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Merge branch 'develop' into fix/windows-tests

release/4.3a0
Varun Agrawal 2023-06-30 15:06:59 -04:00
parent 0bc08b88bc eb5604d037
commit a9d3a10032
28 changed files with 1309 additions and 706 deletions
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33
gtsam/basis/Basis.cpp Normal file
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@ -0,0 +1,33 @@
/* ----------------------------------------------------------------------------
* 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 Basis.cpp
* @brief Compute an interpolating basis
* @author Varun Agrawal
* @date June 20, 2023
*/
#include <gtsam/basis/Basis.h>
namespace gtsam {
Matrix kroneckerProductIdentity(size_t M, const Weights& w) {
Matrix result(M, w.cols() * M);
result.setZero();
for (int i = 0; i < w.cols(); i++) {
result.block(0, i * M, M, M).diagonal().array() = w(i);
}
return result;
}
} // namespace gtsam

124
gtsam/basis/Basis.h
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@ -20,7 +20,6 @@
#include <gtsam/base/Matrix.h>
#include <gtsam/base/OptionalJacobian.h>
#include <gtsam/basis/ParameterMatrix.h>
#include <iostream>
@ -81,16 +80,7 @@ using Weights = Eigen::Matrix<double, 1, -1>; /* 1xN vector */
*
* @ingroup basis
*/
template <size_t M>
Matrix kroneckerProductIdentity(const Weights& w) {
Matrix result(M, w.cols() * M);
result.setZero();
for (int i = 0; i < w.cols(); i++) {
result.block(0, i * M, M, M).diagonal().array() = w(i);
}
return result;
}
Matrix kroneckerProductIdentity(size_t M, const Weights& w);
/**
* CRTP Base class for function bases
@ -169,18 +159,18 @@ class Basis {
};
/**
* VectorEvaluationFunctor at a given x, applied to ParameterMatrix<M>.
* VectorEvaluationFunctor at a given x, applied to a parameter Matrix.
* This functor is used to evaluate a parameterized function at a given scalar
* value x. When given a specific M*N parameters, returns an M-vector the M
* corresponding functions at x, possibly with Jacobians wrpt the parameters.
*/
template <int M>
class VectorEvaluationFunctor : protected EvaluationFunctor {
protected:
using VectorM = Eigen::Matrix<double, M, 1>;
using Jacobian = Eigen::Matrix<double, /*MxMN*/ M, -1>;
using Jacobian = Eigen::Matrix<double, /*MxMN*/ -1, -1>;
Jacobian H_;
size_t M_;
/**
* Calculate the `M*(M*N)` Jacobian of this functor with respect to
* the M*N parameter matrix `P`.
@ -190,7 +180,7 @@ class Basis {
* i.e., the Kronecker product of weights_ with the MxM identity matrix.
*/
void calculateJacobian() {
H_ = kroneckerProductIdentity<M>(this->weights_);
H_ = kroneckerProductIdentity(M_, this->weights_);
}
public:
@ -200,26 +190,27 @@ class Basis {
VectorEvaluationFunctor() {}
/// Default Constructor
VectorEvaluationFunctor(size_t N, double x) : EvaluationFunctor(N, x) {
VectorEvaluationFunctor(size_t M, size_t N, double x)
: EvaluationFunctor(N, x), M_(M) {
calculateJacobian();
}
/// Constructor, with interval [a,b]
VectorEvaluationFunctor(size_t N, double x, double a, double b)
: EvaluationFunctor(N, x, a, b) {
VectorEvaluationFunctor(size_t M, size_t N, double x, double a, double b)
: EvaluationFunctor(N, x, a, b), M_(M) {
calculateJacobian();
}
/// M-dimensional evaluation
VectorM apply(const ParameterMatrix<M>& P,
OptionalJacobian</*MxN*/ -1, -1> H = {}) const {
Vector apply(const Matrix& P,
OptionalJacobian</*MxN*/ -1, -1> H = {}) const {
if (H) *H = H_;
return P.matrix() * this->weights_.transpose();
}
/// c++ sugar
VectorM operator()(const ParameterMatrix<M>& P,
OptionalJacobian</*MxN*/ -1, -1> H = {}) const {
Vector operator()(const Matrix& P,
OptionalJacobian</*MxN*/ -1, -1> H = {}) const {
return apply(P, H);
}
};
@ -231,13 +222,14 @@ class Basis {
*
* This component is specified by the row index i, with 0<i<M.
