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Merge remote-tracking branch 'origin/develop' into feature/LevenbergMarquardt

release/4.3a0
Luca 2014-01-27 22:23:11 -05:00
parent 2a3575fcbc fe55148dd7
commit c9a8ad0ac6
80 changed files with 796 additions and 1726 deletions
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14
gtsam/base/LieMatrix.cpp
View File

@ -19,20 +19,6 @@
namespace gtsam {
/* ************************************************************************* */
LieMatrix::LieMatrix(size_t m, size_t n, ...)
: Matrix(m,n) {
va_list ap;
va_start(ap, n);
for(size_t i = 0; i < m; ++i) {
for(size_t j = 0; j < n; ++j) {
double value = va_arg(ap, double);
(*this)(i,j) = value;
}
}
va_end(ap);
}
/* ************************************************************************* */
void LieMatrix::print(const std::string& name) const {
gtsam::print(matrix(), name);

3
gtsam/base/LieMatrix.h
View File

@ -48,9 +48,6 @@ struct LieMatrix : public Matrix, public DerivedValue<LieMatrix> {
LieMatrix(size_t m, size_t n, const double* const data) :
Matrix(Eigen::Map<const Matrix>(data, m, n)) {}
/** Specify arguments directly, as in Matrix_() - always force these to be doubles */
GTSAM_EXPORT LieMatrix(size_t m, size_t n, ...);
/// @}
/// @name Testable interface
/// @{

15
gtsam/base/LieVector.cpp
View File

@ -24,21 +24,10 @@ namespace gtsam {
/* ************************************************************************* */
LieVector::LieVector(size_t m, const double* const data)
: Vector(Vector_(m,data))
{
}
/* ************************************************************************* */
LieVector::LieVector(size_t m, ...)
: Vector(m)
{
va_list ap;
va_start(ap, m);
for( size_t i = 0 ; i < m ; i++) {
double value = va_arg(ap, double);
(*this)(i) = value;
}
va_end(ap);
for(size_t i = 0; i < m; i++)
(*this)(i) = data[i];
}
/* ************************************************************************* */

3
gtsam/base/LieVector.h
View File

@ -44,9 +44,6 @@ struct LieVector : public Vector, public DerivedValue<LieVector> {
/** constructor with size and initial data, row order ! */
GTSAM_EXPORT LieVector(size_t m, const double* const data);
/** Specify arguments directly, as in Vector_() - always force these to be doubles */
GTSAM_EXPORT LieVector(size_t m, ...);
/** get the underlying vector */
Vector vector() const {
return static_cast<Vector>(*this);

43
gtsam/base/Matrix.cpp
View File

@ -36,38 +36,6 @@ using namespace std;
namespace gtsam {
/* ************************************************************************* */
Matrix Matrix_( size_t m, size_t n, const double* const data) {
MatrixRowMajor A(m,n);
copy(data, data+m*n, A.data());
return Matrix(A);
}
/* ************************************************************************* */
Matrix Matrix_( size_t m, size_t n, const Vector& v)
{
Matrix A(m,n);
// column-wise copy
for( size_t j = 0, k=0 ; j < n ; j++)
for( size_t i = 0; i < m ; i++,k++)
A(i,j) = v(k);
return A;
}
/* ************************************************************************* */
Matrix Matrix_(size_t m, size_t n, ...) {
Matrix A(m,n);
va_list ap;
va_start(ap, n);
for( size_t i = 0 ; i < m ; i++)
for( size_t j = 0 ; j < n ; j++) {
double value = va_arg(ap, double);
A(i,j) = value;
}
va_end(ap);
return A;
}
/* ************************************************************************* */
Matrix zeros( size_t m, size_t n ) {
return Matrix::Zero(m,n);
@ -211,17 +179,6 @@ void transposeMultiplyAdd(double alpha, const Matrix& A, const Vector& e, SubVec
x += alpha * A.transpose() * e;
}
/* ************************************************************************* */
Vector Vector_(const Matrix& A)
{
size_t m = A.rows(), n = A.cols();
Vector v(m*n);
for( size_t j = 0, k=0 ; j < n ; j++)
for( size_t i = 0; i < m ; i++,k++)
v(k) = A(i,j);
return v;
}
/* ************************************************************************* */
void print(const Matrix& A, const string &s, ostream& stream) {
size_t m = A.rows(), n = A.cols();

26
gtsam/base/Matrix.h
View File

@ -45,27 +45,6 @@ typedef Eigen::Matrix<double,6,6> Matrix6;
typedef Eigen::Block<Matrix> SubMatrix;
typedef Eigen::Block<const Matrix> ConstSubMatrix;
/**
* constructor with size and initial data, row order !
*/
GTSAM_EXPORT Matrix Matrix_(size_t m, size_t n, const double* const data);
/**
* constructor with size and vector data, column order !!!
*/
GTSAM_EXPORT Matrix Matrix_(size_t m, size_t n, const Vector& v);
/**
* constructor from Vector yielding v.size()*1 vector
*/
inline Matrix Matrix_(const Vector& v) { return Matrix_(v.size(),1,v);}
/**
* nice constructor, dangerous as number of arguments must be exactly right
* and you have to pass doubles !!! always use 0.0 never 0
*/
GTSAM_EXPORT Matrix Matrix_(size_t m, size_t n, ...);
// Matlab-like syntax
/**
@ -196,11 +175,6 @@ inline MATRIX prod(const MATRIX& A, const MATRIX&B) {
return result;
}
/**
* convert to column vector, column order !!!
*/
GTSAM_EXPORT Vector Vector_(const Matrix& A);
/**
* print a matrix
*/

27
gtsam/base/Vector.cpp
View File

@ -79,33 +79,6 @@ void odprintf(const char *format, ...) {
//#endif
}
/* ************************************************************************* */
Vector Vector_( size_t m, const double* const data) {
Vector A(m);
copy(data, data+m, A.data());
return A;
}
/* ************************************************************************* */
Vector Vector_(size_t m, ...) {
Vector v(m);
va_list ap;
va_start(ap, m);
for( size_t i = 0 ; i < m ; i++) {
double value = va_arg(ap, double);
v(i) = value;
}
va_end(ap);
return v;
}
/* ************************************************************************* */
Vector Vector_(const std::vector<double>& d) {
Vector v(d.size());
copy(d.begin(), d.end(), v.data());
return v;
}
/* ************************************************************************* */
bool zero(const Vector& v) {
bool result = true;

16
gtsam/base/Vector.h
View File

@ -46,22 +46,6 @@ typedef Eigen::VectorBlock<const Vector> ConstSubVector;
*/
GTSAM_EXPORT void odprintf(const char *format, ...);
/**
* constructor with size and initial data, row order !
*/
GTSAM_EXPORT Vector Vector_( size_t m, const double* const data);
/**
* nice constructor, dangerous as number of arguments must be exactly right
* and you have to pass doubles !!! always use 0.0 never 0
*/
GTSAM_EXPORT Vector Vector_(size_t m, ...);
/**
* Create a numeric vector from an STL vector of doubles
*/
GTSAM_EXPORT Vector Vector_(const std::vector<double>& data);
/**
* Create vector initialized to a constant value
* @param n is the size of the vector

2
gtsam/base/numericalDerivative.h
View File

@ -59,7 +59,7 @@ namespace gtsam {
/** global functions for converting to a LieVector for use with numericalDerivative */
inline LieVector makeLieVector(const Vector& v) { return LieVector(v); }
inline LieVector makeLieVectorD(double d) { return LieVector(Vector_(1, d)); }
inline LieVector makeLieVectorD(double d) { return LieVector((Vector) (Vector(1) << d)); }
/**
* Numerically compute gradient of scalar function

11
gtsam/base/tests/testLieMatrix.cpp
View File

@ -39,20 +39,17 @@ TEST( LieMatrix, construction ) {
TEST( LieMatrix, other_constructors ) {
Matrix init = (Matrix(2,2) << 10.0,20.0, 30.0,40.0);
LieMatrix exp(init);
LieMatrix a(2,2,10.0,20.0,30.0,40.0);
double data[] = {10,30,20,40};
LieMatrix b(2,2,data);
EXPECT(assert_equal(exp, a));
EXPECT(assert_equal(exp, b));
EXPECT(assert_equal(b, a));
}
/* ************************************************************************* */
TEST(LieMatrix, retract) {
LieMatrix init(2,2, 1.0,2.0,3.0,4.0);
Vector update = Vector_(4, 3.0, 4.0, 6.0, 7.0);
LieMatrix init((Matrix(2,2) << 1.0,2.0,3.0,4.0));
Vector update = (Vector(4) << 3.0, 4.0, 6.0, 7.0);
LieMatrix expected(2,2, 4.0, 6.0, 9.0, 11.0);
LieMatrix expected((Matrix(2,2) << 4.0, 6.0, 9.0, 11.0));
LieMatrix actual = init.retract(update);
EXPECT(assert_equal(expected, actual));
@ -63,7 +60,7 @@ TEST(LieMatrix, retract) {
EXPECT(assert_equal(expectedUpdate, actualUpdate));
Vector expectedLogmap = (Vector(4) << 1, 2, 3, 4);
Vector actualLogmap = LieMatrix::Logmap(LieMatrix(2,2, 1.0, 2.0, 3.0, 4.0));
Vector actualLogmap = LieMatrix::Logmap(LieMatrix((Matrix(2,2) << 1.0, 2.0, 3.0, 4.0)));
EXPECT(assert_equal(expectedLogmap, actualLogmap));
}

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

@ -42,7 +42,7 @@ TEST( testLieScalar, construction ) {
TEST( testLieScalar, localCoordinates ) {
LieScalar lie1(1.), lie2(3.);
EXPECT(assert_equal(Vector_(1, 2.), lie1.localCoordinates(lie2)));
EXPECT(assert_equal((Vector)(Vector(1) << 2), lie1.localCoordinates(lie2)));
}
/* ************************************************************************* */

5
gtsam/base/tests/testLieVector.cpp
View File

@ -39,14 +39,9 @@ TEST( testLieVector, construction ) {
TEST( testLieVector, other_constructors ) {
Vector init = (Vector(2) << 10.0, 20.0);
LieVector exp(init);
LieVector a(2,10.0,20.0);
double data[] = {10,20};
LieVector b(2,data);
LieVector c(2.3), c_exp(LieVector(1, 2.3));
EXPECT(assert_equal(exp, a));
EXPECT(assert_equal(exp, b));
EXPECT(assert_equal(b, a));
EXPECT(assert_equal(c_exp, c));
}
/* ************************************************************************* */

12
gtsam/base/tests/testMatrix.cpp
View File

@ -43,18 +43,6 @@ TEST( matrix, constructor_data )
EQUALITY(A,B);
}
/* ************************************************************************* */
/*
TEST( matrix, constructor_vector )
{
double data[] = { -5, 3, 0, -5 };
Matrix A = Matrix_(2, 2, -5, 3, 0, -5);
Vector v(4);
copy(data, data + 4, v.data());
Matrix B = Matrix_(2, 2, v); // this one is column order !
EQUALITY(A,trans(B));
}
*/
/* ************************************************************************* */
TEST( matrix, Matrix_ )
{

13
gtsam/base/tests/testNumericalDerivative.cpp
View File

@ -48,7 +48,8 @@ double f2(const LieVector& x) {
/* ************************************************************************* */
TEST(testNumericalDerivative, numericalHessian2) {
LieVector center(2, 0.5, 1.0);
Vector v_center = (Vector(2) << 0.5, 1.0);
LieVector center(v_center);
Matrix expected = (Matrix(2,2) <<
-cos(center(1))*sin(center(0)), -sin(center(1))*cos(center(0)),
@ -67,7 +68,9 @@ double f3(const LieVector& x1, const LieVector& x2) {
/* ************************************************************************* */
TEST(testNumericalDerivative, numericalHessian211) {
LieVector center1(1, 1.0), center2(1, 5.0);
Vector v_center1 = (Vector(1) << 1.0);
Vector v_center2 = (Vector(1) << 5.0);
LieVector center1(v_center1), center2(v_center2);
Matrix expected11 = (Matrix(1, 1) << -sin(center1(0))*cos(center2(0)));
Matrix actual11 = numericalHessian211(f3, center1, center2);
@ -90,7 +93,11 @@ double f4(const LieVector& x, const LieVector& y, const LieVector& z) {
/* ************************************************************************* */
TEST(testNumericalDerivative, numericalHessian311) {
LieVector center1(1, 1.0), center2(1, 2.0), center3(1, 3.0);
Vector v_center1 = (Vector(1) << 1.0);
Vector v_center2 = (Vector(1) << 2.0);
Vector v_center3 = (Vector(1) << 3.0);
LieVector center1(v_center1), center2(v_center2), center3(v_center3);
double x = center1(0), y = center2(0), z = center3(0);
Matrix expected11 = (Matrix(1, 1) << -sin(x)*cos(y)*z*z);
Matrix actual11 = numericalHessian311(f4, center1, center2, center3);

9
gtsam/base/tests/testVector.cpp
View File

@ -23,15 +23,6 @@
using namespace std;
using namespace gtsam;
/* ************************************************************************* */
TEST( TestVector, Vector_variants )
{
Vector a = (Vector(2) << 10.0,20.0);
double data[] = {10,20};
Vector b = Vector_(2, data);
EXPECT(assert_equal(a, b));
}
namespace {
/* ************************************************************************* */
template<typename Derived>

152
gtsam/geometry/EssentialMatrix.cpp Normal file
View File

@ -0,0 +1,152 @@
/*
* @file EssentialMatrix.cpp
* @brief EssentialMatrix class
* @author Frank Dellaert
* @date December 5, 2014
*/
#include <gtsam/geometry/EssentialMatrix.h>
#include <iostream>
using namespace std;
namespace gtsam {
/* ************************************************************************* */
EssentialMatrix EssentialMatrix::FromPose3(const Pose3& _1P2_,
boost::optional<Matrix&> H) {
const Rot3& _1R2_ = _1P2_.rotation();
const Point3& _1T2_ = _1P2_.translation();
if (!H) {
// just make a direction out of translation and create E
Sphere2 direction(_1T2_);
return EssentialMatrix(_1R2_, direction);
} else {
// Calculate the 5*6 Jacobian H = D_E_1P2
// D_E_1P2 = [D_E_1R2 D_E_1T2], 5*3 wrpt rotation, 5*3 wrpt translation
// First get 2*3 derivative from Sphere2::FromPoint3
Matrix D_direction_1T2;
Sphere2 direction = Sphere2::FromPoint3(_1T2_, D_direction_1T2);
H->resize(5, 6);
H->block<3, 3>(0, 0) << Matrix::Identity(3, 3); // upper left
H->block<2, 3>(3, 0) << Matrix::Zero(2, 3); // lower left
H->block<3, 3>(0, 3) << Matrix::Zero(3, 3); // upper right
H->block<2, 3>(3, 3) << D_direction_1T2 * _1R2_.matrix(); // lower right
return EssentialMatrix(_1R2_, direction);
}
}
/* ************************************************************************* */
void EssentialMatrix::print(const string& s) const {
cout << s;
aRb_.print("R:\n");
aTb_.print("d: ");
}
/* ************************************************************************* */
EssentialMatrix EssentialMatrix::retract(const Vector& xi) const {
assert(xi.size() == 5);
Vector3 omega(sub(xi, 0, 3));
Vector2 z(sub(xi, 3, 5));
Rot3 R = aRb_.retract(omega);
Sphere2 t = aTb_.retract(z);
return EssentialMatrix(R, t);
}
/* ************************************************************************* */
Vector EssentialMatrix::localCoordinates(const EssentialMatrix& other) const {
return Vector(5) << //
aRb_.localCoordinates(other.aRb_), aTb_.localCoordinates(other.aTb_);
}
/* ************************************************************************* */
Point3 EssentialMatrix::transform_to(const Point3& p,
boost::optional<Matrix&> DE, boost::optional<Matrix&> Dpoint) const {
Pose3 pose(aRb_, aTb_.point3());
Point3 q = pose.transform_to(p, DE, Dpoint);
if (DE) {
// DE returned by pose.transform_to is 3*6, but we need it to be 3*5
// The last 3 columns are derivative with respect to change in translation
// The derivative of translation with respect to a 2D sphere delta is 3*2 aTb_.basis()
// Duy made an educated guess that this needs to be rotated to the local frame
Matrix H(3, 5);
H << DE->block<3, 3>(0, 0), -aRb_.transpose() * aTb_.basis();
*DE = H;
}
return q;
}
/* ************************************************************************* */
EssentialMatrix EssentialMatrix::rotate(const Rot3& cRb,
boost::optional<Matrix&> HE, boost::optional<Matrix&> HR) const {
// The rotation must be conjugated to act in the camera frame
Rot3 c1Rc2 = aRb_.conjugate(cRb);
if (!HE && !HR) {
// Rotate translation direction and return
Sphere2 c1Tc2 = cRb * aTb_;
return EssentialMatrix(c1Rc2, c1Tc2);
} else {
// Calculate derivatives
Matrix D_c1Tc2_cRb, D_c1Tc2_aTb; // 2*3 and 2*2
Sphere2 c1Tc2 = cRb.rotate(aTb_, D_c1Tc2_cRb, D_c1Tc2_aTb);
if (HE) {
*HE = zeros(5, 5);
HE->block<3, 3>(0, 0) << cRb.matrix(); // a change in aRb_ will yield a rotated change in c1Rc2
HE->block<2, 2>(3, 3) << D_c1Tc2_aTb; // (2*2)
}
if (HR) {
throw runtime_error(
"EssentialMatrix::rotate: derivative HR not implemented yet");
/*
HR->resize(5, 3);
HR->block<3, 3>(0, 0) << zeros(3, 3); // a change in the rotation yields ?
HR->block<2, 3>(3, 0) << zeros(2, 3); // (2*3) * (3*3) ?
*/
}
return EssentialMatrix(c1Rc2, c1Tc2);
}
}
/* ************************************************************************* */
double EssentialMatrix::error(const Vector& vA, const Vector& vB, //
boost::optional<Matrix&> H) const {
if (H) {
H->resize(1, 5);
// See math.lyx
Matrix HR = vA.transpose() * E_ * skewSymmetric(-vB);
Matrix HD = vA.transpose() * skewSymmetric(-aRb_.matrix() * vB)
* aTb_.basis();
*H << HR, HD;
}
return dot(vA, E_ * vB);
}
/* ************************************************************************* */
ostream& operator <<(ostream& os, const EssentialMatrix& E) {
Rot3 R = E.rotation();
Sphere2 d = E.direction();
os.precision(10);
os << R.xyz().transpose() << " " << d.point3().vector().transpose() << " ";
return os;
}
/* ************************************************************************* */
istream& operator >>(istream& is, EssentialMatrix& E) {
double rx, ry, rz, dx, dy, dz;
is >> rx >> ry >> rz; // Read the rotation rxyz
is >> dx >> dy >> dz; // Read the translation dxyz
// Create EssentialMatrix from rotation and translation
Rot3 rot = Rot3::RzRyRx(rx, ry, rz);
Sphere2 dt = Sphere2(dx, dy, dz);
E = EssentialMatrix(rot, dt);
return is;
}
/* ************************************************************************* */
} // gtsam

