x, Vector& e) { e += A * x; }
inline void transposeMultiplyAdd(double alpha, const Matrix& A, const Vector& e, Vector& x) { x += alpha * A.transpose() * e; }
inline void transposeMultiplyAdd(const Matrix& A, const Vector& e, Vector& x) { x += A.transpose() * e; }
inline void transposeMultiplyAdd(double alpha, const Matrix& A, const Vector& e, SubVector x) { x += alpha * A.transpose() * e; }
inline void insertColumn(Matrix& A, const Vector& col, size_t j) { A.col(j) = col; }
inline void insertColumn(Matrix& A, const Vector& col, size_t i, size_t j) { A.col(j).segment(i, col.size()) = col; }
inline void solve(Matrix& A, Matrix& B) { B = A.fullPivLu().solve(B); }
inline Matrix inverse(const Matrix& A) { return A.inverse(); }
#endif
GTSAM_EXPORT Matrix LLt(const Matrix& A);
GTSAM_EXPORT Matrix RtR(const Matrix& A);
GTSAM_EXPORT Vector columnNormSquare(const Matrix &A);
} // namespace gtsam
#include <boost/serialization/nvp.hpp>

75
gtsam/base/Vector.cpp
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@ -33,25 +33,6 @@ using namespace std;
namespace gtsam {
/* ************************************************************************* */
bool zero(const Vector& v) {
bool result = true;
size_t n = v.size();
for( size_t j = 0 ; j < n ; j++)
result = result && (v(j) == 0.0);
return result;
}
/* ************************************************************************* */
Vector repeat(size_t n, double value) {
return Vector::Constant(n, value);
}
/* ************************************************************************* */
Vector delta(size_t n, size_t i, double value) {
return Vector::Unit(n, i) * value;
}
/* ************************************************************************* */
//3 argument call
void print(const Vector& v, const string& s, ostream& stream) {
@ -176,28 +157,6 @@ bool linear_dependent(const Vector& vec1, const Vector& vec2, double tol) {
return flag;
}
/* ************************************************************************* */
ConstSubVector sub(const Vector &v, size_t i1, size_t i2) {
return v.segment(i1,i2-i1);
}
/* ************************************************************************* */
void subInsert(Vector& fullVector, const Vector& subVector, size_t i) {
fullVector.segment(i, subVector.size()) = subVector;
}
/* ************************************************************************* */
Vector emul(const Vector &a, const Vector &b) {
assert (b.size()==a.size());
return a.cwiseProduct(b);
}
/* ************************************************************************* */
Vector ediv(const Vector &a, const Vector &b) {
assert (b.size()==a.size());
return a.cwiseQuotient(b);
}
/* ************************************************************************* */
Vector ediv_(const Vector &a, const Vector &b) {
size_t n = a.size();
@ -210,36 +169,6 @@ Vector ediv_(const Vector &a, const Vector &b) {
return c;
}
/* ************************************************************************* */
double sum(const Vector &a) {
return a.sum();
}
/* ************************************************************************* */
double norm_2(const Vector& v) {
return v.norm();
}
/* ************************************************************************* */
Vector reciprocal(const Vector &a) {
return a.array().inverse();
}
/* ************************************************************************* */
Vector esqrt(const Vector& v) {
return v.cwiseSqrt();
}
/* ************************************************************************* */
Vector abs(const Vector& v) {
return v.cwiseAbs();
}
/* ************************************************************************* */
double max(const Vector &a) {
return a.maxCoeff();
}
/* ************************************************************************* */
// imperative version, pass in x
double houseInPlace(Vector &v) {
@ -292,7 +221,7 @@ double weightedPseudoinverse(const Vector& a, const Vector& weights,
// Basically, instead of doing a normal QR step with the weighted
// pseudoinverse, we enforce the constraint by turning
// ax + AS = b into x + (A/a)S = b/a, for the first row where a!=0
pseudo = delta(m, i, 1.0 / a[i]);
pseudo = Vector::Unit(m,i)*(1.0/a[i]);
return inf;
}
}
@ -363,6 +292,4 @@ Vector concatVectors(size_t nrVectors, ...)
return concatVectors(vs);
}
/* ************************************************************************* */
} // namespace gtsam

146
gtsam/base/Vector.h
View File

@ -14,13 +14,12 @@
* @brief typedef and functions to augment Eigen's VectorXd
* @author Kai Ni
* @author Frank Dellaert
* @author Alex Hagiopol
*/
// \callgraph
#pragma once
#ifndef MKL_BLAS
#define MKL_BLAS MKL_DOMAIN_BLAS
#endif
@ -64,54 +63,6 @@ GTSAM_MAKE_VECTOR_DEFS(12);
typedef Eigen::VectorBlock<Vector> SubVector;
typedef Eigen::VectorBlock<const Vector> ConstSubVector;
/**
* Create vector initialized to a constant value
* @param n is the size of the vector
* @param value is a constant value to insert into the vector
*/
GTSAM_EXPORT Vector repeat(size_t n, double value);
/**
* Create basis vector of dimension n,
* with a constant in spot i
* @param n is the size of the vector
* @param i index of the one
* @param value is the value to insert into the vector
* @return delta vector
*/
GTSAM_EXPORT Vector delta(size_t n, size_t i, double value);
/**
* Create basis vector of dimension n,
* with one in spot i
* @param n is the size of the vector
* @param i index of the one
* @return basis vector
*/
inline Vector basis(size_t n, size_t i) { return delta(n, i, 1.0); }
/**
* Create zero vector
* @param n size
*/
inline Vector zero(size_t n) { return Vector::Zero(n);}
/**
* Create vector initialized to ones
* @param n size
*/
inline Vector ones(size_t n) { return Vector::Ones(n); }
/**
* check if all zero
*/
GTSAM_EXPORT bool zero(const Vector& v);
/**
* dimensionality == size
*/
inline size_t dim(const Vector& v) { return v.size(); }
/**
* print without optional string, must specify cout yourself
*/
@ -196,39 +147,6 @@ GTSAM_EXPORT bool assert_equal(const ConstSubVector& vec1, const ConstSubVector&
*/
GTSAM_EXPORT bool linear_dependent(const Vector& vec1, const Vector& vec2, double tol=1e-9);
/**
* extract subvector, slice semantics, i.e. range = [i1,i2[ excluding i2
* @param v Vector
* @param i1 first row index
* @param i2 last row index + 1
* @return subvector v(i1:i2)
*/
GTSAM_EXPORT ConstSubVector sub(const Vector &v, size_t i1, size_t i2);
/**
* Inserts a subvector into a vector IN PLACE
* @param fullVector is the vector to be changed
* @param subVector is the vector to insert
* @param i is the index where the subvector should be inserted
*/
GTSAM_EXPORT void subInsert(Vector& fullVector, const Vector& subVector, size_t i);
/**
* elementwise multiplication
* @param a first vector
* @param b second vector
* @return vector [a(i)*b(i)]
*/
GTSAM_EXPORT Vector emul(const Vector &a, const Vector &b);
/**
* elementwise division
* @param a first vector
* @param b second vector
* @return vector [a(i)/b(i)]
*/
GTSAM_EXPORT Vector ediv(const Vector &a, const Vector &b);
/**
* elementwise division, but 0/0 = 0, not inf
* @param a first vector
@ -237,49 +155,6 @@ GTSAM_EXPORT Vector ediv(const Vector &a, const Vector &b);
*/
GTSAM_EXPORT Vector ediv_(const Vector &a, const Vector &b);
/**
* sum vector elements
* @param a vector
* @return sum_i a(i)
*/
GTSAM_EXPORT double sum(const Vector &a);
/**
* Calculates L2 norm for a vector
* modeled after boost.ublas for compatibility
* @param v vector
* @return the L2 norm
*/
GTSAM_EXPORT double norm_2(const Vector& v);
/**
* Elementwise reciprocal of vector elements
* @param a vector
* @return [1/a(i)]
*/
GTSAM_EXPORT Vector reciprocal(const Vector &a);
/**
* Elementwise sqrt of vector elements
* @param v is a vector
* @return [sqrt(a(i))]
*/
GTSAM_EXPORT Vector esqrt(const Vector& v);
/**
* Absolute values of vector elements
* @param v is a vector
* @return [abs(a(i))]
*/
GTSAM_EXPORT Vector abs(const Vector& v);
/**
* Return the max element of a vector
* @param a is a vector
* @return max(a)
*/
GTSAM_EXPORT double max(const Vector &a);
/**
* Dot product
*/
@ -356,6 +231,25 @@ GTSAM_EXPORT Vector concatVectors(const std::list<Vector>& vs);
*/
GTSAM_EXPORT Vector concatVectors(size_t nrVectors, ...);
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
inline Vector abs(const Vector& v){return v.cwiseAbs();}
inline Vector basis(size_t n, size_t i) { return Vector::Unit(n,i); }
inline Vector delta(size_t n, size_t i, double value){ return Vector::Unit(n, i) * value;}
inline size_t dim(const Vector& v) { return v.size(); }
inline Vector ediv(const Vector &a, const Vector &b) {assert (b.size()==a.size()); return a.cwiseQuotient(b);}
inline Vector esqrt(const Vector& v) { return v.cwiseSqrt();}
inline Vector emul(const Vector &a, const Vector &b) {assert (b.size()==a.size()); return a.cwiseProduct(b);}
inline double max(const Vector &a){return a.maxCoeff();}
inline double norm_2(const Vector& v) {return v.norm();}
inline Vector ones(size_t n) { return Vector::Ones(n); }
inline Vector reciprocal(const Vector &a) {return a.array().inverse();}
inline Vector repeat(size_t n, double value) {return Vector::Constant(n, value);}
inline const Vector sub(const Vector &v, size_t i1, size_t i2) {return v.segment(i1,i2-i1);}
inline void subInsert(Vector& fullVector, const Vector& subVector, size_t i) {fullVector.segment(i, subVector.size()) = subVector;}
inline double sum(const Vector &a){return a.sum();}
inline bool zero(const Vector& v){ return v.isZero(); }
inline Vector zero(size_t n) { return Vector::Zero(n); }
#endif
} // namespace gtsam
#include <boost/serialization/nvp.hpp>

4
gtsam/base/numericalDerivative.h
View File

@ -88,7 +88,7 @@ typename internal::FixedSizeMatrix<X>::type numericalGradient(boost::function<do
TangentX d;
d.setZero();
Vector g = zero(N); // Can be fixed size
Eigen::Matrix<double,N,1> g; g.setZero(); // Can be fixed size
for (int j = 0; j < N; j++) {
d(j) = delta;
double hxplus = h(traits<X>::Retract(x, d));
@ -142,7 +142,7 @@ typename internal::FixedSizeMatrix<Y,X>::type numericalDerivative11(boost::funct
dx.setZero();
// Fill in Jacobian H
Matrix H = zeros(m, N);
Matrix H = Matrix::Zero(m, N);
const double factor = 1.0 / (2.0 * delta);
for (int j = 0; j < N; j++) {
dx(j) = delta;

114
gtsam/base/tests/testMatrix.cpp
View File

@ -156,8 +156,8 @@ TEST(Matrix, collect2 )
TEST(Matrix, collect3 )
{
Matrix A, B;
A = eye(2, 3);
B = eye(2, 3);
A = Matrix::Identity(2,3);
B = Matrix::Identity(2,3);
vector<const Matrix*> matrices;
matrices.push_back(&A);
matrices.push_back(&B);
@ -211,48 +211,6 @@ TEST(Matrix, column )
EXPECT(assert_equal(a3, exp3));
}
/* ************************************************************************* */
TEST(Matrix, insert_column )
{
Matrix big = zeros(5, 6);
Vector col = ones(5);
size_t j = 3;
insertColumn(big, col, j);
Matrix expected = (Matrix(5, 6) <<
0.0, 0.0, 0.0, 1.0, 0.0, 0.0,
0.0, 0.0, 0.0, 1.0, 0.0, 0.0,
0.0, 0.0, 0.0, 1.0, 0.0, 0.0,
0.0, 0.0, 0.0, 1.0, 0.0, 0.0,
0.0, 0.0, 0.0, 1.0, 0.0, 0.0).finished();
EXPECT(assert_equal(expected, big));
}
/* ************************************************************************* */
TEST(Matrix, insert_subcolumn )
{
Matrix big = zeros(5, 6);
Vector col1 = ones(2);
size_t i = 1;
size_t j = 3;
insertColumn(big, col1, i, j); // check 1
Vector col2 = ones(1);
insertColumn(big, col2, 4, 5); // check 2
Matrix expected = (Matrix(5, 6) <<
0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 1.0, 0.0, 0.0,
0.0, 0.0, 0.0, 1.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 1.0).finished();
EXPECT(assert_equal(expected, big));
}
/* ************************************************************************* */
TEST(Matrix, row )
{
@ -272,26 +230,10 @@ TEST(Matrix, row )
EXPECT(assert_equal(a3, exp3));
}
/* ************************************************************************* */
TEST(Matrix, zeros )
{
Matrix A(2, 3);
A(0, 0) = 0;
A(0, 1) = 0;
A(0, 2) = 0;
A(1, 0) = 0;
A(1, 1) = 0;
A(1, 2) = 0;
Matrix zero = zeros(2, 3);
EQUALITY(A , zero);
}
/* ************************************************************************* */
TEST(Matrix, insert_sub )
{
Matrix big = zeros(5, 6), small = (Matrix(2, 3) << 1.0, 1.0, 1.0, 1.0, 1.0,
Matrix big = Matrix::Zero(5,6), small = (Matrix(2, 3) << 1.0, 1.0, 1.0, 1.0, 1.0,
1.0).finished();
insertSub(big, small, 1, 2);
@ -307,9 +249,9 @@ TEST(Matrix, insert_sub )
TEST(Matrix, diagMatrices )
{
std::vector<Matrix> Hs;
Hs.push_back(ones(3,3));
Hs.push_back(ones(4,4)*2);
Hs.push_back(ones(2,2)*3);
Hs.push_back(Matrix::Ones(3,3));
Hs.push_back(Matrix::Ones(4,4)*2);
Hs.push_back(Matrix::Ones(2,2)*3);
Matrix actual = diag(Hs);
@ -723,9 +665,9 @@ TEST(Matrix, inverse )
A(2, 1) = 0;
A(2, 2) = 6;
Matrix Ainv = inverse(A);
EXPECT(assert_equal(eye(3), A*Ainv));
EXPECT(assert_equal(eye(3), Ainv*A));
Matrix Ainv = A.inverse();
EXPECT(assert_equal((Matrix) I_3x3, A*Ainv));
EXPECT(assert_equal((Matrix) I_3x3, Ainv*A));
Matrix expected(3, 3);
expected(0, 0) = 1.0909;
@ -746,13 +688,13 @@ TEST(Matrix, inverse )
0.0, -1.0, 1.0,
1.0, 0.0, 2.0,
0.0, 0.0, 1.0).finished(),
inverse(lMg)));
lMg.inverse()));
Matrix gMl((Matrix(3, 3) << 0.0, -1.0, 1.0, 1.0, 0.0, 2.0, 0.0, 0.0, 1.0).finished());
EXPECT(assert_equal((Matrix(3, 3) <<
0.0, 1.0,-2.0,
-1.0, 0.0, 1.0,
0.0, 0.0, 1.0).finished(),
inverse(gMl)));
gMl.inverse()));
}
/* ************************************************************************* */
@ -769,7 +711,7 @@ TEST(Matrix, inverse2 )
A(2, 1) = 0;
A(2, 2) = 1;
Matrix Ainv = inverse(A);
Matrix Ainv = A.inverse();
Matrix expected(3, 3);
expected(0, 0) = 0;
@ -996,7 +938,7 @@ TEST(Matrix, inverse_square_root )
10.0).finished();
EQUALITY(expected,actual);
EQUALITY(measurement_covariance,inverse(actual*actual));
EQUALITY(measurement_covariance,(actual*actual).inverse());
// Randomly generated test. This test really requires inverse to
// be working well; if it's not, there's the possibility of a
@ -1052,28 +994,6 @@ TEST(Matrix, cholesky_inverse )
EQUALITY(cholesky::M.inverse(), cholesky_inverse(cholesky::M));
}
/* ************************************************************************* */
TEST(Matrix, multiplyAdd )
{
Matrix A = (Matrix(3, 4) << 4., 0., 0., 1., 0., 4., 0., 2., 0., 0., 1., 3.).finished();
Vector x = (Vector(4) << 1., 2., 3., 4.).finished(), e = Vector3(5., 6., 7.),
expected = e + A * x;
multiplyAdd(1, A, x, e);
EXPECT(assert_equal(expected, e));
}
/* ************************************************************************* */
TEST(Matrix, transposeMultiplyAdd )
{
Matrix A = (Matrix(3, 4) << 4., 0., 0., 1., 0., 4., 0., 2., 0., 0., 1., 3.).finished();
Vector x = (Vector(4) << 1., 2., 3., 4.).finished(), e = Vector3(5., 6., 7.),
expected = x + trans(A) * e;
transposeMultiplyAdd(1, A, e, x);
EXPECT(assert_equal(expected, x));
}
/* ************************************************************************* */
TEST(Matrix, linear_dependent )
{
@ -1102,12 +1022,12 @@ TEST(Matrix, linear_dependent3 )
TEST(Matrix, svd1 )
{
Vector v = Vector3(2., 1., 0.);
Matrix U1 = eye(4, 3), S1 = diag(v), V1 = eye(3, 3), A = (U1 * S1)
Matrix U1 = Matrix::Identity(4, 3), S1 = v.asDiagonal(), V1 = I_3x3, A = (U1 * S1)
* Matrix(trans(V1));
Matrix U, V;
Vector s;
svd(A, U, s, V);
Matrix S = diag(s);
Matrix S = s.asDiagonal();
EXPECT(assert_equal(U*S*Matrix(trans(V)),A));
EXPECT(assert_equal(S,S1));
}
@ -1158,7 +1078,7 @@ TEST(Matrix, svd3 )
V = -V;
}
Matrix S = diag(s);
Matrix S = s.asDiagonal();
Matrix t = U * S;
Matrix Vt = trans(V);
@ -1202,7 +1122,7 @@ TEST(Matrix, svd4 )
V.col(1) = -V.col(1);
}
Matrix reconstructed = U * diag(s) * trans(V);
Matrix reconstructed = U * s.asDiagonal() * trans(V);
EXPECT(assert_equal(A, reconstructed, 1e-4));
EXPECT(assert_equal(expectedU,U, 1e-3));

2
gtsam/base/tests/testSymmetricBlockMatrix.cpp
View File

@ -42,7 +42,7 @@ TEST(SymmetricBlockMatrix, ReadBlocks)
23, 29).finished();
Matrix actual1 = testBlockMatrix(1, 1);
// Test only writing the upper triangle for efficiency
Matrix actual1t = Matrix::Zero(2, 2);
Matrix actual1t = Z_2x2;
actual1t.triangularView<Eigen::Upper>() = testBlockMatrix(1, 1).triangularView();
EXPECT(assert_equal(expected1, actual1));
EXPECT(assert_equal(Matrix(expected1.triangularView<Eigen::Upper>()), actual1t));