*/
template <int M>
class VectorComponentFunctor : public EvaluationFunctor {
protected:
using Jacobian = Eigen::Matrix<double, /*1xMN*/ 1, -1>;
size_t rowIndex_;
Jacobian H_;
size_t M_;
size_t rowIndex_;
/*
* Calculate the `1*(M*N)` Jacobian of this functor with respect to
* the M*N parameter matrix `P`.
@ -248,9 +240,9 @@ class Basis {
* MxM identity matrix. See also VectorEvaluationFunctor.
*/
void calculateJacobian(size_t N) {
H_.setZero(1, M * N);
H_.setZero(1, M_ * N);
for (int j = 0; j < EvaluationFunctor::weights_.size(); j++)
H_(0, rowIndex_ + j * M) = EvaluationFunctor::weights_(j);
H_(0, rowIndex_ + j * M_) = EvaluationFunctor::weights_(j);
}
public:
@ -258,33 +250,34 @@ class Basis {
VectorComponentFunctor() {}
/// Construct with row index
VectorComponentFunctor(size_t N, size_t i, double x)
: EvaluationFunctor(N, x), rowIndex_(i) {
VectorComponentFunctor(size_t M, size_t N, size_t i, double x)
: EvaluationFunctor(N, x), M_(M), rowIndex_(i) {
calculateJacobian(N);
}
/// Construct with row index and interval
VectorComponentFunctor(size_t N, size_t i, double x, double a, double b)
: EvaluationFunctor(N, x, a, b), rowIndex_(i) {
VectorComponentFunctor(size_t M, size_t N, size_t i, double x, double a,
double b)
: EvaluationFunctor(N, x, a, b), M_(M), rowIndex_(i) {
calculateJacobian(N);
}
/// Calculate component of component rowIndex_ of P
double apply(const ParameterMatrix<M>& P,
double apply(const Matrix& P,
OptionalJacobian</*1xMN*/ -1, -1> H = {}) const {
if (H) *H = H_;
return P.row(rowIndex_) * EvaluationFunctor::weights_.transpose();
}
/// c++ sugar
double operator()(const ParameterMatrix<M>& P,
double operator()(const Matrix& P,
OptionalJacobian</*1xMN*/ -1, -1> H = {}) const {
return apply(P, H);
}
};
/**
* Manifold EvaluationFunctor at a given x, applied to ParameterMatrix<M>.
* Manifold EvaluationFunctor at a given x, applied to a parameter Matrix.
* This functor is used to evaluate a parameterized function at a given scalar
* value x. When given a specific M*N parameters, returns an M-vector the M
* corresponding functions at x, possibly with Jacobians wrpt the parameters.
@ -297,25 +290,23 @@ class Basis {
* 3D rotation.
*/
template <class T>
class ManifoldEvaluationFunctor
: public VectorEvaluationFunctor<traits<T>::dimension> {
class ManifoldEvaluationFunctor : public VectorEvaluationFunctor {
enum { M = traits<T>::dimension };
using Base = VectorEvaluationFunctor<M>;
using Base = VectorEvaluationFunctor;
public:
/// For serialization
ManifoldEvaluationFunctor() {}
/// Default Constructor
ManifoldEvaluationFunctor(size_t N, double x) : Base(N, x) {}
ManifoldEvaluationFunctor(size_t N, double x) : Base(M, N, x) {}
/// Constructor, with interval [a,b]
ManifoldEvaluationFunctor(size_t N, double x, double a, double b)
: Base(N, x, a, b) {}
: Base(M, N, x, a, b) {}
/// Manifold evaluation
T apply(const ParameterMatrix<M>& P,
OptionalJacobian</*MxMN*/ -1, -1> H = {}) const {
T apply(const Matrix& P, OptionalJacobian</*MxMN*/ -1, -1> H = {}) const {
// Interpolate the M-dimensional vector to yield a vector in tangent space
Eigen::Matrix<double, M, 1> xi = Base::operator()(P, H);
@ -333,7 +324,7 @@ class Basis {
}
/// c++ sugar
T operator()(const ParameterMatrix<M>& P,
T operator()(const Matrix& P,
OptionalJacobian</*MxN*/ -1, -1> H = {}) const {
return apply(P, H); // might call apply in derived
}
@ -389,20 +380,20 @@ class Basis {
};
/**
* VectorDerivativeFunctor at a given x, applied to ParameterMatrix<M>.