101
gtsam/geometry/EssentialMatrix.h
View File

@ -26,7 +26,7 @@ private:
Rot3 aRb_; ///< Rotation between a and b
Sphere2 aTb_; ///< translation direction from a to b
Matrix E_; ///< Essential matrix
Matrix3 E_; ///< Essential matrix
public:
@ -48,6 +48,10 @@ public:
aRb_(aRb), aTb_(aTb), E_(aTb_.skew() * aRb_.matrix()) {
}
/// Named constructor converting a Pose3 with scale to EssentialMatrix (no scale)
static EssentialMatrix FromPose3(const Pose3& _1P2_,
boost::optional<Matrix&> H = boost::none);
/// Random, using Rot3::Random and Sphere2::Random
template<typename Engine>
static EssentialMatrix Random(Engine & rng) {
@ -60,11 +64,7 @@ public:
/// @{
/// print with optional string
void print(const std::string& s = "") const {
std::cout << s;
aRb_.print("R:\n");
aTb_.print("d: ");
}
void print(const std::string& s = "") const;
/// assert equality up to a tolerance
bool equals(const EssentialMatrix& other, double tol = 1e-8) const {
@ -87,20 +87,10 @@ public:
}
/// Retract delta to manifold
virtual EssentialMatrix retract(const Vector& xi) const {
assert(xi.size() == 5);
Vector3 omega(sub(xi, 0, 3));
Vector2 z(sub(xi, 3, 5));
Rot3 R = aRb_.retract(omega);
Sphere2 t = aTb_.retract(z);
return EssentialMatrix(R, t);
}
virtual EssentialMatrix retract(const Vector& xi) const;
/// Compute the coordinates in the tangent space
virtual Vector localCoordinates(const EssentialMatrix& other) const {
return Vector(5) << //
aRb_.localCoordinates(other.aRb_), aTb_.localCoordinates(other.aTb_);
}
virtual Vector localCoordinates(const EssentialMatrix& other) const;
/// @}
@ -108,17 +98,17 @@ public:
/// @{
/// Rotation
const Rot3& rotation() const {
inline const Rot3& rotation() const {
return aRb_;
}
/// Direction
const Sphere2& direction() const {
inline const Sphere2& direction() const {
return aTb_;
}
/// Return 3*3 matrix representation
const Matrix& matrix() const {
inline const Matrix3& matrix() const {
return E_;
}
@ -131,20 +121,7 @@ public:
*/
Point3 transform_to(const Point3& p,
boost::optional<Matrix&> DE = boost::none,
boost::optional<Matrix&> Dpoint = boost::none) const {
Pose3 pose(aRb_, aTb_.point3());
Point3 q = pose.transform_to(p, DE, Dpoint);
if (DE) {
// DE returned by pose.transform_to is 3*6, but we need it to be 3*5
// The last 3 columns are derivative with respect to change in translation
// The derivative of translation with respect to a 2D sphere delta is 3*2 aTb_.basis()
// Duy made an educated guess that this needs to be rotated to the local frame
Matrix H(3, 5);
H << DE->block<3, 3>(0, 0), -aRb_.transpose() * aTb_.basis();
*DE = H;
}
return q;
}
boost::optional<Matrix&> Dpoint = boost::none) const;
/**
* Given essential matrix E in camera frame B, convert to body frame C
@ -152,36 +129,7 @@ public:
* @param E essential matrix E in camera frame C
*/
EssentialMatrix rotate(const Rot3& cRb, boost::optional<Matrix&> HE =
boost::none, boost::optional<Matrix&> HR = boost::none) const {
// The rotation must be conjugated to act in the camera frame
Rot3 c1Rc2 = aRb_.conjugate(cRb);
if (!HE && !HR) {
// Rotate translation direction and return
Sphere2 c1Tc2 = cRb * aTb_;
return EssentialMatrix(c1Rc2, c1Tc2);
} else {
// Calculate derivatives
Matrix D_c1Tc2_cRb, D_c1Tc2_aTb; // 2*3 and 2*2
Sphere2 c1Tc2 = cRb.rotate(aTb_, D_c1Tc2_cRb, D_c1Tc2_aTb);
if (HE) {
*HE = zeros(5, 5);
HE->block<3, 3>(0, 0) << cRb.matrix(); // a change in aRb_ will yield a rotated change in c1Rc2
HE->block<2, 2>(3, 3) << D_c1Tc2_aTb; // (2*2)
}
if (HR) {
throw std::runtime_error(
"EssentialMatrix::rotate: derivative HR not implemented yet");
/*
HR->resize(5, 3);
HR->block<3, 3>(0, 0) << zeros(3, 3); // a change in the rotation yields ?
HR->block<2, 3>(3, 0) << zeros(2, 3); // (2*3) * (3*3) ?
*/
}
return EssentialMatrix(c1Rc2, c1Tc2);
}
}
boost::none, boost::optional<Matrix&> HR = boost::none) const;
/**
* Given essential matrix E in camera frame B, convert to body frame C
@ -194,17 +142,18 @@ public:
/// epipolar error, algebraic
double error(const Vector& vA, const Vector& vB, //
boost::optional<Matrix&> H = boost::none) const {
if (H) {
H->resize(1, 5);
// See math.lyx
Matrix HR = vA.transpose() * E_ * skewSymmetric(-vB);
Matrix HD = vA.transpose() * skewSymmetric(-aRb_.matrix() * vB)
* aTb_.basis();
*H << HR, HD;
}
return dot(vA, E_ * vB);
}
boost::optional<Matrix&> H = boost::none) const;
/// @}
/// @name Streaming operators
/// @{
/// stream to stream
friend std::ostream& operator <<(std::ostream& os, const EssentialMatrix& E);
/// stream from stream
friend std::istream& operator >>(std::istream& is, EssentialMatrix& E);
/// @}

2
gtsam/geometry/Point2.h
View File

@ -172,7 +172,7 @@ public:
static inline Point2 Expmap(const Vector& v) { return Point2(v); }
/// Log map around identity - just return the Point2 as a vector
static inline Vector Logmap(const Point2& dp) { return Vector_(2, dp.x(), dp.y()); }
static inline Vector Logmap(const Point2& dp) { return (Vector(2) << dp.x(), dp.y()); }
/// @}
/// @name Vector Space

54
gtsam/geometry/Point3.cpp
View File

@ -26,7 +26,8 @@ INSTANTIATE_LIE(Point3);
/* ************************************************************************* */
bool Point3::equals(const Point3 & q, double tol) const {
return (fabs(x_ - q.x()) < tol && fabs(y_ - q.y()) < tol && fabs(z_ - q.z()) < tol);
return (fabs(x_ - q.x()) < tol && fabs(y_ - q.y()) < tol
&& fabs(z_ - q.z()) < tol);
}
/* ************************************************************************* */
@ -37,18 +38,18 @@ void Point3::print(const string& s) const {
/* ************************************************************************* */
bool Point3::operator== (const Point3& q) const {
bool Point3::operator==(const Point3& q) const {
return x_ == q.x_ && y_ == q.y_ && z_ == q.z_;
}
/* ************************************************************************* */
Point3 Point3::operator+(const Point3& q) const {
return Point3( x_ + q.x_, y_ + q.y_, z_ + q.z_ );
return Point3(x_ + q.x_, y_ + q.y_, z_ + q.z_);
}
/* ************************************************************************* */
Point3 Point3::operator- (const Point3& q) const {
return Point3( x_ - q.x_, y_ - q.y_, z_ - q.z_ );
Point3 Point3::operator-(const Point3& q) const {
return Point3(x_ - q.x_, y_ - q.y_, z_ - q.z_);
}
/* ************************************************************************* */
@ -62,36 +63,53 @@ Point3 Point3::operator/(double s) const {
}
/* ************************************************************************* */
Point3 Point3::add(const Point3 &q,
boost::optional<Matrix&> H1, boost::optional<Matrix&> H2) const {
if (H1) *H1 = eye(3,3);
if (H2) *H2 = eye(3,3);
Point3 Point3::add(const Point3 &q, boost::optional<Matrix&> H1,
boost::optional<Matrix&> H2) const {
if (H1)
*H1 = eye(3, 3);
if (H2)
*H2 = eye(3, 3);
return *this + q;
}
/* ************************************************************************* */
Point3 Point3::sub(const Point3 &q,
boost::optional<Matrix&> H1, boost::optional<Matrix&> H2) const {
if (H1) *H1 = eye(3,3);
if (H2) *H2 = -eye(3,3);
Point3 Point3::sub(const Point3 &q, boost::optional<Matrix&> H1,
boost::optional<Matrix&> H2) const {
if (H1)
*H1 = eye(3, 3);
if (H2)
*H2 = -eye(3, 3);
return *this - q;
}
/* ************************************************************************* */
Point3 Point3::cross(const Point3 &q) const {
return Point3( y_*q.z_ - z_*q.y_,
z_*q.x_ - x_*q.z_,
x_*q.y_ - y_*q.x_ );
return Point3(y_ * q.z_ - z_ * q.y_, z_ * q.x_ - x_ * q.z_,
x_ * q.y_ - y_ * q.x_);
}
/* ************************************************************************* */
double Point3::dot(const Point3 &q) const {
return ( x_*q.x_ + y_*q.y_ + z_*q.z_ );
return (x_ * q.x_ + y_ * q.y_ + z_ * q.z_);
}
/* ************************************************************************* */
double Point3::norm() const {
return sqrt( x_*x_ + y_*y_ + z_*z_ );
return sqrt(x_ * x_ + y_ * y_ + z_ * z_);
}
/* ************************************************************************* */
Point3 Point3::normalize(boost::optional<Matrix&> H) const {
Point3 normalized = *this / norm();
if (H) {
// 3*3 Derivative
double x2 = x_ * x_, y2 = y_ * y_, z2 = z_ * z_;
double xy = x_ * y_, xz = x_ * z_, yz = y_ * z_;
H->resize(3, 3);
*H << y2 + z2, -xy, -xz, /**/-xy, x2 + z2, -yz, /**/-xz, -yz, x2 + y2;
*H /= pow(x2 + y2 + z2, 1.5);
}
return normalized;
}
/* ************************************************************************* */

3
gtsam/geometry/Point3.h
View File

@ -176,6 +176,9 @@ namespace gtsam {
/** Distance of the point from the origin */
double norm() const;
/** normalize, with optional Jacobian */
Point3 normalize(boost::optional<Matrix&> H = boost::none) const;
/** cross product @return this x q */
Point3 cross(const Point3 &q) const;

2
gtsam/geometry/Pose3.cpp
View File

@ -98,7 +98,7 @@ Vector Pose3::adjointTranspose(const Vector& xi, const Vector& y,
/* ************************************************************************* */
Matrix6 Pose3::dExpInv_exp(const Vector& xi) {
// Bernoulli numbers, from Wikipedia
static const Vector B = Vector_(9, 1.0, -1.0 / 2.0, 1. / 6., 0.0, -1.0 / 30.0,
static const Vector B = (Vector(9) << 1.0, -1.0 / 2.0, 1. / 6., 0.0, -1.0 / 30.0,
0.0, 1.0 / 42.0, 0.0, -1.0 / 30);
static const int N = 5; // order of approximation
Matrix res = I6;

2
gtsam/geometry/Rot2.h
View File

@ -170,7 +170,7 @@ namespace gtsam {
///Log map at identity - return the canonical coordinates of this rotation
static inline Vector Logmap(const Rot2& r) {
return Vector_(1, r.theta());
return (Vector(1) << r.theta());
}
/// @}

2
gtsam/geometry/Rot3.h
View File

@ -194,7 +194,7 @@ namespace gtsam {
* @return incremental rotation matrix
*/
static Rot3 rodriguez(double wx, double wy, double wz)
{ return rodriguez(Vector_(3,wx,wy,wz));}
{ return rodriguez((Vector(3) << wx, wy, wz));}
/// @}
/// @name Testable

17
gtsam/geometry/Sphere2.cpp
View File

@ -28,6 +28,21 @@ using namespace std;
namespace gtsam {
/* ************************************************************************* */
Sphere2 Sphere2::FromPoint3(const Point3& point,
boost::optional<Matrix&> H) {
Sphere2 direction(point);
if (H) {
// 3*3 Derivative of representation with respect to point is 3*3:
Matrix D_p_point;
point.normalize(D_p_point); // TODO, this calculates norm a second time :-(
// Calculate the 2*3 Jacobian
H->resize(2, 3);
*H << direction.basis().transpose() * D_p_point;
}
return direction;
}
/* ************************************************************************* */
Sphere2 Sphere2::Random(boost::random::mt19937 & rng) {
// TODO allow any engine without including all of boost :-(
@ -170,7 +185,7 @@ Sphere2 Sphere2::retract(const Vector& v, Sphere2::CoordinatesMode mode) const {
double alpha = p.transpose() * q;
assert(alpha != 0.0);
Matrix coeffs = (B.transpose() * q) / alpha;
Vector result = Vector_(2, coeffs(0, 0), coeffs(1, 0));
Vector result = (Vector(2) << coeffs(0, 0), coeffs(1, 0));
return result;
} else {
assert (false);

10
gtsam/geometry/Sphere2.h
View File

@ -70,6 +70,10 @@ public:
p_ = p_ / p_.norm();
}
/// Named constructor from Point3 with optional Jacobian
static Sphere2 FromPoint3(const Point3& point,
boost::optional<Matrix&> H = boost::none);
/// Random direction, using boost::uniform_on_sphere
static Sphere2 Random(boost::random::mt19937 & rng);
@ -90,7 +94,11 @@ public:
/// @name Other functionality
/// @{
/// Returns the local coordinate frame to tangent plane
/**
* Returns the local coordinate frame to tangent plane
* It is a 3*2 matrix [b1 b2] composed of two orthogonal directions
* tangent to the sphere at the current direction.
*/
Matrix basis() const;
/// Return skew-symmetric associated with 3D point on unit sphere

2
gtsam/geometry/tests/testCal3DS2.cpp
View File

@ -36,7 +36,7 @@ TEST( Cal3DS2, uncalibrate)
double g = 1+k[0]*r+k[1]*r*r ;
double tx = 2*k[2]*p.x()*p.y() + k[3]*(r+2*p.x()*p.x()) ;
double ty = k[2]*(r+2*p.y()*p.y()) + 2*k[3]*p.x()*p.y() ;
Vector v_hat = Vector_(3, g*p.x() + tx, g*p.y() + ty, 1.0) ;
Vector v_hat = (Vector(3) << g*p.x() + tx, g*p.y() + ty, 1.0) ;
Vector v_i = K.K() * v_hat ;
Point2 p_i(v_i(0)/v_i(2), v_i(1)/v_i(2)) ;
Point2 q = K.uncalibrate(p);

33
gtsam/geometry/tests/testEssentialMatrix.cpp
View File

@ -10,6 +10,7 @@
#include <gtsam/base/numericalDerivative.h>
#include <gtsam/base/Testable.h>
#include <CppUnitLite/TestHarness.h>
#include <sstream>
using namespace std;
using namespace gtsam;
@ -95,6 +96,38 @@ TEST (EssentialMatrix, rotate) {
// Does not work yet EXPECT(assert_equal(expH2, actH2, 1e-8));
}
//*************************************************************************
TEST (EssentialMatrix, FromPose3_a) {
Matrix actualH;
Pose3 pose(c1Rc2, c1Tc2); // Pose between two cameras
EXPECT(assert_equal(trueE, EssentialMatrix::FromPose3(pose, actualH), 1e-8));
Matrix expectedH = numericalDerivative11<EssentialMatrix, Pose3>(
boost::bind(EssentialMatrix::FromPose3, _1, boost::none), pose);
EXPECT(assert_equal(expectedH, actualH, 1e-8));
}
//*************************************************************************
TEST (EssentialMatrix, FromPose3_b) {
Matrix actualH;
Rot3 c1Rc2 = Rot3::ypr(0.1, -0.2, 0.3);
Point3 c1Tc2(0.4, 0.5, 0.6);
EssentialMatrix trueE(c1Rc2, c1Tc2);
Pose3 pose(c1Rc2, c1Tc2); // Pose between two cameras
EXPECT(assert_equal(trueE, EssentialMatrix::FromPose3(pose, actualH), 1e-8));
Matrix expectedH = numericalDerivative11<EssentialMatrix, Pose3>(
boost::bind(EssentialMatrix::FromPose3, _1, boost::none), pose);
EXPECT(assert_equal(expectedH, actualH, 1e-8));
}
//*************************************************************************
TEST (EssentialMatrix, streaming) {
EssentialMatrix expected(c1Rc2, c1Tc2), actual;
stringstream ss;
ss << expected;
ss >> actual;
EXPECT(assert_equal(expected, actual));
}
/* ************************************************************************* */
int main() {
TestResult tr;

69
gtsam/geometry/tests/testPoint3.cpp
View File

@ -14,73 +14,84 @@
* @brief Unit tests for Point3 class
*/
#include <CppUnitLite/TestHarness.h>
#include <gtsam/base/Testable.h>
#include <gtsam/geometry/Point3.h>
#include <gtsam/base/Testable.h>
#include <gtsam/base/numericalDerivative.h>
#include <CppUnitLite/TestHarness.h>
using namespace gtsam;
GTSAM_CONCEPT_TESTABLE_INST(Point3)
GTSAM_CONCEPT_LIE_INST(Point3)
static Point3 P(0.2,0.7,-2);
static Point3 P(0.2, 0.7, -2);
/* ************************************************************************* */
TEST(Point3, Lie) {
Point3 p1(1,2,3);
Point3 p2(4,5,6);
Point3 p1(1, 2, 3);
Point3 p2(4, 5, 6);
Matrix H1, H2;
EXPECT(assert_equal(Point3(5,7,9), p1.compose(p2, H1, H2)));
EXPECT(assert_equal(Point3(5, 7, 9), p1.compose(p2, H1, H2)));
EXPECT(assert_equal(eye(3), H1));
EXPECT(assert_equal(eye(3), H2));
EXPECT(assert_equal(Point3(3,3,3), p1.between(p2, H1, H2)));
EXPECT(assert_equal(Point3(3, 3, 3), p1.between(p2, H1, H2)));
EXPECT(assert_equal(-eye(3), H1));
EXPECT(assert_equal(eye(3), H2));
EXPECT(assert_equal(Point3(5,7,9), p1.retract((Vector(3) << 4., 5., 6.))));
EXPECT(assert_equal(Vector_(3, 3.,3.,3.), p1.localCoordinates(p2)));
EXPECT(assert_equal(Point3(5, 7, 9), p1.retract((Vector(3) << 4., 5., 6.))));
EXPECT(assert_equal((Vector)(Vector(3) << 3.,3.,3.), p1.localCoordinates(p2)));
}
/* ************************************************************************* */
TEST( Point3, arithmetic)
{
CHECK(P*3==3*P);
CHECK(assert_equal( Point3(-1,-5,-6), -Point3(1,5,6) ));
CHECK(assert_equal( Point3(2,5,6), Point3(1,4,5)+Point3(1,1,1)));
CHECK(assert_equal( Point3(0,3,4), Point3(1,4,5)-Point3(1,1,1)));
CHECK(assert_equal( Point3(2,8,6), Point3(1,4,3)*2));
CHECK(assert_equal( Point3(2,2,6), 2*Point3(1,1,3)));
CHECK(assert_equal( Point3(1,2,3), Point3(2,4,6)/2));
TEST( Point3, arithmetic) {
CHECK(P * 3 == 3 * P);
CHECK(assert_equal(Point3(-1, -5, -6), -Point3(1, 5, 6)));
CHECK(assert_equal(Point3(2, 5, 6), Point3(1, 4, 5) + Point3(1, 1, 1)));
CHECK(assert_equal(Point3(0, 3, 4), Point3(1, 4, 5) - Point3(1, 1, 1)));
CHECK(assert_equal(Point3(2, 8, 6), Point3(1, 4, 3) * 2));
CHECK(assert_equal(Point3(2, 2, 6), 2 * Point3(1, 1, 3)));
CHECK(assert_equal(Point3(1, 2, 3), Point3(2, 4, 6) / 2));
}
/* ************************************************************************* */
TEST( Point3, equals)
{
TEST( Point3, equals) {
CHECK(P.equals(P));
Point3 Q;
CHECK(!P.equals(Q));
}
/* ************************************************************************* */
TEST( Point3, dot)
{
Point3 origin, ones(1,1,1);
CHECK(origin.dot(Point3(1,1,0)) == 0);
CHECK(ones.dot(Point3(1,1,0)) == 2);
TEST( Point3, dot) {
Point3 origin, ones(1, 1, 1);
CHECK(origin.dot(Point3(1, 1, 0)) == 0);
CHECK(ones.dot(Point3(1, 1, 0)) == 2);
}
/* ************************************************************************* */
TEST( Point3, stream)
{
Point3 p(1,2, -3);
TEST( Point3, stream) {
Point3 p(1, 2, -3);
std::ostringstream os;
os << p;
EXPECT(os.str() == "[1, 2, -3]';");
}
//*************************************************************************
TEST (Point3, normalize) {
Matrix actualH;
Point3 point(1, -2, 3); // arbitrary point
Point3 expected(point / sqrt(14));
EXPECT(assert_equal(expected, point.normalize(actualH), 1e-8));
Matrix expectedH = numericalDerivative11<Point3, Point3>(
boost::bind(&Point3::normalize, _1, boost::none), point);
EXPECT(assert_equal(expectedH, actualH, 1e-8));
}
/* ************************************************************************* */
int main() { TestResult tr; return TestRegistry::runAllTests(tr); }
int main() {
TestResult tr;
return TestRegistry::runAllTests(tr);
}
/* ************************************************************************* */

2
gtsam/geometry/tests/testRot2.cpp
View File

@ -96,7 +96,7 @@ TEST(Rot2, logmap)
{
Rot2 rot0(Rot2::fromAngle(M_PI/2.0));
Rot2 rot(Rot2::fromAngle(M_PI));
Vector expected = Vector_(1, M_PI/2.0);
Vector expected = (Vector(1) << M_PI/2.0);
Vector actual = rot0.localCoordinates(rot);
CHECK(assert_equal(expected, actual));
}

28
gtsam/geometry/tests/testSphere2.cpp
View File

@ -27,6 +27,7 @@
#include <boost/foreach.hpp>
#include <boost/random.hpp>
#include <boost/assign/std/vector.hpp>
#include <cmath>
using namespace boost::assign;
using namespace gtsam;
@ -212,9 +213,9 @@ inline static Vector randomVector(const Vector& minLimits,
TEST(Sphere2, localCoordinates_retract) {
size_t numIterations = 10000;
Vector minSphereLimit = Vector_(3, -1.0, -1.0, -1.0), maxSphereLimit =
Vector_(3, 1.0, 1.0, 1.0);
Vector minXiLimit = Vector_(2, -1.0, -1.0), maxXiLimit = Vector_(2, 1.0, 1.0);
Vector minSphereLimit = (Vector(3) << -1.0, -1.0, -1.0), maxSphereLimit =
(Vector(3) << 1.0, 1.0, 1.0);
Vector minXiLimit = (Vector(2) << -1.0, -1.0), maxXiLimit = (Vector(2) << 1.0, 1.0);
for (size_t i = 0; i < numIterations; i++) {
// Sleep for the random number generator (TODO?: Better create all of them first).
@ -242,9 +243,9 @@ TEST(Sphere2, localCoordinates_retract) {
TEST(Sphere2, localCoordinates_retract_expmap) {
size_t numIterations = 10000;
Vector minSphereLimit = Vector_(3, -1.0, -1.0, -1.0), maxSphereLimit =
Vector_(3, 1.0, 1.0, 1.0);
Vector minXiLimit = Vector_(2, -M_PI, -M_PI), maxXiLimit = Vector_(2, M_PI, M_PI);
Vector minSphereLimit = (Vector(3) << -1.0, -1.0, -1.0), maxSphereLimit =
(Vector(3) << 1.0, 1.0, 1.0);
Vector minXiLimit = (Vector(2) << -M_PI, -M_PI), maxXiLimit = (Vector(2) << M_PI, M_PI);
for (size_t i = 0; i < numIterations; i++) {
// Sleep for the random number generator (TODO?: Better create all of them first).
@ -287,7 +288,7 @@ TEST(Sphere2, localCoordinates_retract_expmap) {
// EXPECT(assert_equal(expected,actual1));
// EXPECT(assert_equal(expected,actual2));
//
// Matrix expectedH1 = Matrix_(3,3,
// Matrix expectedH1 = (Matrix(3,3) <<
// 0.0,-1.0,-2.0,
// 1.0, 0.0,-2.0,
// 0.0, 0.0,-1.0
@ -298,7 +299,7 @@ TEST(Sphere2, localCoordinates_retract_expmap) {
// // Assert H1 = -AdjointMap(between(p2,p1)) as in doc/math.lyx
// EXPECT(assert_equal(-gT2.between(gT1).AdjointMap(),actualH1));
//
// Matrix expectedH2 = Matrix_(3,3,
// Matrix expectedH2 = (Matrix(3,3) <<
// 1.0, 0.0, 0.0,
// 0.0, 1.0, 0.0,
// 0.0, 0.0, 1.0
@ -324,6 +325,17 @@ TEST(Sphere2, Random) {
EXPECT(assert_equal(expectedMean,actualMean,0.1));
}
//*************************************************************************
TEST (Sphere2, FromPoint3) {
Matrix actualH;
Point3 point(1, -2, 3); // arbitrary point
Sphere2 expected(point);
EXPECT(assert_equal(expected, Sphere2::FromPoint3(point, actualH), 1e-8));
Matrix expectedH = numericalDerivative11<Sphere2, Point3>(
boost::bind(Sphere2::FromPoint3, _1, boost::none), point);
EXPECT(assert_equal(expectedH, actualH, 1e-8));
}
/* ************************************************************************* */
int main() {
srand(time(NULL));