73
gtsam/base/tests/testVector.cpp
View File

@ -79,22 +79,6 @@ TEST(Vector, copy )
EXPECT(assert_equal(a, b));
}
/* ************************************************************************* */
TEST(Vector, zero1 )
{
Vector v = Vector::Zero(2);
EXPECT(zero(v));
}
/* ************************************************************************* */
TEST(Vector, zero2 )
{
Vector a = zero(2);
Vector b = Vector::Zero(2);
EXPECT(a==b);
EXPECT(assert_equal(a, b));
}
/* ************************************************************************* */
TEST(Vector, scalar_multiply )
{
@ -119,36 +103,6 @@ TEST(Vector, negate )
EXPECT(assert_equal(b, -a));
}
/* ************************************************************************* */
TEST(Vector, sub )
{
Vector a(6);
a(0) = 10; a(1) = 20; a(2) = 3;
a(3) = 34; a(4) = 11; a(5) = 2;
Vector result(sub(a,2,5));
Vector b(3);
b(0) = 3; b(1) = 34; b(2) =11;
EXPECT(b==result);
EXPECT(assert_equal(b, result));
}
/* ************************************************************************* */
TEST(Vector, subInsert )
{
Vector big = zero(6),
small = ones(3);
size_t i = 2;
subInsert(big, small, i);
Vector expected = (Vector(6) << 0.0, 0.0, 1.0, 1.0, 1.0, 0.0).finished();
EXPECT(assert_equal(expected, big));
}
/* ************************************************************************* */
TEST(Vector, householder )
{
@ -198,7 +152,7 @@ TEST(Vector, weightedPseudoinverse )
// create sigmas
Vector sigmas(2);
sigmas(0) = 0.1; sigmas(1) = 0.2;
Vector weights = reciprocal(emul(sigmas,sigmas));
Vector weights = sigmas.array().square().inverse();
// perform solve
Vector actual; double precision;
@ -224,8 +178,7 @@ TEST(Vector, weightedPseudoinverse_constraint )
// create sigmas
Vector sigmas(2);
sigmas(0) = 0.0; sigmas(1) = 0.2;
Vector weights = reciprocal(emul(sigmas,sigmas));
Vector weights = sigmas.array().square().inverse();
// perform solve
Vector actual; double precision;
boost::tie(actual, precision) = weightedPseudoinverse(x, weights);
@ -244,7 +197,7 @@ TEST(Vector, weightedPseudoinverse_nan )
{
Vector a = (Vector(4) << 1., 0., 0., 0.).finished();
Vector sigmas = (Vector(4) << 0.1, 0.1, 0., 0.).finished();
Vector weights = reciprocal(emul(sigmas,sigmas));
Vector weights = sigmas.array().square().inverse();
Vector pseudo; double precision;
boost::tie(pseudo, precision) = weightedPseudoinverse(a, weights);
@ -253,17 +206,6 @@ TEST(Vector, weightedPseudoinverse_nan )
DOUBLES_EQUAL(100, precision, 1e-5);
}
/* ************************************************************************* */
TEST(Vector, ediv )
{
Vector a = Vector3(10., 20., 30.);
Vector b = Vector3(2.0, 5.0, 6.0);
Vector actual(ediv(a,b));
Vector c = Vector3(5.0, 4.0, 5.0);
EXPECT(assert_equal(c,actual));
}
/* ************************************************************************* */
TEST(Vector, dot )
{
@ -298,18 +240,11 @@ TEST(Vector, equals )
TEST(Vector, greater_than )
{
Vector v1 = Vector3(1.0, 2.0, 3.0),
v2 = zero(3);
v2 = Z_3x1;
EXPECT(greaterThanOrEqual(v1, v1)); // test basic greater than
EXPECT(greaterThanOrEqual(v1, v2)); // test equals
}
/* ************************************************************************* */
TEST(Vector, reciprocal )
{
Vector v = Vector3(1.0, 2.0, 4.0);
EXPECT(assert_equal(Vector3(1.0, 0.5, 0.25),reciprocal(v)));
}
/* ************************************************************************* */
TEST(Vector, linear_dependent )
{

2
gtsam/discrete/tests/testDiscreteMarginals.cpp
View File

@ -167,7 +167,7 @@ TEST_UNSAFE( DiscreteMarginals, truss2 ) {
// Calculate the marginals by brute force
vector<DiscreteFactor::Values> allPosbValues = cartesianProduct(
key[0] & key[1] & key[2] & key[3] & key[4]);
Vector T = zero(5), F = zero(5);
Vector T = Z_5x1, F = Z_5x1;
for (size_t i = 0; i < allPosbValues.size(); ++i) {
DiscreteFactor::Values x = allPosbValues[i];
double px = graph(x);

2
gtsam/geometry/OrientedPlane3.cpp
View File

@ -42,7 +42,7 @@ OrientedPlane3 OrientedPlane3::transform(const Pose3& xr, OptionalJacobian<3, 3>
double pred_d = n_.unitVector().dot(xr.translation()) + d_;
if (Hr) {
*Hr = zeros(3, 6);
*Hr = Matrix::Zero(3,6);
Hr->block<2, 3>(0, 0) = D_rotated_plane;
Hr->block<1, 3>(2, 3) = unit_vec;
}

2
gtsam/geometry/Pose2.cpp
View File

@ -132,7 +132,7 @@ Matrix3 Pose2::AdjointMap() const {
/* ************************************************************************* */
Matrix3 Pose2::adjointMap(const Vector3& v) {
// See Chirikjian12book2, vol.2, pg. 36
Matrix3 ad = zeros(3,3);
Matrix3 ad = Z_3x3;
ad(0,1) = -v[2];
ad(1,0) = v[2];
ad(0,2) = v[1];

2
gtsam/geometry/Rot2.cpp
View File

@ -63,7 +63,7 @@ Rot2& Rot2::normalize() {
Rot2 Rot2::Expmap(const Vector1& v, OptionalJacobian<1, 1> H) {
if (H)
*H = I_1x1;
if (zero(v))
if (v.isZero())
return (Rot2());
else
return Rot2::fromAngle(v(0));

4
gtsam/geometry/Rot2.h
View File

@ -119,12 +119,12 @@ namespace gtsam {
/// Left-trivialized derivative of the exponential map
static Matrix ExpmapDerivative(const Vector& /*v*/) {
return ones(1);
return I_1x1;
}
/// Left-trivialized derivative inverse of the exponential map
static Matrix LogmapDerivative(const Vector& /*v*/) {
return ones(1);
return I_1x1;
}
// Chart at origin simply uses exponential map and its inverse

4
gtsam/geometry/tests/testCal3_S2.cpp
View File

@ -122,8 +122,8 @@ TEST(Cal3_S2, between) {
Matrix H1, H2;
EXPECT(assert_equal(Cal3_S2(0,1,2,3,4), k1.between(k2, H1, H2)));
EXPECT(assert_equal(-eye(5), H1));
EXPECT(assert_equal(eye(5), H2));
EXPECT(assert_equal(-I_5x5, H1));
EXPECT(assert_equal(I_5x5, H2));
}

6
gtsam/geometry/tests/testEssentialMatrix.cpp
View File

@ -62,7 +62,7 @@ TEST (EssentialMatrix, FromPose3) {
//*******************************************************************************
TEST(EssentialMatrix, localCoordinates0) {
EssentialMatrix E;
Vector expected = zero(5);
Vector expected = Z_5x1;
Vector actual = E.localCoordinates(E);
EXPECT(assert_equal(expected, actual, 1e-8));
}
@ -74,7 +74,7 @@ TEST (EssentialMatrix, localCoordinates) {
Pose3 pose(trueRotation, trueTranslation);
EssentialMatrix hx = EssentialMatrix::FromPose3(pose);
Vector actual = hx.localCoordinates(EssentialMatrix::FromPose3(pose));
EXPECT(assert_equal(zero(5), actual, 1e-8));
EXPECT(assert_equal(Z_5x1, actual, 1e-8));
Vector6 d;
d << 0.1, 0.2, 0.3, 0, 0, 0;
@ -85,7 +85,7 @@ TEST (EssentialMatrix, localCoordinates) {
//*************************************************************************
TEST (EssentialMatrix, retract0) {
EssentialMatrix actual = trueE.retract(zero(5));
EssentialMatrix actual = trueE.retract(Z_5x1);
EXPECT(assert_equal(trueE, actual));
}

6
gtsam/geometry/tests/testOrientedPlane3.cpp
View File

@ -96,8 +96,8 @@ inline static Vector randomVector(const Vector& minLimits,
const Vector& maxLimits) {
// Get the number of dimensions and create the return vector
size_t numDims = dim(minLimits);
Vector vector = zero(numDims);
size_t numDims = minLimits.size();
Vector vector = Vector::Zero(numDims);
// Create the random vector
for (size_t i = 0; i < numDims; i++) {
@ -145,7 +145,7 @@ TEST (OrientedPlane3, error2) {
OrientedPlane3 plane2(-1.1, 0.2, 0.3, 5.4);
// Hard-coded regression values, to ensure the result doesn't change.
EXPECT(assert_equal(zero(3), plane1.errorVector(plane1), 1e-8));
EXPECT(assert_equal((Vector) Z_3x1, plane1.errorVector(plane1), 1e-8));
EXPECT(assert_equal(Vector3(-0.0677674148, -0.0760543588, -0.4), plane1.errorVector(plane2), 1e-5));
// Test the jacobians of transform

2
gtsam/geometry/tests/testPinholeCamera.cpp
View File

@ -116,7 +116,7 @@ TEST( PinholeCamera, lookat)
Matrix R = camera2.pose().rotation().matrix();
Matrix I = trans(R)*R;
EXPECT(assert_equal(I, eye(3)));
EXPECT(assert_equal(I, I_3x3));
}
/* ************************************************************************* */

2
gtsam/geometry/tests/testPinholePose.cpp
View File

@ -87,7 +87,7 @@ TEST( PinholePose, lookat)
Matrix R = camera2.pose().rotation().matrix();
Matrix I = trans(R)*R;
EXPECT(assert_equal(I, eye(3)));
EXPECT(assert_equal(I, I_3x3));
}
/* ************************************************************************* */

8
gtsam/geometry/tests/testPoint2.cpp
View File

@ -73,12 +73,12 @@ TEST(Point2, Lie) {
Matrix H1, H2;
EXPECT(assert_equal(Point2(5,7), traits<Point2>::Compose(p1, p2, H1, H2)));
EXPECT(assert_equal(eye(2), H1));
EXPECT(assert_equal(eye(2), H2));
EXPECT(assert_equal(I_2x2, H1));
EXPECT(assert_equal(I_2x2, H2));
EXPECT(assert_equal(Point2(3,3), traits<Point2>::Between(p1, p2, H1, H2)));
EXPECT(assert_equal(-eye(2), H1));
EXPECT(assert_equal(eye(2), H2));
EXPECT(assert_equal(-I_2x2, H1));
EXPECT(assert_equal(I_2x2, H2));
EXPECT(assert_equal(Point2(5,7), traits<Point2>::Retract(p1, Vector2(4., 5.))));
EXPECT(assert_equal(Vector2(3.,3.), traits<Point2>::Local(p1,p2)));

8
gtsam/geometry/tests/testPoint3.cpp
View File

@ -47,12 +47,12 @@ TEST(Point3, Lie) {
Matrix H1, H2;
EXPECT(assert_equal(Point3(5, 7, 9), traits<Point3>::Compose(p1, p2, H1, H2)));
EXPECT(assert_equal(eye(3), H1));
EXPECT(assert_equal(eye(3), H2));
EXPECT(assert_equal(I_3x3, H1));
EXPECT(assert_equal(I_3x3, H2));
EXPECT(assert_equal(Point3(3, 3, 3), traits<Point3>::Between(p1, p2, H1, H2)));
EXPECT(assert_equal(-eye(3), H1));
EXPECT(assert_equal(eye(3), H2));
EXPECT(assert_equal(-I_3x3, H1));
EXPECT(assert_equal(I_3x3, H2));
EXPECT(assert_equal(Point3(5, 7, 9), traits<Point3>::Retract(p1, Vector3(4,5,6))));
EXPECT(assert_equal(Vector3(3, 3, 3), traits<Point3>::Local(p1,p2)));

4
gtsam/geometry/tests/testPose2.cpp
View File

@ -102,7 +102,7 @@ TEST(Pose2, expmap3) {
0.99, 0.0, -0.015,
0.0, 0.0, 0.0).finished();
Matrix A2 = A*A/2.0, A3 = A2*A/3.0, A4=A3*A/4.0;
Matrix expected = eye(3) + A + A2 + A3 + A4;
Matrix expected = I_3x3 + A + A2 + A3 + A4;
Vector v = Vector3(0.01, -0.015, 0.99);
Pose2 pose = Pose2::Expmap(v);
@ -311,7 +311,7 @@ TEST(Pose2, compose_a)
-1.0, 0.0, 2.0,
0.0, 0.0, 1.0
).finished();
Matrix expectedH2 = eye(3);
Matrix expectedH2 = I_3x3;
Matrix numericalH1 = numericalDerivative21<Pose2, Pose2, Pose2>(testing::compose, pose1, pose2);
Matrix numericalH2 = numericalDerivative22<Pose2, Pose2, Pose2>(testing::compose, pose1, pose2);
EXPECT(assert_equal(expectedH1,actualDcompose1));

23
gtsam/geometry/tests/testPose3.cpp
View File

@ -61,7 +61,7 @@ TEST( Pose3, constructors)
TEST( Pose3, retract_first_order)
{
Pose3 id;
Vector v = zero(6);
Vector v = Z_6x1;
v(0) = 0.3;
EXPECT(assert_equal(Pose3(R, Point3(0,0,0)), id.retract(v),1e-2));
v(3)=0.2;v(4)=0.7;v(5)=-2;
@ -71,7 +71,7 @@ TEST( Pose3, retract_first_order)
/* ************************************************************************* */
TEST( Pose3, retract_expmap)
{
Vector v = zero(6); v(0) = 0.3;
Vector v = Z_6x1; v(0) = 0.3;
Pose3 pose = Pose3::Expmap(v);
EXPECT(assert_equal(Pose3(R, Point3(0,0,0)), pose, 1e-2));
EXPECT(assert_equal(v,Pose3::Logmap(pose),1e-2));
@ -81,7 +81,7 @@ TEST( Pose3, retract_expmap)
TEST( Pose3, expmap_a_full)
{
Pose3 id;
Vector v = zero(6);
Vector v = Z_6x1;
v(0) = 0.3;
EXPECT(assert_equal(expmap_default<Pose3>(id, v), Pose3(R, Point3(0,0,0))));
v(3)=0.2;v(4)=0.394742;v(5)=-2.08998;
@ -92,7 +92,7 @@ TEST( Pose3, expmap_a_full)
TEST( Pose3, expmap_a_full2)
{
Pose3 id;
Vector v = zero(6);
Vector v = Z_6x1;
v(0) = 0.3;
EXPECT(assert_equal(expmap_default<Pose3>(id, v), Pose3(R, Point3(0,0,0))));
v(3)=0.2;v(4)=0.394742;v(5)=-2.08998;
@ -148,12 +148,12 @@ TEST(Pose3, Adjoint_full)
Pose3 Agrawal06iros(const Vector& xi) {
Vector w = xi.head(3);
Vector v = xi.tail(3);
double t = norm_2(w);
double t = w.norm();
if (t < 1e-5)
return Pose3(Rot3(), Point3(v));
else {
Matrix W = skewSymmetric(w/t);
Matrix A = eye(3) + ((1 - cos(t)) / t) * W + ((t - sin(t)) / t) * (W * W);
Matrix A = I_3x3 + ((1 - cos(t)) / t) * W + ((t - sin(t)) / t) * (W * W);
return Pose3(Rot3::Expmap (w), Point3(A * v));
}
}
@ -267,7 +267,7 @@ TEST( Pose3, inverse)
{
Matrix actualDinverse;
Matrix actual = T.inverse(actualDinverse).matrix();
Matrix expected = inverse(T.matrix());
Matrix expected = T.matrix().inverse();
EXPECT(assert_equal(actual,expected,1e-8));
Matrix numericalH = numericalDerivative11(testing::inverse<Pose3>, T);
@ -293,7 +293,7 @@ TEST( Pose3, inverseDerivatives2)
TEST( Pose3, compose_inverse)
{
Matrix actual = (T*T.inverse()).matrix();
Matrix expected = eye(4,4);
Matrix expected = I_4x4;
EXPECT(assert_equal(actual,expected,1e-8));
}
@ -538,7 +538,7 @@ TEST(Pose3, retract_localCoordinates)
/* ************************************************************************* */
TEST(Pose3, expmap_logmap)
{
Vector d12 = repeat(6,0.1);
Vector d12 = Vector6::Constant(0.1);
Pose3 t1 = T, t2 = t1.expmap(d12);
EXPECT(assert_equal(d12, t1.logmap(t2)));
}
@ -712,7 +712,7 @@ TEST(Pose3, Bearing2) {
TEST( Pose3, unicycle )
{
// velocity in X should be X in inertial frame, rather than global frame
Vector x_step = delta(6,3,1.0);
Vector x_step = Vector::Unit(6,3)*1.0;
EXPECT(assert_equal(Pose3(Rot3::Ypr(0,0,0), l1), expmap_default<Pose3>(x1, x_step), tol));
EXPECT(assert_equal(Pose3(Rot3::Ypr(0,0,0), Point3(2,1,0)), expmap_default<Pose3>(x2, x_step), tol));
EXPECT(assert_equal(Pose3(Rot3::Ypr(M_PI/4.0,0,0), Point3(2,2,0)), expmap_default<Pose3>(x3, sqrt(2.0) * x_step), tol));
@ -723,9 +723,8 @@ TEST( Pose3, adjointMap) {
Matrix res = Pose3::adjointMap(screwPose3::xi);
Matrix wh = skewSymmetric(screwPose3::xi(0), screwPose3::xi(1), screwPose3::xi(2));
Matrix vh = skewSymmetric(screwPose3::xi(3), screwPose3::xi(4), screwPose3::xi(5));
Matrix Z3 = zeros(3,3);
Matrix6 expected;
expected << wh, Z3, vh, wh;
expected << wh, Z_3x3, vh, wh;
EXPECT(assert_equal(expected,res,1e-5));
}

10
gtsam/geometry/tests/testRot2.cpp
View File

@ -62,8 +62,8 @@ TEST( Rot2, compose)
Matrix H1, H2;
(void) Rot2::fromAngle(1.0).compose(Rot2::fromAngle(2.0), H1, H2);
EXPECT(assert_equal(eye(1), H1));
EXPECT(assert_equal(eye(1), H2));
EXPECT(assert_equal(I_1x1, H1));
EXPECT(assert_equal(I_1x1, H2));
}
/* ************************************************************************* */
@ -74,8 +74,8 @@ TEST( Rot2, between)
Matrix H1, H2;
(void) Rot2::fromAngle(1.0).between(Rot2::fromAngle(2.0), H1, H2);
EXPECT(assert_equal(-eye(1), H1));
EXPECT(assert_equal(eye(1), H2));
EXPECT(assert_equal(-I_1x1, H1));
EXPECT(assert_equal(I_1x1, H2));
}
/* ************************************************************************* */
@ -89,7 +89,7 @@ TEST( Rot2, equals)
/* ************************************************************************* */
TEST( Rot2, expmap)
{
Vector v = zero(1);
Vector v = Z_1x1;
CHECK(assert_equal(R.retract(v), R));
}

18
gtsam/geometry/tests/testRot3.cpp
View File

@ -96,7 +96,7 @@ TEST( Rot3, equals)
/* ************************************************************************* */
// Notice this uses J^2 whereas fast uses w*w', and has cos(t)*I + ....
Rot3 slow_but_correct_Rodrigues(const Vector& w) {
double t = norm_2(w);
double t = w.norm();
Matrix3 J = skewSymmetric(w / t);
if (t < 1e-5) return Rot3();
Matrix3 R = I_3x3 + sin(t) * J + (1.0 - cos(t)) * (J * J);
@ -130,7 +130,7 @@ TEST( Rot3, Rodrigues2)
TEST( Rot3, Rodrigues3)
{
Vector w = Vector3(0.1, 0.2, 0.3);
Rot3 R1 = Rot3::AxisAngle(w / norm_2(w), norm_2(w));
Rot3 R1 = Rot3::AxisAngle(w / w.norm(), w.norm());
Rot3 R2 = slow_but_correct_Rodrigues(w);
CHECK(assert_equal(R2,R1));
}
@ -152,7 +152,7 @@ TEST( Rot3, Rodrigues4)
/* ************************************************************************* */
TEST( Rot3, retract)
{
Vector v = zero(3);
Vector v = Z_3x1;
CHECK(assert_equal(R, R.retract(v)));
// // test Canonical coordinates
@ -213,7 +213,7 @@ TEST(Rot3, log)
#define CHECK_OMEGA_ZERO(X,Y,Z) \
w = (Vector(3) << (double)X, (double)Y, double(Z)).finished(); \
R = Rot3::Rodrigues(w); \
EXPECT(assert_equal(zero(3), Rot3::Logmap(R)));
EXPECT(assert_equal((Vector) Z_3x1, Rot3::Logmap(R)));
CHECK_OMEGA_ZERO( 2.0*PI, 0, 0)
CHECK_OMEGA_ZERO( 0, 2.0*PI, 0)
@ -224,16 +224,16 @@ TEST(Rot3, log)
/* ************************************************************************* */
TEST(Rot3, retract_localCoordinates)
{
Vector3 d12 = repeat(3,0.1);
Vector3 d12 = Vector3::Constant(0.1);
Rot3 R2 = R.retract(d12);
EXPECT(assert_equal(d12, R.localCoordinates(R2)));
}
/* ************************************************************************* */
TEST(Rot3, expmap_logmap)
{
Vector3 d12 = repeat(3,0.1);
Vector3 d12 = Vector3::Constant(0.1);
Rot3 R2 = R.expmap(d12);
EXPECT(assert_equal(d12, R.logmap(R2)));
EXPECT(assert_equal(d12, (Vector) R.logmap(R2)));
}
/* ************************************************************************* */
@ -555,8 +555,8 @@ TEST(Rot3, quaternion) {
/* ************************************************************************* */
Matrix Cayley(const Matrix& A) {
Matrix::Index n = A.cols();
const Matrix I = eye(n);
return (I-A)*inverse(I+A);
const Matrix I = Matrix::Identity(n,n);
return (I-A)*(I+A).inverse();
}
TEST( Rot3, Cayley ) {