* VectorDerivativeFunctor at a given x, applied to a parameter Matrix.
*
* This functor is used to evaluate the derivatives of a parameterized
* function at a given scalar value x. When given a specific M*N parameters,
* returns an M-vector the M corresponding function derivatives at x, possibly
* with Jacobians wrpt the parameters.
*/
template <int M>
class VectorDerivativeFunctor : protected DerivativeFunctorBase {
protected:
using VectorM = Eigen::Matrix<double, M, 1>;
using Jacobian = Eigen::Matrix<double, /*MxMN*/ M, -1>;
using Jacobian = Eigen::Matrix<double, /*MxMN*/ -1, -1>;
Jacobian H_;
size_t M_;
/**
* Calculate the `M*(M*N)` Jacobian of this functor with respect to
* the M*N parameter matrix `P`.
@ -412,7 +403,7 @@ class Basis {
* i.e., the Kronecker product of weights_ with the MxM identity matrix.
*/
void calculateJacobian() {
H_ = kroneckerProductIdentity<M>(this->weights_);
H_ = kroneckerProductIdentity(M_, this->weights_);
}
public:
@ -422,25 +413,25 @@ class Basis {
VectorDerivativeFunctor() {}
/// Default Constructor
VectorDerivativeFunctor(size_t N, double x) : DerivativeFunctorBase(N, x) {
VectorDerivativeFunctor(size_t M, size_t N, double x)
: DerivativeFunctorBase(N, x), M_(M) {
calculateJacobian();
}
/// Constructor, with optional interval [a,b]
VectorDerivativeFunctor(size_t N, double x, double a, double b)
: DerivativeFunctorBase(N, x, a, b) {
VectorDerivativeFunctor(size_t M, size_t N, double x, double a, double b)
: DerivativeFunctorBase(N, x, a, b), M_(M) {
calculateJacobian();
}
VectorM apply(const ParameterMatrix<M>& P,
OptionalJacobian</*MxMN*/ -1, -1> H = {}) const {
Vector apply(const Matrix& P,
OptionalJacobian</*MxMN*/ -1, -1> H = {}) const {
if (H) *H = H_;
return P.matrix() * this->weights_.transpose();
}
/// c++ sugar
VectorM operator()(
const ParameterMatrix<M>& P,
OptionalJacobian</*MxMN*/ -1, -1> H = {}) const {
Vector operator()(const Matrix& P,
OptionalJacobian</*MxMN*/ -1, -1> H = {}) const {
return apply(P, H);
}
};
@ -452,13 +443,14 @@ class Basis {
*
* This component is specified by the row index i, with 0<i<M.
*/
template <int M>
class ComponentDerivativeFunctor : protected DerivativeFunctorBase {
protected:
using Jacobian = Eigen::Matrix<double, /*1xMN*/ 1, -1>;
size_t rowIndex_;
Jacobian H_;
size_t M_;
size_t rowIndex_;
/*
* Calculate the `1*(M*N)` Jacobian of this functor with respect to
* the M*N parameter matrix `P`.
@ -469,9 +461,9 @@ class Basis {
* MxM identity matrix. See also VectorDerivativeFunctor.