2
gtsam/geometry/tests/timePose2.cpp
View File

@ -138,7 +138,7 @@ int main()
// create a random pose:
double x = 4.0, y = 2.0, r = 0.3;
Vector v = Vector_(3,x,y,r);
Vector v = (Vector(3) << x, y, r);
Pose2 X = Pose2(3,2,0.4), X2 = X.retract(v), X3(5,6,0.3);
TEST(Expmap, Pose2::Expmap(v));

2
gtsam/geometry/tests/timeRot2.cpp
View File

@ -95,7 +95,7 @@ int main()
// create a random direction:
double norm=sqrt(16.0+4.0);
double x=4.0/norm, y=2.0/norm;
Vector v = Vector_(2,x,y);
Vector v = (Vector(2) << x, y);
Rot2 R = Rot2(0.4), R2 = R.retract(v), R3(0.6);
TEST(Rot2_Expmap, Rot2::Expmap(v));

2
gtsam/geometry/tests/timeRot3.cpp
View File

@ -39,7 +39,7 @@ int main()
// create a random direction:
double norm=sqrt(1.0+16.0+4.0);
double x=1.0/norm, y=4.0/norm, z=2.0/norm;
Vector v = Vector_(3,x,y,z);
Vector v = (Vector(3) << x, y, z);
Rot3 R = Rot3::rodriguez(0.1, 0.4, 0.2), R2 = R.retract(v);
TEST("Rodriguez formula given axis angle", Rot3::rodriguez(v,0.001))

8
gtsam/linear/VectorValues.h
View File

@ -49,15 +49,15 @@ namespace gtsam {
* Example:
* \code
VectorValues values;
values.insert(3, Vector_(3, 1.0, 2.0, 3.0));
values.insert(4, Vector_(2, 4.0, 5.0));
values.insert(0, Vector_(4, 6.0, 7.0, 8.0, 9.0));
values.insert(3, (Vector(3) << 1.0, 2.0, 3.0));
values.insert(4, (Vector(2) << 4.0, 5.0));
values.insert(0, (Vector(4) << 6.0, 7.0, 8.0, 9.0));
// Prints [ 3.0 4.0 ]
gtsam::print(values[1]);
// Prints [ 8.0 9.0 ]
values[1] = Vector_(2, 8.0, 9.0);
values[1] = (Vector(2) << 8.0, 9.0);
gtsam::print(values[1]);
\endcode
*

2
gtsam/linear/tests/testSampler.cpp
View File

@ -24,7 +24,7 @@ const double tol = 1e-5;
/* ************************************************************************* */
TEST(testSampler, basic) {
Vector sigmas = Vector_(3, 1.0, 0.1, 0.0);
Vector sigmas = (Vector(3) << 1.0, 0.1, 0.0);
noiseModel::Diagonal::shared_ptr model = noiseModel::Diagonal::Sigmas(sigmas);
char seed = 'A';
Sampler sampler1(model, seed), sampler2(model, 1), sampler3(model, 1);

10
gtsam/navigation/CombinedImuFactor.h
View File

@ -236,7 +236,7 @@ namespace gtsam {
// overall Jacobian wrt preintegrated measurements (df/dx)
Matrix F(15,15);
F << H_pos_pos, H_pos_vel, H_pos_angles, Z_3x3, Z_3x3,
H_vel_pos, H_vel_vel, H_vel_angles, H_vel_biasacc, Z_3x3,
H_vel_pos, H_vel_vel, H_vel_angles, H_vel_biasacc, Z_3x3,
H_angles_pos, H_angles_vel, H_angles_angles, Z_3x3, H_angles_biasomega,
Z_3x3, Z_3x3, Z_3x3, I_3x3, Z_3x3,
Z_3x3, Z_3x3, Z_3x3, Z_3x3, I_3x3;
@ -274,6 +274,7 @@ namespace gtsam {
// Update preintegrated measurements
/* ----------------------------------------------------------------------------------------------------------------------- */
// deltaPij += deltaVij * deltaT;
deltaPij += deltaVij * deltaT + 0.5 * deltaRij.matrix() * biasHat.correctAccelerometer(measuredAcc) * deltaT*deltaT;
deltaVij += deltaRij.matrix() * correctedAcc * deltaT;
deltaRij = deltaRij * Rincr;
@ -341,8 +342,11 @@ namespace gtsam {
public:
/** Shorthand for a smart pointer to a factor */
typedef boost::shared_ptr<CombinedImuFactor> shared_ptr;
#ifndef _MSC_VER
typedef typename boost::shared_ptr<CombinedImuFactor> shared_ptr;
#else
typedef boost::shared_ptr<CombinedImuFactor> shared_ptr;
#endif
/** Default constructor - only use for serialization */
CombinedImuFactor() : preintegratedMeasurements_(imuBias::ConstantBias(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix::Zero(6,6)) {}

2
gtsam/navigation/ImuBias.h
View File

@ -113,7 +113,7 @@ namespace imuBias {
//
// return measurement - biasGyro_ - w_earth_rate_I;
//
//// Vector bias_gyro_temp(Vector_(3, -bias_gyro_(0), bias_gyro_(1), bias_gyro_(2)));
//// Vector bias_gyro_temp((Vector(3) << -bias_gyro_(0), bias_gyro_(1), bias_gyro_(2)));
//// return measurement - bias_gyro_temp - R_G_to_I * w_earth_rate_G;
// }

4
gtsam/navigation/ImuFactor.h
View File

@ -304,7 +304,11 @@ namespace gtsam {
public:
/** Shorthand for a smart pointer to a factor */
#ifndef _MSC_VER
typedef typename boost::shared_ptr<ImuFactor> shared_ptr;
#else
typedef boost::shared_ptr<ImuFactor> shared_ptr;
#endif
/** Default constructor - only use for serialization */
ImuFactor() : preintegratedMeasurements_(imuBias::ConstantBias(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero()) {}

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

@ -168,9 +168,9 @@ TEST( CombinedImuFactor, ErrorWithBiases )
imuBias::ConstantBias bias(Vector3(0.2, 0, 0), Vector3(0, 0, 0.3)); // Biases (acc, rot)
imuBias::ConstantBias bias2(Vector3(0.2, 0.2, 0), Vector3(1, 0, 0.3)); // Biases (acc, rot)
Pose3 x1(Rot3::Expmap(Vector3(0, 0, M_PI/4.0)), Point3(5.0, 1.0, -50.0));
LieVector v1(3, 0.5, 0.0, 0.0);
LieVector v1((Vector(3) << 0.5, 0.0, 0.0));
Pose3 x2(Rot3::Expmap(Vector3(0, 0, M_PI/4.0 + M_PI/10.0)), Point3(5.5, 1.0, -50.0));
LieVector v2(3, 0.5, 0.0, 0.0);
LieVector v2((Vector(3) << 0.5, 0.0, 0.0));
// Measurements
Vector3 gravity; gravity << 0, 0, 9.81;

20
gtsam/navigation/tests/testImuFactor.cpp
View File

@ -167,9 +167,9 @@ TEST( ImuFactor, Error )
// Linearization point
imuBias::ConstantBias bias; // Bias
Pose3 x1(Rot3::RzRyRx(M_PI/12.0, M_PI/6.0, M_PI/4.0), Point3(5.0, 1.0, -50.0));
LieVector v1(3, 0.5, 0.0, 0.0);
LieVector v1((Vector(3) << 0.5, 0.0, 0.0));
Pose3 x2(Rot3::RzRyRx(M_PI/12.0 + M_PI/100.0, M_PI/6.0, M_PI/4.0), Point3(5.5, 1.0, -50.0));
LieVector v2(3, 0.5, 0.0, 0.0);
LieVector v2((Vector(3) << 0.5, 0.0, 0.0));
// Measurements
Vector3 gravity; gravity << 0, 0, 9.81;
@ -238,16 +238,16 @@ TEST( ImuFactor, ErrorWithBiases )
// Linearization point
// Vector bias(6); bias << 0.2, 0, 0, 0.1, 0, 0; // Biases (acc, rot)
// Pose3 x1(Rot3::RzRyRx(M_PI/12.0, M_PI/6.0, M_PI/4.0), Point3(5.0, 1.0, -50.0));
// LieVector v1(3, 0.5, 0.0, 0.0);
// LieVector v1((Vector(3) << 0.5, 0.0, 0.0));
// Pose3 x2(Rot3::RzRyRx(M_PI/12.0 + M_PI/10.0, M_PI/6.0, M_PI/4.0), Point3(5.5, 1.0, -50.0));
// LieVector v2(3, 0.5, 0.0, 0.0);
// LieVector v2((Vector(3) << 0.5, 0.0, 0.0));
imuBias::ConstantBias bias(Vector3(0.2, 0, 0), Vector3(0, 0, 0.3)); // Biases (acc, rot)
Pose3 x1(Rot3::Expmap(Vector3(0, 0, M_PI/4.0)), Point3(5.0, 1.0, -50.0));
LieVector v1(3, 0.5, 0.0, 0.0);
LieVector v1((Vector(3) << 0.5, 0.0, 0.0));
Pose3 x2(Rot3::Expmap(Vector3(0, 0, M_PI/4.0 + M_PI/10.0)), Point3(5.5, 1.0, -50.0));
LieVector v2(3, 0.5, 0.0, 0.0);
LieVector v2((Vector(3) << 0.5, 0.0, 0.0));
// Measurements
Vector3 gravity; gravity << 0, 0, 9.81;
@ -445,9 +445,9 @@ TEST( ImuFactor, FirstOrderPreIntegratedMeasurements )
//{
// // Linearization point
// Pose3 x1(Rot3::RzRyRx(M_PI/12.0, M_PI/6.0, M_PI/4.0), Point3(5.0, 1.0, -50.0));
// LieVector v1(3, 0.5, 0.0, 0.0);
// LieVector v1((Vector(3) << 0.5, 0.0, 0.0));
// Pose3 x2(Rot3::RzRyRx(M_PI/12.0 + M_PI/100.0, M_PI/6.0, M_PI/4.0), Point3(5.5, 1.0, -50.0));
// LieVector v2(3, 0.5, 0.0, 0.0);
// LieVector v2((Vector(3) << 0.5, 0.0, 0.0));
// imuBias::ConstantBias bias(Vector3(0.001, 0.002, 0.008), Vector3(0.002, 0.004, 0.012));
//
// // Pre-integrator
@ -501,9 +501,9 @@ TEST( ImuFactor, ErrorWithBiasesAndSensorBodyDisplacement )
imuBias::ConstantBias bias(Vector3(0.2, 0, 0), Vector3(0, 0, 0.3)); // Biases (acc, rot)
Pose3 x1(Rot3::Expmap(Vector3(0, 0, M_PI/4.0)), Point3(5.0, 1.0, -50.0));
LieVector v1(3, 0.5, 0.0, 0.0);
LieVector v1((Vector(3) << 0.5, 0.0, 0.0));
Pose3 x2(Rot3::Expmap(Vector3(0, 0, M_PI/4.0 + M_PI/10.0)), Point3(5.5, 1.0, -50.0));
LieVector v2(3, 0.5, 0.0, 0.0);
LieVector v2((Vector(3) << 0.5, 0.0, 0.0));
// Measurements
Vector3 gravity; gravity << 0, 0, 9.81;

4
gtsam/nonlinear/ISAM2.h
View File

@ -122,8 +122,8 @@ struct GTSAM_EXPORT ISAM2Params {
* entries would be added with:
* \code
FastMap<char,Vector> thresholds;
thresholds['x'] = Vector_(6, 0.1, 0.1, 0.1, 0.5, 0.5, 0.5); // 0.1 rad rotation threshold, 0.5 m translation threshold
thresholds['l'] = Vector_(3, 1.0, 1.0, 1.0); // 1.0 m landmark position threshold
thresholds['x'] = (Vector(6) << 0.1, 0.1, 0.1, 0.5, 0.5, 0.5); // 0.1 rad rotation threshold, 0.5 m translation threshold
thresholds['l'] = (Vector(3) << 1.0, 1.0, 1.0); // 1.0 m landmark position threshold
params.relinearizeThreshold = thresholds;
\endcode
*/

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

@ -19,7 +19,7 @@ using namespace std;
using namespace boost::assign;
using namespace gtsam;
const gtsam::noiseModel::Diagonal::shared_ptr diag_model2 = noiseModel::Diagonal::Sigmas(Vector_(2, 1.0, 1.0));
const gtsam::noiseModel::Diagonal::shared_ptr diag_model2 = noiseModel::Diagonal::Sigmas((Vector(2) << 1.0, 1.0));
const double tol = 1e-5;
gtsam::Key l1 = 101, l2 = 102, x1 = 1, x2 = 2;

83
gtsam/nonlinear/tests/testValues.cpp
View File

@ -65,7 +65,8 @@ public:
TEST( Values, equals1 )
{
Values expected;
LieVector v(3, 5.0, 6.0, 7.0);
LieVector v((Vector(3) << 5.0, 6.0, 7.0));
expected.insert(key1,v);
Values actual;
actual.insert(key1,v);
@ -76,8 +77,9 @@ TEST( Values, equals1 )
TEST( Values, equals2 )
{
Values cfg1, cfg2;
LieVector v1(3, 5.0, 6.0, 7.0);
LieVector v2(3, 5.0, 6.0, 8.0);
LieVector v1((Vector(3) << 5.0, 6.0, 7.0));
LieVector v2((Vector(3) << 5.0, 6.0, 8.0));
cfg1.insert(key1, v1);
cfg2.insert(key1, v2);
CHECK(!cfg1.equals(cfg2));
@ -88,8 +90,9 @@ TEST( Values, equals2 )
TEST( Values, equals_nan )
{
Values cfg1, cfg2;
LieVector v1(3, 5.0, 6.0, 7.0);
LieVector v2(3, inf, inf, inf);
LieVector v1((Vector(3) << 5.0, 6.0, 7.0));
LieVector v2((Vector(3) << inf, inf, inf));
cfg1.insert(key1, v1);
cfg2.insert(key1, v2);
CHECK(!cfg1.equals(cfg2));
@ -100,10 +103,11 @@ TEST( Values, equals_nan )
TEST( Values, insert_good )
{
Values cfg1, cfg2, expected;
LieVector v1(3, 5.0, 6.0, 7.0);
LieVector v2(3, 8.0, 9.0, 1.0);
LieVector v3(3, 2.0, 4.0, 3.0);
LieVector v4(3, 8.0, 3.0, 7.0);
LieVector v1((Vector(3) << 5.0, 6.0, 7.0));
LieVector v2((Vector(3) << 8.0, 9.0, 1.0));
LieVector v3((Vector(3) << 2.0, 4.0, 3.0));
LieVector v4((Vector(3) << 8.0, 3.0, 7.0));
cfg1.insert(key1, v1);
cfg1.insert(key2, v2);
cfg2.insert(key3, v4);
@ -121,10 +125,11 @@ TEST( Values, insert_good )
TEST( Values, insert_bad )
{
Values cfg1, cfg2;
LieVector v1(3, 5.0, 6.0, 7.0);
LieVector v2(3, 8.0, 9.0, 1.0);
LieVector v3(3, 2.0, 4.0, 3.0);
LieVector v4(3, 8.0, 3.0, 7.0);
LieVector v1((Vector(3) << 5.0, 6.0, 7.0));
LieVector v2((Vector(3) << 8.0, 9.0, 1.0));
LieVector v3((Vector(3) << 2.0, 4.0, 3.0));
LieVector v4((Vector(3) << 8.0, 3.0, 7.0));
cfg1.insert(key1, v1);
cfg1.insert(key2, v2);
cfg2.insert(key2, v3);
@ -137,8 +142,8 @@ TEST( Values, insert_bad )
TEST( Values, update_element )
{
Values cfg;
LieVector v1(3, 5.0, 6.0, 7.0);
LieVector v2(3, 8.0, 9.0, 1.0);
LieVector v1((Vector(3) << 5.0, 6.0, 7.0));
LieVector v2((Vector(3) << 8.0, 9.0, 1.0));
cfg.insert(key1, v1);
CHECK(cfg.size() == 1);
@ -181,8 +186,8 @@ TEST(Values, basic_functions)
//TEST(Values, dim_zero)
//{
// Values config0;
// config0.insert(key1, LieVector(2, 2.0, 3.0));
// config0.insert(key2, LieVector(3, 5.0, 6.0, 7.0));
// config0.insert(key1, LieVector((Vector(2) << 2.0, 3.0));
// config0.insert(key2, LieVector((Vector(3) << 5.0, 6.0, 7.0));
// LONGS_EQUAL(5, config0.dim());
//
// VectorValues expected;
@ -195,16 +200,16 @@ TEST(Values, basic_functions)
TEST(Values, expmap_a)
{
Values config0;
config0.insert(key1, LieVector(3, 1.0, 2.0, 3.0));
config0.insert(key2, LieVector(3, 5.0, 6.0, 7.0));
config0.insert(key1, LieVector((Vector(3) << 1.0, 2.0, 3.0)));
config0.insert(key2, LieVector((Vector(3) << 5.0, 6.0, 7.0)));
VectorValues increment = pair_list_of<Key, Vector>
(key1, (Vector(3) << 1.0, 1.1, 1.2))
(key2, (Vector(3) << 1.3, 1.4, 1.5));
Values expected;
expected.insert(key1, LieVector(3, 2.0, 3.1, 4.2));
expected.insert(key2, LieVector(3, 6.3, 7.4, 8.5));
expected.insert(key1, LieVector((Vector(3) << 2.0, 3.1, 4.2)));
expected.insert(key2, LieVector((Vector(3) << 6.3, 7.4, 8.5)));
CHECK(assert_equal(expected, config0.retract(increment)));
}
@ -213,15 +218,15 @@ TEST(Values, expmap_a)
TEST(Values, expmap_b)
{
Values config0;
config0.insert(key1, LieVector(3, 1.0, 2.0, 3.0));
config0.insert(key2, LieVector(3, 5.0, 6.0, 7.0));
config0.insert(key1, LieVector((Vector(3) << 1.0, 2.0, 3.0)));
config0.insert(key2, LieVector((Vector(3) << 5.0, 6.0, 7.0)));
VectorValues increment = pair_list_of<Key, Vector>
(key2, (Vector(3) << 1.3, 1.4, 1.5));
Values expected;
expected.insert(key1, LieVector(3, 1.0, 2.0, 3.0));
expected.insert(key2, LieVector(3, 6.3, 7.4, 8.5));
expected.insert(key1, LieVector((Vector(3) << 1.0, 2.0, 3.0)));
expected.insert(key2, LieVector((Vector(3) << 6.3, 7.4, 8.5)));
CHECK(assert_equal(expected, config0.retract(increment)));
}
@ -230,16 +235,16 @@ TEST(Values, expmap_b)
//TEST(Values, expmap_c)
//{
// Values config0;
// config0.insert(key1, LieVector(3, 1.0, 2.0, 3.0));
// config0.insert(key2, LieVector(3, 5.0, 6.0, 7.0));
// config0.insert(key1, LieVector((Vector(3) << 1.0, 2.0, 3.0)));
// config0.insert(key2, LieVector((Vector(3) << 5.0, 6.0, 7.0)));
//
// Vector increment = LieVector(6,
// 1.0, 1.1, 1.2,
// 1.3, 1.4, 1.5);
//
// Values expected;
// expected.insert(key1, LieVector(3, 2.0, 3.1, 4.2));
// expected.insert(key2, LieVector(3, 6.3, 7.4, 8.5));
// expected.insert(key1, LieVector((Vector(3) << 2.0, 3.1, 4.2)));
// expected.insert(key2, LieVector((Vector(3) << 6.3, 7.4, 8.5)));
//
// CHECK(assert_equal(expected, config0.retract(increment)));
//}
@ -248,8 +253,8 @@ TEST(Values, expmap_b)
TEST(Values, expmap_d)
{
Values config0;
config0.insert(key1, LieVector(3, 1.0, 2.0, 3.0));
config0.insert(key2, LieVector(3, 5.0, 6.0, 7.0));
config0.insert(key1, LieVector((Vector(3) << 1.0, 2.0, 3.0)));
config0.insert(key2, LieVector((Vector(3) << 5.0, 6.0, 7.0)));
//config0.print("config0");
CHECK(equal(config0, config0));
CHECK(config0.equals(config0));
@ -266,8 +271,8 @@ TEST(Values, expmap_d)
TEST(Values, localCoordinates)
{
Values valuesA;
valuesA.insert(key1, LieVector(3, 1.0, 2.0, 3.0));
valuesA.insert(key2, LieVector(3, 5.0, 6.0, 7.0));
valuesA.insert(key1, LieVector((Vector(3) << 1.0, 2.0, 3.0)));
valuesA.insert(key2, LieVector((Vector(3) << 5.0, 6.0, 7.0)));
VectorValues expDelta = pair_list_of<Key, Vector>
(key1, (Vector(3) << 0.1, 0.2, 0.3))
@ -314,17 +319,17 @@ TEST(Values, exists_)
TEST(Values, update)
{
Values config0;
config0.insert(key1, LieVector(1, 1.));
config0.insert(key2, LieVector(1, 2.));
config0.insert(key1, LieVector((Vector(1) << 1.)));
config0.insert(key2, LieVector((Vector(1) << 2.)));
Values superset;
superset.insert(key1, LieVector(1, -1.));
superset.insert(key2, LieVector(1, -2.));
superset.insert(key1, LieVector((Vector(1) << -1.)));
superset.insert(key2, LieVector((Vector(1) << -2.)));
config0.update(superset);
Values expected;
expected.insert(key1, LieVector(1, -1.));
expected.insert(key2, LieVector(1, -2.));
expected.insert(key1, LieVector((Vector(1) << -1.)));
expected.insert(key2, LieVector((Vector(1) << -2.)));
CHECK(assert_equal(expected,config0));
}