2
gtsam/geometry/tests/testSimpleCamera.cpp
View File

@ -76,7 +76,7 @@ TEST( SimpleCamera, lookat)
Matrix R = camera2.pose().rotation().matrix();
Matrix I = trans(R)*R;
CHECK(assert_equal(I, eye(3)));
CHECK(assert_equal(I, I_3x3));
}
/* ************************************************************************* */

6
gtsam/geometry/tests/testUnit3.cpp
View File

@ -237,7 +237,7 @@ TEST(Unit3, distance) {
TEST(Unit3, localCoordinates0) {
Unit3 p;
Vector actual = p.localCoordinates(p);
EXPECT(assert_equal(zero(2), actual, 1e-8));
EXPECT(assert_equal(Z_2x1, actual, 1e-8));
}
TEST(Unit3, localCoordinates) {
@ -245,14 +245,14 @@ TEST(Unit3, localCoordinates) {
Unit3 p, q;
Vector2 expected = Vector2::Zero();
Vector2 actual = p.localCoordinates(q);
EXPECT(assert_equal(zero(2), actual, 1e-8));
EXPECT(assert_equal((Vector) Z_2x1, actual, 1e-8));
EXPECT(assert_equal(q, p.retract(expected), 1e-8));
}
{
Unit3 p, q(1, 6.12385e-21, 0);
Vector2 expected = Vector2::Zero();
Vector2 actual = p.localCoordinates(q);
EXPECT(assert_equal(zero(2), actual, 1e-8));
EXPECT(assert_equal((Vector) Z_2x1, actual, 1e-8));
EXPECT(assert_equal(q, p.retract(expected), 1e-8));
}
{

2
gtsam/geometry/triangulation.cpp
View File

@ -31,7 +31,7 @@ Vector4 triangulateHomogeneousDLT(
size_t m = projection_matrices.size();
// Allocate DLT matrix
Matrix A = zeros(m * 2, 4);
Matrix A = Matrix::Zero(m * 2, 4);
for (size_t i = 0; i < m; i++) {
size_t row = i * 2;

21
gtsam/geometry/triangulation.h
View File

@ -363,6 +363,18 @@ struct TriangulationParameters {
<< p.dynamicOutlierRejectionThreshold << std::endl;
return os;
}
private:
/// Serialization function
friend class boost::serialization::access;
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int version) {
ar & BOOST_SERIALIZATION_NVP(rankTolerance);
ar & BOOST_SERIALIZATION_NVP(enableEPI);
ar & BOOST_SERIALIZATION_NVP(landmarkDistanceThreshold);
ar & BOOST_SERIALIZATION_NVP(dynamicOutlierRejectionThreshold);
}
};
/**
@ -411,6 +423,15 @@ public:
os << "no point, status = " << result.status_ << std::endl;
return os;
}
private:
/// Serialization function
friend class boost::serialization::access;
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int version) {
ar & BOOST_SERIALIZATION_NVP(status_);
}
};
/// triangulateSafe: extensive checking of the outcome

2
gtsam/linear/GaussianConditional.cpp
View File

@ -183,7 +183,7 @@ namespace gtsam {
if (frontalVec.hasNaN()) throw IndeterminantLinearSystemException(this->keys().front());
for (const_iterator it = beginParents(); it!= endParents(); it++)
gtsam::transposeMultiplyAdd(-1.0, Matrix(getA(it)), frontalVec, gy[*it]);
gy[*it] += -1.0 * Matrix(getA(it)).transpose() * frontalVec;
// Scale by sigmas
if(model_)

4
gtsam/linear/HessianFactor.cpp
View File

@ -377,7 +377,7 @@ void HessianFactor::multiplyHessianAdd(double alpha, const VectorValues& x,
vector<Vector> y;
y.reserve(size());
for (const_iterator it = begin(); it != end(); it++)
y.push_back(zero(getDim(it)));
y.push_back(Vector::Zero(getDim(it)));
// Accessing the VectorValues one by one is expensive
// So we will loop over columns to access x only once per column
@ -427,7 +427,7 @@ void HessianFactor::gradientAtZero(double* d) const {
Vector HessianFactor::gradient(Key key, const VectorValues& x) const {
Factor::const_iterator i = find(key);
// Sum over G_ij*xj for all xj connecting to xi
Vector b = zero(x.at(key).size());
Vector b = Vector::Zero(x.at(key).size());
for (Factor::const_iterator j = begin(); j != end(); ++j) {
// Obtain Gij from the Hessian factor
// Hessian factor only stores an upper triangular matrix, so be careful when i>j

7
gtsam/linear/JacobianFactor.cpp
View File

@ -543,7 +543,7 @@ void JacobianFactor::updateHessian(const FastVector<Key>& infoKeys,
/* ************************************************************************* */
Vector JacobianFactor::operator*(const VectorValues& x) const {
Vector Ax = zero(Ab_.rows());
Vector Ax = Vector::Zero(Ab_.rows());
if (empty())
return Ax;
@ -565,8 +565,7 @@ void JacobianFactor::transposeMultiplyAdd(double alpha, const Vector& e,
pair<VectorValues::iterator, bool> xi = x.tryInsert(j, Vector());
if (xi.second)
xi.first->second = Vector::Zero(getDim(begin() + pos));
gtsam::transposeMultiplyAdd(Ab_(pos), E, xi.first->second);
xi.first->second += Ab_(pos).transpose()*E;
}
}
@ -595,7 +594,7 @@ void JacobianFactor::multiplyHessianAdd(double alpha, const double* x, double* y
if (empty())
return;
Vector Ax = zero(Ab_.rows());
Vector Ax = Vector::Zero(Ab_.rows());
/// Just iterate over all A matrices and multiply in correct config part (looping over keys)
/// E.g.: Jacobian A = [A0 A1 A2] multiplies x = [x0 x1 x2]'

4
gtsam/linear/KalmanFilter.cpp
View File

@ -115,7 +115,7 @@ KalmanFilter::State KalmanFilter::predictQ(const State& p, const Matrix& F,
// f2(x_{t},x_{t+1}) = (F*x_{t} + B*u - x_{t+1}) * Q^-1 * (F*x_{t} + B*u - x_{t+1})^T
// See documentation in HessianFactor, we have A1 = -F, A2 = I_, b = B*u:
// TODO: starts to seem more elaborate than straight-up KF equations?
Matrix M = inverse(Q), Ft = trans(F);
Matrix M = Q.inverse(), Ft = trans(F);
Matrix G12 = -Ft * M, G11 = -G12 * F, G22 = M;
Vector b = B * u, g2 = M * b, g1 = -Ft * g2;
double f = dot(b, g2);
@ -147,7 +147,7 @@ KalmanFilter::State KalmanFilter::update(const State& p, const Matrix& H,
KalmanFilter::State KalmanFilter::updateQ(const State& p, const Matrix& H,
const Vector& z, const Matrix& Q) const {
Key k = step(p);
Matrix M = inverse(Q), Ht = trans(H);
Matrix M = Q.inverse(), Ht = trans(H);
Matrix G = Ht * M * H;
Vector g = Ht * M * z;
double f = dot(z, M * z);

2
gtsam/linear/KalmanFilter.h
View File

@ -69,7 +69,7 @@ public:
// Constructor
KalmanFilter(size_t n, Factorization method =
KALMANFILTER_DEFAULT_FACTORIZATION) :
n_(n), I_(eye(n_, n_)), function_(
n_(n), I_(Matrix::Identity(n_, n_)), function_(
method == QR ? GaussianFactorGraph::Eliminate(EliminateQR) :
GaussianFactorGraph::Eliminate(EliminateCholesky)) {
}

6
gtsam/linear/NoiseModel.cpp
View File

@ -258,7 +258,7 @@ Diagonal::shared_ptr Diagonal::Variances(const Vector& variances, bool smart) {
if (variances(j) != variances(0)) goto full;
return Isotropic::Variance(n, variances(0), true);
}
full: return shared_ptr(new Diagonal(esqrt(variances)));
full: return shared_ptr(new Diagonal(variances.cwiseSqrt()));
}
/* ************************************************************************* */
@ -326,7 +326,7 @@ static void fix(const Vector& sigmas, Vector& precisions, Vector& invsigmas) {
/* ************************************************************************* */
Constrained::Constrained(const Vector& sigmas)
: Diagonal(sigmas), mu_(repeat(sigmas.size(), 1000.0)) {
: Diagonal(sigmas), mu_(Vector::Constant(sigmas.size(), 1000.0)) {
internal::fix(sigmas, precisions_, invsigmas_);
}
@ -405,7 +405,7 @@ void Constrained::WhitenInPlace(Eigen::Block<Matrix> H) const {
/* ************************************************************************* */
Constrained::shared_ptr Constrained::unit() const {
Vector sigmas = ones(dim());
Vector sigmas = Vector::Ones(dim());
for (size_t i=0; i<dim(); ++i)
if (constrained(i))
sigmas(i) = 0.0;

28
gtsam/linear/NoiseModel.h
View File

@ -309,7 +309,7 @@ namespace gtsam {
* i.e. the diagonal of the information matrix, i.e., weights
*/
static shared_ptr Precisions(const Vector& precisions, bool smart = true) {
return Variances(reciprocal(precisions), smart);
return Variances(precisions.array().inverse(), smart);
}
virtual void print(const std::string& name) const;
@ -341,7 +341,7 @@ namespace gtsam {
* Return R itself, but note that Whiten(H) is cheaper than R*H
*/
virtual Matrix R() const {
return diag(invsigmas());
return invsigmas().asDiagonal();
}
private:
@ -381,7 +381,7 @@ namespace gtsam {
* from appearing in invsigmas or precisions.
* mu set to large default value (1000.0)
*/
Constrained(const Vector& sigmas = zero(1));
Constrained(const Vector& sigmas = Z_1x1);
/**
* Constructor that prevents any inf values
@ -416,7 +416,7 @@ namespace gtsam {
* standard devations, some of which might be zero
*/
static shared_ptr MixedSigmas(const Vector& sigmas) {
return MixedSigmas(repeat(sigmas.size(), 1000.0), sigmas);
return MixedSigmas(Vector::Constant(sigmas.size(), 1000.0), sigmas);
}
/**
@ -424,7 +424,7 @@ namespace gtsam {
* standard devations, some of which might be zero
*/
static shared_ptr MixedSigmas(double m, const Vector& sigmas) {
return MixedSigmas(repeat(sigmas.size(), m), sigmas);
return MixedSigmas(Vector::Constant(sigmas.size(), m), sigmas);
}
/**
@ -432,10 +432,10 @@ namespace gtsam {
* standard devations, some of which might be zero
*/
static shared_ptr MixedVariances(const Vector& mu, const Vector& variances) {
return shared_ptr(new Constrained(mu, esqrt(variances)));
return shared_ptr(new Constrained(mu, variances.cwiseSqrt()));
}
static shared_ptr MixedVariances(const Vector& variances) {
return shared_ptr(new Constrained(esqrt(variances)));
return shared_ptr(new Constrained(variances.cwiseSqrt()));
}
/**
@ -443,10 +443,10 @@ namespace gtsam {
* precisions, some of which might be inf
*/
static shared_ptr MixedPrecisions(const Vector& mu, const Vector& precisions) {
return MixedVariances(mu, reciprocal(precisions));
return MixedVariances(mu, precisions.array().inverse());
}
static shared_ptr MixedPrecisions(const Vector& precisions) {
return MixedVariances(reciprocal(precisions));
return MixedVariances(precisions.array().inverse());
}
/**
@ -458,17 +458,17 @@ namespace gtsam {
/** Fully constrained variations */
static shared_ptr All(size_t dim) {
return shared_ptr(new Constrained(repeat(dim, 1000.0), repeat(dim,0)));
return shared_ptr(new Constrained(Vector::Constant(dim, 1000.0), Vector::Constant(dim,0)));
}
/** Fully constrained variations */
static shared_ptr All(size_t dim, const Vector& mu) {
return shared_ptr(new Constrained(mu, repeat(dim,0)));
return shared_ptr(new Constrained(mu, Vector::Constant(dim,0)));
}
/** Fully constrained variations with a mu parameter */
static shared_ptr All(size_t dim, double mu) {
return shared_ptr(new Constrained(repeat(dim, mu), repeat(dim,0)));
return shared_ptr(new Constrained(Vector::Constant(dim, mu), Vector::Constant(dim,0)));
}
virtual void print(const std::string& name) const;
@ -522,10 +522,10 @@ namespace gtsam {
/** protected constructor takes sigma */
Isotropic(size_t dim, double sigma) :
Diagonal(repeat(dim, sigma)),sigma_(sigma),invsigma_(1.0/sigma) {}
Diagonal(Vector::Constant(dim, sigma)),sigma_(sigma),invsigma_(1.0/sigma) {}
/* dummy constructor to allow for serialization */
Isotropic() : Diagonal(repeat(1, 1.0)),sigma_(1.0),invsigma_(1.0) {}
Isotropic() : Diagonal(Vector1::Constant(1.0)),sigma_(1.0),invsigma_(1.0) {}
public:

4
gtsam/linear/RegularJacobianFactor.h
View File

@ -83,7 +83,7 @@ public:
void multiplyHessianAdd(double alpha, const double* x, double* y) const {
if (empty())
return;
Vector Ax = zero(Ab_.rows());
Vector Ax = Vector::Zero(Ab_.rows());
// Just iterate over all A matrices and multiply in correct config part
for (size_t pos = 0; pos < size(); ++pos)
@ -173,7 +173,7 @@ public:
* RAW memory access! Assumes keys start at 0 and go to M-1, and x is laid out that way
*/
Vector operator*(const double* x) const {
Vector Ax = zero(Ab_.rows());
Vector Ax = Vector::Zero(Ab_.rows());
if (empty())
return Ax;

2
gtsam/linear/VectorValues.cpp
View File

@ -52,7 +52,7 @@ namespace gtsam {
Key key;
size_t n;
boost::tie(key, n) = v;
values_.insert(make_pair(key, sub(x, j, j + n)));
values_.insert(make_pair(key, x.segment(j,n)));
j += n;
}
}

2
gtsam/linear/iterative.h
View File

@ -86,7 +86,7 @@ namespace gtsam {
/** x += alpha* A'*e */
void transposeMultiplyAdd(double alpha, const Vector& e, Vector& x) const {
gtsam::transposeMultiplyAdd(alpha, A(), e, x);
x += alpha * A().transpose() * e;
}
};

4
gtsam/linear/tests/testGaussianBayesTree.cpp
View File

@ -170,7 +170,7 @@ TEST(GaussianBayesTree, complicatedMarginal) {
LONGS_EQUAL(1, (long)actualJacobianQR.size());
LONGS_EQUAL(5, (long)actualJacobianQR.keys()[0]);
Matrix actualA = actualJacobianQR.getA(actualJacobianQR.begin());
Matrix actualCov = inverse(actualA.transpose() * actualA);
Matrix actualCov = (actualA.transpose() * actualA).inverse();
EXPECT(assert_equal(expectedCov, actualCov, 1e-1));
// Marginal on 6
@ -187,7 +187,7 @@ TEST(GaussianBayesTree, complicatedMarginal) {
LONGS_EQUAL(1, (long)actualJacobianQR.size());
LONGS_EQUAL(6, (long)actualJacobianQR.keys()[0]);
actualA = actualJacobianQR.getA(actualJacobianQR.begin());
actualCov = inverse(actualA.transpose() * actualA);
actualCov = (actualA.transpose() * actualA).inverse();
EXPECT(assert_equal(expectedCov, actualCov, 1e1));
}

20
gtsam/linear/tests/testGaussianFactorGraph.cpp
View File

@ -47,10 +47,10 @@ TEST(GaussianFactorGraph, initialization) {
SharedDiagonal unit2 = noiseModel::Unit::Create(2);
fg +=
JacobianFactor(0, 10*eye(2), -1.0*ones(2), unit2),
JacobianFactor(0, -10*eye(2),1, 10*eye(2), Vector2(2.0, -1.0), unit2),
JacobianFactor(0, -5*eye(2), 2, 5*eye(2), Vector2(0.0, 1.0), unit2),
JacobianFactor(1, -5*eye(2), 2, 5*eye(2), Vector2(-1.0, 1.5), unit2);
JacobianFactor(0, 10*I_2x2, -1.0*Vector::Ones(2), unit2),
JacobianFactor(0, -10*I_2x2,1, 10*I_2x2, Vector2(2.0, -1.0), unit2),
JacobianFactor(0, -5*I_2x2, 2, 5*I_2x2, Vector2(0.0, 1.0), unit2),
JacobianFactor(1, -5*I_2x2, 2, 5*I_2x2, Vector2(-1.0, 1.5), unit2);
EXPECT_LONGS_EQUAL(4, (long)fg.size());
@ -166,13 +166,13 @@ static GaussianFactorGraph createSimpleGaussianFactorGraph() {
GaussianFactorGraph fg;
SharedDiagonal unit2 = noiseModel::Unit::Create(2);
// linearized prior on x1: c[_x1_]+x1=0 i.e. x1=-c[_x1_]
fg += JacobianFactor(2, 10*eye(2), -1.0*ones(2), unit2);
fg += JacobianFactor(2, 10*I_2x2, -1.0*Vector::Ones(2), unit2);
// odometry between x1 and x2: x2-x1=[0.2;-0.1]
fg += JacobianFactor(0, 10*eye(2), 2, -10*eye(2), Vector2(2.0, -1.0), unit2);
fg += JacobianFactor(0, 10*I_2x2, 2, -10*I_2x2, Vector2(2.0, -1.0), unit2);
// measurement between x1 and l1: l1-x1=[0.0;0.2]
fg += JacobianFactor(1, 5*eye(2), 2, -5*eye(2), Vector2(0.0, 1.0), unit2);
fg += JacobianFactor(1, 5*I_2x2, 2, -5*I_2x2, Vector2(0.0, 1.0), unit2);
// measurement between x2 and l1: l1-x2=[-0.2;0.3]
fg += JacobianFactor(0, -5*eye(2), 1, 5*eye(2), Vector2(-1.0, 1.5), unit2);
fg += JacobianFactor(0, -5*I_2x2, 1, 5*I_2x2, Vector2(-1.0, 1.5), unit2);
return fg;
}
@ -280,8 +280,8 @@ TEST( GaussianFactorGraph, multiplyHessianAdd )
/* ************************************************************************* */
static GaussianFactorGraph createGaussianFactorGraphWithHessianFactor() {
GaussianFactorGraph gfg = createSimpleGaussianFactorGraph();
gfg += HessianFactor(1, 2, 100*eye(2,2), zeros(2,2), Vector2(0.0, 1.0),
400*eye(2,2), Vector2(1.0, 1.0), 3.0);
gfg += HessianFactor(1, 2, 100*I_2x2, Z_2x2, Vector2(0.0, 1.0),
400*I_2x2, Vector2(1.0, 1.0), 3.0);
return gfg;
}