*/
void calculateJacobian(size_t N) {
H_.setZero(1, M * N);
H_.setZero(1, M_ * N);
for (int j = 0; j < this->weights_.size(); j++)
H_(0, rowIndex_ + j * M) = this->weights_(j);
H_(0, rowIndex_ + j * M_) = this->weights_(j);
}
public:
@ -479,29 +471,29 @@ class Basis {
ComponentDerivativeFunctor() {}
/// Construct with row index
ComponentDerivativeFunctor(size_t N, size_t i, double x)
: DerivativeFunctorBase(N, x), rowIndex_(i) {
ComponentDerivativeFunctor(size_t M, size_t N, size_t i, double x)
: DerivativeFunctorBase(N, x), M_(M), rowIndex_(i) {
calculateJacobian(N);
}
/// Construct with row index and interval
ComponentDerivativeFunctor(size_t N, size_t i, double x, double a, double b)
: DerivativeFunctorBase(N, x, a, b), rowIndex_(i) {
ComponentDerivativeFunctor(size_t M, size_t N, size_t i, double x, double a,
double b)
: DerivativeFunctorBase(N, x, a, b), M_(M), rowIndex_(i) {
calculateJacobian(N);
}
/// Calculate derivative of component rowIndex_ of F
double apply(const ParameterMatrix<M>& P,
double apply(const Matrix& P,
OptionalJacobian</*1xMN*/ -1, -1> H = {}) const {
if (H) *H = H_;
return P.row(rowIndex_) * this->weights_.transpose();
}
/// c++ sugar
double operator()(const ParameterMatrix<M>& P,
double operator()(const Matrix& P,
OptionalJacobian</*1xMN*/ -1, -1> H = {}) const {
return apply(P, H);
}
};
};
} // namespace gtsam

128
gtsam/basis/BasisFactors.h
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@ -75,7 +75,7 @@ class EvaluationFactor : public FunctorizedFactor<double, Vector> {
};
/**
* Unary factor for enforcing BASIS polynomial evaluation on a ParameterMatrix
* Unary factor for enforcing BASIS polynomial evaluation on a parameter Matrix
* of size (M, N) is equal to a vector-valued measurement at the same point,
when
* using a pseudo-spectral parameterization.
@ -87,15 +87,13 @@ class EvaluationFactor : public FunctorizedFactor<double, Vector> {
* measurement prediction function.
*
* @param BASIS: The basis class to use e.g. Chebyshev2
* @param M: Size of the evaluated state vector.
*
* @ingroup basis
*/
template <class BASIS, int M>
class VectorEvaluationFactor
: public FunctorizedFactor<Vector, ParameterMatrix<M>> {
template <class BASIS>
class VectorEvaluationFactor : public FunctorizedFactor<Vector, Matrix> {
private:
using Base = FunctorizedFactor<Vector, ParameterMatrix<M>>;
using Base = FunctorizedFactor<Vector, Matrix>;
public:
VectorEvaluationFactor() {}
@ -103,42 +101,43 @@ class VectorEvaluationFactor
/**
* @brief Construct a new VectorEvaluationFactor object.
*
* @param key The key to the ParameterMatrix object used to represent the
* @param key The key to the parameter Matrix object used to represent the
* polynomial.
* @param z The measurement value.
* @param model The noise model.
* @param M Size of the evaluated state vector.
* @param N The degree of the polynomial.
* @param x The point at which to evaluate the basis polynomial.
*/
VectorEvaluationFactor(Key key, const Vector &z,
const SharedNoiseModel &model, const size_t N,
double x)
: Base(key, z, model,
typename BASIS::template VectorEvaluationFunctor<M>(N, x)) {}
const SharedNoiseModel &model, const size_t M,
const size_t N, double x)
: Base(key, z, model, typename BASIS::VectorEvaluationFunctor(M, N, x)) {}
/**
* @brief Construct a new VectorEvaluationFactor object.
*
* @param key The key to the ParameterMatrix object used to represent the
* @param key The key to the parameter Matrix object used to represent the
* polynomial.
* @param z The measurement value.
* @param model The noise model.
* @param M Size of the evaluated state vector.
* @param N The degree of the polynomial.
* @param x The point at which to evaluate the basis polynomial.
* @param a Lower bound for the polynomial.
* @param b Upper bound for the polynomial.
*/
VectorEvaluationFactor(Key key, const Vector &z,
const SharedNoiseModel &model, const size_t N,
double x, double a, double b)
const SharedNoiseModel &model, const size_t M,
const size_t N, double x, double a, double b)
: Base(key, z, model,
typename BASIS::template VectorEvaluationFunctor<M>(N, x, a, b)) {}
typename BASIS::VectorEvaluationFunctor(M, N, x, a, b)) {}
virtual ~VectorEvaluationFactor() {}
};
/**
* Unary factor for enforcing BASIS polynomial evaluation on a ParameterMatrix
* Unary factor for enforcing BASIS polynomial evaluation on a parameter Matrix
* of size (P, N) is equal to specified measurement at the same point, when
* using a pseudo-spectral parameterization.