2
gtsam/slam/BearingRangeFactor.h
View File

@ -96,7 +96,7 @@ namespace gtsam {
Vector e1 = Rot::Logmap(measuredBearing_.between(y1));
double y2 = pose.range(point, H21_, H22_);
Vector e2 = Vector_(1, y2 - measuredRange_);
Vector e2 = (Vector(1) << y2 - measuredRange_);
if (H1) *H1 = gtsam::stack(2, &H11, &H21);
if (H2) *H2 = gtsam::stack(2, &H12, &H22);

75
gtsam/slam/EssentialMatrixConstraint.cpp Normal file
View File

@ -0,0 +1,75 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010-2014, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file EssentialMatrixConstraint.cpp
* @author Frank Dellaert
* @author Pablo Alcantarilla
* @date Jan 5, 2014
**/
#include <gtsam/slam/EssentialMatrixConstraint.h>
//#include <gtsam/linear/GaussianFactor.h>
//#include <gtsam/base/Testable.h>
#include <ostream>
namespace gtsam {
/* ************************************************************************* */
void EssentialMatrixConstraint::print(const std::string& s,
const KeyFormatter& keyFormatter) const {
std::cout << s << "EssentialMatrixConstraint(" << keyFormatter(this->key1())
<< "," << keyFormatter(this->key2()) << ")\n";
measuredE_.print(" measured: ");
this->noiseModel_->print(" noise model: ");
}
/* ************************************************************************* */
bool EssentialMatrixConstraint::equals(const NonlinearFactor& expected,
double tol) const {
const This *e = dynamic_cast<const This*>(&expected);
return e != NULL && Base::equals(*e, tol)
&& this->measuredE_.equals(e->measuredE_, tol);
}
/* ************************************************************************* */
Vector EssentialMatrixConstraint::evaluateError(const Pose3& p1,
const Pose3& p2, boost::optional<Matrix&> Hp1,
boost::optional<Matrix&> Hp2) const {
// compute relative Pose3 between p1 and p2
Pose3 _1P2_ = p1.between(p2, Hp1, Hp2);
// convert to EssentialMatrix
Matrix D_hx_1P2;
EssentialMatrix hx = EssentialMatrix::FromPose3(_1P2_,
(Hp1 || Hp2) ? boost::optional<Matrix&>(D_hx_1P2) : boost::none);
// Calculate derivatives if needed
if (Hp1) {
// Hp1 will already contain the 6*6 derivative D_1P2_p1
const Matrix& D_1P2_p1 = *Hp1;
// The 5*6 derivative is obtained by chaining with 5*6 D_hx_1P2:
*Hp1 = D_hx_1P2 * D_1P2_p1;
}
if (Hp2) {
// Hp2 will already contain the 6*6 derivative D_1P2_p1
const Matrix& D_1P2_p2 = *Hp2;
// The 5*6 derivative is obtained by chaining with 5*6 D_hx_1P2:
*Hp2 = D_hx_1P2 * D_1P2_p2;
}
// manifold equivalent of h(x)-z -> log(z,h(x))
return measuredE_.localCoordinates(hx); // 5D error
}
} /// namespace gtsam

109
gtsam/slam/EssentialMatrixConstraint.h Normal file
View File

@ -0,0 +1,109 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010-2014, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file EssentialMatrixConstraint.h
* @author Frank Dellaert
* @author Pablo Alcantarilla
* @date Jan 5, 2014
**/
#pragma once
#include <gtsam/nonlinear/NonlinearFactor.h>
#include <gtsam/geometry/EssentialMatrix.h>
namespace gtsam {
/**
* Binary factor between two Pose3 variables induced by an EssentialMatrix measurement
* @addtogroup SLAM
*/
class EssentialMatrixConstraint: public NoiseModelFactor2<Pose3, Pose3> {
private:
typedef EssentialMatrixConstraint This;
typedef NoiseModelFactor2<Pose3, Pose3> Base;
EssentialMatrix measuredE_; /** The measurement is an essential matrix */
public:
// shorthand for a smart pointer to a factor
typedef boost::shared_ptr<EssentialMatrixConstraint> shared_ptr;
/** default constructor - only use for serialization */
EssentialMatrixConstraint() {
}
/**
* Constructor
* @param key1 key for first pose
* @param key2 key for second pose
* @param measuredE measured EssentialMatrix
* @param model noise model, 5D !
*/
EssentialMatrixConstraint(Key key1, Key key2,
const EssentialMatrix& measuredE, const SharedNoiseModel& model) :
Base(model, key1, key2), measuredE_(measuredE) {
}
virtual ~EssentialMatrixConstraint() {
}
/// @return a deep copy of this factor
virtual gtsam::NonlinearFactor::shared_ptr clone() const {
return boost::static_pointer_cast<gtsam::NonlinearFactor>(
gtsam::NonlinearFactor::shared_ptr(new This(*this)));
}
/** implement functions needed for Testable */
/** print */
virtual void print(const std::string& s = "",
const KeyFormatter& keyFormatter = DefaultKeyFormatter) const;
/** equals */
virtual bool equals(const NonlinearFactor& expected, double tol = 1e-9) const;
/** implement functions needed to derive from Factor */
/** vector of errors */
virtual Vector evaluateError(const Pose3& p1, const Pose3& p2,
boost::optional<Matrix&> Hp1 = boost::none, //
boost::optional<Matrix&> Hp2 = boost::none) const;
/** return the measured */
const EssentialMatrix& measured() const {
return measuredE_;
}
/** number of variables attached to this factor */
std::size_t size() const {
return 2;
}
private:
/** Serialization function */
friend class boost::serialization::access;
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int version) {
ar
& boost::serialization::make_nvp("NoiseModelFactor2",
boost::serialization::base_object<Base>(*this));
ar & BOOST_SERIALIZATION_NVP(measuredE_);
}
};
// \class EssentialMatrixConstraint
}/// namespace gtsam

2
gtsam/slam/RangeFactor.h
View File

@ -73,7 +73,7 @@ namespace gtsam {
} else {
hx = pose.range(point, H1, H2);
}
return Vector_(1, hx - measured_);
return (Vector(1) << hx - measured_);
}
/** return the measured */

76
gtsam/slam/tests/testEssentialMatrixConstraint.cpp Normal file
View File

@ -0,0 +1,76 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file testEssentialMatrixConstraint.cpp
* @brief Unit tests for EssentialMatrixConstraint Class
* @author Frank Dellaert
* @author Pablo Alcantarilla
* @date Jan 5, 2014
*/
#include <gtsam/slam/EssentialMatrixConstraint.h>
#include <gtsam/nonlinear/Symbol.h>
#include <gtsam/geometry/Pose3.h>
#include <gtsam/base/numericalDerivative.h>
#include <gtsam/base/TestableAssertions.h>
#include <CppUnitLite/TestHarness.h>
using namespace std;
using namespace gtsam;
/* ************************************************************************* */
TEST( EssentialMatrixConstraint, test ) {
// Create a factor
Key poseKey1(1);
Key poseKey2(2);
Rot3 trueRotation = Rot3::RzRyRx(0.15, 0.15, -0.20);
Point3 trueTranslation(+0.5, -1.0, +1.0);
Sphere2 trueDirection(trueTranslation);
EssentialMatrix measurement(trueRotation, trueDirection);
SharedNoiseModel model = noiseModel::Isotropic::Sigma(5, 0.25);
EssentialMatrixConstraint factor(poseKey1, poseKey2, measurement, model);
// Create a linearization point at the zero-error point
Pose3 pose1(Rot3::RzRyRx(0.00, -0.15, 0.30), Point3(-4.0, 7.0, -10.0));
Pose3 pose2(
Rot3::RzRyRx(0.179693265735950, 0.002945368776519, 0.102274823253840),
Point3(-3.37493895, 6.14660244, -8.93650986));
Vector expected = zero(5);
Vector actual = factor.evaluateError(pose1,pose2);
CHECK(assert_equal(expected, actual, 1e-8));
// Calculate numerical derivatives
Matrix expectedH1 = numericalDerivative11<Pose3>(
boost::bind(&EssentialMatrixConstraint::evaluateError, &factor, _1, pose2,
boost::none, boost::none), pose1);
Matrix expectedH2 = numericalDerivative11<Pose3>(
boost::bind(&EssentialMatrixConstraint::evaluateError, &factor, pose1, _1,
boost::none, boost::none), pose2);
// Use the factor to calculate the derivative
Matrix actualH1;
Matrix actualH2;
factor.evaluateError(pose1, pose2, actualH1, actualH2);
// Verify we get the expected error
CHECK(assert_equal(expectedH1, actualH1, 1e-9));
CHECK(assert_equal(expectedH2, actualH2, 1e-9));
}
/* ************************************************************************* */
int main() {
TestResult tr;
return TestRegistry::runAllTests(tr);
}
/* ************************************************************************* */

19
gtsam/slam/tests/testEssentialMatrixFactor.cpp
View File

@ -154,8 +154,8 @@ TEST (EssentialMatrixFactor2, factor) {
EssentialMatrixFactor2 factor(100, i, pA(i), pB(i), model2);
// Check evaluation
Point3 P1 = data.tracks[i].p;
const Point2 pi = camera2.project(P1);
Point3 P1 = data.tracks[i].p, P2 = data.cameras[1].pose().transform_to(P1);
const Point2 pi = SimpleCamera::project_to_camera(P2);
Point2 reprojectionError(pi - pB(i));
Vector expected = reprojectionError.vector();
@ -269,6 +269,21 @@ TEST (EssentialMatrixFactor3, minimization) {
truth.insert(i, LieScalar(baseline / P1.z()));
}
EXPECT_DOUBLES_EQUAL(0, graph.error(truth), 1e-8);
// Optimize
LevenbergMarquardtParams parameters;
// parameters.setVerbosity("ERROR");
LevenbergMarquardtOptimizer optimizer(graph, truth, parameters);
Values result = optimizer.optimize();
// Check result
EssentialMatrix actual = result.at<EssentialMatrix>(100);
EXPECT(assert_equal(bodyE, actual, 1e-1));
for (size_t i = 0; i < 5; i++)
EXPECT(assert_equal(truth.at<LieScalar>(i), result.at<LieScalar>(i), 1e-1));
// Check error at result
EXPECT_DOUBLES_EQUAL(0, graph.error(result), 1e-4);
}
} // namespace example1

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

@ -110,7 +110,7 @@ TEST( GeneralSFMFactor, error ) {
Pose3 x1(R,t1);
values.insert(X(1), GeneralCamera(x1));
Point3 l1; values.insert(L(1), l1);
EXPECT(assert_equal((Vector(2) << -3.0, 0.0), factor->unwhitenedError(values)));
EXPECT(assert_equal(((Vector) (Vector(2) << -3.0, 0.0)), factor->unwhitenedError(values)));
}
static const double baseline = 5.0 ;

17
gtsam_unstable/base/FixedVector.h
View File

@ -45,23 +45,6 @@ public:
std::copy(values, values+N, this->data());
}
/**
* nice constructor, dangerous as number of arguments must be exactly right
* and you have to pass doubles !!! always use 0.0 never 0
*
* NOTE: this will throw warnings/explode if there is no argument
* before the variadic section, so there is a meaningless size argument.
*/
FixedVector(size_t n, ...) {
va_list ap;
va_start(ap, n);
for(size_t i = 0 ; i < N ; i++) {
double value = va_arg(ap, double);
(*this)(i) = value;
}
va_end(ap);
}
/**
* Create vector initialized to a constant value
* @param value constant value

19
gtsam_unstable/base/tests/testFixedVector.cpp
View File

@ -28,7 +28,7 @@ static const double tol = 1e-9;
/* ************************************************************************* */
TEST( testFixedVector, conversions ) {
double data1[] = {1.0, 2.0, 3.0};
Vector v1 = Vector_(3, data1);
Vector v1 = (Vector(3) << data1[0], data1[1], data1[2]);
TestVector3 fv1(v1), fv2(data1);
Vector actFv2(fv2);
@ -37,7 +37,7 @@ TEST( testFixedVector, conversions ) {
/* ************************************************************************* */
TEST( testFixedVector, variable_constructor ) {
TestVector3 act(3, 1.0, 2.0, 3.0);
TestVector3 act((Vector(3) << 1.0, 2.0, 3.0));
EXPECT_DOUBLES_EQUAL(1.0, act(0), tol);
EXPECT_DOUBLES_EQUAL(2.0, act(1), tol);
EXPECT_DOUBLES_EQUAL(3.0, act(2), tol);
@ -45,8 +45,9 @@ TEST( testFixedVector, variable_constructor ) {
/* ************************************************************************* */
TEST( testFixedVector, equals ) {
TestVector3 vec1(3, 1.0, 2.0, 3.0), vec2(3, 1.0, 2.0, 3.0), vec3(3, 2.0, 3.0, 4.0);
TestVector5 vec4(5, 1.0, 2.0, 3.0, 4.0, 5.0);
TestVector3 vec1((Vector(3) << 1.0, 2.0, 3.0)), vec2((Vector(3) << 1.0, 2.0, 3.0)),
vec3((Vector(3) << 2.0, 3.0, 4.0));
TestVector5 vec4((Vector(5) << 1.0, 2.0, 3.0, 4.0, 5.0));
EXPECT(assert_equal(vec1, vec1, tol));
EXPECT(assert_equal(vec1, vec2, tol));
@ -60,23 +61,23 @@ TEST( testFixedVector, equals ) {
/* ************************************************************************* */
TEST( testFixedVector, static_constructors ) {
TestVector3 actZero = TestVector3::zero();
TestVector3 expZero(3, 0.0, 0.0, 0.0);
TestVector3 expZero((Vector(3) << 0.0, 0.0, 0.0));
EXPECT(assert_equal(expZero, actZero, tol));
TestVector3 actOnes = TestVector3::ones();
TestVector3 expOnes(3, 1.0, 1.0, 1.0);
TestVector3 expOnes((Vector(3) << 1.0, 1.0, 1.0));
EXPECT(assert_equal(expOnes, actOnes, tol));
TestVector3 actRepeat = TestVector3::repeat(2.3);
TestVector3 expRepeat(3, 2.3, 2.3, 2.3);
TestVector3 expRepeat((Vector(3) << 2.3, 2.3, 2.3));
EXPECT(assert_equal(expRepeat, actRepeat, tol));
TestVector3 actBasis = TestVector3::basis(1);
TestVector3 expBasis(3, 0.0, 1.0, 0.0);
TestVector3 expBasis((Vector(3) << 0.0, 1.0, 0.0));
EXPECT(assert_equal(expBasis, actBasis, tol));
TestVector3 actDelta = TestVector3::delta(1, 2.3);
TestVector3 expDelta(3, 0.0, 2.3, 0.0);
TestVector3 expDelta((Vector(3) << 0.0, 2.3, 0.0));
EXPECT(assert_equal(expDelta, actDelta, tol));
}

2
gtsam_unstable/geometry/BearingS2.cpp
View File

@ -69,7 +69,7 @@ BearingS2 BearingS2::retract(const Vector& v) const {
/* ************************************************************************* */
Vector BearingS2::localCoordinates(const BearingS2& x) const {
return Vector_(2, azimuth_.localCoordinates(x.azimuth_)(0),
return (Vector(2) << azimuth_.localCoordinates(x.azimuth_)(0),
elevation_.localCoordinates(x.elevation_)(0));
}

2
gtsam_unstable/geometry/InvDepthCamera3.h
View File

@ -166,7 +166,7 @@ public:
gtsam::Point3 ray = pw - pt;
double theta = atan2(ray.y(), ray.x()); // longitude
double phi = atan2(ray.z(), sqrt(ray.x()*ray.x()+ray.y()*ray.y()));
return std::make_pair(gtsam::LieVector(5, pt.x(),pt.y(),pt.z(), theta, phi),
return std::make_pair(gtsam::LieVector((Vector(5) << pt.x(),pt.y(),pt.z(), theta, phi)),
gtsam::LieScalar(1./depth));
}

2
gtsam_unstable/geometry/SimPolygon2D.cpp
View File

@ -145,7 +145,7 @@ SimPolygon2D SimPolygon2D::randomTriangle(
// extend line by random dist and angle to get BC
double dAB = randomDistance(mean_side_len, sigma_side_len, min_side_len);
double tABC = randomAngle().theta();
Pose2 xB = xA.retract(Vector_(3, dAB, 0.0, tABC));
Pose2 xB = xA.retract((Vector(3) << dAB, 0.0, tABC));
// extend from B to find C
double dBC = randomDistance(mean_side_len, sigma_side_len, min_side_len);

2
gtsam_unstable/geometry/SimWall2D.cpp
View File

@ -128,7 +128,7 @@ std::pair<Pose2, bool> moveWithBounce(const Pose2& cur_pose, double step_size,
// calculate angle to change by
Rot2 dtheta = Rot2::fromAngle(angle_drift.sample()(0) + bias.theta());
Pose2 test_pose = cur_pose.retract(Vector_(3, step_size, 0.0, Rot2::Logmap(dtheta)(0)));
Pose2 test_pose = cur_pose.retract((Vector(3) << step_size, 0.0, Rot2::Logmap(dtheta)(0)));
// create a segment to use for intersection checking
// find the closest intersection

18
gtsam_unstable/geometry/tests/testInvDepthCamera3.cpp
View File

@ -29,7 +29,7 @@ TEST( InvDepthFactor, Project1) {
Point2 expected_uv = level_camera.project(landmark);
InvDepthCamera3<Cal3_S2> inv_camera(level_pose, K);
LieVector inv_landmark(5, 1., 0., 1., 0., 0.);
LieVector inv_landmark((Vector(5) << 1., 0., 1., 0., 0.));
LieScalar inv_depth(1./4);
Point2 actual_uv = inv_camera.project(inv_landmark, inv_depth);
EXPECT(assert_equal(expected_uv, actual_uv));
@ -45,7 +45,7 @@ TEST( InvDepthFactor, Project2) {
Point2 expected = level_camera.project(landmark);
InvDepthCamera3<Cal3_S2> inv_camera(level_pose, K);
LieVector diag_landmark(5, 0., 0., 1., M_PI/4., atan(1.0/sqrt(2.0)));
LieVector diag_landmark((Vector(5) << 0., 0., 1., M_PI/4., atan(1.0/sqrt(2.0))));
LieScalar inv_depth(1/sqrt(3.0));
Point2 actual = inv_camera.project(diag_landmark, inv_depth);
EXPECT(assert_equal(expected, actual));
@ -60,7 +60,7 @@ TEST( InvDepthFactor, Project3) {
Point2 expected = level_camera.project(landmark);
InvDepthCamera3<Cal3_S2> inv_camera(level_pose, K);
LieVector diag_landmark(5, 0., 0., 0., M_PI/4., atan(2./sqrt(2.0)));
LieVector diag_landmark((Vector(5) << 0., 0., 0., M_PI/4., atan(2./sqrt(2.0))));
LieScalar inv_depth( 1./sqrt(1.0+1+4));
Point2 actual = inv_camera.project(diag_landmark, inv_depth);
EXPECT(assert_equal(expected, actual));
@ -75,7 +75,7 @@ TEST( InvDepthFactor, Project4) {
Point2 expected = level_camera.project(landmark);
InvDepthCamera3<Cal3_S2> inv_camera(level_pose, K);
LieVector diag_landmark(5, 0., 0., 0., atan(4.0/1), atan(2./sqrt(1.+16.)));
LieVector diag_landmark((Vector(5) << 0., 0., 0., atan(4.0/1), atan(2./sqrt(1.+16.))));
LieScalar inv_depth(1./sqrt(1.+16.+4.));
Point2 actual = inv_camera.project(diag_landmark, inv_depth);
EXPECT(assert_equal(expected, actual));
@ -88,7 +88,7 @@ Point2 project_(const Pose3& pose, const LieVector& landmark, const LieScalar& i
TEST( InvDepthFactor, Dproject_pose)
{
LieVector landmark(6,0.1,0.2,0.3, 0.1,0.2);
LieVector landmark((Vector(5) << 0.1,0.2,0.3, 0.1,0.2));
LieScalar inv_depth(1./4);
Matrix expected = numericalDerivative31<Point2,Pose3,LieVector>(project_,level_pose, landmark, inv_depth);
InvDepthCamera3<Cal3_S2> inv_camera(level_pose,K);
@ -100,7 +100,7 @@ TEST( InvDepthFactor, Dproject_pose)
/* ************************************************************************* */
TEST( InvDepthFactor, Dproject_landmark)
{
LieVector landmark(5,0.1,0.2,0.3, 0.1,0.2);
LieVector landmark((Vector(5) << 0.1,0.2,0.3, 0.1,0.2));
LieScalar inv_depth(1./4);
Matrix expected = numericalDerivative32<Point2,Pose3,LieVector>(project_,level_pose, landmark, inv_depth);
InvDepthCamera3<Cal3_S2> inv_camera(level_pose,K);
@ -112,7 +112,7 @@ TEST( InvDepthFactor, Dproject_landmark)
/* ************************************************************************* */
TEST( InvDepthFactor, Dproject_inv_depth)
{
LieVector landmark(5,0.1,0.2,0.3, 0.1,0.2);
LieVector landmark((Vector(5) << 0.1,0.2,0.3, 0.1,0.2));
LieScalar inv_depth(1./4);
Matrix expected = numericalDerivative33<Point2,Pose3,LieVector>(project_,level_pose, landmark, inv_depth);
InvDepthCamera3<Cal3_S2> inv_camera(level_pose,K);
@ -124,7 +124,7 @@ TEST( InvDepthFactor, Dproject_inv_depth)
/* ************************************************************************* */
TEST(InvDepthFactor, backproject)
{
LieVector expected(5,0.,0.,1., 0.1,0.2);
LieVector expected((Vector(5) << 0.,0.,1., 0.1,0.2));
LieScalar inv_depth(1./4);
InvDepthCamera3<Cal3_S2> inv_camera(level_pose,K);
Point2 z = inv_camera.project(expected, inv_depth);
@ -140,7 +140,7 @@ TEST(InvDepthFactor, backproject)
TEST(InvDepthFactor, backproject2)
{
// backwards facing camera
LieVector expected(5,-5.,-5.,2., 3., -0.1);
LieVector expected((Vector(5) << -5.,-5.,2., 3., -0.1));
LieScalar inv_depth(1./10);
InvDepthCamera3<Cal3_S2> inv_camera(Pose3(Rot3::ypr(1.5,0.1, -1.5), Point3(-5, -5, 2)),K);
Point2 z = inv_camera.project(expected, inv_depth);