8
gtsam/linear/tests/testHessianFactor.cpp
View File

@ -54,7 +54,7 @@ TEST(HessianFactor, emptyConstructor)
HessianFactor f;
DOUBLES_EQUAL(0.0, f.constantTerm(), 1e-9); // Constant term should be zero
EXPECT(assert_equal(Vector(), f.linearTerm())); // Linear term should be empty
EXPECT(assert_equal(zeros(1,1), f.info())); // Full matrix should be 1-by-1 zero matrix
EXPECT(assert_equal((Matrix) Z_1x1, f.info())); // Full matrix should be 1-by-1 zero matrix
DOUBLES_EQUAL(0.0, f.error(VectorValues()), 1e-9); // Should have zero error
}
@ -123,11 +123,11 @@ TEST(HessianFactor, Constructor1)
TEST(HessianFactor, Constructor1b)
{
Vector mu = Vector2(1.0,2.0);
Matrix Sigma = eye(2,2);
Matrix Sigma = I_2x2;
HessianFactor factor(0, mu, Sigma);
Matrix G = eye(2,2);
Matrix G = I_2x2;
Vector g = G*mu;
double f = dot(g,mu);
@ -484,7 +484,7 @@ TEST(HessianFactor, combine) {
-8.94427191, 0.0,
0.0, -8.94427191).finished();
Vector b = Vector2(2.23606798,-1.56524758);
SharedDiagonal model = noiseModel::Diagonal::Sigmas(ones(2));
SharedDiagonal model = noiseModel::Diagonal::Sigmas(Vector::Ones(2));
GaussianFactor::shared_ptr f(new JacobianFactor(0, A0, 1, A1, 2, A2, b, model));
GaussianFactorGraph factors = list_of(f);

40
gtsam/linear/tests/testJacobianFactor.cpp
View File

@ -168,19 +168,19 @@ namespace simple_graph {
Key keyX(10), keyY(8), keyZ(12);
double sigma1 = 0.1;
Matrix A11 = Matrix::Identity(2, 2);
Matrix A11 = I_2x2;
Vector2 b1(2, -1);
JacobianFactor factor1(keyX, A11, b1, noiseModel::Isotropic::Sigma(2, sigma1));
double sigma2 = 0.5;
Matrix A21 = -2 * Matrix::Identity(2, 2);
Matrix A22 = 3 * Matrix::Identity(2, 2);
Matrix A21 = -2 * I_2x2;
Matrix A22 = 3 * I_2x2;
Vector2 b2(4, -5);
JacobianFactor factor2(keyX, A21, keyY, A22, b2, noiseModel::Isotropic::Sigma(2, sigma2));
double sigma3 = 1.0;
Matrix A32 = -4 * Matrix::Identity(2, 2);
Matrix A33 = 5 * Matrix::Identity(2, 2);
Matrix A32 = -4 * I_2x2;
Matrix A33 = 5 * I_2x2;
Vector2 b3(3, -6);
JacobianFactor factor3(keyY, A32, keyZ, A33, b3, noiseModel::Isotropic::Sigma(2, sigma3));
@ -193,8 +193,8 @@ TEST( JacobianFactor, construct_from_graph)
{
using namespace simple_graph;
Matrix A1(6,2); A1 << A11, A21, Matrix::Zero(2,2);
Matrix A2(6,2); A2 << Matrix::Zero(2,2), A22, A32;
Matrix A1(6,2); A1 << A11, A21, Z_2x2;
Matrix A2(6,2); A2 << Z_2x2, A22, A32;
Matrix A3(6,2); A3 << Matrix::Zero(4,2), A33;
Vector b(6); b << b1, b2, b3;
Vector sigmas(6); sigmas << sigma1, sigma1, sigma2, sigma2, sigma3, sigma3;
@ -260,17 +260,17 @@ TEST(JacobianFactor, matrices_NULL)
// hessianDiagonal
VectorValues expectDiagonal;
expectDiagonal.insert(5, ones(3));
expectDiagonal.insert(10, 4*ones(3));
expectDiagonal.insert(15, 9*ones(3));
expectDiagonal.insert(5, Vector::Ones(3));
expectDiagonal.insert(10, 4*Vector::Ones(3));
expectDiagonal.insert(15, 9*Vector::Ones(3));
EXPECT(assert_equal(expectDiagonal, factor.hessianDiagonal()));
// hessianBlockDiagonal
map<Key,Matrix> actualBD = factor.hessianBlockDiagonal();
LONGS_EQUAL(3,actualBD.size());
EXPECT(assert_equal(1*eye(3),actualBD[5]));
EXPECT(assert_equal(4*eye(3),actualBD[10]));
EXPECT(assert_equal(9*eye(3),actualBD[15]));
EXPECT(assert_equal(1*I_3x3,actualBD[5]));
EXPECT(assert_equal(4*I_3x3,actualBD[10]));
EXPECT(assert_equal(9*I_3x3,actualBD[15]));
}
/* ************************************************************************* */
@ -314,9 +314,9 @@ TEST(JacobianFactor, matrices)
// hessianBlockDiagonal
map<Key,Matrix> actualBD = factor.hessianBlockDiagonal();
LONGS_EQUAL(3,actualBD.size());
EXPECT(assert_equal(4*eye(3),actualBD[5]));
EXPECT(assert_equal(16*eye(3),actualBD[10]));
EXPECT(assert_equal(36*eye(3),actualBD[15]));
EXPECT(assert_equal(4*I_3x3,actualBD[5]));
EXPECT(assert_equal(16*I_3x3,actualBD[10]));
EXPECT(assert_equal(36*I_3x3,actualBD[15]));
}
/* ************************************************************************* */
@ -324,7 +324,7 @@ TEST(JacobianFactor, operators )
{
SharedDiagonal sigma0_1 = noiseModel::Isotropic::Sigma(2,0.1);
Matrix I = eye(2);
Matrix I = I_2x2;
Vector b = Vector2(0.2,-0.1);
JacobianFactor lf(1, -I, 2, I, b, sigma0_1);
@ -405,7 +405,7 @@ TEST(JacobianFactor, eliminate)
gfg.add(0, A10, 1, A11, b1, noiseModel::Diagonal::Sigmas(s1, true));
gfg.add(1, A21, b2, noiseModel::Diagonal::Sigmas(s2, true));
Matrix zero3x3 = zeros(3,3);
Matrix zero3x3 = Matrix::Zero(3,3);
Matrix A0 = gtsam::stack(3, &A10, &zero3x3, &zero3x3);
Matrix A1 = gtsam::stack(3, &A11, &A01, &A21);
Vector9 b; b << b1, b0, b2;
@ -561,7 +561,7 @@ TEST ( JacobianFactor, constraint_eliminate1 )
{
// construct a linear constraint
Vector v(2); v(0)=1.2; v(1)=3.4;
JacobianFactor lc(1, eye(2), v, noiseModel::Constrained::All(2));
JacobianFactor lc(1, I_2x2, v, noiseModel::Constrained::All(2));
// eliminate it
pair<GaussianConditional::shared_ptr, JacobianFactor::shared_ptr>
@ -572,7 +572,7 @@ TEST ( JacobianFactor, constraint_eliminate1 )
// verify conditional Gaussian
Vector sigmas = Vector2(0.0, 0.0);
GaussianConditional expCG(1, v, eye(2), noiseModel::Diagonal::Sigmas(sigmas));
GaussianConditional expCG(1, v, I_2x2, noiseModel::Diagonal::Sigmas(sigmas));
EXPECT(assert_equal(expCG, *actual.first));
}

34
gtsam/linear/tests/testKalmanFilter.cpp
View File

@ -51,7 +51,7 @@ TEST( KalmanFilter, constructor ) {
EXPECT(assert_equal(x_initial, p1->mean()));
Matrix Sigma = (Matrix(2, 2) << 0.01, 0.0, 0.0, 0.01).finished();
EXPECT(assert_equal(Sigma, p1->covariance()));
EXPECT(assert_equal(inverse(Sigma), p1->information()));
EXPECT(assert_equal(Sigma.inverse(), p1->information()));
// Create one with a sharedGaussian
KalmanFilter::State p2 = kf1.init(x_initial, Sigma);
@ -65,33 +65,33 @@ TEST( KalmanFilter, constructor ) {
TEST( KalmanFilter, linear1 ) {
// Create the controls and measurement properties for our example
Matrix F = eye(2, 2);
Matrix B = eye(2, 2);
Matrix F = I_2x2;
Matrix B = I_2x2;
Vector u = Vector2(1.0, 0.0);
SharedDiagonal modelQ = noiseModel::Isotropic::Sigma(2, 0.1);
Matrix Q = 0.01*eye(2, 2);
Matrix H = eye(2, 2);
Matrix Q = 0.01*I_2x2;
Matrix H = I_2x2;
State z1(1.0, 0.0);
State z2(2.0, 0.0);
State z3(3.0, 0.0);
SharedDiagonal modelR = noiseModel::Isotropic::Sigma(2, 0.1);
Matrix R = 0.01*eye(2, 2);
Matrix R = 0.01*I_2x2;
// Create the set of expected output TestValues
State expected0(0.0, 0.0);
Matrix P00 = 0.01*eye(2, 2);
Matrix P00 = 0.01*I_2x2;
State expected1(1.0, 0.0);
Matrix P01 = P00 + Q;
Matrix I11 = inverse(P01) + inverse(R);
Matrix I11 = P01.inverse() + R.inverse();
State expected2(2.0, 0.0);
Matrix P12 = inverse(I11) + Q;
Matrix I22 = inverse(P12) + inverse(R);
Matrix P12 = I11.inverse() + Q;
Matrix I22 = P12.inverse() + R.inverse();
State expected3(3.0, 0.0);
Matrix P23 = inverse(I22) + Q;
Matrix I33 = inverse(P23) + inverse(R);
Matrix P23 = I22.inverse() + Q;
Matrix I33 = P23.inverse() + R.inverse();
// Create a Kalman filter of dimension 2
KalmanFilter kf(2);
@ -140,7 +140,7 @@ TEST( KalmanFilter, predict ) {
Vector u = Vector3(1.0, 0.0, 2.0);
Matrix R = (Matrix(2, 2) << 1.0, 0.5, 0.0, 3.0).finished();
Matrix M = trans(R)*R;
Matrix Q = inverse(M);
Matrix Q = M.inverse();
// Create a Kalman filter of dimension 2
KalmanFilter kf(2);
@ -167,7 +167,7 @@ TEST( KalmanFilter, predict ) {
// Test both QR and Cholesky versions in case of a realistic (AHRS) dynamics update
TEST( KalmanFilter, QRvsCholesky ) {
Vector mean = ones(9);
Vector mean = Vector::Ones(9);
Matrix covariance = 1e-6 * (Matrix(9, 9) <<
15.0, -6.2, 0.0, 0.0, 0.0, 0.0, 0.0, 63.8, -0.6,
-6.2, 21.9, -0.0, 0.0, 0.0, 0.0, -63.8, -0.0, -0.1,
@ -197,8 +197,8 @@ TEST( KalmanFilter, QRvsCholesky ) {
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1000000.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1000000.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1000000.0).finished();
Matrix B = zeros(9, 1);
Vector u = zero(1);
Matrix B = Matrix::Zero(9, 1);
Vector u = Z_1x1;
Matrix dt_Q_k = 1e-6 * (Matrix(9, 9) <<
33.7, 3.1, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
3.1, 126.4, -0.3, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
@ -270,7 +270,7 @@ TEST( KalmanFilter, QRvsCholesky ) {
EXPECT(assert_equal(expected2, pb2->covariance(), 1e-7));
// do the above update again, this time with a full Matrix Q
Matrix modelQ = diag(emul(sigmas,sigmas));
Matrix modelQ = ((Matrix) sigmas.array().square()).asDiagonal();
KalmanFilter::State pa3 = kfa.updateQ(pa, H, z, modelQ);
KalmanFilter::State pb3 = kfb.updateQ(pb, H, z, modelQ);

28
gtsam/linear/tests/testNoiseModel.cpp
View File

@ -112,7 +112,7 @@ TEST(NoiseModel, Unit)
TEST(NoiseModel, equals)
{
Gaussian::shared_ptr g1 = Gaussian::SqrtInformation(R),
g2 = Gaussian::SqrtInformation(eye(3,3));
g2 = Gaussian::SqrtInformation(I_3x3);
Diagonal::shared_ptr d1 = Diagonal::Sigmas(Vector3(kSigma, kSigma, kSigma)),
d2 = Diagonal::Sigmas(Vector3(0.1, 0.2, 0.3));
Isotropic::shared_ptr i1 = Isotropic::Sigma(3, kSigma),
@ -155,13 +155,13 @@ TEST(NoiseModel, ConstrainedConstructors )
Vector3 mu(200.0, 300.0, 400.0);
actual = Constrained::All(d);
// TODO: why should this be a thousand ??? Dummy variable?
EXPECT(assert_equal(gtsam::repeat(d, 1000.0), actual->mu()));
EXPECT(assert_equal(gtsam::repeat(d, 0), actual->sigmas()));
EXPECT(assert_equal(gtsam::repeat(d, 0), actual->invsigmas())); // Actually zero as dummy value
EXPECT(assert_equal(gtsam::repeat(d, 0), actual->precisions())); // Actually zero as dummy value
EXPECT(assert_equal(Vector::Constant(d, 1000.0), actual->mu()));
EXPECT(assert_equal(Vector::Constant(d, 0), actual->sigmas()));
EXPECT(assert_equal(Vector::Constant(d, 0), actual->invsigmas())); // Actually zero as dummy value
EXPECT(assert_equal(Vector::Constant(d, 0), actual->precisions())); // Actually zero as dummy value
actual = Constrained::All(d, m);
EXPECT(assert_equal(gtsam::repeat(d, m), actual->mu()));
EXPECT(assert_equal(Vector::Constant(d, m), actual->mu()));
actual = Constrained::All(d, mu);
EXPECT(assert_equal(mu, actual->mu()));
@ -170,7 +170,7 @@ TEST(NoiseModel, ConstrainedConstructors )
EXPECT(assert_equal(mu, actual->mu()));
actual = Constrained::MixedSigmas(m, sigmas);
EXPECT(assert_equal( gtsam::repeat(d, m), actual->mu()));
EXPECT(assert_equal(Vector::Constant(d, m), actual->mu()));
}
/* ************************************************************************* */
@ -395,7 +395,7 @@ TEST(NoiseModel, SmartSqrtInformation )
{
bool smart = true;
gtsam::SharedGaussian expected = Unit::Create(3);
gtsam::SharedGaussian actual = Gaussian::SqrtInformation(eye(3), smart);
gtsam::SharedGaussian actual = Gaussian::SqrtInformation(I_3x3, smart);
EXPECT(assert_equal(*expected,*actual));
}
@ -404,7 +404,7 @@ TEST(NoiseModel, SmartSqrtInformation2 )
{
bool smart = true;
gtsam::SharedGaussian expected = Unit::Isotropic::Sigma(3,2);
gtsam::SharedGaussian actual = Gaussian::SqrtInformation(0.5*eye(3), smart);
gtsam::SharedGaussian actual = Gaussian::SqrtInformation(0.5*I_3x3, smart);
EXPECT(assert_equal(*expected,*actual));
}
@ -413,7 +413,7 @@ TEST(NoiseModel, SmartInformation )
{
bool smart = true;
gtsam::SharedGaussian expected = Unit::Isotropic::Variance(3,2);
Matrix M = 0.5*eye(3);
Matrix M = 0.5*I_3x3;
EXPECT(checkIfDiagonal(M));
gtsam::SharedGaussian actual = Gaussian::Information(M, smart);
EXPECT(assert_equal(*expected,*actual));
@ -424,7 +424,7 @@ TEST(NoiseModel, SmartCovariance )
{
bool smart = true;
gtsam::SharedGaussian expected = Unit::Create(3);
gtsam::SharedGaussian actual = Gaussian::Covariance(eye(3), smart);
gtsam::SharedGaussian actual = Gaussian::Covariance(I_3x3, smart);
EXPECT(assert_equal(*expected,*actual));
}
@ -433,7 +433,7 @@ TEST(NoiseModel, ScalarOrVector )
{
bool smart = true;
SharedGaussian expected = Unit::Create(3);
SharedGaussian actual = Gaussian::Covariance(eye(3), smart);
SharedGaussian actual = Gaussian::Covariance(I_3x3, smart);
EXPECT(assert_equal(*expected,*actual));
}
@ -442,9 +442,9 @@ TEST(NoiseModel, WhitenInPlace)
{
Vector sigmas = Vector3(0.1, 0.1, 0.1);
SharedDiagonal model = Diagonal::Sigmas(sigmas);
Matrix A = eye(3);
Matrix A = I_3x3;
model->WhitenInPlace(A);
Matrix expected = eye(3) * 10;
Matrix expected = I_3x3 * 10;
EXPECT(assert_equal(expected, A));
}

10
gtsam/linear/tests/testSerializationLinear.cpp
View File

@ -51,7 +51,7 @@ BOOST_CLASS_EXPORT_GUID(gtsam::SharedDiagonal, "gtsam_SharedDiagonal");
/* ************************************************************************* */
// example noise models
static noiseModel::Diagonal::shared_ptr diag3 = noiseModel::Diagonal::Sigmas(Vector3(0.1, 0.2, 0.3));
static noiseModel::Gaussian::shared_ptr gaussian3 = noiseModel::Gaussian::SqrtInformation(2.0 * eye(3,3));
static noiseModel::Gaussian::shared_ptr gaussian3 = noiseModel::Gaussian::SqrtInformation(2.0 * I_3x3);
static noiseModel::Isotropic::shared_ptr iso3 = noiseModel::Isotropic::Sigma(3, 0.2);
static noiseModel::Constrained::shared_ptr constrained3 = noiseModel::Constrained::MixedSigmas(Vector3(0.0, 0.0, 0.1));
static noiseModel::Unit::shared_ptr unit3 = noiseModel::Unit::Create(3);
@ -144,8 +144,8 @@ TEST (Serialization, linear_factors) {
EXPECT(equalsBinary<VectorValues>(values));
Key i1 = 4, i2 = 7;
Matrix A1 = eye(3), A2 = -1.0 * eye(3);
Vector b = ones(3);
Matrix A1 = I_3x3, A2 = -1.0 * I_3x3;
Vector b = Vector::Ones(3);
SharedDiagonal model = noiseModel::Diagonal::Sigmas(Vector3(1.0, 2.0, 3.0));
JacobianFactor jacobianfactor(i1, A1, i2, A2, b, model);
EXPECT(equalsObj(jacobianfactor));
@ -185,8 +185,8 @@ TEST (Serialization, gaussian_factor_graph) {
{
Key i1 = 4, i2 = 7;
Matrix A1 = eye(3), A2 = -1.0 * eye(3);
Vector b = ones(3);
Matrix A1 = I_3x3, A2 = -1.0 * I_3x3;
Vector b = Vector::Ones(3);
SharedDiagonal model = noiseModel::Diagonal::Sigmas(Vector3(1.0, 2.0, 3.0));
JacobianFactor jacobianfactor(i1, A1, i2, A2, b, model);
HessianFactor hessianfactor(jacobianfactor);

2
gtsam/navigation/AttitudeFactor.h
View File

@ -189,7 +189,7 @@ public:
Vector e = attitudeError(nTb.rotation(), H);
if (H) {
Matrix H23 = *H;
*H = Matrix::Zero(2, 6);
*H = Matrix::Zero(2,6);
H->block<2,3>(0,0) = H23;
}
return e;

4
gtsam/navigation/MagFactor.h
View File

@ -154,7 +154,7 @@ public:
// measured bM = nRb<52> * nM + b, where b is unknown bias
Point3 hx = bRn_.rotate(nM, boost::none, H1) + bias;
if (H2)
*H2 = eye(3);
*H2 = I_3x3;
return (hx - measured_);
}
};
@ -205,7 +205,7 @@ public:
*H2 = scale * H * (*H2);
}
if (H3)
*H3 = eye(3);
*H3 = I_3x3;
return (hx - measured_);
}
};

