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More slow progress. Added a variable-size specialization for OptionalJacobian

release/4.3a0
Paul Furgale 2014-12-16 12:30:11 +01:00
parent e9ae4f3c4d
commit 37fc86b595
7 changed files with 269 additions and 191 deletions
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3
gtsam/base/LieVector.cpp
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@ -35,5 +35,6 @@ void LieVector::print(const std::string& name) const {
gtsam::print(vector(), name);
}
GTSAM_CONCEPT_LIE_INST(LieVector)
// Does not compile because LieVector is not fixed size.
// GTSAM_CONCEPT_LIE_INST(LieVector)
} // \namespace gtsam

96
gtsam/base/OptionalJacobian.h
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@ -48,8 +48,8 @@ private:
// Trick from http://eigen.tuxfamily.org/dox/group__TutorialMapClass.html
// uses "placement new" to make map_ usurp the memory of the fixed size matrix
void usurp(double* data) {
new (&map_) Eigen::Map<Fixed>(data);
void usurp(double* data, int rows, int cols) {
new (&map_) Eigen::Map<Fixed>(data, rows, cols);
}
public:
@ -62,22 +62,22 @@ public:
/// Constructor that will usurp data of a fixed-size matrix
OptionalJacobian(Fixed& fixed) :
map_(NULL) {
usurp(fixed.data());
usurp(fixed.data(), fixed.rows(), fixed.cols());
}
/// Constructor that will usurp data of a fixed-size matrix, pointer version
OptionalJacobian(Fixed* fixedPtr) :
map_(NULL) {
if (fixedPtr)
usurp(fixedPtr->data());
usurp(fixedPtr->data(), fixedPtr->rows(), fixedPtr->cols());
}
// Disabled to deal with dynamic types.
/// Constructor that will resize a dynamic matrix (unless already correct)
OptionalJacobian(Eigen::MatrixXd& dynamic) :
map_(NULL) {
dynamic.resize(Rows, Cols); // no malloc if correct size
usurp(dynamic.data());
usurp(dynamic.data(), dynamic.rows(), dynamic.cols());
}
#ifndef OPTIONALJACOBIAN_NOBOOST
@ -92,7 +92,7 @@ public:
map_(NULL) {
if (optional) {
optional->resize(Rows, Cols);
usurp(optional->data());
usurp(optional->data(), optional->rows(), optional->cols());
}
}
@ -112,11 +112,83 @@ public:
Eigen::Map<Fixed>* operator->(){ return &map_; }
};
// these make sure that no dynamic sized matrices are compiled.
template<int Cols>
class OptionalJacobian<-1, Cols> {};
template<int Rows>
class OptionalJacobian<Rows, -1> {};
// The pure dynamic specialization of this is needed to support
// variable-sized types.
template<>
class OptionalJacobian<-1, -1> {
public:
/// Fixed size type
typedef Eigen::Matrix<double, -1, -1> Fixed;
private:
Eigen::Map<Fixed> map_; /// View on constructor argument, if given
// Trick from http://eigen.tuxfamily.org/dox/group__TutorialMapClass.html
// uses "placement new" to make map_ usurp the memory of the fixed size matrix
void usurp(double* data, int rows, int cols) {
new (&map_) Eigen::Map<Fixed>(data, rows, cols);
}
public:
/// Default constructor acts like boost::none
OptionalJacobian() :
map_(NULL, 0, 0) {
}
/// Constructor that will usurp data of a fixed-size matrix
OptionalJacobian(Fixed& fixed) :
map_(NULL, 0, 0) {
usurp(fixed.data(), fixed.rows(), fixed.cols());
}
/// Constructor that will usurp data of a fixed-size matrix, pointer version
OptionalJacobian(Fixed* fixedPtr) :
map_(NULL, 0, 0) {
if (fixedPtr)
usurp(fixedPtr->data(), fixedPtr->rows(), fixedPtr->cols());
}
#ifndef OPTIONALJACOBIAN_NOBOOST
/// Constructor with boost::none just makes empty
OptionalJacobian(boost::none_t none) :
map_(NULL, 0, 0) {
}
/// Constructor compatible with old-style derivatives
OptionalJacobian(const boost::optional<Eigen::MatrixXd&> optional) :
map_(NULL, 0, 0) {
if (optional) {
usurp(optional->data(), optional->rows(), optional->cols());
}
}
#endif
/// Return true is allocated, false if default constructor was used
operator bool() const {
return map_.data();
}
/// De-reference, like boost optional
Eigen::Map<Fixed>& operator*() {
return map_;
}
/// TODO: operator->()
Eigen::Map<Fixed>* operator->(){ return &map_; }
};
//// these make sure that no dynamic sized matrices are compiled.