2
gtsam_unstable/geometry/tests/testPose3Upright.cpp
View File

@ -72,7 +72,7 @@ TEST( testPose3Upright, manifold ) {
EXPECT(assert_equal(x1, x1.retract(zero(4)), tol));
EXPECT(assert_equal(x2, x2.retract(zero(4)), tol));
Vector delta12 = Vector_(4, 3.0, 0.0, 4.0, 0.0), delta21 = -delta12;
Vector delta12 = (Vector(4) << 3.0, 0.0, 4.0, 0.0), delta21 = -delta12;
EXPECT(assert_equal(x2, x1.retract(delta12), tol));
EXPECT(assert_equal(x1, x2.retract(delta21), tol));

2
gtsam_unstable/slam/BetweenFactorEM.h
View File

@ -266,7 +266,7 @@ namespace gtsam {
}
}
return Vector_(2, p_inlier, p_outlier);
return (Vector(2) << p_inlier, p_outlier);
}
/* ************************************************************************* */

673
gtsam_unstable/slam/CombinedImuFactor.h
View File

@ -1,673 +0,0 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file CombinedImuFactor.h
* @author Luca Carlone, Stephen Williams
**/
#pragma once
/* GTSAM includes */
#include <gtsam/nonlinear/NonlinearFactor.h>
#include <gtsam/linear/GaussianFactor.h>
#include <gtsam/navigation/ImuBias.h>
#include <gtsam/geometry/Pose3.h>
#include <gtsam/base/LieVector.h>
#include <gtsam/base/debug.h>
/* External or standard includes */
#include <ostream>
namespace gtsam {
/**
*
* @addtogroup SLAM
*
* REFERENCES:
* [1] G.S. Chirikjian, "Stochastic Models, Information Theory, and Lie Groups", Volume 2, 2008.
* [2] T. Lupton and S.Sukkarieh, "Visual-Inertial-Aided Navigation for High-Dynamic Motion in Built
* Environments Without Initial Conditions", TRO, 28(1):61-76, 2012.
* [3] L. Carlone, S. Williams, R. Roberts, "Preintegrated IMU factor: Computation of the Jacobian Matrices", Tech. Report, 2013.
*/
class CombinedImuFactor: public NoiseModelFactor6<Pose3,LieVector,Pose3,LieVector,imuBias::ConstantBias,imuBias::ConstantBias> {
public:
/** Struct to store results of preintegrating IMU measurements. Can be build
* incrementally so as to avoid costly integration at time of factor construction. */
/** Right Jacobian for Exponential map in SO(3) - equation (10.86) and following equations in [1] */
static Matrix3 rightJacobianExpMapSO3(const Vector3& x) {
// x is the axis-angle representation (exponential coordinates) for a rotation
double normx = norm_2(x); // rotation angle
Matrix3 Jr;
if (normx < 10e-8){
Jr = Matrix3::Identity();
}
else{
const Matrix3 X = skewSymmetric(x); // element of Lie algebra so(3): X = x^
Jr = Matrix3::Identity() - ((1-cos(normx))/(normx*normx)) * X +
((normx-sin(normx))/(normx*normx*normx)) * X * X; // right Jacobian
}
return Jr;
}
/** Right Jacobian for Log map in SO(3) - equation (10.86) and following equations in [1] */
static Matrix3 rightJacobianExpMapSO3inverse(const Vector3& x) {
// x is the axis-angle representation (exponential coordinates) for a rotation
double normx = norm_2(x); // rotation angle
Matrix3 Jrinv;
if (normx < 10e-8){
Jrinv = Matrix3::Identity();
}
else{
const Matrix3 X = skewSymmetric(x); // element of Lie algebra so(3): X = x^
Jrinv = Matrix3::Identity() +
0.5 * X + (1/(normx*normx) - (1+cos(normx))/(2*normx * sin(normx)) ) * X * X;
}
return Jrinv;
}
/** CombinedPreintegratedMeasurements accumulates (integrates) the IMU measurements (rotation rates and accelerations)
* and the corresponding covariance matrix. The measurements are then used to build the Preintegrated IMU factor*/
class CombinedPreintegratedMeasurements {
public:
imuBias::ConstantBias biasHat; ///< Acceleration and angular rate bias values used during preintegration
Matrix measurementCovariance; ///< (Raw measurements uncertainty) Covariance of the vector
///< [integrationError measuredAcc measuredOmega biasAccRandomWalk biasOmegaRandomWalk biasAccInit biasOmegaInit] in R^(21 x 21)
Vector3 deltaPij; ///< Preintegrated relative position (does not take into account velocity at time i, see deltap+, in [2]) (in frame i)
Vector3 deltaVij; ///< Preintegrated relative velocity (in global frame)
Rot3 deltaRij; ///< Preintegrated relative orientation (in frame i)
double deltaTij; ///< Time interval from i to j
Matrix3 delPdelBiasAcc; ///< Jacobian of preintegrated position w.r.t. acceleration bias
Matrix3 delPdelBiasOmega; ///< Jacobian of preintegrated position w.r.t. angular rate bias
Matrix3 delVdelBiasAcc; ///< Jacobian of preintegrated velocity w.r.t. acceleration bias
Matrix3 delVdelBiasOmega; ///< Jacobian of preintegrated velocity w.r.t. angular rate bias
Matrix3 delRdelBiasOmega; ///< Jacobian of preintegrated rotation w.r.t. angular rate bias
Matrix PreintMeasCov; ///< Covariance matrix of the preintegrated measurements (first-order propagation from *measurementCovariance*)
///< In the combined factor is also includes the biases and keeps the correlation between the preintegrated measurements and the biases
///< COVARIANCE OF: [PreintPOSITION PreintVELOCITY PreintROTATION BiasAcc BiasOmega]
/** Default constructor, initialize with no IMU measurements */
CombinedPreintegratedMeasurements(
const imuBias::ConstantBias& bias, ///< Current estimate of acceleration and rotation rate biases
const Matrix3& measuredAccCovariance, ///< Covariance matrix of measuredAcc
const Matrix3& measuredOmegaCovariance, ///< Covariance matrix of measuredAcc
const Matrix3& integrationErrorCovariance, ///< Covariance matrix of measuredAcc
const Matrix3& biasAccCovariance, ///< Covariance matrix of biasAcc (random walk describing BIAS evolution)
const Matrix3& biasOmegaCovariance, ///< Covariance matrix of biasOmega (random walk describing BIAS evolution)
const Matrix& biasAccOmegaInit ///< Covariance of biasAcc & biasOmega when preintegrating measurements
///< (this allows to consider the uncertainty of the BIAS choice when integrating the measurements)
) : biasHat(bias), measurementCovariance(21,21), deltaPij(Vector3::Zero()), deltaVij(Vector3::Zero()), deltaTij(0.0),
delPdelBiasAcc(Matrix3::Zero()), delPdelBiasOmega(Matrix3::Zero()),
delVdelBiasAcc(Matrix3::Zero()), delVdelBiasOmega(Matrix3::Zero()),
delRdelBiasOmega(Matrix3::Zero()), PreintMeasCov(Matrix::Zero(15,15))
{
// COVARIANCE OF: [Integration AccMeasurement OmegaMeasurement BiasAccRandomWalk BiasOmegaRandomWalk (BiasAccInit BiasOmegaInit)] SIZE (21x21)
measurementCovariance << integrationErrorCovariance , Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(),
Matrix3::Zero(), measuredAccCovariance, Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(),
Matrix3::Zero(), Matrix3::Zero(), measuredOmegaCovariance, Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(),
Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), biasAccCovariance, Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(),
Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), biasOmegaCovariance, Matrix3::Zero(), Matrix3::Zero(),
Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), biasAccOmegaInit.block(0,0,3,3), biasAccOmegaInit.block(0,3,3,3),
Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), biasAccOmegaInit.block(3,0,3,3), biasAccOmegaInit.block(3,3,3,3);
}
CombinedPreintegratedMeasurements() :
biasHat(imuBias::ConstantBias()), measurementCovariance(21,21), deltaPij(Vector3::Zero()), deltaVij(Vector3::Zero()), deltaTij(0.0),
delPdelBiasAcc(Matrix3::Zero()), delPdelBiasOmega(Matrix3::Zero()),
delVdelBiasAcc(Matrix3::Zero()), delVdelBiasOmega(Matrix3::Zero()),
delRdelBiasOmega(Matrix3::Zero()), PreintMeasCov(Matrix::Zero(15,15))
{
}
/** print */
void print(const std::string& s = "Preintegrated Measurements:") const {
std::cout << s << std::endl;
biasHat.print(" biasHat");
std::cout << " deltaTij " << deltaTij << std::endl;
std::cout << " deltaPij [ " << deltaPij.transpose() << " ]" << std::endl;
std::cout << " deltaVij [ " << deltaVij.transpose() << " ]" << std::endl;
deltaRij.print(" deltaRij ");
std::cout << " measurementCovariance [ " << measurementCovariance << " ]" << std::endl;
std::cout << " PreintMeasCov [ " << PreintMeasCov << " ]" << std::endl;
}
/** equals */
bool equals(const CombinedPreintegratedMeasurements& expected, double tol=1e-9) const {
return biasHat.equals(expected.biasHat, tol)
&& equal_with_abs_tol(measurementCovariance, expected.measurementCovariance, tol)
&& equal_with_abs_tol(deltaPij, expected.deltaPij, tol)
&& equal_with_abs_tol(deltaVij, expected.deltaVij, tol)
&& deltaRij.equals(expected.deltaRij, tol)
&& std::fabs(deltaTij - expected.deltaTij) < tol
&& equal_with_abs_tol(delPdelBiasAcc, expected.delPdelBiasAcc, tol)
&& equal_with_abs_tol(delPdelBiasOmega, expected.delPdelBiasOmega, tol)
&& equal_with_abs_tol(delVdelBiasAcc, expected.delVdelBiasAcc, tol)
&& equal_with_abs_tol(delVdelBiasOmega, expected.delVdelBiasOmega, tol)
&& equal_with_abs_tol(delRdelBiasOmega, expected.delRdelBiasOmega, tol);
}
/** Add a single IMU measurement to the preintegration. */
void integrateMeasurement(
const Vector3& measuredAcc, ///< Measured linear acceleration (in body frame)
const Vector3& measuredOmega, ///< Measured angular velocity (in body frame)
double deltaT, ///< Time step
boost::optional<Pose3> body_P_sensor = boost::none ///< Sensor frame
) {
// NOTE: order is important here because each update uses old values, e.g., velocity and position updates are based on previous rotation estimate.
// First we compensate the measurements for the bias: since we have only an estimate of the bias, the covariance includes the corresponding uncertainty
Vector3 correctedAcc = biasHat.correctAccelerometer(measuredAcc);
Vector3 correctedOmega = biasHat.correctGyroscope(measuredOmega);
// Then compensate for sensor-body displacement: we express the quantities (originally in the IMU frame) into the body frame
if(body_P_sensor){
Matrix3 body_R_sensor = body_P_sensor->rotation().matrix();
correctedOmega = body_R_sensor * correctedOmega; // rotation rate vector in the body frame
Matrix3 body_omega_body__cross = skewSymmetric(correctedOmega);
correctedAcc = body_R_sensor * correctedAcc - body_omega_body__cross * body_omega_body__cross * body_P_sensor->translation().vector();
// linear acceleration vector in the body frame
}
const Vector3 theta_incr = correctedOmega * deltaT; // rotation vector describing rotation increment computed from the current rotation rate measurement
const Rot3 Rincr = Rot3::Expmap(theta_incr); // rotation increment computed from the current rotation rate measurement
const Matrix3 Jr_theta_incr = rightJacobianExpMapSO3(theta_incr); // Right jacobian computed at theta_incr
// Update Jacobians
/* ----------------------------------------------------------------------------------------------------------------------- */
delPdelBiasAcc += delVdelBiasAcc * deltaT;
delPdelBiasOmega += delVdelBiasOmega * deltaT;
delVdelBiasAcc += -deltaRij.matrix() * deltaT;
delVdelBiasOmega += -deltaRij.matrix() * skewSymmetric(correctedAcc) * deltaT * delRdelBiasOmega;
delRdelBiasOmega = Rincr.inverse().matrix() * delRdelBiasOmega - Jr_theta_incr * deltaT;
// Update preintegrated measurements covariance: as in [2] we consider a first order propagation that
// can be seen as a prediction phase in an EKF framework. In this implementation, contrarily to [2] we
// consider the uncertainty of the bias selection and we keep correlation between biases and preintegrated measurements
/* ----------------------------------------------------------------------------------------------------------------------- */
Matrix3 Z_3x3 = Matrix3::Zero();
Matrix3 I_3x3 = Matrix3::Identity();
const Vector3 theta_i = Rot3::Logmap(deltaRij); // parametrization of so(3)
const Matrix3 Jr_theta_i = rightJacobianExpMapSO3(theta_i);
Rot3 Rot_j = deltaRij * Rincr;
const Vector3 theta_j = Rot3::Logmap(Rot_j); // parametrization of so(3)
const Matrix3 Jrinv_theta_j = rightJacobianExpMapSO3inverse(theta_j);
// Single Jacobians to propagate covariance
Matrix3 H_pos_pos = I_3x3;
Matrix3 H_pos_vel = I_3x3 * deltaT;
Matrix3 H_pos_angles = Z_3x3;
Matrix3 H_vel_pos = Z_3x3;
Matrix3 H_vel_vel = I_3x3;
Matrix3 H_vel_angles = - deltaRij.matrix() * skewSymmetric(correctedAcc) * Jr_theta_i * deltaT;
// analytic expression corresponding to the following numerical derivative
// Matrix H_vel_angles = numericalDerivative11<LieVector, LieVector>(boost::bind(&PreIntegrateIMUObservations_delta_vel, correctedOmega, correctedAcc, deltaT, _1, deltaVij), theta_i);
Matrix3 H_vel_biasacc = - deltaRij.matrix() * deltaT;
Matrix3 H_angles_pos = Z_3x3;
Matrix3 H_angles_vel = Z_3x3;
Matrix3 H_angles_angles = Jrinv_theta_j * Rincr.inverse().matrix() * Jr_theta_i;
Matrix3 H_angles_biasomega =- Jrinv_theta_j * Jr_theta_incr * deltaT;
// analytic expression corresponding to the following numerical derivative
// Matrix H_angles_angles = numericalDerivative11<LieVector, LieVector>(boost::bind(&PreIntegrateIMUObservations_delta_angles, correctedOmega, deltaT, _1), thetaij);
// overall Jacobian wrt preintegrated measurements (df/dx)
Matrix F(15,15);
F << H_pos_pos, H_pos_vel, H_pos_angles, Z_3x3, Z_3x3,
H_vel_pos, H_vel_vel, H_vel_angles, H_vel_biasacc, Z_3x3,
H_angles_pos, H_angles_vel, H_angles_angles, Z_3x3, H_angles_biasomega,
Z_3x3, Z_3x3, Z_3x3, I_3x3, Z_3x3,
Z_3x3, Z_3x3, Z_3x3, Z_3x3, I_3x3;
// first order uncertainty propagation
// Optimized matrix multiplication (1/deltaT) * G * measurementCovariance * G.transpose()
Matrix G_measCov_Gt = Matrix::Zero(15,15);
// BLOCK DIAGONAL TERMS
G_measCov_Gt.block(0,0,3,3) = deltaT * measurementCovariance.block(0,0,3,3);
// G_measCov_Gt.block(3,3,3,3) = (H_vel_biasacc) * (1/deltaT) * measurementCovariance.block(3,3,3,3) * (H_vel_biasacc.transpose()) +
// (H_vel_biasacc) * (1/deltaT) *
// ( measurementCovariance.block(9,9,3,3) + measurementCovariance.block(15,15,3,3) ) *
// (H_vel_biasacc.transpose());
G_measCov_Gt.block(3,3,3,3) = (1/deltaT) * (H_vel_biasacc) *
(measurementCovariance.block(3,3,3,3) + measurementCovariance.block(9,9,3,3) + measurementCovariance.block(15,15,3,3) ) *
(H_vel_biasacc.transpose());
G_measCov_Gt.block(6,6,3,3) = (1/deltaT) * (H_angles_biasomega) *
(measurementCovariance.block(6,6,3,3) + measurementCovariance.block(12,12,3,3) + measurementCovariance.block(18,18,3,3) ) *
(H_angles_biasomega.transpose());
G_measCov_Gt.block(9,9,3,3) = deltaT * measurementCovariance.block(9,9,3,3);
G_measCov_Gt.block(12,12,3,3) = deltaT * measurementCovariance.block(12,12,3,3);
// OFF BLOCK DIAGONAL TERMS
Matrix3 block24 = H_vel_biasacc * measurementCovariance.block(9,9,3,3);
G_measCov_Gt.block(3,9,3,3) = block24;
G_measCov_Gt.block(9,3,3,3) = block24.transpose();
Matrix3 block35 = H_angles_biasomega * measurementCovariance.block(12,12,3,3);
G_measCov_Gt.block(6,12,3,3) = block35;
G_measCov_Gt.block(12,6,3,3) = block35.transpose();
/*
// overall Jacobian wrt raw measurements (df/du)
Matrix3 H_vel_initbiasacc = H_vel_biasacc;
Matrix3 H_angles_initbiasomega = H_angles_biasomega;
// COMBINED IMU FACTOR, preserves correlation with bias evolution and considers initial uncertainty on biases
Matrix G(15,21);
G << I_3x3 * deltaT, Z_3x3, Z_3x3, Z_3x3, Z_3x3, Z_3x3, Z_3x3,
Z_3x3, - H_vel_biasacc, Z_3x3, H_vel_biasacc, Z_3x3, H_vel_initbiasacc, Z_3x3,
Z_3x3, Z_3x3, - H_angles_biasomega, Z_3x3, H_angles_biasomega, Z_3x3, H_angles_initbiasomega,
Z_3x3, Z_3x3, Z_3x3, I_3x3 * deltaT, Z_3x3, Z_3x3, Z_3x3,
Z_3x3, Z_3x3, Z_3x3, Z_3x3, I_3x3 * deltaT, Z_3x3, Z_3x3;
Matrix ErrorMatrix = (1/deltaT) * G * measurementCovariance * G.transpose() - G_measCov_Gt;
std::cout << "---- matrix multiplication error = [" << ErrorMatrix << "];"<< std::endl;
double max_err=0;
for(int i=0;i<15;i++)
{
for(int j=0;j<15;j++)
{
if(fabs(ErrorMatrix(i,j))>max_err)
max_err = fabs(ErrorMatrix(i,j));
}
}
std::cout << "---- max matrix multiplication error = [" << max_err << "];"<< std::endl;
if(max_err>10e-15)
std::cout << "---- max matrix multiplication error *large* = [" << max_err << "];"<< std::endl;
PreintMeasCov = F * PreintMeasCov * F.transpose() + (1/deltaT) * G * measurementCovariance * G.transpose();
*/
PreintMeasCov = F * PreintMeasCov * F.transpose() + G_measCov_Gt;
// Update preintegrated measurements
/* ----------------------------------------------------------------------------------------------------------------------- */
deltaPij += deltaVij * deltaT;
deltaVij += deltaRij.matrix() * correctedAcc * deltaT;
deltaRij = deltaRij * Rincr;
deltaTij += deltaT;
}
/* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */
// This function is only used for test purposes (compare numerical derivatives wrt analytic ones)
static inline Vector PreIntegrateIMUObservations_delta_vel(const Vector& msr_gyro_t, const Vector& msr_acc_t, const double msr_dt,
const Vector3& delta_angles, const Vector& delta_vel_in_t0){
// Note: all delta terms refer to an IMU\sensor system at t0
Vector body_t_a_body = msr_acc_t;
Rot3 R_t_to_t0 = Rot3::Expmap(delta_angles);
return delta_vel_in_t0 + R_t_to_t0.matrix() * body_t_a_body * msr_dt;
}
// This function is only used for test purposes (compare numerical derivatives wrt analytic ones)
static inline Vector PreIntegrateIMUObservations_delta_angles(const Vector& msr_gyro_t, const double msr_dt,
const Vector3& delta_angles){
// Note: all delta terms refer to an IMU\sensor system at t0
// Calculate the corrected measurements using the Bias object
Vector body_t_omega_body= msr_gyro_t;
Rot3 R_t_to_t0 = Rot3::Expmap(delta_angles);
R_t_to_t0 = R_t_to_t0 * Rot3::Expmap( body_t_omega_body*msr_dt );
return Rot3::Logmap(R_t_to_t0);
}
/* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */
private:
/** Serialization function */
friend class boost::serialization::access;
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int version) {
ar & BOOST_SERIALIZATION_NVP(biasHat);
ar & BOOST_SERIALIZATION_NVP(measurementCovariance);
ar & BOOST_SERIALIZATION_NVP(deltaPij);
ar & BOOST_SERIALIZATION_NVP(deltaVij);
ar & BOOST_SERIALIZATION_NVP(deltaRij);
ar & BOOST_SERIALIZATION_NVP(deltaTij);
ar & BOOST_SERIALIZATION_NVP(delPdelBiasAcc);
ar & BOOST_SERIALIZATION_NVP(delPdelBiasOmega);
ar & BOOST_SERIALIZATION_NVP(delVdelBiasAcc);
ar & BOOST_SERIALIZATION_NVP(delVdelBiasOmega);
ar & BOOST_SERIALIZATION_NVP(delRdelBiasOmega);
}
};
private:
typedef CombinedImuFactor This;
typedef NoiseModelFactor6<Pose3,LieVector,Pose3,LieVector,imuBias::ConstantBias,imuBias::ConstantBias> Base;
CombinedPreintegratedMeasurements preintegratedMeasurements_;
Vector3 gravity_;
Vector3 omegaCoriolis_;
public:
/** Shorthand for a smart pointer to a factor */
#ifndef _MSC_VER
typedef typename boost::shared_ptr<CombinedImuFactor> shared_ptr;
#else
typedef boost::shared_ptr<CombinedImuFactor> shared_ptr;
#endif
/** Default constructor - only use for serialization */
CombinedImuFactor() : preintegratedMeasurements_(imuBias::ConstantBias(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero(), Matrix::Zero(6,6)) {}
/** Constructor */
CombinedImuFactor(Key pose_i, Key vel_i, Key pose_j, Key vel_j, Key bias_i, Key bias_j,
const CombinedPreintegratedMeasurements& preintegratedMeasurements, const Vector3& gravity, const Vector3& omegaCoriolis,
const SharedNoiseModel& model) :
Base(model, pose_i, vel_i, pose_j, vel_j, bias_i, bias_j),
preintegratedMeasurements_(preintegratedMeasurements),
gravity_(gravity),
omegaCoriolis_(omegaCoriolis) {
}
virtual ~CombinedImuFactor() {}
/// @return a deep copy of this factor
virtual gtsam::NonlinearFactor::shared_ptr clone() const {
return boost::static_pointer_cast<gtsam::NonlinearFactor>(
gtsam::NonlinearFactor::shared_ptr(new This(*this))); }
/** implement functions needed for Testable */
/** print */
virtual void print(const std::string& s, const KeyFormatter& keyFormatter = DefaultKeyFormatter) const {
std::cout << s << "CombinedImuFactor("
<< keyFormatter(this->key1()) << ","
<< keyFormatter(this->key2()) << ","
<< keyFormatter(this->key3()) << ","
<< keyFormatter(this->key4()) << ","
<< keyFormatter(this->key5()) << ","
<< keyFormatter(this->key6()) << ")\n";
preintegratedMeasurements_.print(" preintegrated measurements:");
std::cout << " gravity: [ " << gravity_.transpose() << " ]" << std::endl;
std::cout << " omegaCoriolis: [ " << omegaCoriolis_.transpose() << " ]" << std::endl;
this->noiseModel_->print(" noise model: ");
}
/** equals */
virtual bool equals(const NonlinearFactor& expected, double tol=1e-9) const {
const This *e = dynamic_cast<const This*> (&expected);
return e != NULL && Base::equals(*e, tol)
&& preintegratedMeasurements_.equals(e->preintegratedMeasurements_)
&& equal_with_abs_tol(gravity_, e->gravity_, tol)
&& equal_with_abs_tol(omegaCoriolis_, e->omegaCoriolis_, tol);
}
/** Access the preintegrated measurements. */
const CombinedPreintegratedMeasurements& preintegratedMeasurements() const {
return preintegratedMeasurements_; }
/** implement functions needed to derive from Factor */
/** vector of errors */
Vector evaluateError(const Pose3& pose_i, const LieVector& vel_i, const Pose3& pose_j, const LieVector& vel_j,
const imuBias::ConstantBias& bias_i, const imuBias::ConstantBias& bias_j,
boost::optional<Matrix&> H1 = boost::none,
boost::optional<Matrix&> H2 = boost::none,
boost::optional<Matrix&> H3 = boost::none,
boost::optional<Matrix&> H4 = boost::none,
boost::optional<Matrix&> H5 = boost::none,
boost::optional<Matrix&> H6 = boost::none) const
{
const double& deltaTij = preintegratedMeasurements_.deltaTij;
const Vector3 biasAccIncr = bias_i.accelerometer() - preintegratedMeasurements_.biasHat.accelerometer();
const Vector3 biasOmegaIncr = bias_i.gyroscope() - preintegratedMeasurements_.biasHat.gyroscope();
// we give some shorter name to rotations and translations
const Rot3 Rot_i = pose_i.rotation();
const Rot3 Rot_j = pose_j.rotation();
const Vector3 pos_i = pose_i.translation().vector();
const Vector3 pos_j = pose_j.translation().vector();
// We compute factor's Jacobians, according to [3]
/* ---------------------------------------------------------------------------------------------------- */
const Rot3 deltaRij_biascorrected = preintegratedMeasurements_.deltaRij.retract(preintegratedMeasurements_.delRdelBiasOmega * biasOmegaIncr, Rot3::EXPMAP);
// deltaRij_biascorrected is expmap(deltaRij) * expmap(delRdelBiasOmega * biasOmegaIncr)
Vector3 theta_biascorrected = Rot3::Logmap(deltaRij_biascorrected);
Vector3 theta_biascorrected_corioliscorrected = theta_biascorrected -
Rot_i.inverse().matrix() * omegaCoriolis_ * deltaTij; // Coriolis term
const Rot3 deltaRij_biascorrected_corioliscorrected =
Rot3::Expmap( theta_biascorrected_corioliscorrected );
const Rot3 fRhat = deltaRij_biascorrected_corioliscorrected.between(Rot_i.between(Rot_j));
const Matrix3 Jr_theta_bcc = rightJacobianExpMapSO3(theta_biascorrected_corioliscorrected);
const Matrix3 Jtheta = -Jr_theta_bcc * skewSymmetric(Rot_i.inverse().matrix() * omegaCoriolis_ * deltaTij);
const Matrix3 Jrinv_fRhat = rightJacobianExpMapSO3inverse(Rot3::Logmap(fRhat));
if(H1) {
H1->resize(15,6);
(*H1) <<
// dfP/dRi
Rot_i.matrix() * skewSymmetric(preintegratedMeasurements_.deltaPij
+ preintegratedMeasurements_.delPdelBiasOmega * biasOmegaIncr + preintegratedMeasurements_.delPdelBiasAcc * biasAccIncr),
// dfP/dPi
- Rot_i.matrix(),
// dfV/dRi
Rot_i.matrix() * skewSymmetric(preintegratedMeasurements_.deltaVij
+ preintegratedMeasurements_.delVdelBiasOmega * biasOmegaIncr + preintegratedMeasurements_.delVdelBiasAcc * biasAccIncr),
// dfV/dPi
Matrix3::Zero(),
// dfR/dRi
Jrinv_fRhat * (- Rot_j.between(Rot_i).matrix() - fRhat.inverse().matrix() * Jtheta),
// dfR/dPi
Matrix3::Zero(),
//dBiasAcc/dPi
Matrix3::Zero(), Matrix3::Zero(),
//dBiasOmega/dPi
Matrix3::Zero(), Matrix3::Zero();
}
if(H2) {
H2->resize(15,3);
(*H2) <<
// dfP/dVi
- Matrix3::Identity() * deltaTij
+ skewSymmetric(omegaCoriolis_) * deltaTij * deltaTij, // Coriolis term - we got rid of the 2 wrt ins paper
// dfV/dVi
- Matrix3::Identity()
+ 2 * skewSymmetric(omegaCoriolis_) * deltaTij, // Coriolis term
// dfR/dVi
Matrix3::Zero(),
//dBiasAcc/dVi
Matrix3::Zero(),
//dBiasOmega/dVi
Matrix3::Zero();
}
if(H3) {
H3->resize(15,6);
(*H3) <<
// dfP/dPosej
Matrix3::Zero(), Rot_j.matrix(),
// dfV/dPosej
Matrix::Zero(3,6),
// dfR/dPosej
Jrinv_fRhat * ( Matrix3::Identity() ), Matrix3::Zero(),
//dBiasAcc/dPosej
Matrix3::Zero(), Matrix3::Zero(),
//dBiasOmega/dPosej
Matrix3::Zero(), Matrix3::Zero();
}
if(H4) {
H4->resize(15,3);
(*H4) <<
// dfP/dVj
Matrix3::Zero(),
// dfV/dVj
Matrix3::Identity(),
// dfR/dVj
Matrix3::Zero(),
//dBiasAcc/dVj
Matrix3::Zero(),
//dBiasOmega/dVj
Matrix3::Zero();
}
if(H5) {
const Matrix3 Jrinv_theta_bc = rightJacobianExpMapSO3inverse(theta_biascorrected);
const Matrix3 Jr_JbiasOmegaIncr = rightJacobianExpMapSO3(preintegratedMeasurements_.delRdelBiasOmega * biasOmegaIncr);
const Matrix3 JbiasOmega = Jr_theta_bcc * Jrinv_theta_bc * Jr_JbiasOmegaIncr * preintegratedMeasurements_.delRdelBiasOmega;
H5->resize(15,6);
(*H5) <<
// dfP/dBias_i
- Rot_i.matrix() * preintegratedMeasurements_.delPdelBiasAcc,
- Rot_i.matrix() * preintegratedMeasurements_.delPdelBiasOmega,
// dfV/dBias_i
- Rot_i.matrix() * preintegratedMeasurements_.delVdelBiasAcc,
- Rot_i.matrix() * preintegratedMeasurements_.delVdelBiasOmega,
// dfR/dBias_i
Matrix::Zero(3,3),
Jrinv_fRhat * ( - fRhat.inverse().matrix() * JbiasOmega),
//dBiasAcc/dBias_i
-Matrix3::Identity(), Matrix3::Zero(),
//dBiasOmega/dBias_i
Matrix3::Zero(), -Matrix3::Identity();
}
if(H6) {
H6->resize(15,6);
(*H6) <<
// dfP/dBias_j
Matrix3::Zero(), Matrix3::Zero(),
// dfV/dBias_j
Matrix3::Zero(), Matrix3::Zero(),
// dfR/dBias_j
Matrix3::Zero(), Matrix3::Zero(),
//dBiasAcc/dBias_j
Matrix3::Identity(), Matrix3::Zero(),
//dBiasOmega/dBias_j
Matrix3::Zero(), Matrix3::Identity();
}
// Evaluate residual error, according to [3]
/* ---------------------------------------------------------------------------------------------------- */
const Vector3 fp =
pos_j - pos_i
- Rot_i.matrix() * (preintegratedMeasurements_.deltaPij
+ preintegratedMeasurements_.delPdelBiasAcc * biasAccIncr
+ preintegratedMeasurements_.delPdelBiasOmega * biasOmegaIncr)
- vel_i * deltaTij
+ skewSymmetric(omegaCoriolis_) * vel_i * deltaTij*deltaTij // Coriolis term - we got rid of the 2 wrt ins paper
- 0.5 * gravity_ * deltaTij*deltaTij;
const Vector3 fv =
vel_j - vel_i - Rot_i.matrix() * (preintegratedMeasurements_.deltaVij
+ preintegratedMeasurements_.delVdelBiasAcc * biasAccIncr
+ preintegratedMeasurements_.delVdelBiasOmega * biasOmegaIncr)
+ 2 * skewSymmetric(omegaCoriolis_) * vel_i * deltaTij // Coriolis term
- gravity_ * deltaTij;
const Vector3 fR = Rot3::Logmap(fRhat);
const Vector3 fbiasAcc = bias_j.accelerometer() - bias_i.accelerometer();
const Vector3 fbiasOmega = bias_j.gyroscope() - bias_i.gyroscope();
Vector r(15); r << fp, fv, fR, fbiasAcc, fbiasOmega; // vector of size 15
return r;
}
/** predicted states from IMU */
static void Predict(const Pose3& pose_i, const LieVector& vel_i, Pose3& pose_j, LieVector& vel_j,
const imuBias::ConstantBias& bias_i, imuBias::ConstantBias& bias_j,
const CombinedPreintegratedMeasurements preintegratedMeasurements,
const Vector3& gravity, const Vector3& omegaCoriolis)
{
const double& deltaTij = preintegratedMeasurements.deltaTij;
const Vector3 biasAccIncr = bias_i.accelerometer() - preintegratedMeasurements.biasHat.accelerometer();
const Vector3 biasOmegaIncr = bias_i.gyroscope() - preintegratedMeasurements.biasHat.gyroscope();
const Rot3 Rot_i = pose_i.rotation();
const Vector3 pos_i = pose_i.translation().vector();
// Predict state at time j
/* ---------------------------------------------------------------------------------------------------- */
const Vector3 pos_j = pos_i + Rot_i.matrix() * (preintegratedMeasurements.deltaPij
+ preintegratedMeasurements.delPdelBiasAcc * biasAccIncr
+ preintegratedMeasurements.delPdelBiasOmega * biasOmegaIncr)
+ vel_i * deltaTij
- skewSymmetric(omegaCoriolis) * vel_i * deltaTij*deltaTij // Coriolis term - we got rid of the 2 wrt ins paper
+ 0.5 * gravity * deltaTij*deltaTij;
vel_j = LieVector(vel_i + Rot_i.matrix() * (preintegratedMeasurements.deltaVij
+ preintegratedMeasurements.delVdelBiasAcc * biasAccIncr
+ preintegratedMeasurements.delVdelBiasOmega * biasOmegaIncr)
- 2 * skewSymmetric(omegaCoriolis) * vel_i * deltaTij // Coriolis term
+ gravity * deltaTij);
const Rot3 deltaRij_biascorrected = preintegratedMeasurements.deltaRij.retract(preintegratedMeasurements.delRdelBiasOmega * biasOmegaIncr, Rot3::EXPMAP);
// deltaRij_biascorrected is expmap(deltaRij) * expmap(delRdelBiasOmega * biasOmegaIncr)
Vector3 theta_biascorrected = Rot3::Logmap(deltaRij_biascorrected);
Vector3 theta_biascorrected_corioliscorrected = theta_biascorrected -
Rot_i.inverse().matrix() * omegaCoriolis * deltaTij; // Coriolis term
const Rot3 deltaRij_biascorrected_corioliscorrected =
Rot3::Expmap( theta_biascorrected_corioliscorrected );
const Rot3 Rot_j = Rot_i.compose( deltaRij_biascorrected_corioliscorrected );
pose_j = Pose3( Rot_j, Point3(pos_j) );
bias_j = bias_i;
}
private:
/** Serialization function */
friend class boost::serialization::access;
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int version) {
ar & boost::serialization::make_nvp("NoiseModelFactor6",
boost::serialization::base_object<Base>(*this));
ar & BOOST_SERIALIZATION_NVP(preintegratedMeasurements_);
ar & BOOST_SERIALIZATION_NVP(gravity_);
ar & BOOST_SERIALIZATION_NVP(omegaCoriolis_);
}
}; // \class CombinedImuFactor
} /// namespace gtsam