36
gtsam/navigation/PreintegratedRotation.h
View File

@ -26,24 +26,25 @@
namespace gtsam {
/**
* PreintegratedRotation is the base class for all PreintegratedMeasurements
* classes (in AHRSFactor, ImuFactor, and CombinedImuFactor).
* It includes the definitions of the preintegrated rotation.
*/
class PreintegratedRotation {
public:
/// Parameters for pre-integration:
/// Usage: Create just a single Params and pass a shared pointer to the constructor
struct Params {
/// Parameters for pre-integration:
/// Usage: Create just a single Params and pass a shared pointer to the constructor
struct PreintegratedRotationParams {
Matrix3 gyroscopeCovariance; ///< continuous-time "Covariance" of gyroscope measurements
boost::optional<Vector3> omegaCoriolis; ///< Coriolis constant
boost::optional<Pose3> body_P_sensor; ///< The pose of the sensor in the body frame
Params() : gyroscopeCovariance(I_3x3) {}
PreintegratedRotationParams() : gyroscopeCovariance(I_3x3) {}
virtual void print(const std::string& s) const;
virtual bool equals(const Params& other, double tol=1e-9) const;
virtual bool equals(const PreintegratedRotationParams& other, double tol=1e-9) const;
void setGyroscopeCovariance(const Matrix3& cov) { gyroscopeCovariance = cov; }
void setOmegaCoriolis(const Vector3& omega) { omegaCoriolis.reset(omega); }
void setBodyPSensor(const Pose3& pose) { body_P_sensor.reset(pose); }
const Matrix3& getGyroscopeCovariance() const { return gyroscopeCovariance; }
boost::optional<Vector3> getOmegaCoriolis() const { return omegaCoriolis; }
boost::optional<Pose3> getBodyPSensor() const { return body_P_sensor; }
private:
/** Serialization function */
@ -55,7 +56,16 @@ class PreintegratedRotation {
ar & BOOST_SERIALIZATION_NVP(omegaCoriolis);
ar & BOOST_SERIALIZATION_NVP(body_P_sensor);
}
};
};
/**
* PreintegratedRotation is the base class for all PreintegratedMeasurements
* classes (in AHRSFactor, ImuFactor, and CombinedImuFactor).
* It includes the definitions of the preintegrated rotation.
*/
class PreintegratedRotation {
public:
typedef PreintegratedRotationParams Params;
protected:
/// Parameters

2
gtsam/navigation/PreintegrationBase.cpp
View File

@ -105,7 +105,7 @@ pair<Vector3, Vector3> PreintegrationBase::correctMeasurementsBySensorPose(
// Update derivative: centrifugal causes the correlation between acc and omega!!!
if (correctedAcc_H_unbiasedOmega) {
double wdp = correctedOmega.dot(b_arm);
*correctedAcc_H_unbiasedOmega = -(diag(Vector3::Constant(wdp))
*correctedAcc_H_unbiasedOmega = -( (Matrix) Vector3::Constant(wdp).asDiagonal()
+ correctedOmega * b_arm.transpose()) * bRs.matrix()
+ 2 * b_arm * unbiasedOmega.transpose();
}

12
gtsam/navigation/PreintegrationParams.h
View File

@ -23,7 +23,7 @@ namespace gtsam {
/// Parameters for pre-integration:
/// Usage: Create just a single Params and pass a shared pointer to the constructor
struct PreintegrationParams: PreintegratedRotation::Params {
struct PreintegrationParams: PreintegratedRotationParams {
Matrix3 accelerometerCovariance; ///< continuous-time "Covariance" of accelerometer
Matrix3 integrationCovariance; ///< continuous-time "Covariance" describing integration uncertainty
bool use2ndOrderCoriolis; ///< Whether to use second order Coriolis integration
@ -50,6 +50,14 @@ struct PreintegrationParams: PreintegratedRotation::Params {
void print(const std::string& s) const;
bool equals(const PreintegratedRotation::Params& other, double tol) const;
void setAccelerometerCovariance(const Matrix3& cov) { accelerometerCovariance = cov; }
void setIntegrationCovariance(const Matrix3& cov) { integrationCovariance = cov; }
void setUse2ndOrderCoriolis(bool flag) { use2ndOrderCoriolis = flag; }
const Matrix3& getAccelerometerCovariance() const { return accelerometerCovariance; }
const Matrix3& getIntegrationCovariance() const { return integrationCovariance; }
bool getUse2ndOrderCoriolis() const { return use2ndOrderCoriolis; }
protected:
/// Default constructor for serialization only: uninitialized!
PreintegrationParams() {}
@ -60,7 +68,7 @@ protected:
void serialize(ARCHIVE & ar, const unsigned int /*version*/) {
namespace bs = ::boost::serialization;
ar & boost::serialization::make_nvp("PreintegratedRotation_Params",
boost::serialization::base_object<PreintegratedRotation::Params>(*this));
boost::serialization::base_object<PreintegratedRotationParams>(*this));
ar & bs::make_nvp("accelerometerCovariance", bs::make_array(accelerometerCovariance.data(), accelerometerCovariance.size()));
ar & bs::make_nvp("integrationCovariance", bs::make_array(integrationCovariance.data(), integrationCovariance.size()));
ar & BOOST_SERIALIZATION_NVP(use2ndOrderCoriolis);

2
gtsam/navigation/tests/testAHRSFactor.cpp
View File

@ -268,7 +268,7 @@ TEST( AHRSFactor, PartialDerivativeLogmap ) {
const Vector3 x = thetahat; // parametrization of so(3)
const Matrix3 X = skewSymmetric(x); // element of Lie algebra so(3): X = x^
double normx = norm_2(x);
double normx = x.norm();
const Matrix3 actualDelFdeltheta = I_3x3 + 0.5 * X
+ (1 / (normx * normx) - (1 + cos(normx)) / (2 * normx * sin(normx))) * X
* X;

4
gtsam/navigation/tests/testAttitudeFactor.cpp
View File

@ -42,7 +42,7 @@ TEST( Rot3AttitudeFactor, Constructor ) {
// Create a linearization point at the zero-error point
Rot3 nRb;
EXPECT(assert_equal(zero(2),factor.evaluateError(nRb),1e-5));
EXPECT(assert_equal((Vector) Z_2x1,factor.evaluateError(nRb),1e-5));
// Calculate numerical derivatives
Matrix expectedH = numericalDerivative11<Vector,Rot3>(
@ -75,7 +75,7 @@ TEST( Pose3AttitudeFactor, Constructor ) {
// Create a linearization point at the zero-error point
Pose3 T(Rot3(), Point3(-5.0, 8.0, -11.0));
EXPECT(assert_equal(zero(2),factor.evaluateError(T),1e-5));
EXPECT(assert_equal((Vector) Z_2x1,factor.evaluateError(T),1e-5));
// Calculate numerical derivatives
Matrix expectedH = numericalDerivative11<Vector,Pose3>(

4
gtsam/navigation/tests/testGPSFactor.cpp
View File

@ -58,7 +58,7 @@ TEST( GPSFactor, Constructor ) {
// Create a linearization point at zero error
Pose3 T(Rot3::RzRyRx(0.15, -0.30, 0.45), Point3(E, N, U));
EXPECT(assert_equal(zero(3),factor.evaluateError(T),1e-5));
EXPECT(assert_equal(Z_3x3,factor.evaluateError(T),1e-5));
// Calculate numerical derivatives
Matrix expectedH = numericalDerivative11<Vector,Pose3>(
@ -87,7 +87,7 @@ TEST( GPSFactor2, Constructor ) {
// Create a linearization point at zero error
NavState T(Rot3::RzRyRx(0.15, -0.30, 0.45), Point3(E, N, U), Vector3::Zero());
EXPECT(assert_equal(zero(3),factor.evaluateError(T),1e-5));
EXPECT(assert_equal(Z_3x3,factor.evaluateError(T),1e-5));
// Calculate numerical derivatives
Matrix expectedH = numericalDerivative11<Vector,NavState>(

8
gtsam/navigation/tests/testMagFactor.cpp
View File

@ -71,19 +71,19 @@ TEST( MagFactor, Factors ) {
// MagFactor
MagFactor f(1, measured, s, dir, bias, model);
EXPECT( assert_equal(zero(3),f.evaluateError(theta,H1),1e-5));
EXPECT( assert_equal(Z_3x3,f.evaluateError(theta,H1),1e-5));
EXPECT( assert_equal((Matrix)numericalDerivative11<Vector,Rot2> //
(boost::bind(&MagFactor::evaluateError, &f, _1, none), theta), H1, 1e-7));
// MagFactor1
MagFactor1 f1(1, measured, s, dir, bias, model);
EXPECT( assert_equal(zero(3),f1.evaluateError(nRb,H1),1e-5));
EXPECT( assert_equal(Z_3x3,f1.evaluateError(nRb,H1),1e-5));
EXPECT( assert_equal(numericalDerivative11<Vector,Rot3> //
(boost::bind(&MagFactor1::evaluateError, &f1, _1, none), nRb), H1, 1e-7));
// MagFactor2
MagFactor2 f2(1, 2, measured, nRb, model);
EXPECT( assert_equal(zero(3),f2.evaluateError(scaled,bias,H1,H2),1e-5));
EXPECT( assert_equal(Z_3x3,f2.evaluateError(scaled,bias,H1,H2),1e-5));
EXPECT( assert_equal(numericalDerivative11<Vector,Point3> //
(boost::bind(&MagFactor2::evaluateError, &f2, _1, bias, none, none), scaled),//
H1, 1e-7));
@ -93,7 +93,7 @@ TEST( MagFactor, Factors ) {
// MagFactor2
MagFactor3 f3(1, 2, 3, measured, nRb, model);
EXPECT(assert_equal(zero(3),f3.evaluateError(s,dir,bias,H1,H2,H3),1e-5));
EXPECT(assert_equal(Z_3x3,f3.evaluateError(s,dir,bias,H1,H2,H3),1e-5));
EXPECT(assert_equal((Matrix)numericalDerivative11<Vector,double> //
(boost::bind(&MagFactor3::evaluateError, &f3, _1, dir, bias, none, none, none), s),//
H1, 1e-7));

14
gtsam/nonlinear/NonlinearEquality.h
View File

@ -142,18 +142,18 @@ public:
const size_t nj = traits<T>::GetDimension(feasible_);
if (allow_error_) {
if (H)
*H = eye(nj); // FIXME: this is not the right linearization for nonlinear compare
*H = Matrix::Identity(nj,nj); // FIXME: this is not the right linearization for nonlinear compare
return traits<T>::Local(xj,feasible_);
} else if (compare_(feasible_, xj)) {
if (H)
*H = eye(nj);
return zero(nj); // set error to zero if equal
*H = Matrix::Identity(nj,nj);
return Vector::Zero(nj); // set error to zero if equal
} else {
if (H)
throw std::invalid_argument(
"Linearization point not feasible for "
+ DefaultKeyFormatter(this->key()) + "!");
return repeat(nj, std::numeric_limits<double>::infinity()); // set error to infinity if not equal
return Vector::Constant(nj, std::numeric_limits<double>::infinity()); // set error to infinity if not equal
}
}
@ -249,7 +249,7 @@ public:
Vector evaluateError(const X& x1,
boost::optional<Matrix&> H = boost::none) const {
if (H)
(*H) = eye(traits<X>::GetDimension(x1));
(*H) = Matrix::Identity(traits<X>::GetDimension(x1),traits<X>::GetDimension(x1));
// manifold equivalent of h(x)-z -> log(z,h(x))
return traits<X>::Local(value_,x1);
}
@ -322,8 +322,8 @@ public:
Vector evaluateError(const X& x1, const X& x2, boost::optional<Matrix&> H1 =
boost::none, boost::optional<Matrix&> H2 = boost::none) const {
static const size_t p = traits<X>::dimension;
if (H1) *H1 = -eye(p);
if (H2) *H2 = eye(p);
if (H1) *H1 = -Matrix::Identity(p,p);
if (H2) *H2 = Matrix::Identity(p,p);
return traits<X>::Local(x1,x2);
}

12
gtsam/nonlinear/NonlinearFactor.h
View File

@ -313,7 +313,7 @@ public:
return evaluateError(x1);
}
} else {
return zero(this->dim());
return Vector::Zero(this->dim());
}
}
@ -388,7 +388,7 @@ public:
return evaluateError(x1, x2);
}
} else {
return zero(this->dim());
return Vector::Zero(this->dim());
}
}
@ -463,7 +463,7 @@ public:
else
return evaluateError(x.at<X1>(keys_[0]), x.at<X2>(keys_[1]), x.at<X3>(keys_[2]));
} else {
return zero(this->dim());
return Vector::Zero(this->dim());
}
}
@ -543,7 +543,7 @@ public:
else
return evaluateError(x.at<X1>(keys_[0]), x.at<X2>(keys_[1]), x.at<X3>(keys_[2]), x.at<X4>(keys_[3]));
} else {
return zero(this->dim());
return Vector::Zero(this->dim());
}
}
@ -627,7 +627,7 @@ public:
else
return evaluateError(x.at<X1>(keys_[0]), x.at<X2>(keys_[1]), x.at<X3>(keys_[2]), x.at<X4>(keys_[3]), x.at<X5>(keys_[4]));
} else {
return zero(this->dim());
return Vector::Zero(this->dim());
}
}
@ -715,7 +715,7 @@ public:
else
return evaluateError(x.at<X1>(keys_[0]), x.at<X2>(keys_[1]), x.at<X3>(keys_[2]), x.at<X4>(keys_[3]), x.at<X5>(keys_[4]), x.at<X6>(keys_[5]));
} else {
return zero(this->dim());
return Vector::Zero(this->dim());
}
}

145
gtsam/nonlinear/Values-inl.h
View File

@ -270,24 +270,113 @@ namespace gtsam {
}
/* ************************************************************************* */
namespace internal {
// Check the type and throw exception if incorrect
// Generic version, partially specialized below for various Eigen Matrix types
template<typename ValueType>
const ValueType& Values::at(Key j) const {
struct handle {
ValueType operator()(Key j, const gtsam::Value * const pointer) {
try {
// value returns a const ValueType&, and the return makes a copy !!!!!
return dynamic_cast<const GenericValue<ValueType>&>(*pointer).value();
} catch (std::bad_cast &) {
throw ValuesIncorrectType(j, typeid(*pointer), typeid(ValueType));
}
}
};
// Handle dynamic vectors
template<>
struct handle<Eigen::Matrix<double, -1, 1> > {
Eigen::Matrix<double, -1, 1> operator()(Key j,
const gtsam::Value * const pointer) {
try {
// value returns a const Vector&, and the return makes a copy !!!!!
return dynamic_cast<const GenericValue<Eigen::Matrix<double, -1, 1> >&>(*pointer).value();
} catch (std::bad_cast &) {
// If a fixed vector was stored, we end up here as well.
throw ValuesIncorrectType(j, typeid(*pointer),
typeid(Eigen::Matrix<double, -1, 1>));
}
}
};
// Handle dynamic matrices
template<int N>
struct handle<Eigen::Matrix<double, -1, N> > {
Eigen::Matrix<double, -1, N> operator()(Key j,
const gtsam::Value * const pointer) {
try {
// value returns a const Matrix&, and the return makes a copy !!!!!
return dynamic_cast<const GenericValue<Eigen::Matrix<double, -1, N> >&>(*pointer).value();
} catch (std::bad_cast &) {
// If a fixed matrix was stored, we end up here as well.
throw ValuesIncorrectType(j, typeid(*pointer),
typeid(Eigen::Matrix<double, -1, N>));
}
}
};
// Request for a fixed vector
// TODO(jing): is this piece of code really needed ???
template<int M>
struct handle<Eigen::Matrix<double, M, 1> > {
Eigen::Matrix<double, M, 1> operator()(Key j,
const gtsam::Value * const pointer) {
try {
// value returns a const VectorM&, and the return makes a copy !!!!!
return dynamic_cast<const GenericValue<Eigen::Matrix<double, M, 1> >&>(*pointer).value();
} catch (std::bad_cast &) {
// Check if a dynamic vector was stored
Matrix A = handle<Eigen::VectorXd>()(j, pointer); // will throw if not....
// Yes: check size, and throw if not a match
if (A.rows() != M || A.cols() != 1)
throw NoMatchFoundForFixed(M, 1, A.rows(), A.cols());
else
// This is not a copy because of RVO
return A;
}
}
};
// Request for a fixed matrix
template<int M, int N>
struct handle<Eigen::Matrix<double, M, N> > {
Eigen::Matrix<double, M, N> operator()(Key j,
const gtsam::Value * const pointer) {
try {
// value returns a const MatrixMN&, and the return makes a copy !!!!!
return dynamic_cast<const GenericValue<Eigen::Matrix<double, M, N> >&>(*pointer).value();
} catch (std::bad_cast &) {
// Check if a dynamic matrix was stored
Matrix A = handle<Eigen::MatrixXd>()(j, pointer); // will throw if not....
// Yes: check size, and throw if not a match
if (A.rows() != M || A.cols() != N)
throw NoMatchFoundForFixed(M, N, A.rows(), A.cols());
else
// This is not a copy because of RVO
return A;
}
}
};
} // internal
/* ************************************************************************* */
template<typename ValueType>
ValueType Values::at(Key j) const {
// Find the item
KeyValueMap::const_iterator item = values_.find(j);
// Throw exception if it does not exist
if(item == values_.end())
throw ValuesKeyDoesNotExist("retrieve", j);
throw ValuesKeyDoesNotExist("at", j);
// Check the type and throw exception if incorrect
const Value& value = *item->second;
try {
return dynamic_cast<const GenericValue<ValueType>&>(value).value();
} catch (std::bad_cast &) {
// NOTE(abe): clang warns about potential side effects if done in typeid
const Value* value = item->second;
throw ValuesIncorrectType(j, typeid(*value), typeid(ValueType));
}
return internal::handle<ValueType>()(j,item->second);
}
/* ************************************************************************* */
@ -312,16 +401,48 @@ namespace gtsam {
}
/* ************************************************************************* */
// wrap all fixed in dynamics when insert and update
namespace internal {
// general type
template<typename ValueType>
struct handle_wrap {
inline gtsam::GenericValue<ValueType> operator()(Key j, const ValueType& val) {
return gtsam::GenericValue<ValueType>(val);
}
};
// when insert/update a fixed size vector: convert to dynamic size
template<int M>
struct handle_wrap<Eigen::Matrix<double, M, 1> > {
inline gtsam::GenericValue<Eigen::Matrix<double, -1, 1> > operator()(
Key j, const Eigen::Matrix<double, M, 1>& val) {
return gtsam::GenericValue<Eigen::Matrix<double, -1, 1> >(val);
}
};
// when insert/update a fixed size matrix: convert to dynamic size
template<int M, int N>
struct handle_wrap<Eigen::Matrix<double, M, N> > {
inline gtsam::GenericValue<Eigen::Matrix<double, -1, -1> > operator()(
Key j, const Eigen::Matrix<double, M, N>& val) {
return gtsam::GenericValue<Eigen::Matrix<double, -1, -1> >(val);
}
};
}
// insert a templated value
template<typename ValueType>
void Values::insert(Key j, const ValueType& val) {
insert(j, static_cast<const Value&>(GenericValue<ValueType>(val)));
insert(j, static_cast<const Value&>(internal::handle_wrap<ValueType>()(j, val)));
}
// update with templated value
template <typename ValueType>
void Values::update(Key j, const ValueType& val) {
update(j, static_cast<const Value&>(GenericValue<ValueType >(val)));
update(j, static_cast<const Value&>(internal::handle_wrap<ValueType>()(j, val)));
}
}

55
gtsam/nonlinear/Values.cpp
View File

@ -25,8 +25,6 @@
#include <gtsam/nonlinear/Values.h>
#include <gtsam/linear/VectorValues.h>
#include <list>
#include <boost/foreach.hpp>
#ifdef __GNUC__
#pragma GCC diagnostic push
@ -38,6 +36,9 @@
#endif
#include <boost/iterator/transform_iterator.hpp>
#include <list>
#include <sstream>
using namespace std;
namespace gtsam {
@ -112,24 +113,6 @@ namespace gtsam {
return result;
}
/* ************************************************************************* */
Vector Values::atFixed(Key j, size_t n) {
switch (n) {
case 1: return at<Vector1>(j);
case 2: return at<Vector2>(j);
case 3: return at<Vector3>(j);
case 4: return at<Vector4>(j);
case 5: return at<Vector5>(j);
case 6: return at<Vector6>(j);
case 7: return at<Vector7>(j);
case 8: return at<Vector8>(j);
case 9: return at<Vector9>(j);
default:
throw runtime_error(
"Values::at fixed size can only handle n in 1..9");
}
}
/* ************************************************************************* */
const Value& Values::at(Key j) const {
// Find the item
@ -148,24 +131,6 @@ namespace gtsam {
throw ValuesKeyAlreadyExists(j);
}
/* ************************************************************************* */
void Values::insertFixed(Key j, const Vector& v, size_t n) {
switch (n) {
case 1: insert<Vector1>(j,v); break;
case 2: insert<Vector2>(j,v); break;
case 3: insert<Vector3>(j,v); break;
case 4: insert<Vector4>(j,v); break;
case 5: insert<Vector5>(j,v); break;
case 6: insert<Vector6>(j,v); break;
case 7: insert<Vector7>(j,v); break;
case 8: insert<Vector8>(j,v); break;
case 9: insert<Vector9>(j,v); break;
default:
throw runtime_error(
"Values::insert fixed size can only handle n in 1..9");
}
}
/* ************************************************************************* */
void Values::insert(const Values& values) {
for(const_iterator key_value = values.begin(); key_value != values.end(); ++key_value) {
@ -269,4 +234,18 @@ namespace gtsam {
return message_.c_str();
}
/* ************************************************************************* */
const char* NoMatchFoundForFixed::what() const throw() {
if(message_.empty()) {
ostringstream oss;
oss
<< "Attempting to retrieve fixed-size matrix with dimensions " //
<< M1_ << "x" << N1_
<< ", but found dynamic Matrix with mismatched dimensions " //
<< M2_ << "x" << N2_;
message_ = oss.str();
}
return message_.c_str();
}
}