*
@ -147,20 +146,18 @@ class VectorEvaluationFactor
* indexed by `i`.
*
* @param BASIS: The basis class to use e.g. Chebyshev2
* @param P: Size of the fixed-size vector.
*
* Example:
* VectorComponentFactor<BASIS, P> controlPrior(key, measured, model,
* N, i, t, a, b);
* VectorComponentFactor<BASIS> controlPrior(key, measured, model,
* N, i, t, a, b);
* where N is the degree and i is the component index.
*
* @ingroup basis
*/
template <class BASIS, size_t P>
class VectorComponentFactor
: public FunctorizedFactor<double, ParameterMatrix<P>> {
template <class BASIS>
class VectorComponentFactor : public FunctorizedFactor<double, Matrix> {
private:
using Base = FunctorizedFactor<double, ParameterMatrix<P>>;
using Base = FunctorizedFactor<double, Matrix>;
public:
VectorComponentFactor() {}
@ -168,29 +165,31 @@ class VectorComponentFactor
/**
* @brief Construct a new VectorComponentFactor object.
*
* @param key The key to the ParameterMatrix object used to represent the
* @param key The key to the parameter Matrix object used to represent the
* polynomial.
* @param z The scalar value at a specified index `i` of the full measurement
* vector.
* @param model The noise model.
* @param P Size of the fixed-size vector.
* @param N The degree of the polynomial.
* @param i The index for the evaluated vector to give us the desired scalar
* value.
* @param x The point at which to evaluate the basis polynomial.
*/
VectorComponentFactor(Key key, const double &z, const SharedNoiseModel &model,
const size_t N, size_t i, double x)
const size_t P, const size_t N, size_t i, double x)
: Base(key, z, model,
typename BASIS::template VectorComponentFunctor<P>(N, i, x)) {}
typename BASIS::VectorComponentFunctor(P, N, i, x)) {}
/**
* @brief Construct a new VectorComponentFactor object.
*
* @param key The key to the ParameterMatrix object used to represent the
* @param key The key to the parameter Matrix object used to represent the
* polynomial.
* @param z The scalar value at a specified index `i` of the full measurement
* vector.
* @param model The noise model.
* @param P Size of the fixed-size vector.
* @param N The degree of the polynomial.
* @param i The index for the evaluated vector to give us the desired scalar
* value.
@ -199,11 +198,10 @@ class VectorComponentFactor
* @param b Upper bound for the polynomial.
*/
VectorComponentFactor(Key key, const double &z, const SharedNoiseModel &model,
const size_t N, size_t i, double x, double a, double b)
: Base(
key, z, model,
typename BASIS::template VectorComponentFunctor<P>(N, i, x, a, b)) {
}
const size_t P, const size_t N, size_t i, double x,
double a, double b)
: Base(key, z, model,
typename BASIS::VectorComponentFunctor(P, N, i, x, a, b)) {}
virtual ~VectorComponentFactor() {}
};
@ -226,10 +224,9 @@ class VectorComponentFactor
* where `x` is the value (e.g. timestep) at which the rotation was evaluated.
*/
template <class BASIS, typename T>
class ManifoldEvaluationFactor
: public FunctorizedFactor<T, ParameterMatrix<traits<T>::dimension>> {
class ManifoldEvaluationFactor : public FunctorizedFactor<T, Matrix> {
private:
using Base = FunctorizedFactor<T, ParameterMatrix<traits<T>::dimension>>;
using Base = FunctorizedFactor<T, Matrix>;
public:
ManifoldEvaluationFactor() {}
@ -289,7 +286,7 @@ class DerivativeFactor
/**
* @brief Construct a new DerivativeFactor object.
*
* @param key The key to the ParameterMatrix which represents the basis
* @param key The key to the parameter Matrix which represents the basis
* polynomial.