//template<int Cols>
//class OptionalJacobian<-1, Cols> {};
//template<int Rows>
//class OptionalJacobian<Rows, -1> {};
} // namespace gtsam

4
gtsam/geometry/Point3.h
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@ -132,6 +132,10 @@ namespace gtsam {
/// Returns inverse retraction
inline Vector3 localCoordinates(const Point3& q) const { return (q -*this).vector(); }
/// Returns inverse retraction
inline Vector3 localCoordinates(const Point3& q, OptionalJacobian<3,3> Horigin, OptionalJacobian<3,3> Ha) const {
CONCEPT_NOT_IMPLEMENTED; return (q -*this).vector();
}
/// @}
/// @name Lie Group
/// @{

9
gtsam/navigation/AttitudeFactor.cpp
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@ -24,13 +24,14 @@ namespace gtsam {
//***************************************************************************
Vector AttitudeFactor::attitudeError(const Rot3& nRb,
boost::optional<Matrix&> H) const {
OptionalJacobian<2, 3> H) const {
if (H) {
Matrix D_nRef_R, D_e_nRef;
Matrix23 D_nRef_R;
Matrix22 D_e_nRef;
Unit3 nRef = nRb.rotate(bRef_, D_nRef_R);
Vector e = nZ_.error(nRef, D_e_nRef);
H->resize(2, 3);
H->block < 2, 3 > (0, 0) = D_e_nRef * D_nRef_R;
(*H) = D_e_nRef * D_nRef_R;
return e;
} else {
Unit3 nRef = nRb * bRef_;

2
gtsam/navigation/AttitudeFactor.h
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@ -54,7 +54,7 @@ public:
/** vector of errors */
Vector attitudeError(const Rot3& p,
boost::optional<Matrix&> H = boost::none) const;
OptionalJacobian<2,3> H = boost::none) const;
};
/**

10
gtsam/navigation/tests/testAHRSFactor.cpp
View File

@ -134,19 +134,19 @@ TEST(AHRSFactor, Error) {
// Create factor
AHRSFactor factor(X(1), X(2), B(1), pre_int_data, omegaCoriolis, false);
Vector errorActual = factor.evaluateError(x1, x2, bias);
Vector3 errorActual = factor.evaluateError(x1, x2, bias);
// Expected error
Vector errorExpected(3);
Vector3 errorExpected(3);
errorExpected << 0, 0, 0;
EXPECT(assert_equal(errorExpected, errorActual, 1e-6));
// Expected Jacobians
Matrix H1e = numericalDerivative11<Vector, Rot3>(
Matrix H1e = numericalDerivative11<Vector3, Rot3>(
boost::bind(&callEvaluateError, factor, _1, x2, bias), x1);
Matrix H2e = numericalDerivative11<Vector, Rot3>(
Matrix H2e = numericalDerivative11<Vector3, Rot3>(
boost::bind(&callEvaluateError, factor, x1, _1, bias), x2);
Matrix H3e = numericalDerivative11<Vector, Vector3>(
Matrix H3e = numericalDerivative11<Vector3, Vector3>(
boost::bind(&callEvaluateError, factor, x1, x2, _1), bias);
// Check rotation Jacobians

336
gtsam/nonlinear/tests/testAdaptAutoDiff.cpp
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@ -37,175 +37,175 @@ using boost::assign::map_list_of;
using namespace std;
using namespace gtsam;
// The DefaultChart of Camera below is laid out like Snavely's 9-dim vector
typedef PinholeCamera<Cal3Bundler> Camera;
/* ************************************************************************* */
// Some Ceres Snippets copied for testing
// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
template<typename T> inline T &RowMajorAccess(T *base, int rows, int cols,
int i, int j) {
return base[cols * i + j];
}
inline double RandDouble() {
double r = static_cast<double>(rand());
return r / RAND_MAX;
}
// A structure for projecting a 3x4 camera matrix and a
// homogeneous 3D point, to a 2D inhomogeneous point.