39
gtsam/nonlinear/Values.h
View File

@ -168,16 +168,13 @@ namespace gtsam {
/** Retrieve a variable by key \c j. The type of the value associated with
* this key is supplied as a template argument to this function.
* @param j Retrieve the value associated with this key
* @tparam Value The type of the value stored with this key, this method
* throws DynamicValuesIncorrectType if this requested type is not correct.
* @return A const reference to the stored value
* @tparam ValueType The type of the value stored with this key, this method
* Throws DynamicValuesIncorrectType if this requested type is not correct.
* Dynamic matrices/vectors can be retrieved as fixed-size, but not vice-versa.
* @return The stored value
*/
template<typename ValueType>
const ValueType& at(Key j) const;
/// Special version for small fixed size vectors, for matlab/python
/// throws truntime error if n<1 || n>9
Vector atFixed(Key j, size_t n);
ValueType at(Key j) const;
/// version for double
double atDouble(size_t key) const { return at<double>(key);}
@ -259,10 +256,6 @@ namespace gtsam {
template <typename ValueType>
void insert(Key j, const ValueType& val);
/// Special version for small fixed size vectors, for matlab/python
/// throws truntime error if n<1 || n>9
void insertFixed(Key j, const Vector& v, size_t n);
/// version for double
void insertDouble(Key j, double c) { insert<double>(j,c); }
@ -500,6 +493,28 @@ namespace gtsam {
}
};
/* ************************************************************************* */
class GTSAM_EXPORT NoMatchFoundForFixed: public std::exception {
protected:
const size_t M1_, N1_;
const size_t M2_, N2_;
private:
mutable std::string message_;
public:
NoMatchFoundForFixed(size_t M1, size_t N1, size_t M2, size_t N2) throw () :
M1_(M1), N1_(N1), M2_(M2), N2_(N2) {
}
virtual ~NoMatchFoundForFixed() throw () {
}
virtual const char* what() const throw ();
};
/* ************************************************************************* */
/// traits
template<>
struct traits<Values> : public Testable<Values> {

2
gtsam/nonlinear/expressions.h
View File

@ -25,7 +25,7 @@ Expression<T> compose(const Expression<T>& t1, const Expression<T>& t2) {
}
// Some typedefs
typedef Expression<double> double_;
typedef Expression<double> Double_;
typedef Expression<Vector1> Vector1_;
typedef Expression<Vector2> Vector2_;
typedef Expression<Vector3> Vector3_;

20
gtsam/nonlinear/tests/testExpression.cpp
View File

@ -17,7 +17,7 @@
* @brief unit tests for Block Automatic Differentiation
*/
#include <gtsam/nonlinear/Expression.h>
#include <gtsam/nonlinear/expressions.h>
#include <gtsam/geometry/Cal3_S2.h>
#include <gtsam/geometry/PinholeCamera.h>
#include <gtsam/geometry/Point3.h>
@ -32,9 +32,7 @@ using boost::assign::map_list_of;
using namespace std;
using namespace gtsam;
typedef Expression<double> double_;
typedef Expression<Point3> Point3_;
typedef Expression<Vector3> Vector3_;
typedef Expression<Pose3> Pose3_;
typedef Expression<Rot3> Rot3_;
@ -101,7 +99,7 @@ TEST(Expression, Unary1) {
}
TEST(Expression, Unary2) {
using namespace unary;
double_ e(f2, p);
Double_ e(f2, p);
EXPECT(expected == e.keys());
}
@ -156,7 +154,7 @@ Point3_ p_cam(x, &Pose3::transform_to, p);
// Check that creating an expression to double compiles
TEST(Expression, BinaryToDouble) {
using namespace binary;
double_ p_cam(doubleF, x, p);
Double_ p_cam(doubleF, x, p);
}
/* ************************************************************************* */
@ -269,11 +267,11 @@ Rot3 composeThree(const Rot3& R1, const Rot3& R2, const Rot3& R3, OptionalJacobi
OptionalJacobian<3, 3> H2, OptionalJacobian<3, 3> H3) {
// return dummy derivatives (not correct, but that's ok for testing here)
if (H1)
*H1 = eye(3);
*H1 = I_3x3;
if (H2)
*H2 = eye(3);
*H2 = I_3x3;
if (H3)
*H3 = eye(3);
*H3 = I_3x3;
return R1 * (R2 * R3);
}
@ -372,8 +370,8 @@ TEST(Expression, TripleSum) {
/* ************************************************************************* */
TEST(Expression, SumOfUnaries) {
const Key key(67);
const double_ norm_(&gtsam::norm, Point3_(key));
const double_ sum_ = norm_ + norm_;
const Double_ norm_(&gtsam::norm, Point3_(key));
const Double_ sum_ = norm_ + norm_;
Values values;
values.insert<Point3>(key, Point3(6, 0, 0));
@ -391,7 +389,7 @@ TEST(Expression, SumOfUnaries) {
TEST(Expression, UnaryOfSum) {
const Key key1(42), key2(67);
const Point3_ sum_ = Point3_(key1) + Point3_(key2);
const double_ norm_(&gtsam::norm, sum_);
const Double_ norm_(&gtsam::norm, sum_);
map<Key, int> actual_dims, expected_dims = map_list_of<Key, int>(key1, 3)(key2, 3);
norm_.dims(actual_dims);

2
gtsam/nonlinear/tests/testLinearContainerFactor.cpp
View File

@ -234,7 +234,7 @@ TEST( testLinearContainerFactor, creation ) {
// create a linear factor
SharedDiagonal model = noiseModel::Unit::Create(2);
JacobianFactor::shared_ptr linear_factor(new JacobianFactor(
l3, eye(2,2), l5, 2.0 * eye(2,2), zero(2), model));
l3, I_2x2, l5, 2.0 * I_2x2, Z_2x1, model));
// create a set of values - build with full set of values
gtsam::Values full_values, exp_values;

54
gtsam/nonlinear/tests/testValues.cpp
View File

@ -477,13 +477,59 @@ TEST(Values, Destructors) {
}
/* ************************************************************************* */
TEST(Values, FixedSize) {
TEST(Values, VectorDynamicInsertFixedRead) {
Values values;
Vector v(3); v << 5.0, 6.0, 7.0;
values.insertFixed(key1, v, 3);
values.insert(key1, v);
Vector3 expected(5.0, 6.0, 7.0);
CHECK(assert_equal((Vector)expected, values.at<Vector3>(key1)));
CHECK_EXCEPTION(values.insertFixed(key1, v, 12),runtime_error);
Vector3 actual = values.at<Vector3>(key1);
CHECK(assert_equal(expected, actual));
CHECK_EXCEPTION(values.at<Vector7>(key1), exception);
}
/* ************************************************************************* */
TEST(Values, VectorDynamicInsertDynamicRead) {
Values values;
Vector v(3); v << 5.0, 6.0, 7.0;
values.insert(key1, v);
Vector expected(3); expected << 5.0, 6.0, 7.0;
Vector actual = values.at<Vector>(key1);
LONGS_EQUAL(3, actual.rows());
LONGS_EQUAL(1, actual.cols());
CHECK(assert_equal(expected, actual));
}
/* ************************************************************************* */
TEST(Values, VectorFixedInsertFixedRead) {
Values values;
Vector3 v; v << 5.0, 6.0, 7.0;
values.insert(key1, v);
Vector3 expected; expected << 5.0, 6.0, 7.0;
Vector3 actual = values.at<Vector3>(key1);
CHECK(assert_equal(expected, actual));
CHECK_EXCEPTION(values.at<Vector7>(key1), exception);
}
/* ************************************************************************* */
TEST(Values, VectorFixedInsertDynamicRead) {
Values values;
Vector3 v; v << 5.0, 6.0, 7.0;
values.insert(key1, v);
Vector expected(3); expected << 5.0, 6.0, 7.0;
Vector actual = values.at<Vector>(key1);
LONGS_EQUAL(3, actual.rows());
LONGS_EQUAL(1, actual.cols());
CHECK(assert_equal(expected, actual));
}
/* ************************************************************************* */
TEST(Values, MatrixDynamicInsertFixedRead) {
Values values;
Matrix v(1,3); v << 5.0, 6.0, 7.0;
values.insert(key1, v);
Vector3 expected(5.0, 6.0, 7.0);
CHECK(assert_equal((Vector)expected, values.at<Matrix13>(key1)));
CHECK_EXCEPTION(values.at<Matrix23>(key1), exception);
}
/* ************************************************************************* */
int main() { TestResult tr; return TestRegistry::runAllTests(tr); }

10
gtsam/slam/GeneralSFMFactor.h
View File

@ -129,7 +129,7 @@ public:
if (H1) *H1 = JacobianC::Zero();
if (H2) *H2 = JacobianL::Zero();
// TODO warn if verbose output asked for
return zero(2);
return Z_2x1;
}
}
@ -266,13 +266,13 @@ public:
return reprojError.vector();
}
catch( CheiralityException& e) {
if (H1) *H1 = zeros(2, 6);
if (H2) *H2 = zeros(2, 3);
if (H3) *H3 = zeros(2, DimK);
if (H1) *H1 = Matrix::Zero(2, 6);
if (H2) *H2 = Matrix::Zero(2, 3);
if (H3) *H3 = Matrix::Zero(2, DimK);
std::cout << e.what() << ": Landmark "<< DefaultKeyFormatter(this->key2())
<< " behind Camera " << DefaultKeyFormatter(this->key1()) << std::endl;
}
return zero(2);
return Z_2x1;
}
/** return the measured */

11
gtsam/slam/InitializePose3.cpp
View File

@ -31,10 +31,9 @@ using namespace std;
namespace gtsam {
namespace InitializePose3 {
//static const Matrix I = eye(1);
static const Matrix I9 = eye(9);
static const Matrix I9 = I_9x9;
static const Vector zero9 = Vector::Zero(9);
static const Matrix zero33= Matrix::Zero(3,3);
static const Matrix zero33 = Z_3x3;
static const Key keyAnchor = symbol('Z', 9999999);
@ -54,11 +53,9 @@ GaussianFactorGraph buildLinearOrientationGraph(const NonlinearFactorGraph& g) {
else
std::cout << "Error in buildLinearOrientationGraph" << std::endl;
// std::cout << "Rij \n" << Rij << std::endl;
const FastVector<Key>& keys = factor->keys();
Key key1 = keys[0], key2 = keys[1];
Matrix M9 = Matrix::Zero(9,9);
Matrix M9 = Z_9x9;
M9.block(0,0,3,3) = Rij;
M9.block(3,3,3,3) = Rij;
M9.block(6,6,3,3) = Rij;
@ -74,7 +71,7 @@ GaussianFactorGraph buildLinearOrientationGraph(const NonlinearFactorGraph& g) {
Values normalizeRelaxedRotations(const VectorValues& relaxedRot3) {
gttic(InitializePose3_computeOrientationsChordal);
Matrix ppm = Matrix::Zero(3,3); // plus plus minus
Matrix ppm = Z_3x3; // plus plus minus
ppm(0,0) = 1; ppm(1,1) = 1; ppm(2,2) = -1;
Values validRot3;

2
gtsam/slam/JacobianFactorQ.h
View File

@ -56,7 +56,7 @@ public:
Base() {
size_t j = 0, m2 = E.rows(), m = m2 / ZDim;
// Calculate projector Q
Matrix Q = eye(m2) - E * P * E.transpose();
Matrix Q = Matrix::Identity(m2,m2) - E * P * E.transpose();
// Calculate pre-computed Jacobian matrices
// TODO: can we do better ?
std::vector<KeyMatrix> QF;

2
gtsam/slam/OrientedPlane3Factor.cpp
View File

@ -47,7 +47,7 @@ Vector OrientedPlane3DirectionPrior::evaluateError(const OrientedPlane3& plane,
Vector e = n_hat_p.error(n_hat_q, H_p);
H->resize(2, 3);
H->block<2, 2>(0, 0) << H_p;
H->block<2, 1>(0, 2) << Matrix::Zero(2, 1);
H->block<2, 1>(0, 2) << Z_2x1;
return e;
} else {
Unit3 n_hat_p = measured_p_.normal();

2
gtsam/slam/PoseRotationPrior.h
View File

@ -75,7 +75,7 @@ public:
Vector evaluateError(const Pose& pose, boost::optional<Matrix&> H = boost::none) const {
const Rotation& newR = pose.rotation();
if (H) {
*H = gtsam::zeros(rDim, xDim);
*H = Matrix::Zero(rDim, xDim);
std::pair<size_t, size_t> rotInterval = POSE::rotationInterval();
(*H).middleCols(rotInterval.first, rDim).setIdentity(rDim, rDim);
}

2
gtsam/slam/PoseTranslationPrior.h
View File

@ -65,7 +65,7 @@ public:
const int tDim = traits<Translation>::GetDimension(newTrans);
const int xDim = traits<Pose>::GetDimension(pose);
if (H) {
*H = gtsam::zeros(tDim, xDim);
*H = Matrix::Zero(tDim, xDim);
std::pair<size_t, size_t> transInterval = POSE::translationInterval();
(*H).middleCols(transInterval.first, tDim) = R.matrix();
}

2
gtsam/slam/PriorFactor.h
View File

@ -86,7 +86,7 @@ namespace gtsam {
/** vector of errors */
Vector evaluateError(const T& x, boost::optional<Matrix&> H = boost::none) const {
if (H) (*H) = eye(traits<T>::GetDimension(x));
if (H) (*H) = Matrix::Identity(traits<T>::GetDimension(x),traits<T>::GetDimension(x));
// manifold equivalent of z-x -> Local(x,z)
// TODO(ASL) Add Jacobians.
return -traits<T>::Local(x, prior_);

6
gtsam/slam/ProjectionFactor.h
View File

@ -146,15 +146,15 @@ namespace gtsam {
return reprojectionError.vector();
}
} catch( CheiralityException& e) {
if (H1) *H1 = zeros(2,6);
if (H2) *H2 = zeros(2,3);
if (H1) *H1 = Matrix::Zero(2,6);
if (H2) *H2 = Matrix::Zero(2,3);
if (verboseCheirality_)
std::cout << e.what() << ": Landmark "<< DefaultKeyFormatter(this->key2()) <<
" moved behind camera " << DefaultKeyFormatter(this->key1()) << std::endl;
if (throwCheirality_)
throw e;
}
return ones(2) * 2.0 * K_->fx();
return Vector2::Constant(2.0 * K_->fx());
}
/** return the measurement */

2
gtsam/slam/ReferenceFrameFactor.h
View File

@ -100,7 +100,7 @@ public:
boost::optional<Matrix&> Dlocal = boost::none) const {
Point newlocal = transform_point<Transform,Point>(trans, global, Dtrans, Dforeign);
if (Dlocal)
*Dlocal = -1* gtsam::eye(Point::dimension);
*Dlocal = -1* Matrix::Identity(Point::dimension,Point::dimension);
return traits<Point>::Local(local,newlocal);
}

5
gtsam/slam/SmartFactorBase.h
View File

@ -29,6 +29,7 @@
#include <gtsam/geometry/CameraSet.h>
#include <boost/optional.hpp>
#include <boost/serialization/optional.hpp>
#include <boost/make_shared.hpp>
#include <vector>
@ -73,7 +74,7 @@ protected:
std::vector<Z> measured_;
/// @name Pose of the camera in the body frame
const boost::optional<Pose3> body_P_sensor_; ///< Pose of the camera in the body frame
boost::optional<Pose3> body_P_sensor_; ///< Pose of the camera in the body frame
/// @}
// Cache for Fblocks, to avoid a malloc ever time we re-linearize
@ -385,7 +386,9 @@ private:
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int /*version*/) {
ar & BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base);
ar & BOOST_SERIALIZATION_NVP(noiseModel_);
ar & BOOST_SERIALIZATION_NVP(measured_);
ar & BOOST_SERIALIZATION_NVP(body_P_sensor_);
}
};
// end class SmartFactorBase

25
gtsam/slam/SmartProjectionFactor.h
View File

@ -112,6 +112,20 @@ struct GTSAM_EXPORT SmartProjectionParams {
void setDynamicOutlierRejectionThreshold(double dynOutRejectionThreshold) {
triangulation.dynamicOutlierRejectionThreshold = dynOutRejectionThreshold;
}
private:
/// Serialization function
friend class boost::serialization::access;
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int version) {
ar & BOOST_SERIALIZATION_NVP(linearizationMode);
ar & BOOST_SERIALIZATION_NVP(degeneracyMode);
ar & BOOST_SERIALIZATION_NVP(triangulation);
ar & BOOST_SERIALIZATION_NVP(retriangulationThreshold);
ar & BOOST_SERIALIZATION_NVP(throwCheirality);
ar & BOOST_SERIALIZATION_NVP(verboseCheirality);
}
};
/**
@ -277,9 +291,9 @@ public:
if (params_.degeneracyMode == ZERO_ON_DEGENERACY && !result_) {
// failed: return"empty" Hessian
BOOST_FOREACH(Matrix& m, Gs)
m = zeros(Base::Dim, Base::Dim);
m = Matrix::Zero(Base::Dim, Base::Dim);
BOOST_FOREACH(Vector& v, gs)
v = zero(Base::Dim);
v = Vector::Zero(Base::Dim);
return boost::make_shared<RegularHessianFactor<Base::Dim> >(this->keys_,
Gs, gs, 0.0);
}
@ -463,7 +477,7 @@ public:
if (nonDegenerate)
return Base::unwhitenedError(cameras, *result_);
else
return zero(cameras.size() * 2);
return Vector::Zero(cameras.size() * 2);
}
/**
@ -535,8 +549,9 @@ private:
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int version) {
ar & BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base);
ar & BOOST_SERIALIZATION_NVP(params_.throwCheirality);
ar & BOOST_SERIALIZATION_NVP(params_.verboseCheirality);
ar & BOOST_SERIALIZATION_NVP(params_);
ar & BOOST_SERIALIZATION_NVP(result_);
ar & BOOST_SERIALIZATION_NVP(cameraPosesTriangulation_);
}
}
;

6
gtsam/slam/StereoFactor.h
View File

@ -138,15 +138,15 @@ public:
return (stereoCam.project(point, H1, H2) - measured_).vector();
}
} catch(StereoCheiralityException& e) {
if (H1) *H1 = zeros(3,6);
if (H2) *H2 = zeros(3,3);
if (H1) *H1 = Matrix::Zero(3,6);
if (H2) *H2 = Z_3x3;
if (verboseCheirality_)
std::cout << e.what() << ": Landmark "<< DefaultKeyFormatter(this->key2()) <<
" moved behind camera " << DefaultKeyFormatter(this->key1()) << std::endl;
if (throwCheirality_)
throw e;
}
return ones(3) * 2.0 * K_->fx();
return Vector3::Constant(2.0 * K_->fx());
}
/** return the measured */