* @param z The measurement value.
* @param model The noise model.
@ -303,7 +300,7 @@ class DerivativeFactor
/**
* @brief Construct a new DerivativeFactor object.
*
* @param key The key to the ParameterMatrix which represents the basis
* @param key The key to the parameter Matrix which represents the basis
* polynomial.
* @param z The measurement value.
* @param model The noise model.
@ -324,14 +321,12 @@ class DerivativeFactor
* polynomial at a specified point `x` is equal to the vector value `z`.
*
* @param BASIS: The basis class to use e.g. Chebyshev2
* @param M: Size of the evaluated state vector derivative.
*/
template <class BASIS, int M>
class VectorDerivativeFactor
: public FunctorizedFactor<Vector, ParameterMatrix<M>> {
template <class BASIS>
class VectorDerivativeFactor : public FunctorizedFactor<Vector, Matrix> {
private:
using Base = FunctorizedFactor<Vector, ParameterMatrix<M>>;
using Func = typename BASIS::template VectorDerivativeFunctor<M>;
using Base = FunctorizedFactor<Vector, Matrix>;
using Func = typename BASIS::VectorDerivativeFunctor;
public:
VectorDerivativeFactor() {}
@ -339,34 +334,36 @@ class VectorDerivativeFactor
/**
* @brief Construct a new VectorDerivativeFactor object.
*
* @param key The key to the ParameterMatrix which represents the basis
* @param key The key to the parameter Matrix which represents the basis
* polynomial.
* @param z The measurement value.
* @param model The noise model.
* @param M Size of the evaluated state vector derivative.
* @param N The degree of the polynomial.
* @param x The point at which to evaluate the basis polynomial.
*/
VectorDerivativeFactor(Key key, const Vector &z,
const SharedNoiseModel &model, const size_t N,
double x)
: Base(key, z, model, Func(N, x)) {}
const SharedNoiseModel &model, const size_t M,
const size_t N, double x)
: Base(key, z, model, Func(M, N, x)) {}
/**
* @brief Construct a new VectorDerivativeFactor object.
*
* @param key The key to the ParameterMatrix which represents the basis
* @param key The key to the parameter Matrix which represents the basis
* polynomial.
* @param z The measurement value.
* @param model The noise model.
* @param M Size of the evaluated state vector derivative.
* @param N The degree of the polynomial.
* @param x The point at which to evaluate the basis polynomial.
* @param a Lower bound for the polynomial.
* @param b Upper bound for the polynomial.
*/
VectorDerivativeFactor(Key key, const Vector &z,
const SharedNoiseModel &model, const size_t N,
double x, double a, double b)
: Base(key, z, model, Func(N, x, a, b)) {}
const SharedNoiseModel &model, const size_t M,
const size_t N, double x, double a, double b)
: Base(key, z, model, Func(M, N, x, a, b)) {}
virtual ~VectorDerivativeFactor() {}
};
@ -377,14 +374,12 @@ class VectorDerivativeFactor
* vector-valued measurement `z`.
*
* @param BASIS: The basis class to use e.g. Chebyshev2
* @param P: Size of the control component derivative.
*/
template <class BASIS, int P>
class ComponentDerivativeFactor
: public FunctorizedFactor<double, ParameterMatrix<P>> {
template <class BASIS>
class ComponentDerivativeFactor : public FunctorizedFactor<double, Matrix> {
private:
using Base = FunctorizedFactor<double, ParameterMatrix<P>>;
using Func = typename BASIS::template ComponentDerivativeFunctor<P>;
using Base = FunctorizedFactor<double, Matrix>;
using Func = typename BASIS::ComponentDerivativeFunctor;
public:
ComponentDerivativeFactor() {}
@ -392,29 +387,31 @@ class ComponentDerivativeFactor
/**
* @brief Construct a new ComponentDerivativeFactor object.
*
* @param key The key to the ParameterMatrix which represents the basis
* @param key The key to the parameter Matrix which represents the basis
* polynomial.
* @param z The scalar measurement value at a specific index `i` of the full
* measurement vector.
* @param model The degree of the polynomial.
* @param P: Size of the control component derivative.
* @param N The degree of the polynomial.