struct Projective {
// Function that takes P and X as separate vectors:
// P, X -> x
template<typename A>
bool operator()(A const P[12], A const X[4], A x[2]) const {
A PX[3];
for (int i = 0; i < 3; ++i) {
PX[i] = RowMajorAccess(P, 3, 4, i, 0) * X[0]
+ RowMajorAccess(P, 3, 4, i, 1) * X[1]
+ RowMajorAccess(P, 3, 4, i, 2) * X[2]
+ RowMajorAccess(P, 3, 4, i, 3) * X[3];
}
if (PX[2] != 0.0) {
x[0] = PX[0] / PX[2];
x[1] = PX[1] / PX[2];
return true;
}
return false;
}
// Adapt to eigen types
Vector2 operator()(const MatrixRowMajor& P, const Vector4& X) const {
Vector2 x;
if (operator()(P.data(), X.data(), x.data()))
return x;
else
throw std::runtime_error("Projective fail");
}
};
/* ************************************************************************* */
// Test Ceres AutoDiff
TEST(Expression, AutoDiff) {
using ceres::internal::AutoDiff;
// Instantiate function
Projective projective;
// Make arguments
typedef Eigen::Matrix<double, 3, 4, Eigen::RowMajor> M;
M P;
P << 1, 0, 0, 0, 0, 1, 0, 5, 0, 0, 1, 0;
Vector4 X(10, 0, 5, 1);
// Apply the mapping, to get image point b_x.
Vector expected = Vector2(2, 1);
Vector2 actual = projective(P, X);
EXPECT(assert_equal(expected,actual,1e-9));
// Get expected derivatives
Matrix E1 = numericalDerivative21<Vector2, M, Vector4>(Projective(), P, X);
Matrix E2 = numericalDerivative22<Vector2, M, Vector4>(Projective(), P, X);
// Get derivatives with AutoDiff
Vector2 actual2;
MatrixRowMajor H1(2, 12), H2(2, 4);
double *parameters[] = { P.data(), X.data() };
double *jacobians[] = { H1.data(), H2.data() };
CHECK(
(AutoDiff<Projective, double, 12, 4>::Differentiate( projective, parameters, 2, actual2.data(), jacobians)));
EXPECT(assert_equal(E1,H1,1e-8));
EXPECT(assert_equal(E2,H2,1e-8));
}
/* ************************************************************************* */
// Test Ceres AutoDiff on Snavely, defined in ceres_example.h
// Adapt to GTSAM types
Vector2 adapted(const Vector9& P, const Vector3& X) {
SnavelyProjection snavely;
Vector2 x;
if (snavely(P.data(), X.data(), x.data()))
return x;
else
throw std::runtime_error("Snavely fail");
}
TEST(Expression, AutoDiff2) {
using ceres::internal::AutoDiff;
// Instantiate function
SnavelyProjection snavely;
// Make arguments
Vector9 P; // zero rotation, (0,5,0) translation, focal length 1
P << 0, 0, 0, 0, 5, 0, 1, 0, 0;
Vector3 X(10, 0, -5); // negative Z-axis convention of Snavely!
// Apply the mapping, to get image point b_x.
Vector expected = Vector2(2, 1);
Vector2 actual = adapted(P, X);
EXPECT(assert_equal(expected,actual,1e-9));
// Get expected derivatives
Matrix E1 = numericalDerivative21<Vector2, Vector9, Vector3>(adapted, P, X);
Matrix E2 = numericalDerivative22<Vector2, Vector9, Vector3>(adapted, P, X);
// Get derivatives with AutoDiff
Vector2 actual2;
MatrixRowMajor H1(2, 9), H2(2, 3);
double *parameters[] = { P.data(), X.data() };
double *jacobians[] = { H1.data(), H2.data() };
CHECK(
(AutoDiff<SnavelyProjection, double, 9, 3>::Differentiate( snavely, parameters, 2, actual2.data(), jacobians)));
EXPECT(assert_equal(E1,H1,1e-8));
EXPECT(assert_equal(E2,H2,1e-8));
}
/* ************************************************************************* */
// Test AutoDiff wrapper Snavely
TEST(Expression, AutoDiff3) {
// Make arguments
Camera P(Pose3(Rot3(), Point3(0, 5, 0)), Cal3Bundler(1, 0, 0));
Point3 X(10, 0, -5); // negative Z-axis convention of Snavely!
typedef AdaptAutoDiff<SnavelyProjection, Point2, Camera, Point3> Adaptor;
Adaptor snavely;
// Apply the mapping, to get image point b_x.