4
gtsam/slam/TriangulationFactor.h
View File

@ -124,14 +124,14 @@ public:
return error.vector();
} catch (CheiralityException& e) {
if (H2)
*H2 = zeros(Measurement::dimension, 3);
*H2 = Matrix::Zero(Measurement::dimension, 3);
if (verboseCheirality_)
std::cout << e.what() << ": Landmark "
<< DefaultKeyFormatter(this->key()) << " moved behind camera"
<< std::endl;
if (throwCheirality_)
throw e;
return ones(Measurement::dimension) * 2.0 * camera_.calibration().fx();
return Eigen::Matrix<double,Measurement::dimension,1>::Constant(2.0 * camera_.calibration().fx());
}
}

18
gtsam/slam/dataset.cpp
View File

@ -472,7 +472,7 @@ void writeG2o(const NonlinearFactorGraph& graph, const Values& estimate,
<< p.x() << " " << p.y() << " " << p.z() << " " << R.toQuaternion().x()
<< " " << R.toQuaternion().y() << " " << R.toQuaternion().z() << " " << R.toQuaternion().w();
Matrix InfoG2o = eye(6);
Matrix InfoG2o = I_6x6;
InfoG2o.block(0,0,3,3) = Info.block(3,3,3,3); // cov translation
InfoG2o.block(3,3,3,3) = Info.block(0,0,3,3); // cov rotation
InfoG2o.block(0,3,3,3) = Info.block(0,3,3,3); // off diagonal
@ -539,7 +539,7 @@ GraphAndValues load3D(const string& filename) {
ls >> id1 >> id2 >> x >> y >> z >> roll >> pitch >> yaw;
Rot3 R = Rot3::Ypr(yaw,pitch,roll);
Point3 t = Point3(x, y, z);
Matrix m = eye(6);
Matrix m = I_6x6;
for (int i = 0; i < 6; i++)
for (int j = i; j < 6; j++)
ls >> m(i, j);
@ -549,7 +549,7 @@ GraphAndValues load3D(const string& filename) {
graph->push_back(factor);
}
if (tag == "EDGE_SE3:QUAT") {
Matrix m = eye(6);
Matrix m = I_6x6;
Key id1, id2;
double x, y, z, qx, qy, qz, qw;
ls >> id1 >> id2 >> x >> y >> z >> qx >> qy >> qz >> qw;
@ -563,11 +563,13 @@ GraphAndValues load3D(const string& filename) {
m(j, i) = mij;
}
}
Matrix mgtsam = eye(6);
mgtsam.block(0,0,3,3) = m.block(3,3,3,3); // cov rotation
mgtsam.block(3,3,3,3) = m.block(0,0,3,3); // cov translation
mgtsam.block(0,3,3,3) = m.block(0,3,3,3); // off diagonal
mgtsam.block(3,0,3,3) = m.block(3,0,3,3); // off diagonal
Matrix mgtsam = I_6x6;
mgtsam.block<3,3>(0,0) = m.block<3,3>(3,3); // cov rotation
mgtsam.block<3,3>(3,3) = m.block<3,3>(0,0); // cov translation
mgtsam.block<3,3>(0,3) = m.block<3,3>(0,3); // off diagonal
mgtsam.block<3,3>(3,0) = m.block<3,3>(3,0); // off diagonal
SharedNoiseModel model = noiseModel::Gaussian::Information(mgtsam);
NonlinearFactor::shared_ptr factor(new BetweenFactor<Pose3>(id1, id2, Pose3(R,t), model));
graph->push_back(factor);

4
gtsam/slam/lago.cpp
View File

@ -30,8 +30,8 @@ using namespace std;
namespace gtsam {
namespace lago {
static const Matrix I = eye(1);
static const Matrix I3 = eye(3);
static const Matrix I = I_1x1;
static const Matrix I3 = I_3x3;
static const Key keyAnchor = symbol('Z', 9999999);
static const noiseModel::Diagonal::shared_ptr priorOrientationNoise =

2
gtsam/slam/tests/testEssentialMatrixConstraint.cpp
View File

@ -45,7 +45,7 @@ TEST( EssentialMatrixConstraint, test ) {
Rot3::RzRyRx(0.179693265735950, 0.002945368776519, 0.102274823253840),
Point3(-3.37493895, 6.14660244, -8.93650986));
Vector expected = zero(5);
Vector expected = Z_5x1;
Vector actual = factor.evaluateError(pose1,pose2);
CHECK(assert_equal(expected, actual, 1e-8));

4
gtsam/slam/tests/testPoseRotationPrior.cpp
View File

@ -52,7 +52,7 @@ TEST( testPoseRotationFactor, level3_zero_error ) {
Pose3 pose1(rot3A, point3A);
Pose3RotationPrior factor(poseKey, rot3A, model3);
Matrix actH1;
EXPECT(assert_equal(zero(3), factor.evaluateError(pose1, actH1)));
EXPECT(assert_equal(Z_3x1, factor.evaluateError(pose1, actH1)));
Matrix expH1 = numericalDerivative22<Vector3,Pose3RotationPrior,Pose3>(evalFactorError3, factor, pose1, 1e-5);
EXPECT(assert_equal(expH1, actH1, tol));
}
@ -78,7 +78,7 @@ TEST( testPoseRotationFactor, level2_zero_error ) {
Pose2 pose1(rot2A, point2A);
Pose2RotationPrior factor(poseKey, rot2A, model1);
Matrix actH1;
EXPECT(assert_equal(zero(1), factor.evaluateError(pose1, actH1)));
EXPECT(assert_equal(Z_1x1, factor.evaluateError(pose1, actH1)));
Matrix expH1 = numericalDerivative22<Vector1,Pose2RotationPrior,Pose2>(evalFactorError2, factor, pose1, 1e-5);
EXPECT(assert_equal(expH1, actH1, tol));
}

8
gtsam/slam/tests/testPoseTranslationPrior.cpp
View File

@ -48,7 +48,7 @@ TEST( testPoseTranslationFactor, level3_zero_error ) {
Pose3 pose1(rot3A, point3A);
Pose3TranslationPrior factor(poseKey, point3A, model3);
Matrix actH1;
EXPECT(assert_equal(zero(3), factor.evaluateError(pose1, actH1)));
EXPECT(assert_equal(Z_3x1, factor.evaluateError(pose1, actH1)));
Matrix expH1 = numericalDerivative22<Vector3,Pose3TranslationPrior,Pose3>(evalFactorError3, factor, pose1, 1e-5);
EXPECT(assert_equal(expH1, actH1, tol));
}
@ -68,7 +68,7 @@ TEST( testPoseTranslationFactor, pitched3_zero_error ) {
Pose3 pose1(rot3B, point3A);
Pose3TranslationPrior factor(poseKey, point3A, model3);
Matrix actH1;
EXPECT(assert_equal(zero(3), factor.evaluateError(pose1, actH1)));
EXPECT(assert_equal(Z_3x1, factor.evaluateError(pose1, actH1)));
Matrix expH1 = numericalDerivative22<Vector3,Pose3TranslationPrior,Pose3>(evalFactorError3, factor, pose1, 1e-5);
EXPECT(assert_equal(expH1, actH1, tol));
}
@ -88,7 +88,7 @@ TEST( testPoseTranslationFactor, smallrot3_zero_error ) {
Pose3 pose1(rot3C, point3A);
Pose3TranslationPrior factor(poseKey, point3A, model3);
Matrix actH1;
EXPECT(assert_equal(zero(3), factor.evaluateError(pose1, actH1)));
EXPECT(assert_equal(Z_3x1, factor.evaluateError(pose1, actH1)));
Matrix expH1 = numericalDerivative22<Vector3,Pose3TranslationPrior,Pose3>(evalFactorError3, factor, pose1, 1e-5);
EXPECT(assert_equal(expH1, actH1, tol));
}
@ -108,7 +108,7 @@ TEST( testPoseTranslationFactor, level2_zero_error ) {
Pose2 pose1(rot2A, point2A);
Pose2TranslationPrior factor(poseKey, point2A, model2);
Matrix actH1;
EXPECT(assert_equal(zero(2), factor.evaluateError(pose1, actH1)));
EXPECT(assert_equal(Z_2x1, factor.evaluateError(pose1, actH1)));
Matrix expH1 = numericalDerivative22<Vector2,Pose2TranslationPrior,Pose2>(evalFactorError2, factor, pose1, 1e-5);
EXPECT(assert_equal(expH1, actH1, tol));
}

2
gtsam/slam/tests/testReferenceFrameFactor.cpp
View File

@ -93,7 +93,7 @@ TEST( ReferenceFrameFactor, jacobians_zero ) {
PointReferenceFrameFactor tc(lA1, tA1, lB1);
Vector actCost = tc.evaluateError(global, trans, local),
expCost = zero(2);
expCost = Z_2x1;
EXPECT(assert_equal(expCost, actCost, 1e-5));
Matrix actualDT, actualDL, actualDF;

54
gtsam/slam/tests/testRegularImplicitSchurFactor.cpp
View File

@ -49,9 +49,9 @@ const Vector b = (Vector(6) << 1., 2., 3., 4., 5., 6.).finished();
//*************************************************************************************
TEST( regularImplicitSchurFactor, creation ) {
// Matrix E = Matrix::Ones(6,3);
Matrix E = zeros(6, 3);
E.block<2,2>(0, 0) = eye(2);
E.block<2,3>(2, 0) = 2 * ones(2, 3);
Matrix E = Matrix::Zero(6, 3);
E.block<2,2>(0, 0) = I_2x2;
E.block<2,3>(2, 0) = 2 * Matrix::Ones(2, 3);
Matrix3 P = (E.transpose() * E).inverse();
RegularImplicitSchurFactor<CalibratedCamera> expected(keys, FBlocks, E, P, b);
Matrix expectedP = expected.getPointCovariance();
@ -61,35 +61,35 @@ TEST( regularImplicitSchurFactor, creation ) {
/* ************************************************************************* */
TEST( regularImplicitSchurFactor, addHessianMultiply ) {
Matrix E = zeros(6, 3);
E.block<2,2>(0, 0) = eye(2);
E.block<2,3>(2, 0) = 2 * ones(2, 3);
E.block<2,2>(4, 1) = eye(2);
Matrix E = Matrix::Zero(6, 3);
E.block<2,2>(0, 0) = I_2x2;
E.block<2,3>(2, 0) = 2 * Matrix::Ones(2, 3);
E.block<2,2>(4, 1) = I_2x2;
Matrix3 P = (E.transpose() * E).inverse();
double alpha = 0.5;
VectorValues xvalues = map_list_of //
(0, gtsam::repeat(6, 2))//
(1, gtsam::repeat(6, 4))//
(2, gtsam::repeat(6, 0))// distractor
(3, gtsam::repeat(6, 8));
(0, Vector::Constant(6, 2))//
(1, Vector::Constant(6, 4))//
(2, Vector::Constant(6, 0))// distractor
(3, Vector::Constant(6, 8));
VectorValues yExpected = map_list_of//
(0, gtsam::repeat(6, 27))//
(1, gtsam::repeat(6, -40))//
(2, gtsam::repeat(6, 0))// distractor
(3, gtsam::repeat(6, 279));
(0, Vector::Constant(6, 27))//
(1, Vector::Constant(6, -40))//
(2, Vector::Constant(6, 0))// distractor
(3, Vector::Constant(6, 279));
// Create full F
size_t M=4, m = 3, d = 6;
Matrix F(2 * m, d * M);
F << F0, zeros(2, d * 3), zeros(2, d), F1, zeros(2, d*2), zeros(2, d * 3), F3;
F << F0, Matrix::Zero(2, d * 3), Matrix::Zero(2, d), F1, Matrix::Zero(2, d*2), Matrix::Zero(2, d * 3), F3;
// Calculate expected result F'*alpha*(I - E*P*E')*F*x
FastVector<Key> keys2;
keys2 += 0,1,2,3;
Vector x = xvalues.vector(keys2);
Vector expected = zero(24);
Vector expected = Vector::Zero(24);
RegularImplicitSchurFactor<CalibratedCamera>::multiplyHessianAdd(F, E, P, alpha, x, expected);
EXPECT(assert_equal(expected, yExpected.vector(keys2), 1e-8));
@ -170,9 +170,9 @@ TEST( regularImplicitSchurFactor, addHessianMultiply ) {
}
VectorValues expectedVV;
expectedVV.insert(0,-3.5*ones(6));
expectedVV.insert(1,10*ones(6)/3);
expectedVV.insert(3,-19.5*ones(6));
expectedVV.insert(0,-3.5*Vector::Ones(6));
expectedVV.insert(1,10*Vector::Ones(6)/3);
expectedVV.insert(3,-19.5*Vector::Ones(6));
{ // Check gradientAtZero
VectorValues actual = implicitFactor.gradientAtZero();
EXPECT(assert_equal(expectedVV, jfQ.gradientAtZero(), 1e-8));
@ -210,9 +210,9 @@ TEST( regularImplicitSchurFactor, addHessianMultiply ) {
TEST(regularImplicitSchurFactor, hessianDiagonal)
{
/* TESTED AGAINST MATLAB
* F = [ones(2,6) zeros(2,6) zeros(2,6)
zeros(2,6) 2*ones(2,6) zeros(2,6)
zeros(2,6) zeros(2,6) 3*ones(2,6)]
* F = [Vector::Ones(2,6) zeros(2,6) zeros(2,6)
zeros(2,6) 2*Vector::Ones(2,6) zeros(2,6)
zeros(2,6) zeros(2,6) 3*Vector::Ones(2,6)]
E = [[1:6] [1:6] [0.5 1:5]];
E = reshape(E',3,6)'
P = inv(E' * E)
@ -228,9 +228,9 @@ TEST(regularImplicitSchurFactor, hessianDiagonal)
// hessianDiagonal
VectorValues expected;
expected.insert(0, 1.195652*ones(6));
expected.insert(1, 4.782608*ones(6));
expected.insert(3, 7.043478*ones(6));
expected.insert(0, 1.195652*Vector::Ones(6));
expected.insert(1, 4.782608*Vector::Ones(6));
expected.insert(3, 7.043478*Vector::Ones(6));
EXPECT(assert_equal(expected, factor.hessianDiagonal(),1e-5));
// hessianBlockDiagonal
@ -246,7 +246,7 @@ TEST(regularImplicitSchurFactor, hessianDiagonal)
EXPECT(assert_equal(F3.transpose()*F3-FtE3*P*FtE3.transpose(),actualBD[3]));
// variant two
Matrix I2 = eye(2);
Matrix I2 = I_2x2;
Matrix E0 = E.block<2,3>(0, 0);
Matrix F0t = F0.transpose();
EXPECT(assert_equal(F0t*F0-F0t*E0*P*E0.transpose()*F0,actualBD[0]));

4
gtsam/slam/tests/testRotateFactor.cpp
View File

@ -58,7 +58,7 @@ TEST (RotateFactor, checkMath) {
TEST (RotateFactor, test) {
Model model = noiseModel::Isotropic::Sigma(3, 0.01);
RotateFactor f(1, i1Ri2, c1Zc2, model);
EXPECT(assert_equal(zero(3), f.evaluateError(iRc), 1e-8));
EXPECT(assert_equal(Z_3x1, f.evaluateError(iRc), 1e-8));
Rot3 R = iRc.retract(Vector3(0.1, 0.2, 0.1));
#if defined(GTSAM_ROT3_EXPMAP) || defined(GTSAM_USE_QUATERNIONS)
@ -127,7 +127,7 @@ TEST (RotateFactor, minimization) {
TEST (RotateDirectionsFactor, test) {
Model model = noiseModel::Isotropic::Sigma(2, 0.01);
RotateDirectionsFactor f(1, p1, z1, model);
EXPECT(assert_equal(zero(2), f.evaluateError(iRc), 1e-8));
EXPECT(assert_equal(Z_2x1, f.evaluateError(iRc), 1e-8));
Rot3 R = iRc.retract(Vector3(0.1, 0.2, 0.1));

4
gtsam/slam/tests/testSmartProjectionCameraFactor.cpp
View File

@ -123,7 +123,7 @@ TEST( SmartProjectionCameraFactor, noiseless ) {
double expectedError = 0.0;
DOUBLES_EQUAL(expectedError, factor1->error(values), 1e-7);
CHECK(
assert_equal(zero(4),
assert_equal(Z_4x1,
factor1->reprojectionErrorAfterTriangulation(values), 1e-7));
}
@ -652,7 +652,7 @@ TEST( SmartProjectionCameraFactor, comparisonGeneralSfMFactor ) {
Vector e1 = sfm1.evaluateError(values.at<Camera>(c1), values.at<Point3>(l1));
Vector e2 = sfm2.evaluateError(values.at<Camera>(c2), values.at<Point3>(l1));
double actualError = 0.5
* (norm_2(e1) * norm_2(e1) + norm_2(e2) * norm_2(e2));
* (e1.norm() * e1.norm() + e2.norm() * e2.norm());
double actualErrorGraph = generalGraph.error(values);
DOUBLES_EQUAL(expectedErrorGraph, actualErrorGraph, 1e-7);

22
gtsam/slam/tests/testSmartProjectionPoseFactor.cpp
View File

@ -140,7 +140,7 @@ TEST( SmartProjectionPoseFactor, noiseless ) {
Vector actualErrors = factor.unwhitenedError(cameras, *point, F, E);
EXPECT(assert_equal(expectedE, E, 1e-7));
EXPECT(assert_equal(zero(4), actualErrors, 1e-7));
EXPECT(assert_equal(Z_4x1, actualErrors, 1e-7));
// Calculate using computeJacobians
Vector b;
@ -1409,6 +1409,26 @@ TEST(SmartProjectionPoseFactor, serialize) {
EXPECT(equalsBinary(factor));
}
TEST(SmartProjectionPoseFactor, serialize2) {
using namespace vanillaPose;
using namespace gtsam::serializationTestHelpers;
SmartProjectionParams params;
params.setRankTolerance(rankTol);
Pose3 bts;
SmartFactor factor(model, sharedK, bts, params);
// insert some measurments
vector<Key> key_view;
vector<Point2> meas_view;
key_view.push_back(Symbol('x', 1));
meas_view.push_back(Point2(10, 10));
factor.add(meas_view, key_view);
EXPECT(equalsObj(factor));
EXPECT(equalsXML(factor));
EXPECT(equalsBinary(factor));
}
/* ************************************************************************* */
int main() {
TestResult tr;

8
gtsam_unstable/dynamics/DynamicsPriors.h
View File

@ -57,7 +57,7 @@ struct VelocityPrior : public gtsam::PartialPriorFactor<PoseRTV> {
this->mask_[0] = 6;
this->mask_[1] = 7;
this->mask_[2] = 8;
this->H_ = zeros(3, 9);
this->H_ = Matrix::Zero(3, 9);
this->fillH();
}
};
@ -75,13 +75,13 @@ struct DGroundConstraint : public gtsam::PartialPriorFactor<PoseRTV> {
*/
DGroundConstraint(Key key, double height, const gtsam::SharedNoiseModel& model)
: Base(key, model) {
this->prior_ = delta(4, 0, height); // [z, vz, roll, pitch]
this->prior_ = Vector::Unit(4,0)*height; // [z, vz, roll, pitch]
this->mask_.resize(4);
this->mask_[0] = 5; // z = height
this->mask_[1] = 8; // vz
this->mask_[2] = 0; // roll
this->mask_[3] = 1; // pitch
this->H_ = zeros(3, 9);
this->H_ = Matrix::Zero(3, 9);
this->fillH();
}
@ -97,7 +97,7 @@ struct DGroundConstraint : public gtsam::PartialPriorFactor<PoseRTV> {
this->mask_[1] = 8; // vz
this->mask_[2] = 0; // roll
this->mask_[3] = 1; // pitch
this->H_ = zeros(3, 9);
this->H_ = Matrix::Zero(3, 9);
this->fillH();
}
};