* @param i The index for the evaluated vector to give us the desired scalar
* value.
* @param x The point at which to evaluate the basis polynomial.
*/
ComponentDerivativeFactor(Key key, const double &z,
const SharedNoiseModel &model, const size_t N,
size_t i, double x)
: Base(key, z, model, Func(N, i, x)) {}
const SharedNoiseModel &model, const size_t P,
const size_t N, size_t i, double x)
: Base(key, z, model, Func(P, N, i, x)) {}
/**
* @brief Construct a new ComponentDerivativeFactor object.
*
* @param key The key to the ParameterMatrix which represents the basis
* @param key The key to the parameter Matrix which represents the basis
* polynomial.
* @param z The scalar measurement value at a specific index `i` of the full
* measurement vector.
* @param model The degree of the polynomial.
* @param P: Size of the control component derivative.
* @param N The degree of the polynomial.
* @param i The index for the evaluated vector to give us the desired scalar
* value.
@ -423,9 +420,10 @@ class ComponentDerivativeFactor
* @param b Upper bound for the polynomial.
*/
ComponentDerivativeFactor(Key key, const double &z,
const SharedNoiseModel &model, const size_t N,
size_t i, double x, double a, double b)
: Base(key, z, model, Func(N, i, x, a, b)) {}
const SharedNoiseModel &model, const size_t P,
const size_t N, size_t i, double x, double a,
double b)
: Base(key, z, model, Func(P, N, i, x, a, b)) {}
virtual ~ComponentDerivativeFactor() {}
};

215
gtsam/basis/ParameterMatrix.h
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@ -1,215 +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 ParameterMatrix.h
* @brief Define ParameterMatrix class which is used to store values at
* interpolation points.
* @author Varun Agrawal, Frank Dellaert
* @date September 21, 2020
*/
#pragma once
#include <gtsam/base/Matrix.h>
#include <gtsam/base/Testable.h>
#include <gtsam/base/VectorSpace.h>
#include <iostream>
namespace gtsam {
/**
* A matrix abstraction of MxN values at the Basis points.
* This class serves as a wrapper over an Eigen matrix.
* @tparam M: The dimension of the type you wish to evaluate.
* @param N: the number of Basis points (e.g. Chebyshev points of the second
* kind).
*/
template <int M>
class ParameterMatrix {
using MatrixType = Eigen::Matrix<double, M, -1>;
private:
MatrixType matrix_;
public:
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
enum { dimension = Eigen::Dynamic };
/**
* Create ParameterMatrix using the number of basis points.
* @param N: The number of basis points (the columns).
*/
ParameterMatrix(const size_t N) : matrix_(M, N) { matrix_.setZero(); }
/**
* Create ParameterMatrix from an MxN Eigen Matrix.
* @param matrix: An Eigen matrix used to initialze the ParameterMatrix.
*/
ParameterMatrix(const MatrixType& matrix) : matrix_(matrix) {}
/// Get the number of rows.
size_t rows() const { return matrix_.rows(); }
/// Get the number of columns.
size_t cols() const { return matrix_.cols(); }
/// Get the underlying matrix.
MatrixType matrix() const { return matrix_; }
/// Return the tranpose of the underlying matrix.
Eigen::Matrix<double, -1, M> transpose() const { return matrix_.transpose(); }
/**
* Get the matrix row specified by `index`.
* @param index: The row index to retrieve.
*/
Eigen::Matrix<double, 1, -1> row(size_t index) const {
return matrix_.row(index);
}
/**
* Set the matrix row specified by `index`.
* @param index: The row index to set.
*/
auto row(size_t index) -> Eigen::Block<MatrixType, 1, -1, false> {
return matrix_.row(index);
}
/**
* Get the matrix column specified by `index`.
* @param index: The column index to retrieve.
*/
Eigen::Matrix<double, M, 1> col(size_t index) const {
return matrix_.col(index);
}
/**
* Set the matrix column specified by `index`.
* @param index: The column index to set.
*/
auto col(size_t index) -> Eigen::Block<MatrixType, M, 1, true> {
return matrix_.col(index);
}
/**
* Set all matrix coefficients to zero.