6
gtsam_unstable/slam/EquivInertialNavFactor_GlobalVel_NoBias.h
View File

@ -537,7 +537,7 @@ public:
Rot3 R_ECEF_to_ENU( UEN_to_ENU * C2 * C1 );
Vector omega_earth_ECEF(Vector_(3, 0.0, 0.0, 7.292115e-5));
Vector omega_earth_ECEF((Vector(3) << 0.0, 0.0, 7.292115e-5));
omega_earth_ENU = R_ECEF_to_ENU.matrix() * omega_earth_ECEF;
// Calculating g
@ -551,7 +551,7 @@ public:
double Ro( sqrt(Rp*Rm) ); // mean earth radius of curvature
double g0( 9.780318*( 1 + 5.3024e-3 * pow(sin(lat_new),2) - 5.9e-6 * pow(sin(2*lat_new),2) ) );
double g_calc( g0/( pow(1 + height/Ro, 2) ) );
g_ENU = Vector_(3, 0.0, 0.0, -g_calc);
g_ENU = (Vector(3) << 0.0, 0.0, -g_calc);
// Calculate rho
@ -560,7 +560,7 @@ public:
double rho_E = -Vn/(Rm + height);
double rho_N = Ve/(Rp + height);
double rho_U = Ve*tan(lat_new)/(Rp + height);
rho_ENU = Vector_(3, rho_E, rho_N, rho_U);
rho_ENU = (Vector(3) << rho_E, rho_N, rho_U);
}
static inline noiseModel::Gaussian::shared_ptr calc_descrete_noise_model(const noiseModel::Gaussian::shared_ptr& model, double delta_t){