Point2 expected(2, 1);
Point2 actual = snavely(P, X);
EXPECT(assert_equal(expected,actual,1e-9));
//// The DefaultChart of Camera below is laid out like Snavely's 9-dim vector
//typedef PinholeCamera<Cal3Bundler> Camera;
//
///* ************************************************************************* */
//// Some Ceres Snippets copied for testing
//// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
//template<typename T> inline T &RowMajorAccess(T *base, int rows, int cols,
// int i, int j) {
// return base[cols * i + j];
//}
//
//inline double RandDouble() {
// double r = static_cast<double>(rand());
// return r / RAND_MAX;
//}
//
//// A structure for projecting a 3x4 camera matrix and a
//// homogeneous 3D point, to a 2D inhomogeneous point.
//struct Projective {
// // Function that takes P and X as separate vectors:
// // P, X -> x
// template<typename A>
// bool operator()(A const P[12], A const X[4], A x[2]) const {
// A PX[3];
// for (int i = 0; i < 3; ++i) {
// PX[i] = RowMajorAccess(P, 3, 4, i, 0) * X[0]
// + RowMajorAccess(P, 3, 4, i, 1) * X[1]
// + RowMajorAccess(P, 3, 4, i, 2) * X[2]
// + RowMajorAccess(P, 3, 4, i, 3) * X[3];
// }
// if (PX[2] != 0.0) {
// x[0] = PX[0] / PX[2];
// x[1] = PX[1] / PX[2];
// return true;
// }
// return false;
// }
//
// // Adapt to eigen types
// Vector2 operator()(const MatrixRowMajor& P, const Vector4& X) const {
// Vector2 x;
// if (operator()(P.data(), X.data(), x.data()))
// return x;
// else
// throw std::runtime_error("Projective fail");
// }
//};
//
///* ************************************************************************* */
//// Test Ceres AutoDiff
//TEST(Expression, AutoDiff) {
// using ceres::internal::AutoDiff;
//
// // Instantiate function
// Projective projective;
//
// // Make arguments
// typedef Eigen::Matrix<double, 3, 4, Eigen::RowMajor> M;
// M P;
// P << 1, 0, 0, 0, 0, 1, 0, 5, 0, 0, 1, 0;
// Vector4 X(10, 0, 5, 1);
//
// // Apply the mapping, to get image point b_x.
// Vector expected = Vector2(2, 1);
// Vector2 actual = projective(P, X);
// EXPECT(assert_equal(expected,actual,1e-9));
//
// // Get expected derivatives
Matrix E1 = numericalDerivative21<Point2, Camera, Point3>(Adaptor(), P, X);
Matrix E2 = numericalDerivative22<Point2, Camera, Point3>(Adaptor(), P, X);
// Get derivatives with AutoDiff, not gives RowMajor results!
OptionalJacobian<2,9> H1;
OptionalJacobian<2,3> H2;
Point2 actual2 = snavely(P, X, H1, H2);
EXPECT(assert_equal(expected,actual2,1e-9));
EXPECT(assert_equal(E1,*H1,1e-8));
EXPECT(assert_equal(E2,*H2,1e-8));
}
/* ************************************************************************* */
// Test AutoDiff wrapper in an expression
TEST(Expression, Snavely) {
Expression<Camera> P(1);
Expression<Point3> X(2);
typedef AdaptAutoDiff<SnavelyProjection, Point2, Camera, Point3> Adaptor;
Expression<Point2> expression(Adaptor(), P, X);
#ifdef GTSAM_USE_QUATERNIONS
EXPECT_LONGS_EQUAL(400,expression.traceSize()); // Todo, should be zero
#else
EXPECT_LONGS_EQUAL(432,expression.traceSize()); // Todo, should be zero
#endif
set<Key> expected = list_of(1)(2);
EXPECT(expected == expression.keys());
}
// Matrix E1 = numericalDerivative21<Vector2, M, Vector4>(Projective(), P, X);
// Matrix E2 = numericalDerivative22<Vector2, M, Vector4>(Projective(), P, X);
//
// // Get derivatives with AutoDiff
// Vector2 actual2;
// MatrixRowMajor H1(2, 12), H2(2, 4);
// double *parameters[] = { P.data(), X.data() };
// double *jacobians[] = { H1.data(), H2.data() };
// CHECK(
// (AutoDiff<Projective, double, 12, 4>::Differentiate( projective, parameters, 2, actual2.data(), jacobians)));
// EXPECT(assert_equal(E1,H1,1e-8));
// EXPECT(assert_equal(E2,H2,1e-8));
//}
//
///* ************************************************************************* */
//// Test Ceres AutoDiff on Snavely, defined in ceres_example.h
//// Adapt to GTSAM types
//Vector2 adapted(const Vector9& P, const Vector3& X) {
// SnavelyProjection snavely;
// Vector2 x;
// if (snavely(P.data(), X.data(), x.data()))
// return x;
// else
// throw std::runtime_error("Snavely fail");
//}
//
//TEST(Expression, AutoDiff2) {
// using ceres::internal::AutoDiff;
//
// // Instantiate function
// SnavelyProjection snavely;
//
// // Make arguments
// Vector9 P; // zero rotation, (0,5,0) translation, focal length 1
// P << 0, 0, 0, 0, 5, 0, 1, 0, 0;
// Vector3 X(10, 0, -5); // negative Z-axis convention of Snavely!