24
gtsam_unstable/dynamics/Pendulum.h
View File

@ -50,9 +50,9 @@ public:
boost::optional<Matrix&> H2 = boost::none,
boost::optional<Matrix&> H3 = boost::none) const {
const size_t p = 1;
if (H1) *H1 = -eye(p);
if (H2) *H2 = eye(p);
if (H3) *H3 = eye(p)*h_;
if (H1) *H1 = -Matrix::Identity(p,p);
if (H2) *H2 = Matrix::Identity(p,p);
if (H3) *H3 = Matrix::Identity(p,p)*h_;
return (Vector(1) << qk+v*h_-qk1).finished();
}
@ -98,9 +98,9 @@ public:
boost::optional<Matrix&> H2 = boost::none,
boost::optional<Matrix&> H3 = boost::none) const {
const size_t p = 1;
if (H1) *H1 = -eye(p);
if (H2) *H2 = eye(p);
if (H3) *H3 = -eye(p)*h_*g_/r_*cos(q);
if (H1) *H1 = -Matrix::Identity(p,p);
if (H2) *H2 = Matrix::Identity(p,p);
if (H3) *H3 = -Matrix::Identity(p,p)*h_*g_/r_*cos(q);
return (Vector(1) << vk - h_ * g_ / r_ * sin(q) - vk1).finished();
}
@ -154,9 +154,9 @@ public:
double mr2_h = 1/h_*m_*r_*r_;
double mgrh = m_*g_*r_*h_;
if (H1) *H1 = -eye(p);
if (H2) *H2 = eye(p)*(-mr2_h + mgrh*(1-alpha_)*(1-alpha_)*cos(qmid));
if (H3) *H3 = eye(p)*( mr2_h + mgrh*(1-alpha_)*(alpha_)*cos(qmid));
if (H1) *H1 = -Matrix::Identity(p,p);
if (H2) *H2 = Matrix::Identity(p,p)*(-mr2_h + mgrh*(1-alpha_)*(1-alpha_)*cos(qmid));
if (H3) *H3 = Matrix::Identity(p,p)*( mr2_h + mgrh*(1-alpha_)*(alpha_)*cos(qmid));
return (Vector(1) << mr2_h * (qk1 - qk) + mgrh * (1 - alpha_) * sin(qmid) - pk).finished();
}
@ -210,9 +210,9 @@ public:
double mr2_h = 1/h_*m_*r_*r_;
double mgrh = m_*g_*r_*h_;
if (H1) *H1 = -eye(p);
if (H2) *H2 = eye(p)*(-mr2_h - mgrh*(1-alpha_)*alpha_*cos(qmid));
if (H3) *H3 = eye(p)*( mr2_h - mgrh*alpha_*alpha_*cos(qmid));
if (H1) *H1 = -Matrix::Identity(p,p);
if (H2) *H2 = Matrix::Identity(p,p)*(-mr2_h - mgrh*(1-alpha_)*alpha_*cos(qmid));
if (H3) *H3 = Matrix::Identity(p,p)*( mr2_h - mgrh*alpha_*alpha_*cos(qmid));
return (Vector(1) << mr2_h * (qk1 - qk) - mgrh * alpha_ * sin(qmid) - pk1).finished();
}

4
gtsam_unstable/dynamics/PoseRTV.cpp
View File

@ -11,7 +11,7 @@ namespace gtsam {
using namespace std;
static const Vector kGravity = delta(3, 2, 9.81);
static const Vector kGravity = Vector::Unit(3,2)*9.81;
/* ************************************************************************* */
double bound(double a, double min, double max) {
@ -105,7 +105,7 @@ PoseRTV PoseRTV::flyingDynamics(
Vector Acc_n =
yaw_correction_bn.rotate(forward) // applies locally forward force in the global frame
- drag * (Vector(3) << v1.x(), v1.y(), 0.0).finished() // drag term dependent on v1
+ delta(3, 2, loss_lift - lift_control); // falling due to lift lost from pitch
+ Vector::Unit(3,2)*(loss_lift - lift_control); // falling due to lift lost from pitch
// Update Vehicle Position and Velocity
Velocity3 v2 = v1 + Velocity3(Acc_n * dt);

2
gtsam_unstable/dynamics/SimpleHelicopter.h
View File

@ -140,7 +140,7 @@ public:
}
if (H3) {
*H3 = zeros(6,6);
*H3 = Z_6x6;
insertSub(*H3, -h_*D_gravityBody_gk, 3, 0);
}

6
gtsam_unstable/dynamics/VelocityConstraint3.h
View File

@ -41,9 +41,9 @@ public:
boost::optional<Matrix&> H2 = boost::none,
boost::optional<Matrix&> H3 = boost::none) const {
const size_t p = 1;
if (H1) *H1 = eye(p);
if (H2) *H2 = -eye(p);
if (H3) *H3 = eye(p)*dt_;
if (H1) *H1 = Matrix::Identity(p,p);
if (H2) *H2 = -Matrix::Identity(p,p);
if (H3) *H3 = Matrix::Identity(p,p)*dt_;
return (Vector(1) << x1+v*dt_-x2).finished();
}

10
gtsam_unstable/dynamics/tests/testIMUSystem.cpp
View File

@ -26,7 +26,7 @@ using namespace gtsam;
const double tol=1e-5;
static const Key x0 = 0, x1 = 1, x2 = 2, x3 = 3, x4 = 4;
static const Vector g = delta(3, 2, -9.81);
static const Vector g = Vector::Unit(3,2)*(-9.81);
/* ************************************************************************* */
TEST(testIMUSystem, instantiations) {
@ -38,7 +38,7 @@ TEST(testIMUSystem, instantiations) {
gtsam::SharedNoiseModel model6 = gtsam::noiseModel::Unit::Create(6);
gtsam::SharedNoiseModel model9 = gtsam::noiseModel::Unit::Create(9);
Vector accel = ones(3), gyro = ones(3);
Vector accel = Vector::Ones(3), gyro = Vector::Ones(3);
IMUFactor<PoseRTV> imu(accel, gyro, 0.01, x1, x2, model6);
FullIMUFactor<PoseRTV> full_imu(accel, gyro, 0.01, x1, x2, model9);
@ -48,7 +48,7 @@ TEST(testIMUSystem, instantiations) {
VelocityConstraint constraint(x1, x2, 0.1, 10000);
PriorFactor<gtsam::PoseRTV> posePrior(x1, x1_v, model9);
DHeightPrior heightPrior(x1, 0.1, model1);
VelocityPrior velPrior(x1, ones(3), model3);
VelocityPrior velPrior(x1, Vector::Ones(3), model3);
}
/* ************************************************************************* */
@ -149,8 +149,8 @@ TEST( testIMUSystem, linear_trajectory) {
const double dt = 1.0;
PoseRTV start;
Vector accel = delta(3, 0, 0.5); // forward force
Vector gyro = delta(3, 0, 0.1); // constant rotation
Vector accel = Vector::Unit(3,0)*0.5; // forward force
Vector gyro = Vector::Unit(3,0)*0.1; // constant rotation
SharedDiagonal model = noiseModel::Unit::Create(9);
Values true_traj, init_traj;

8
gtsam_unstable/dynamics/tests/testPendulumFactors.cpp
View File

@ -29,7 +29,7 @@ TEST( testPendulumFactor1, evaluateError) {
PendulumFactor1 constraint(Q(2), Q(1), V(1), h);
// verify error function
EXPECT(assert_equal(zero(1), constraint.evaluateError(q2, q1, v1), tol));
EXPECT(assert_equal(Z_1x1, constraint.evaluateError(q2, q1, v1), tol));
}
/* ************************************************************************* */
@ -38,7 +38,7 @@ TEST( testPendulumFactor2, evaluateError) {
PendulumFactor2 constraint(V(2), V(1), Q(1), h);
// verify error function
EXPECT(assert_equal(zero(1), constraint.evaluateError(v2, v1, q1), tol));
EXPECT(assert_equal(Z_1x1, constraint.evaluateError(v2, v1, q1), tol));
}
/* ************************************************************************* */
@ -49,7 +49,7 @@ TEST( testPendulumFactorPk, evaluateError) {
double pk( 1/h * (q2-q1) + h*g*sin(q1) );
// verify error function
EXPECT(assert_equal(zero(1), constraint.evaluateError(pk, q1, q2), tol));
EXPECT(assert_equal(Z_1x1, constraint.evaluateError(pk, q1, q2), tol));
}
/* ************************************************************************* */
@ -60,7 +60,7 @@ TEST( testPendulumFactorPk1, evaluateError) {
double pk1( 1/h * (q2-q1) );
// verify error function
EXPECT(assert_equal(zero(1), constraint.evaluateError(pk1, q1, q2), tol));
EXPECT(assert_equal(Z_1x1, constraint.evaluateError(pk1, q1, q2), tol));
}

18
gtsam_unstable/dynamics/tests/testPoseRTV.cpp
View File

@ -76,12 +76,12 @@ TEST( testPoseRTV, equals ) {
TEST( testPoseRTV, Lie ) {
// origin and zero deltas
PoseRTV identity;
EXPECT(assert_equal(identity, (PoseRTV)identity.retract(zero(9))));
EXPECT(assert_equal(zero(9), identity.localCoordinates(identity)));
EXPECT(assert_equal(identity, (PoseRTV)identity.retract(Z_9x1)));
EXPECT(assert_equal((Vector) Z_9x1, identity.localCoordinates(identity)));
PoseRTV state1(pt, rot, vel);
EXPECT(assert_equal(state1, (PoseRTV)state1.retract(zero(9))));
EXPECT(assert_equal(zero(9), state1.localCoordinates(state1)));
EXPECT(assert_equal(state1, (PoseRTV)state1.retract(Z_9x1)));
EXPECT(assert_equal((Vector) Z_9x1, state1.localCoordinates(state1)));
Vector delta(9);
delta << 0.1, 0.1, 0.1, 0.2, 0.3, 0.4,-0.1,-0.2,-0.3;
@ -111,7 +111,7 @@ TEST( testPoseRTV, dynamics_identities ) {
const double dt = 0.1;
Vector accel = Vector3(0.2, 0.0, 0.0), gyro = Vector3(0.0, 0.0, 0.2);
Vector imu01 = zero(6), imu12 = zero(6), imu23 = zero(6), imu34 = zero(6);
Vector imu01 = Z_6x1, imu12 = Z_6x1, imu23 = Z_6x1, imu34 = Z_6x1;
x1 = x0.generalDynamics(accel, gyro, dt);
x2 = x1.generalDynamics(accel, gyro, dt);
@ -227,15 +227,15 @@ TEST( testPoseRTV, transformed_from_2 ) {
/* ************************************************************************* */
TEST(testPoseRTV, RRTMbn) {
EXPECT(assert_equal(Matrix::Identity(3,3), PoseRTV::RRTMbn(kZero3)));
EXPECT(assert_equal(Matrix::Identity(3,3), PoseRTV::RRTMbn(Rot3())));
EXPECT(assert_equal(I_3x3, PoseRTV::RRTMbn(kZero3)));
EXPECT(assert_equal(I_3x3, PoseRTV::RRTMbn(Rot3())));
EXPECT(assert_equal(PoseRTV::RRTMbn(Vector3(0.3, 0.2, 0.1)), PoseRTV::RRTMbn(Rot3::Ypr(0.1, 0.2, 0.3))));
}
/* ************************************************************************* */
TEST(testPoseRTV, RRTMnb) {
EXPECT(assert_equal(Matrix::Identity(3,3), PoseRTV::RRTMnb(kZero3)));
EXPECT(assert_equal(Matrix::Identity(3,3), PoseRTV::RRTMnb(Rot3())));
EXPECT(assert_equal(I_3x3, PoseRTV::RRTMnb(kZero3)));
EXPECT(assert_equal(I_3x3, PoseRTV::RRTMnb(Rot3())));
EXPECT(assert_equal(PoseRTV::RRTMnb(Vector3(0.3, 0.2, 0.1)), PoseRTV::RRTMnb(Rot3::Ypr(0.1, 0.2, 0.3))));
}

12
gtsam_unstable/dynamics/tests/testSimpleHelicopter.cpp
View File

@ -29,8 +29,8 @@ Vector gamma2 = Vector2(0.0, 0.0); // no shape
Vector u2 = Vector2(0.0, 0.0); // no control at time 2
double distT = 1.0; // distance from the body-centered x axis to the big top motor
double distR = 5.0; // distance from the body-centered z axis to the small motor
Matrix Mass = diag((Vector(3) << mass, mass, mass).finished());
Matrix Inertia = diag((Vector(6) << 2.0/5.0*mass*distR*distR, 2.0/5.0*mass*distR*distR, 2.0/5.0*mass*distR*distR, mass, mass, mass).finished());
Matrix Mass = ((Vector(3) << mass, mass, mass).finished()).asDiagonal();
Matrix Inertia = (Vector(6) << 2.0/5.0*mass*distR*distR, 2.0/5.0*mass*distR*distR, 2.0/5.0*mass*distR*distR, mass, mass, mass).finished().asDiagonal();
Vector computeFu(const Vector& gamma, const Vector& control) {
double gamma_r = gamma(0), gamma_p = gamma(1);
@ -53,7 +53,7 @@ TEST( Reconstruction, evaluateError) {
// verify error function
Matrix H1, H2, H3;
EXPECT(
assert_equal(zero(6), constraint.evaluateError(g2, g1, V1_g1, H1, H2, H3), tol));
assert_equal(Z_6x1, constraint.evaluateError(g2, g1, V1_g1, H1, H2, H3), tol));
Matrix numericalH1 = numericalDerivative31(
boost::function<Vector(const Pose3&, const Pose3&, const Vector6&)>(
@ -89,8 +89,8 @@ Vector newtonEuler(const Vector& Vb, const Vector& Fb, const Matrix& Inertia) {
TEST( DiscreteEulerPoincareHelicopter, evaluateError) {
Vector Fu = computeFu(gamma2, u2);
Vector fGravity_g1 = zero(6);
subInsert(fGravity_g1, g1.rotation().unrotate(Point3(0.0, 0.0, -mass*9.81)), 3); // gravity force in g1 frame
Vector fGravity_g1 = Z_6x1;
fGravity_g1.segment<3>(3) = g1.rotation().unrotate(Vector3(0, 0, -mass*9.81)); // gravity force in g1 frame
Vector Fb = Fu+fGravity_g1;
Vector dV = newtonEuler(V1_g1, Fb, Inertia);
@ -106,7 +106,7 @@ TEST( DiscreteEulerPoincareHelicopter, evaluateError) {
// verify error function
Matrix H1, H2, H3;
EXPECT(assert_equal(zero(6), constraint.evaluateError(expectedv2, V1_g1, g2, H1, H2, H3), 1e0));
EXPECT(assert_equal(Z_6x1, constraint.evaluateError(expectedv2, V1_g1, g2, H1, H2, H3), 1e0));
Matrix numericalH1 = numericalDerivative31(
boost::function<Vector(const Vector6&, const Vector6&, const Pose3&)>(

24
gtsam_unstable/dynamics/tests/testVelocityConstraint.cpp
View File

@ -25,10 +25,10 @@ TEST( testVelocityConstraint, trapezoidal ) {
VelocityConstraint constraint(x1, x2, dynamics::TRAPEZOIDAL, dt);
// verify error function
EXPECT(assert_equal(zero(3), constraint.evaluateError(origin, pose1), tol));
EXPECT(assert_equal(zero(3), constraint.evaluateError(origin, origin), tol));
EXPECT(assert_equal(delta(3, 0,-1.0), constraint.evaluateError(pose1, pose1), tol));
EXPECT(assert_equal(delta(3, 0, 0.5), constraint.evaluateError(origin, pose1a), tol));
EXPECT(assert_equal(Z_3x1, constraint.evaluateError(origin, pose1), tol));
EXPECT(assert_equal(Z_3x1, constraint.evaluateError(origin, origin), tol));
EXPECT(assert_equal(Vector::Unit(3,0)*(-1.0), constraint.evaluateError(pose1, pose1), tol));
EXPECT(assert_equal(Vector::Unit(3,0)*0.5, constraint.evaluateError(origin, pose1a), tol));
}
/* ************************************************************************* */
@ -37,10 +37,10 @@ TEST( testEulerVelocityConstraint, euler_start ) {
VelocityConstraint constraint(x1, x2, dynamics::EULER_START, dt);
// verify error function
EXPECT(assert_equal(delta(3, 0, 0.5), constraint.evaluateError(origin, pose1), tol));
EXPECT(assert_equal(zero(3), constraint.evaluateError(origin, origin), tol));
EXPECT(assert_equal(zero(3), constraint.evaluateError(pose1, pose2), tol));
EXPECT(assert_equal(delta(3, 0, 0.5), constraint.evaluateError(origin, pose1a), tol));
EXPECT(assert_equal(Vector::Unit(3,0)*0.5, constraint.evaluateError(origin, pose1), tol));
EXPECT(assert_equal(Z_3x1, constraint.evaluateError(origin, origin), tol));
EXPECT(assert_equal(Z_3x1, constraint.evaluateError(pose1, pose2), tol));
EXPECT(assert_equal(Vector::Unit(3,0)*0.5, constraint.evaluateError(origin, pose1a), tol));
}
/* ************************************************************************* */
@ -49,10 +49,10 @@ TEST( testEulerVelocityConstraint, euler_end ) {
VelocityConstraint constraint(x1, x2, dynamics::EULER_END, dt);
// verify error function
EXPECT(assert_equal(delta(3, 0,-0.5), constraint.evaluateError(origin, pose1), tol));
EXPECT(assert_equal(zero(3), constraint.evaluateError(origin, origin), tol));
EXPECT(assert_equal(zero(3), constraint.evaluateError(pose1, pose2), tol));
EXPECT(assert_equal(delta(3, 0, 0.5), constraint.evaluateError(origin, pose1a), tol));
EXPECT(assert_equal(Vector::Unit(3,0)*(-0.5), constraint.evaluateError(origin, pose1), tol));
EXPECT(assert_equal(Z_3x1, constraint.evaluateError(origin, origin), tol));
EXPECT(assert_equal(Z_3x1, constraint.evaluateError(pose1, pose2), tol));
EXPECT(assert_equal(Vector::Unit(3,0)*0.5, constraint.evaluateError(origin, pose1a), tol));
}
/* ************************************************************************* */

2
gtsam_unstable/dynamics/tests/testVelocityConstraint3.cpp
View File

@ -23,7 +23,7 @@ TEST( testVelocityConstraint3, evaluateError) {
VelocityConstraint3 constraint(X(1), X(2), V(1), dt);
// verify error function
EXPECT(assert_equal(zero(1), constraint.evaluateError(x1, x2, v), tol));
EXPECT(assert_equal(Z_1x1, constraint.evaluateError(x1, x2, v), tol));
}
/* ************************************************************************* */

2
gtsam_unstable/geometry/InvDepthCamera3.h
View File

@ -102,7 +102,7 @@ public:
gtsam::Matrix J2;
gtsam::Point2 uv= camera.project(landmark,H1, J2, boost::none);
if (H1) {
*H1 = (*H1) * gtsam::eye(6);
*H1 = (*H1) * I_6x6;
}
double cos_theta = cos(theta);

10
gtsam_unstable/geometry/Pose3Upright.cpp
View File

@ -81,7 +81,7 @@ Pose3 Pose3Upright::pose() const {
Pose3Upright Pose3Upright::inverse(boost::optional<Matrix&> H1) const {
Pose3Upright result(T_.inverse(H1), -z_);
if (H1) {
Matrix H1_ = -eye(4,4);
Matrix H1_ = -I_4x4;
H1_.topLeftCorner(2,2) = H1->topLeftCorner(2,2);
H1_.topRightCorner(2, 1) = H1->topRightCorner(2, 1);
*H1 = H1_;
@ -96,12 +96,12 @@ Pose3Upright Pose3Upright::compose(const Pose3Upright& p2,
return Pose3Upright(T_.compose(p2.T_), z_ + p2.z_);
Pose3Upright result(T_.compose(p2.T_, H1), z_ + p2.z_);
if (H1) {
Matrix H1_ = eye(4,4);
Matrix H1_ = I_4x4;
H1_.topLeftCorner(2,2) = H1->topLeftCorner(2,2);
H1_.topRightCorner(2, 1) = H1->topRightCorner(2, 1);
*H1 = H1_;
}
if (H2) *H2 = eye(4,4);
if (H2) *H2 = I_4x4;
return result;
}
@ -112,12 +112,12 @@ Pose3Upright Pose3Upright::between(const Pose3Upright& p2,
return Pose3Upright(T_.between(p2.T_), p2.z_ - z_);
Pose3Upright result(T_.between(p2.T_, H1, H2), p2.z_ - z_);
if (H1) {
Matrix H1_ = -eye(4,4);
Matrix H1_ = -I_4x4;
H1_.topLeftCorner(2,2) = H1->topLeftCorner(2,2);
H1_.topRightCorner(2, 1) = H1->topRightCorner(2, 1);
*H1 = H1_;
}
if (H2) *H2 = eye(4,4);
if (H2) *H2 = I_4x4;
return result;
}

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