565
gtsam_unstable/slam/ImuFactor.h
View File

@ -1,565 +0,0 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file ImuFactor.h
* @author Luca Carlone, Stephen Williams, Richard Roberts
**/
#pragma once
/* GTSAM includes */
#include <gtsam/nonlinear/NonlinearFactor.h>
#include <gtsam/linear/GaussianFactor.h>
#include <gtsam/navigation/ImuBias.h>
#include <gtsam/geometry/Pose3.h>
#include <gtsam/base/LieVector.h>
#include <gtsam/base/debug.h>
/* External or standard includes */
#include <ostream>
namespace gtsam {
/**
*
* @addtogroup SLAM
* * REFERENCES:
* [1] G.S. Chirikjian, "Stochastic Models, Information Theory, and Lie Groups", Volume 2, 2008.
* [2] T. Lupton and S.Sukkarieh, "Visual-Inertial-Aided Navigation for High-Dynamic Motion in Built
* Environments Without Initial Conditions", TRO, 28(1):61-76, 2012.
* [3] L. Carlone, S. Williams, R. Roberts, "Preintegrated IMU factor: Computation of the Jacobian Matrices", Tech. Report, 2013.
*/
class ImuFactor: public NoiseModelFactor5<Pose3,LieVector,Pose3,LieVector,imuBias::ConstantBias> {
public:
/** Struct to store results of preintegrating IMU measurements. Can be build
* incrementally so as to avoid costly integration at time of factor construction. */
/** Right Jacobian for Exponential map in SO(3) - equation (10.86) and following equations in [1] */
static Matrix3 rightJacobianExpMapSO3(const Vector3& x) {
// x is the axis-angle representation (exponential coordinates) for a rotation
double normx = norm_2(x); // rotation angle
Matrix3 Jr;
if (normx < 10e-8){
Jr = Matrix3::Identity();
}
else{
const Matrix3 X = skewSymmetric(x); // element of Lie algebra so(3): X = x^
Jr = Matrix3::Identity() - ((1-cos(normx))/(normx*normx)) * X +
((normx-sin(normx))/(normx*normx*normx)) * X * X; // right Jacobian
}
return Jr;
}
/** Right Jacobian for Log map in SO(3) - equation (10.86) and following equations in [1] */
static Matrix3 rightJacobianExpMapSO3inverse(const Vector3& x) {
// x is the axis-angle representation (exponential coordinates) for a rotation
double normx = norm_2(x); // rotation angle
Matrix3 Jrinv;
if (normx < 10e-8){
Jrinv = Matrix3::Identity();
}
else{
const Matrix3 X = skewSymmetric(x); // element of Lie algebra so(3): X = x^
Jrinv = Matrix3::Identity() +
0.5 * X + (1/(normx*normx) - (1+cos(normx))/(2*normx * sin(normx)) ) * X * X;
}
return Jrinv;
}
/** CombinedPreintegratedMeasurements accumulates (integrates) the IMU measurements (rotation rates and accelerations)
* and the corresponding covariance matrix. The measurements are then used to build the Preintegrated IMU factor*/
class PreintegratedMeasurements {
public:
imuBias::ConstantBias biasHat; ///< Acceleration and angular rate bias values used during preintegration
Matrix measurementCovariance; ///< (Raw measurements uncertainty) Covariance of the vector [integrationError measuredAcc measuredOmega] in R^(9X9)
Vector3 deltaPij; ///< Preintegrated relative position (does not take into account velocity at time i, see deltap+, , in [2]) (in frame i)
Vector3 deltaVij; ///< Preintegrated relative velocity (in global frame)
Rot3 deltaRij; ///< Preintegrated relative orientation (in frame i)
double deltaTij; ///< Time interval from i to j
Matrix3 delPdelBiasAcc; ///< Jacobian of preintegrated position w.r.t. acceleration bias
Matrix3 delPdelBiasOmega; ///< Jacobian of preintegrated position w.r.t. angular rate bias
Matrix3 delVdelBiasAcc; ///< Jacobian of preintegrated velocity w.r.t. acceleration bias
Matrix3 delVdelBiasOmega; ///< Jacobian of preintegrated velocity w.r.t. angular rate bias
Matrix3 delRdelBiasOmega; ///< Jacobian of preintegrated rotation w.r.t. angular rate bias
Matrix PreintMeasCov; ///< Covariance matrix of the preintegrated measurements (first-order propagation from *measurementCovariance*)
Vector3 initialRotationRate; ///< initial rotation rate reading from the IMU (at time i)
Vector3 finalRotationRate; ///< final rotation rate reading from the IMU (at time j)
/** Default constructor, initialize with no IMU measurements */
PreintegratedMeasurements(
const imuBias::ConstantBias& bias, ///< Current estimate of acceleration and rotation rate biases
const Matrix3& measuredAccCovariance, ///< Covariance matrix of measuredAcc
const Matrix3& measuredOmegaCovariance, ///< Covariance matrix of measuredAcc
const Matrix3& integrationErrorCovariance, ///< Covariance matrix of measuredAcc
const Vector3& initialRotationRate = Vector3::Zero() ///< initial rotation rate reading from the IMU (at time i)
) : biasHat(bias), measurementCovariance(9,9), deltaPij(Vector3::Zero()), deltaVij(Vector3::Zero()), deltaTij(0.0),
delPdelBiasAcc(Matrix3::Zero()), delPdelBiasOmega(Matrix3::Zero()),
delVdelBiasAcc(Matrix3::Zero()), delVdelBiasOmega(Matrix3::Zero()),
delRdelBiasOmega(Matrix3::Zero()), PreintMeasCov(9,9),
initialRotationRate(initialRotationRate), finalRotationRate(initialRotationRate)
{
measurementCovariance << integrationErrorCovariance , Matrix3::Zero(), Matrix3::Zero(),
Matrix3::Zero(), measuredAccCovariance, Matrix3::Zero(),
Matrix3::Zero(), Matrix3::Zero(), measuredOmegaCovariance;
PreintMeasCov = Matrix::Zero(9,9);
}
PreintegratedMeasurements() :
biasHat(imuBias::ConstantBias()), measurementCovariance(9,9), deltaPij(Vector3::Zero()), deltaVij(Vector3::Zero()), deltaTij(0.0),
delPdelBiasAcc(Matrix3::Zero()), delPdelBiasOmega(Matrix3::Zero()),
delVdelBiasAcc(Matrix3::Zero()), delVdelBiasOmega(Matrix3::Zero()),
delRdelBiasOmega(Matrix3::Zero()), PreintMeasCov(9,9),
initialRotationRate(Vector3::Zero()), finalRotationRate(Vector3::Zero())
{
measurementCovariance = Matrix::Zero(9,9);
PreintMeasCov = Matrix::Zero(9,9);
}
/** print */
void print(const std::string& s = "Preintegrated Measurements:") const {
std::cout << s << std::endl;
biasHat.print(" biasHat");
std::cout << " deltaTij " << deltaTij << std::endl;
std::cout << " deltaPij [ " << deltaPij.transpose() << " ]" << std::endl;
std::cout << " deltaVij [ " << deltaVij.transpose() << " ]" << std::endl;
deltaRij.print(" deltaRij ");
std::cout << " measurementCovariance [ " << measurementCovariance << " ]" << std::endl;
std::cout << " PreintMeasCov [ " << PreintMeasCov << " ]" << std::endl;
}
/** equals */
bool equals(const PreintegratedMeasurements& expected, double tol=1e-9) const {
return biasHat.equals(expected.biasHat, tol)
&& equal_with_abs_tol(measurementCovariance, expected.measurementCovariance, tol)
&& equal_with_abs_tol(deltaPij, expected.deltaPij, tol)
&& equal_with_abs_tol(deltaVij, expected.deltaVij, tol)
&& deltaRij.equals(expected.deltaRij, tol)
&& std::fabs(deltaTij - expected.deltaTij) < tol
&& equal_with_abs_tol(delPdelBiasAcc, expected.delPdelBiasAcc, tol)
&& equal_with_abs_tol(delPdelBiasOmega, expected.delPdelBiasOmega, tol)
&& equal_with_abs_tol(delVdelBiasAcc, expected.delVdelBiasAcc, tol)
&& equal_with_abs_tol(delVdelBiasOmega, expected.delVdelBiasOmega, tol)
&& equal_with_abs_tol(delRdelBiasOmega, expected.delRdelBiasOmega, tol);
}
/** Add a single IMU measurement to the preintegration. */
void integrateMeasurement(
const Vector3& measuredAcc, ///< Measured linear acceleration (in body frame)
const Vector3& measuredOmega, ///< Measured angular velocity (in body frame)
double deltaT, ///< Time step
boost::optional<Pose3> body_P_sensor = boost::none ///< Sensor frame
) {
// NOTE: order is important here because each update uses old values.
// First we compensate the measurements for the bias
Vector3 correctedAcc = biasHat.correctAccelerometer(measuredAcc);
Vector3 correctedOmega = biasHat.correctGyroscope(measuredOmega);
finalRotationRate = correctedOmega;
// Then compensate for sensor-body displacement: we express the quantities (originally in the IMU frame) into the body frame
if(body_P_sensor){
Matrix3 body_R_sensor = body_P_sensor->rotation().matrix();
correctedOmega = body_R_sensor * correctedOmega; // rotation rate vector in the body frame
Matrix3 body_omega_body__cross = skewSymmetric(correctedOmega);
correctedAcc = body_R_sensor * correctedAcc - body_omega_body__cross * body_omega_body__cross * body_P_sensor->translation().vector();
// linear acceleration vector in the body frame
}
const Vector3 theta_incr = correctedOmega * deltaT; // rotation vector describing rotation increment computed from the current rotation rate measurement
const Rot3 Rincr = Rot3::Expmap(theta_incr); // rotation increment computed from the current rotation rate measurement
const Matrix3 Jr_theta_incr = rightJacobianExpMapSO3(theta_incr); // Right jacobian computed at theta_incr
// Update Jacobians
/* ----------------------------------------------------------------------------------------------------------------------- */
delPdelBiasAcc += delVdelBiasAcc * deltaT;
delPdelBiasOmega += delVdelBiasOmega * deltaT;
delVdelBiasAcc += -deltaRij.matrix() * deltaT;
delVdelBiasOmega += -deltaRij.matrix() * skewSymmetric(correctedAcc) * deltaT * delRdelBiasOmega;
delRdelBiasOmega = Rincr.inverse().matrix() * delRdelBiasOmega - Jr_theta_incr * deltaT;
// Update preintegrated mesurements covariance
/* ----------------------------------------------------------------------------------------------------------------------- */
Matrix3 Z_3x3 = Matrix3::Zero();
Matrix3 I_3x3 = Matrix3::Identity();
const Vector3 theta_i = Rot3::Logmap(deltaRij); // parametrization of so(3)
const Matrix3 Jr_theta_i = rightJacobianExpMapSO3(theta_i);
Rot3 Rot_j = deltaRij * Rincr;
const Vector3 theta_j = Rot3::Logmap(Rot_j); // parametrization of so(3)
const Matrix3 Jrinv_theta_j = rightJacobianExpMapSO3inverse(theta_j);
// Update preintegrated measurements covariance: as in [2] we consider a first order propagation that
// can be seen as a prediction phase in an EKF framework
Matrix H_pos_pos = I_3x3;
Matrix H_pos_vel = I_3x3 * deltaT;
Matrix H_pos_angles = Z_3x3;
Matrix H_vel_pos = Z_3x3;
Matrix H_vel_vel = I_3x3;
Matrix H_vel_angles = - deltaRij.matrix() * skewSymmetric(correctedAcc) * Jr_theta_i * deltaT;
// analytic expression corresponding to the following numerical derivative
// Matrix H_vel_angles = numericalDerivative11<LieVector, LieVector>(boost::bind(&PreIntegrateIMUObservations_delta_vel, correctedOmega, correctedAcc, deltaT, _1, deltaVij), theta_i);
Matrix H_angles_pos = Z_3x3;
Matrix H_angles_vel = Z_3x3;
Matrix H_angles_angles = Jrinv_theta_j * Rincr.inverse().matrix() * Jr_theta_i;
// analytic expression corresponding to the following numerical derivative
// Matrix H_angles_angles = numericalDerivative11<LieVector, LieVector>(boost::bind(&PreIntegrateIMUObservations_delta_angles, correctedOmega, deltaT, _1), thetaij);
// overall Jacobian wrt preintegrated measurements (df/dx)
Matrix F(9,9);
F << H_pos_pos, H_pos_vel, H_pos_angles,
H_vel_pos, H_vel_vel, H_vel_angles,
H_angles_pos, H_angles_vel, H_angles_angles;
// first order uncertainty propagation
// the deltaT allows to pass from continuous time noise to discrete time noise
PreintMeasCov = F * PreintMeasCov * F.transpose() + measurementCovariance * deltaT ;
// Update preintegrated measurements
/* ----------------------------------------------------------------------------------------------------------------------- */
deltaPij += deltaVij * deltaT;
deltaVij += deltaRij.matrix() * correctedAcc * deltaT;
deltaRij = deltaRij * Rincr;
deltaTij += deltaT;
}
/* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */
// This function is only used for test purposes (compare numerical derivatives wrt analytic ones)
static inline Vector PreIntegrateIMUObservations_delta_vel(const Vector& msr_gyro_t, const Vector& msr_acc_t, const double msr_dt,
const Vector3& delta_angles, const Vector& delta_vel_in_t0){
// Note: all delta terms refer to an IMU\sensor system at t0
Vector body_t_a_body = msr_acc_t;
Rot3 R_t_to_t0 = Rot3::Expmap(delta_angles);
return delta_vel_in_t0 + R_t_to_t0.matrix() * body_t_a_body * msr_dt;
}
// This function is only used for test purposes (compare numerical derivatives wrt analytic ones)
static inline Vector PreIntegrateIMUObservations_delta_angles(const Vector& msr_gyro_t, const double msr_dt,
const Vector3& delta_angles){
// Note: all delta terms refer to an IMU\sensor system at t0
// Calculate the corrected measurements using the Bias object
Vector body_t_omega_body= msr_gyro_t;
Rot3 R_t_to_t0 = Rot3::Expmap(delta_angles);
R_t_to_t0 = R_t_to_t0 * Rot3::Expmap( body_t_omega_body*msr_dt );
return Rot3::Logmap(R_t_to_t0);
}
/* ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ */
private:
/** Serialization function */
friend class boost::serialization::access;
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int version) {
ar & BOOST_SERIALIZATION_NVP(biasHat);
ar & BOOST_SERIALIZATION_NVP(measurementCovariance);
ar & BOOST_SERIALIZATION_NVP(deltaPij);
ar & BOOST_SERIALIZATION_NVP(deltaVij);
ar & BOOST_SERIALIZATION_NVP(deltaRij);
ar & BOOST_SERIALIZATION_NVP(deltaTij);
ar & BOOST_SERIALIZATION_NVP(delPdelBiasAcc);
ar & BOOST_SERIALIZATION_NVP(delPdelBiasOmega);
ar & BOOST_SERIALIZATION_NVP(delVdelBiasAcc);
ar & BOOST_SERIALIZATION_NVP(delVdelBiasOmega);
ar & BOOST_SERIALIZATION_NVP(delRdelBiasOmega);
}
};
private:
typedef ImuFactor This;
typedef NoiseModelFactor5<Pose3,LieVector,Pose3,LieVector,imuBias::ConstantBias> Base;
PreintegratedMeasurements preintegratedMeasurements_;
Vector3 gravity_;
Vector3 omegaCoriolis_;
boost::optional<Pose3> body_P_sensor_; ///< The pose of the sensor in the body frame
public:
/** Shorthand for a smart pointer to a factor */
#ifndef _MSC_VER
typedef typename boost::shared_ptr<ImuFactor> shared_ptr;
#else
typedef boost::shared_ptr<ImuFactor> shared_ptr;
#endif
/** Default constructor - only use for serialization */
ImuFactor() : preintegratedMeasurements_(imuBias::ConstantBias(), Matrix3::Zero(), Matrix3::Zero(), Matrix3::Zero()) {}
/** Constructor */
ImuFactor(Key pose_i, Key vel_i, Key pose_j, Key vel_j, Key bias,
const PreintegratedMeasurements& preintegratedMeasurements, const Vector3& gravity, const Vector3& omegaCoriolis,
const SharedNoiseModel& model, boost::optional<Pose3> body_P_sensor = boost::none) :
Base(model, pose_i, vel_i, pose_j, vel_j, bias),
preintegratedMeasurements_(preintegratedMeasurements),
gravity_(gravity),
omegaCoriolis_(omegaCoriolis),
body_P_sensor_(body_P_sensor) {
}
virtual ~ImuFactor() {}
/// @return a deep copy of this factor
virtual gtsam::NonlinearFactor::shared_ptr clone() const {
return boost::static_pointer_cast<gtsam::NonlinearFactor>(
gtsam::NonlinearFactor::shared_ptr(new This(*this))); }
/** implement functions needed for Testable */
/** print */
virtual void print(const std::string& s, const KeyFormatter& keyFormatter = DefaultKeyFormatter) const {
std::cout << s << "ImuFactor("
<< keyFormatter(this->key1()) << ","
<< keyFormatter(this->key2()) << ","
<< keyFormatter(this->key3()) << ","
<< keyFormatter(this->key4()) << ","
<< keyFormatter(this->key5()) << ")\n";
preintegratedMeasurements_.print(" preintegrated measurements:");
std::cout << " gravity: [ " << gravity_.transpose() << " ]" << std::endl;
std::cout << " omegaCoriolis: [ " << omegaCoriolis_.transpose() << " ]" << std::endl;
this->noiseModel_->print(" noise model: ");
if(this->body_P_sensor_)
this->body_P_sensor_->print(" sensor pose in body frame: ");
}
/** equals */
virtual bool equals(const NonlinearFactor& expected, double tol=1e-9) const {
const This *e = dynamic_cast<const This*> (&expected);
return e != NULL && Base::equals(*e, tol)
&& preintegratedMeasurements_.equals(e->preintegratedMeasurements_)
&& equal_with_abs_tol(gravity_, e->gravity_, tol)
&& equal_with_abs_tol(omegaCoriolis_, e->omegaCoriolis_, tol)
&& ((!body_P_sensor_ && !e->body_P_sensor_) || (body_P_sensor_ && e->body_P_sensor_ && body_P_sensor_->equals(*e->body_P_sensor_)));
}
/** Access the preintegrated measurements. */
const PreintegratedMeasurements& preintegratedMeasurements() const {
return preintegratedMeasurements_; }
/** implement functions needed to derive from Factor */
/** vector of errors */
Vector evaluateError(const Pose3& pose_i, const LieVector& vel_i, const Pose3& pose_j, const LieVector& vel_j,
const imuBias::ConstantBias& bias,
boost::optional<Matrix&> H1 = boost::none,
boost::optional<Matrix&> H2 = boost::none,
boost::optional<Matrix&> H3 = boost::none,
boost::optional<Matrix&> H4 = boost::none,
boost::optional<Matrix&> H5 = boost::none) const
{
const double& deltaTij = preintegratedMeasurements_.deltaTij;
const Vector3 biasAccIncr = bias.accelerometer() - preintegratedMeasurements_.biasHat.accelerometer();
const Vector3 biasOmegaIncr = bias.gyroscope() - preintegratedMeasurements_.biasHat.gyroscope();
// we give some shorter name to rotations and translations
const Rot3 Rot_i = pose_i.rotation();
const Rot3 Rot_j = pose_j.rotation();
const Vector3 pos_i = pose_i.translation().vector();
const Vector3 pos_j = pose_j.translation().vector();
// We compute factor's Jacobians
/* ---------------------------------------------------------------------------------------------------- */
const Rot3 deltaRij_biascorrected = preintegratedMeasurements_.deltaRij.retract(preintegratedMeasurements_.delRdelBiasOmega * biasOmegaIncr, Rot3::EXPMAP);
// deltaRij_biascorrected is expmap(deltaRij) * expmap(delRdelBiasOmega * biasOmegaIncr)
Vector3 theta_biascorrected = Rot3::Logmap(deltaRij_biascorrected);
Vector3 theta_biascorrected_corioliscorrected = theta_biascorrected -
Rot_i.inverse().matrix() * omegaCoriolis_ * deltaTij; // Coriolis term
const Rot3 deltaRij_biascorrected_corioliscorrected =
Rot3::Expmap( theta_biascorrected_corioliscorrected );
const Rot3 fRhat = deltaRij_biascorrected_corioliscorrected.between(Rot_i.between(Rot_j));
const Matrix3 Jr_theta_bcc = rightJacobianExpMapSO3(theta_biascorrected_corioliscorrected);
const Matrix3 Jtheta = -Jr_theta_bcc * skewSymmetric(Rot_i.inverse().matrix() * omegaCoriolis_ * deltaTij);
const Matrix3 Jrinv_fRhat = rightJacobianExpMapSO3inverse(Rot3::Logmap(fRhat));
if(H1) {
H1->resize(9,6);
(*H1) <<
// dfP/dRi
Rot_i.matrix() * skewSymmetric(preintegratedMeasurements_.deltaPij
+ preintegratedMeasurements_.delPdelBiasOmega * biasOmegaIncr + preintegratedMeasurements_.delPdelBiasAcc * biasAccIncr),
// dfP/dPi
- Rot_i.matrix(),
// dfV/dRi
Rot_i.matrix() * skewSymmetric(preintegratedMeasurements_.deltaVij
+ preintegratedMeasurements_.delVdelBiasOmega * biasOmegaIncr + preintegratedMeasurements_.delVdelBiasAcc * biasAccIncr),
// dfV/dPi
Matrix3::Zero(),
// dfR/dRi
Jrinv_fRhat * (- Rot_j.between(Rot_i).matrix() - fRhat.inverse().matrix() * Jtheta),
// dfR/dPi
Matrix3::Zero();
}
if(H2) {
H2->resize(9,3);
(*H2) <<
// dfP/dVi
- Matrix3::Identity() * deltaTij
+ skewSymmetric(omegaCoriolis_) * deltaTij * deltaTij, // Coriolis term - we got rid of the 2 wrt ins paper
// dfV/dVi
- Matrix3::Identity()
+ 2 * skewSymmetric(omegaCoriolis_) * deltaTij, // Coriolis term
// dfR/dVi
Matrix3::Zero();
}
if(H3) {
H3->resize(9,6);
(*H3) <<
// dfP/dPosej
Matrix3::Zero(), Rot_j.matrix(),
// dfV/dPosej
Matrix::Zero(3,6),
// dfR/dPosej
Jrinv_fRhat * ( Matrix3::Identity() ), Matrix3::Zero();
}
if(H4) {
H4->resize(9,3);
(*H4) <<
// dfP/dVj
Matrix3::Zero(),
// dfV/dVj
Matrix3::Identity(),
// dfR/dVj
Matrix3::Zero();
}
if(H5) {
const Matrix3 Jrinv_theta_bc = rightJacobianExpMapSO3inverse(theta_biascorrected);
const Matrix3 Jr_JbiasOmegaIncr = rightJacobianExpMapSO3(preintegratedMeasurements_.delRdelBiasOmega * biasOmegaIncr);
const Matrix3 JbiasOmega = Jr_theta_bcc * Jrinv_theta_bc * Jr_JbiasOmegaIncr * preintegratedMeasurements_.delRdelBiasOmega;
H5->resize(9,6);
(*H5) <<
// dfP/dBias
- Rot_i.matrix() * preintegratedMeasurements_.delPdelBiasAcc,
- Rot_i.matrix() * preintegratedMeasurements_.delPdelBiasOmega,
// dfV/dBias
- Rot_i.matrix() * preintegratedMeasurements_.delVdelBiasAcc,
- Rot_i.matrix() * preintegratedMeasurements_.delVdelBiasOmega,
// dfR/dBias
Matrix::Zero(3,3),
Jrinv_fRhat * ( - fRhat.inverse().matrix() * JbiasOmega);
}
// Evaluate residual error, according to [3]
/* ---------------------------------------------------------------------------------------------------- */
const Vector3 fp =
pos_j - pos_i
- Rot_i.matrix() * (preintegratedMeasurements_.deltaPij
+ preintegratedMeasurements_.delPdelBiasAcc * biasAccIncr
+ preintegratedMeasurements_.delPdelBiasOmega * biasOmegaIncr)
- vel_i * deltaTij
+ skewSymmetric(omegaCoriolis_) * vel_i * deltaTij*deltaTij // Coriolis term - we got rid of the 2 wrt ins paper
- 0.5 * gravity_ * deltaTij*deltaTij;
const Vector3 fv =
vel_j - vel_i - Rot_i.matrix() * (preintegratedMeasurements_.deltaVij
+ preintegratedMeasurements_.delVdelBiasAcc * biasAccIncr
+ preintegratedMeasurements_.delVdelBiasOmega * biasOmegaIncr)
+ 2 * skewSymmetric(omegaCoriolis_) * vel_i * deltaTij // Coriolis term
- gravity_ * deltaTij;
const Vector3 fR = Rot3::Logmap(fRhat);
Vector r(9); r << fp, fv, fR;
return r;
}
/** predicted states from IMU */
static void Predict(const Pose3& pose_i, const LieVector& vel_i, Pose3& pose_j, LieVector& vel_j,
const imuBias::ConstantBias& bias, const PreintegratedMeasurements preintegratedMeasurements,
const Vector3& gravity, const Vector3& omegaCoriolis, boost::optional<Pose3> body_P_sensor = boost::none)
{
const double& deltaTij = preintegratedMeasurements.deltaTij;
const Vector3 biasAccIncr = bias.accelerometer() - preintegratedMeasurements.biasHat.accelerometer();
const Vector3 biasOmegaIncr = bias.gyroscope() - preintegratedMeasurements.biasHat.gyroscope();
const Rot3 Rot_i = pose_i.rotation();
const Vector3 pos_i = pose_i.translation().vector();
// Predict state at time j
/* ---------------------------------------------------------------------------------------------------- */
const Vector3 pos_j = pos_i + Rot_i.matrix() * (preintegratedMeasurements.deltaPij
+ preintegratedMeasurements.delPdelBiasAcc * biasAccIncr
+ preintegratedMeasurements.delPdelBiasOmega * biasOmegaIncr)
+ vel_i * deltaTij
- skewSymmetric(omegaCoriolis) * vel_i * deltaTij*deltaTij // Coriolis term - we got rid of the 2 wrt ins paper
+ 0.5 * gravity * deltaTij*deltaTij;
vel_j = LieVector(vel_i + Rot_i.matrix() * (preintegratedMeasurements.deltaVij
+ preintegratedMeasurements.delVdelBiasAcc * biasAccIncr
+ preintegratedMeasurements.delVdelBiasOmega * biasOmegaIncr)
- 2 * skewSymmetric(omegaCoriolis) * vel_i * deltaTij // Coriolis term
+ gravity * deltaTij);
const Rot3 deltaRij_biascorrected = preintegratedMeasurements.deltaRij.retract(preintegratedMeasurements.delRdelBiasOmega * biasOmegaIncr, Rot3::EXPMAP);
// deltaRij_biascorrected is expmap(deltaRij) * expmap(delRdelBiasOmega * biasOmegaIncr)
Vector3 theta_biascorrected = Rot3::Logmap(deltaRij_biascorrected);
Vector3 theta_biascorrected_corioliscorrected = theta_biascorrected -
Rot_i.inverse().matrix() * omegaCoriolis * deltaTij; // Coriolis term
const Rot3 deltaRij_biascorrected_corioliscorrected =
Rot3::Expmap( theta_biascorrected_corioliscorrected );
const Rot3 Rot_j = Rot_i.compose( deltaRij_biascorrected_corioliscorrected );
pose_j = Pose3( Rot_j, Point3(pos_j) );
}
private:
/** Serialization function */
friend class boost::serialization::access;
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int version) {
ar & boost::serialization::make_nvp("NoiseModelFactor5",
boost::serialization::base_object<Base>(*this));
ar & BOOST_SERIALIZATION_NVP(preintegratedMeasurements_);
ar & BOOST_SERIALIZATION_NVP(gravity_);
ar & BOOST_SERIALIZATION_NVP(omegaCoriolis_);
ar & BOOST_SERIALIZATION_NVP(body_P_sensor_);
}
}; // \class ImuFactor
} /// namespace gtsam