//
// // Apply the mapping, to get image point b_x.
// Vector expected = Vector2(2, 1);
// Vector2 actual = adapted(P, X);
// EXPECT(assert_equal(expected,actual,1e-9));
//
// // Get expected derivatives
// Matrix E1 = numericalDerivative21<Vector2, Vector9, Vector3>(adapted, P, X);
// Matrix E2 = numericalDerivative22<Vector2, Vector9, Vector3>(adapted, P, X);
//
// // Get derivatives with AutoDiff
// Vector2 actual2;
// MatrixRowMajor H1(2, 9), H2(2, 3);
// double *parameters[] = { P.data(), X.data() };
// double *jacobians[] = { H1.data(), H2.data() };
// CHECK(
// (AutoDiff<SnavelyProjection, double, 9, 3>::Differentiate( snavely, parameters, 2, actual2.data(), jacobians)));
// EXPECT(assert_equal(E1,H1,1e-8));
// EXPECT(assert_equal(E2,H2,1e-8));
//}
//
///* ************************************************************************* */
//// Test AutoDiff wrapper Snavely
//TEST(Expression, AutoDiff3) {
//
// // Make arguments
// Camera P(Pose3(Rot3(), Point3(0, 5, 0)), Cal3Bundler(1, 0, 0));
// Point3 X(10, 0, -5); // negative Z-axis convention of Snavely!
//
// typedef AdaptAutoDiff<SnavelyProjection, Point2, Camera, Point3> Adaptor;
// Adaptor snavely;
//
// // Apply the mapping, to get image point b_x.
// Point2 expected(2, 1);
// Point2 actual = snavely(P, X);
// EXPECT(assert_equal(expected,actual,1e-9));
//
//// // Get expected derivatives
// Matrix E1 = numericalDerivative21<Point2, Camera, Point3>(Adaptor(), P, X);
// Matrix E2 = numericalDerivative22<Point2, Camera, Point3>(Adaptor(), P, X);
//
// // Get derivatives with AutoDiff, not gives RowMajor results!
// OptionalJacobian<2,9> H1;
// OptionalJacobian<2,3> H2;
// Point2 actual2 = snavely(P, X, H1, H2);
// EXPECT(assert_equal(expected,actual2,1e-9));
// EXPECT(assert_equal(E1,*H1,1e-8));
// EXPECT(assert_equal(E2,*H2,1e-8));
//}
//
///* ************************************************************************* */
//// Test AutoDiff wrapper in an expression
//TEST(Expression, Snavely) {
// Expression<Camera> P(1);
// Expression<Point3> X(2);
// typedef AdaptAutoDiff<SnavelyProjection, Point2, Camera, Point3> Adaptor;
// Expression<Point2> expression(Adaptor(), P, X);
//#ifdef GTSAM_USE_QUATERNIONS
// EXPECT_LONGS_EQUAL(400,expression.traceSize()); // Todo, should be zero
//#else
// EXPECT_LONGS_EQUAL(432,expression.traceSize()); // Todo, should be zero
//#endif
// set<Key> expected = list_of(1)(2);
// EXPECT(expected == expression.keys());
//}
/* ************************************************************************* */
int main() {