2
gtsam_unstable/slam/InvDepthFactor3.h
View File

@ -96,7 +96,7 @@ public:
" moved behind camera " << DefaultKeyFormatter(this->key1()) << std::endl;
return gtsam::ones(2) * 2.0 * K_->fx();
}
return gtsam::Vector_(1, 0.0);
return (gtsam::Vector(1) << 0.0);
}
/** return the measurement */

2
gtsam_unstable/slam/InvDepthFactorVariant1.h
View File

@ -96,7 +96,7 @@ public:
<< std::endl;
return gtsam::ones(2) * 2.0 * K_->fx();
}
return gtsam::Vector_(1, 0.0);
return (gtsam::Vector(1) << 0.0);
}
/// Evaluate error h(x)-z and optionally derivatives

2
gtsam_unstable/slam/InvDepthFactorVariant2.h
View File

@ -99,7 +99,7 @@ public:
<< std::endl;
return gtsam::ones(2) * 2.0 * K_->fx();
}
return gtsam::Vector_(1, 0.0);
return (gtsam::Vector(1) << 0.0);
}
/// Evaluate error h(x)-z and optionally derivatives

2
gtsam_unstable/slam/Mechanization_bRn2.h
View File

@ -35,7 +35,7 @@ public:
}
Vector b_g(double g_e) {
Vector n_g = Vector_(3, 0.0, 0.0, g_e);
Vector n_g = (Vector(3) << 0.0, 0.0, g_e);
return (bRn_ * n_g).vector();
}

2
gtsam_unstable/slam/PartialPriorFactor.h
View File

@ -70,7 +70,7 @@ namespace gtsam {
/** Single Element Constructor: acts on a single parameter specified by idx */
PartialPriorFactor(Key key, size_t idx, double prior, const SharedNoiseModel& model) :
Base(model, key), prior_(Vector_(1, prior)), mask_(1, idx), H_(zeros(1, T::Dim())) {
Base(model, key), prior_((Vector(1) << prior)), mask_(1, idx), H_(zeros(1, T::Dim())) {
assert(model->dim() == 1);
this->fillH();
}

2
gtsam_unstable/slam/RelativeElevationFactor.cpp
View File

@ -32,7 +32,7 @@ Vector RelativeElevationFactor::evaluateError(const Pose3& pose, const Point3& p
*H2 = zeros(1, 3);
(*H2)(0, 2) = -1.0;
}
return Vector_(1, hx - measured_);
return (Vector(1) << hx - measured_);
}
/* ************************************************************************* */

2
gtsam_unstable/slam/TransformBtwRobotsUnaryFactorEM.h
View File

@ -276,7 +276,7 @@ namespace gtsam {
}
}
return Vector_(2, p_inlier, p_outlier);
return (Vector(2) << p_inlier, p_outlier);
}
/* ************************************************************************* */

2
gtsam_unstable/slam/tests/testAHRS.cpp
View File

@ -75,7 +75,7 @@ TEST (AHRS, Mechanization_integrate) {
Mechanization_bRn2 mech;
KalmanFilter::State state;
// boost::tie(mech,state) = ahrs.initialize(g_e);
// Vector u = Vector_(3,0.05,0.0,0.0);
// Vector u = (Vector(3) << 0.05,0.0,0.0);
// double dt = 2;
// Rot3 expected;
// Mechanization_bRn2 mech2 = mech.integrate(u,dt);

36
gtsam_unstable/slam/tests/testInertialNavFactor_GlobalVelocity.cpp
View File

@ -119,10 +119,10 @@ TEST( InertialNavFactor_GlobalVelocity, Predict)
InertialNavFactor_GlobalVelocity<Pose3, LieVector, imuBias::ConstantBias> f(PoseKey1, VelKey1, BiasKey1, PoseKey2, VelKey2, measurement_acc, measurement_gyro, measurement_dt, world_g, world_rho, world_omega_earth, model);
Pose3 Pose1(Rot3(), Point3(2.00, 1.00, 3.00));
LieVector Vel1(3, 0.50, -0.50, 0.40);
LieVector Vel1((Vector(3) << 0.50, -0.50, 0.40));
imuBias::ConstantBias Bias1;
Pose3 expectedPose2(Rot3(), Point3(2.05, 0.95, 3.04));
LieVector expectedVel2(3, 0.51, -0.48, 0.43);
LieVector expectedVel2((Vector(3) << 0.51, -0.48, 0.43));
Pose3 actualPose2;
LieVector actualVel2;
f.predict(Pose1, Vel1, Bias1, actualPose2, actualVel2);
@ -157,8 +157,8 @@ TEST( InertialNavFactor_GlobalVelocity, ErrorPosVel)
Pose3 Pose1(Rot3(), Point3(2.00, 1.00, 3.00));
Pose3 Pose2(Rot3(), Point3(2.05, 0.95, 3.04));
LieVector Vel1(3, 0.50, -0.50, 0.40);
LieVector Vel2(3, 0.51, -0.48, 0.43);
LieVector Vel1((Vector(3) << 0.50, -0.50, 0.40));
LieVector Vel2((Vector(3) << 0.51, -0.48, 0.43));
imuBias::ConstantBias Bias1;
Vector ActualErr(f.evaluateError(Pose1, Vel1, Bias1, Pose2, Vel2));
@ -192,8 +192,8 @@ TEST( InertialNavFactor_GlobalVelocity, ErrorRot)
Pose3 Pose1(Rot3(), Point3(2.0,1.0,3.0));
Pose3 Pose2(Rot3::Expmap(measurement_gyro*measurement_dt), Point3(2.0,1.0,3.0));
LieVector Vel1(3,0.0,0.0,0.0);
LieVector Vel2(3,0.0,0.0,0.0);
LieVector Vel1((Vector(3) << 0.0, 0.0, 0.0));
LieVector Vel2((Vector(3) << 0.0, 0.0, 0.0));
imuBias::ConstantBias Bias1;
Vector ActualErr(f.evaluateError(Pose1, Vel1, Bias1, Pose2, Vel2));
@ -230,7 +230,7 @@ TEST( InertialNavFactor_GlobalVelocity, ErrorRotPosVel)
-0.652537293, 0.709880342, 0.265075427);
Point3 t1(2.0,1.0,3.0);
Pose3 Pose1(R1, t1);
LieVector Vel1(3,0.5,-0.5,0.4);
LieVector Vel1((Vector(3) << 0.5, -0.5, 0.4));
Rot3 R2(0.473618898, 0.119523052, 0.872582019,
0.609241153, 0.67099888, -0.422594037,
-0.636011287, 0.731761397, 0.244979388);
@ -302,13 +302,13 @@ TEST (InertialNavFactor_GlobalVelocity, Jacobian ) {
-0.652537293, 0.709880342, 0.265075427);
Point3 t1(2.0,1.0,3.0);
Pose3 Pose1(R1, t1);
LieVector Vel1(3,0.5,-0.5,0.4);
LieVector Vel1((Vector(3) << 0.5, -0.5, 0.4));
Rot3 R2(0.473618898, 0.119523052, 0.872582019,
0.609241153, 0.67099888, -0.422594037,
-0.636011287, 0.731761397, 0.244979388);
Point3 t2(2.052670960415706, 0.977252139079380, 2.942482135362800);
Pose3 Pose2(R2, t2);
LieVector Vel2(3,0.510000000000000, -0.480000000000000, 0.430000000000000);
LieVector Vel2((Vector(3) << 0.510000000000000, -0.480000000000000, 0.430000000000000));
imuBias::ConstantBias Bias1;
Matrix H1_actual, H2_actual, H3_actual, H4_actual, H5_actual;
@ -447,10 +447,10 @@ TEST( InertialNavFactor_GlobalVelocity, PredictWithTransform)
InertialNavFactor_GlobalVelocity<Pose3, LieVector, imuBias::ConstantBias> f(PoseKey1, VelKey1, BiasKey1, PoseKey2, VelKey2, measurement_acc, measurement_gyro, measurement_dt, world_g, world_rho, world_omega_earth, model, body_P_sensor);
Pose3 Pose1(Rot3(), Point3(2.00, 1.00, 3.00));
LieVector Vel1(3, 0.50, -0.50, 0.40);
LieVector Vel1((Vector(3) << 0.50, -0.50, 0.40));
imuBias::ConstantBias Bias1;
Pose3 expectedPose2(Rot3(), Point3(2.05, 0.95, 3.04));
LieVector expectedVel2(3, 0.51, -0.48, 0.43);
LieVector expectedVel2((Vector(3) << 0.51, -0.48, 0.43));
Pose3 actualPose2;
LieVector actualVel2;
f.predict(Pose1, Vel1, Bias1, actualPose2, actualVel2);
@ -488,8 +488,8 @@ TEST( InertialNavFactor_GlobalVelocity, ErrorPosVelWithTransform)
Pose3 Pose1(Rot3(), Point3(2.00, 1.00, 3.00));
Pose3 Pose2(Rot3(), Point3(2.05, 0.95, 3.04));
LieVector Vel1(3, 0.50, -0.50, 0.40);
LieVector Vel2(3, 0.51, -0.48, 0.43);
LieVector Vel1((Vector(3) << 0.50, -0.50, 0.40));
LieVector Vel2((Vector(3) << 0.51, -0.48, 0.43));
imuBias::ConstantBias Bias1;
Vector ActualErr(f.evaluateError(Pose1, Vel1, Bias1, Pose2, Vel2));
@ -527,8 +527,8 @@ TEST( InertialNavFactor_GlobalVelocity, ErrorRotWithTransform)
Pose3 Pose1(Rot3(), Point3(2.0,1.0,3.0));
Pose3 Pose2(Rot3::Expmap(body_P_sensor.rotation().matrix()*measurement_gyro*measurement_dt), Point3(2.0, 1.0, 3.0));
LieVector Vel1(3,0.0,0.0,0.0);
LieVector Vel2(3,0.0,0.0,0.0);
LieVector Vel1((Vector(3) << 0.0,0.0,0.0));
LieVector Vel2((Vector(3) << 0.0,0.0,0.0));
imuBias::ConstantBias Bias1;
Vector ActualErr(f.evaluateError(Pose1, Vel1, Bias1, Pose2, Vel2));
@ -569,7 +569,7 @@ TEST( InertialNavFactor_GlobalVelocity, ErrorRotPosVelWithTransform)
-0.652537293, 0.709880342, 0.265075427);
Point3 t1(2.0,1.0,3.0);
Pose3 Pose1(R1, t1);
LieVector Vel1(3,0.5,-0.5,0.4);
LieVector Vel1((Vector(3) << 0.5,-0.5,0.4));
Rot3 R2(0.473618898, 0.119523052, 0.872582019,
0.609241153, 0.67099888, -0.422594037,
-0.636011287, 0.731761397, 0.244979388);
@ -618,13 +618,13 @@ TEST (InertialNavFactor_GlobalVelocity, JacobianWithTransform ) {
-0.652537293, 0.709880342, 0.265075427);
Point3 t1(2.0,1.0,3.0);
Pose3 Pose1(R1, t1);
LieVector Vel1(3,0.5,-0.5,0.4);
LieVector Vel1((Vector(3) << 0.5,-0.5,0.4));
Rot3 R2(0.473618898, 0.119523052, 0.872582019,
0.609241153, 0.67099888, -0.422594037,
-0.636011287, 0.731761397, 0.244979388);
Point3 t2(2.052670960415706, 0.977252139079380, 2.942482135362800);
Pose3 Pose2(R2, t2);
LieVector Vel2(3,0.510000000000000, -0.480000000000000, 0.430000000000000);
LieVector Vel2((Vector(3) << 0.510000000000000, -0.480000000000000, 0.430000000000000));
imuBias::ConstantBias Bias1;
Matrix H1_actual, H2_actual, H3_actual, H4_actual, H5_actual;

2
gtsam_unstable/slam/tests/testInvDepthFactor3.cpp
View File

@ -38,7 +38,7 @@ TEST( InvDepthFactor, optimize) {
Point2 expected_uv = level_camera.project(landmark);
InvDepthCamera3<Cal3_S2> inv_camera(level_pose, K);
LieVector inv_landmark(5, 0., 0., 1., 0., 0.);
LieVector inv_landmark((Vector(5) << 0., 0., 1., 0., 0.));
// initialize inverse depth with "incorrect" depth of 1/4
// in reality this is 1/5, but initial depth is guessed
LieScalar inv_depth(1./4);

2
gtsam_unstable/slam/tests/testInvDepthFactorVariant1.cpp
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@ -45,7 +45,7 @@ TEST( InvDepthFactorVariant1, optimize) {
double theta = atan2(ray.y(), ray.x());
double phi = atan2(ray.z(), sqrt(ray.x()*ray.x()+ray.y()*ray.y()));
double rho = 1./ray.norm();
LieVector expected(6, x, y, z, theta, phi, rho);
LieVector expected((Vector(6) << x, y, z, theta, phi, rho));

2
gtsam_unstable/slam/tests/testInvDepthFactorVariant2.cpp
View File

@ -43,7 +43,7 @@ TEST( InvDepthFactorVariant2, optimize) {
double theta = atan2(ray.y(), ray.x());
double phi = atan2(ray.z(), sqrt(ray.x()*ray.x()+ray.y()*ray.y()));
double rho = 1./ray.norm();
LieVector expected(3, theta, phi, rho);
LieVector expected((Vector(3) << theta, phi, rho));

2
gtsam_unstable/slam/tests/testInvDepthFactorVariant3.cpp
View File

@ -43,7 +43,7 @@ TEST( InvDepthFactorVariant3, optimize) {
double theta = atan2(landmark_p1.x(), landmark_p1.z());
double phi = atan2(landmark_p1.y(), sqrt(landmark_p1.x()*landmark_p1.x()+landmark_p1.z()*landmark_p1.z()));
double rho = 1./landmark_p1.norm();
LieVector expected(3, theta, phi, rho);
LieVector expected((Vector(3) << theta, phi, rho));

2
tests/testBoundingConstraint.cpp
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@ -187,7 +187,7 @@ TEST( testBoundingConstraint, MaxDistance_basics) {
EXPECT(!rangeBound.isGreaterThan());
EXPECT(rangeBound.dim() == 1);
EXPECT(assert_equal(Vector_(1, 2.0), rangeBound.evaluateError(pt1, pt1)));
EXPECT(assert_equal(((Vector)Vector(1) << 2.0), rangeBound.evaluateError(pt1, pt1)));
EXPECT(assert_equal(ones(1), rangeBound.evaluateError(pt1, pt2)));
EXPECT(assert_equal(zero(1), rangeBound.evaluateError(pt1, pt3)));
EXPECT(assert_equal(-1.0*ones(1), rangeBound.evaluateError(pt1, pt4)));

2
tests/testGaussianJunctionTreeB.cpp
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@ -199,7 +199,7 @@ TEST(GaussianJunctionTreeB, simpleMarginal) {
// Create a simple graph
NonlinearFactorGraph fg;
fg.add(PriorFactor<Pose2>(X(0), Pose2(), noiseModel::Isotropic::Sigma(3, 10.0)));
fg.add(BetweenFactor<Pose2>(X(0), X(1), Pose2(1.0, 0.0, 0.0), noiseModel::Diagonal::Sigmas(Vector_(3, 10.0, 1.0, 1.0))));
fg.add(BetweenFactor<Pose2>(X(0), X(1), Pose2(1.0, 0.0, 0.0), noiseModel::Diagonal::Sigmas((Vector(3) << 10.0, 1.0, 1.0))));
Values init;
init.insert(X(0), Pose2());

30
tests/testNonlinearFactor.cpp
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@ -266,10 +266,10 @@ public:
TEST(NonlinearFactor, NoiseModelFactor4) {
TestFactor4 tf;
Values tv;
tv.insert(X(1), LieVector(1, 1.0));
tv.insert(X(2), LieVector(1, 2.0));
tv.insert(X(3), LieVector(1, 3.0));
tv.insert(X(4), LieVector(1, 4.0));
tv.insert(X(1), LieVector((Vector(1) << 1.0)));
tv.insert(X(2), LieVector((Vector(1) << 2.0)));
tv.insert(X(3), LieVector((Vector(1) << 3.0)));
tv.insert(X(4), LieVector((Vector(1) << 4.0)));
EXPECT(assert_equal((Vector(1) << 10.0), tf.unwhitenedError(tv)));
DOUBLES_EQUAL(25.0/2.0, tf.error(tv), 1e-9);
JacobianFactor jf(*boost::dynamic_pointer_cast<JacobianFactor>(tf.linearize(tv)));
@ -312,11 +312,11 @@ public:
TEST(NonlinearFactor, NoiseModelFactor5) {
TestFactor5 tf;
Values tv;
tv.insert(X(1), LieVector(1, 1.0));
tv.insert(X(2), LieVector(1, 2.0));
tv.insert(X(3), LieVector(1, 3.0));
tv.insert(X(4), LieVector(1, 4.0));
tv.insert(X(5), LieVector(1, 5.0));
tv.insert(X(1), LieVector((Vector(1) << 1.0)));
tv.insert(X(2), LieVector((Vector(1) << 2.0)));
tv.insert(X(3), LieVector((Vector(1) << 3.0)));
tv.insert(X(4), LieVector((Vector(1) << 4.0)));
tv.insert(X(5), LieVector((Vector(1) << 5.0)));
EXPECT(assert_equal((Vector(1) << 15.0), tf.unwhitenedError(tv)));
DOUBLES_EQUAL(56.25/2.0, tf.error(tv), 1e-9);
JacobianFactor jf(*boost::dynamic_pointer_cast<JacobianFactor>(tf.linearize(tv)));
@ -364,12 +364,12 @@ public:
TEST(NonlinearFactor, NoiseModelFactor6) {
TestFactor6 tf;
Values tv;
tv.insert(X(1), LieVector(1, 1.0));
tv.insert(X(2), LieVector(1, 2.0));
tv.insert(X(3), LieVector(1, 3.0));
tv.insert(X(4), LieVector(1, 4.0));
tv.insert(X(5), LieVector(1, 5.0));
tv.insert(X(6), LieVector(1, 6.0));
tv.insert(X(1), LieVector((Vector(1) << 1.0)));
tv.insert(X(2), LieVector((Vector(1) << 2.0)));
tv.insert(X(3), LieVector((Vector(1) << 3.0)));
tv.insert(X(4), LieVector((Vector(1) << 4.0)));
tv.insert(X(5), LieVector((Vector(1) << 5.0)));
tv.insert(X(6), LieVector((Vector(1) << 6.0)));
EXPECT(assert_equal((Vector(1) << 21.0), tf.unwhitenedError(tv)));
DOUBLES_EQUAL(110.25/2.0, tf.error(tv), 1e-9);
JacobianFactor jf(*boost::dynamic_pointer_cast<JacobianFactor>(tf.linearize(tv)));

4
tests/testSerializationSLAM.cpp
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@ -287,8 +287,8 @@ TEST (testSerializationSLAM, smallExample_nonlinear) {
/* ************************************************************************* */
TEST (testSerializationSLAM, factors) {
LieVector lieVector(4, 1.0, 2.0, 3.0, 4.0);
LieMatrix lieMatrix(2, 3, 1.0, 2.0, 3.0, 4.0, 5.0 ,6.0);
LieVector lieVector((Vector(4) << 1.0, 2.0, 3.0, 4.0));
LieMatrix lieMatrix((Matrix(2, 3) << 1.0, 2.0, 3.0, 4.0, 5.0 ,6.0));
Point2 point2(1.0, 2.0);
StereoPoint2 stereoPoint2(1.0, 2.0, 3.0);
Point3 point3(1.0, 2.0, 3.0);

2
tests/testSimulated2DOriented.cpp
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@ -58,7 +58,7 @@ TEST( simulated2DOriented, Dprior )
TEST( simulated2DOriented, constructor )
{
Pose2 measurement(0.2, 0.3, 0.1);
SharedDiagonal model = noiseModel::Diagonal::Sigmas(Vector_(3, 1., 1., 1.));
SharedDiagonal model = noiseModel::Diagonal::Sigmas((Vector(3) << 1., 1., 1.));
simulated2DOriented::Odometry factor(measurement, model, X(1), X(2));
simulated2DOriented::Values config;