*/
void setZero() { matrix_.setZero(); }
/**
* Add a ParameterMatrix to another.
* @param other: ParameterMatrix to add.
*/
ParameterMatrix<M> operator+(const ParameterMatrix<M>& other) const {
return ParameterMatrix<M>(matrix_ + other.matrix());
}
/**
* Add a MxN-sized vector to the ParameterMatrix.
* @param other: Vector which is reshaped and added.
*/
ParameterMatrix<M> operator+(
const Eigen::Matrix<double, -1, 1>& other) const {
// This form avoids a deep copy and instead typecasts `other`.
Eigen::Map<const MatrixType> other_(other.data(), M, cols());
return ParameterMatrix<M>(matrix_ + other_);
}
/**
* Subtract a ParameterMatrix from another.
* @param other: ParameterMatrix to subtract.
*/
ParameterMatrix<M> operator-(const ParameterMatrix<M>& other) const {
return ParameterMatrix<M>(matrix_ - other.matrix());
}
/**
* Subtract a MxN-sized vector from the ParameterMatrix.
* @param other: Vector which is reshaped and subracted.
*/
ParameterMatrix<M> operator-(
const Eigen::Matrix<double, -1, 1>& other) const {
Eigen::Map<const MatrixType> other_(other.data(), M, cols());
return ParameterMatrix<M>(matrix_ - other_);
}
/**
* Multiply ParameterMatrix with an Eigen matrix.
* @param other: Eigen matrix which should be multiplication compatible with
* the ParameterMatrix.
*/
MatrixType operator*(const Eigen::Matrix<double, -1, -1>& other) const {
return matrix_ * other;
}
/// @name Vector Space requirements
/// @{
/**
* Print the ParameterMatrix.
* @param s: The prepend string to add more contextual info.
*/
void print(const std::string& s = "") const {
std::cout << (s == "" ? s : s + " ") << matrix_ << std::endl;
}
/**
* Check for equality up to absolute tolerance.
* @param other: The ParameterMatrix to check equality with.
* @param tol: The absolute tolerance threshold.
*/
bool equals(const ParameterMatrix<M>& other, double tol = 1e-8) const {
return gtsam::equal_with_abs_tol(matrix_, other.matrix(), tol);
}
/// Returns dimensionality of the tangent space
inline size_t dim() const { return matrix_.size(); }
/// Convert to vector form, is done row-wise
inline Vector vector() const {
using RowMajor = Eigen::Matrix<double, -1, -1, Eigen::RowMajor>;
Vector result(matrix_.size());
Eigen::Map<RowMajor>(&result(0), rows(), cols()) = matrix_;
return result;
}
/** Identity function to satisfy VectorSpace traits.
*
* NOTE: The size at compile time is unknown so this identity is zero
* length and thus not valid.
*/
inline static ParameterMatrix Identity() {
// throw std::runtime_error(
// "ParameterMatrix::Identity(): Don't use this function");
return ParameterMatrix(0);
}
/// @}
};
// traits for ParameterMatrix
template <int M>
struct traits<ParameterMatrix<M>>
: public internal::VectorSpace<ParameterMatrix<M>> {};
/* ************************************************************************* */
// Stream operator that takes a ParameterMatrix. Used for printing.
template <int M>
inline std::ostream& operator<<(std::ostream& os,
const ParameterMatrix<M>& parameterMatrix) {
os << parameterMatrix.matrix();
return os;
}
} // namespace gtsam

93
gtsam/basis/basis.i
View File

@ -46,18 +46,6 @@ class Chebyshev2 {
static Matrix DifferentiationMatrix(size_t N, double a, double b);
};
#include <gtsam/basis/ParameterMatrix.h>
template <M = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}>
class ParameterMatrix {
ParameterMatrix(const size_t N);
ParameterMatrix(const Matrix& matrix);
Matrix matrix() const;
void print(const string& s = "") const;
};
#include <gtsam/basis/BasisFactors.h>
template <BASIS = {gtsam::Chebyshev2, gtsam::Chebyshev1Basis,
@ -72,45 +60,36 @@ virtual class EvaluationFactor : gtsam::NoiseModelFactor {
double x, double a, double b);
};
template <BASIS, M>
template <BASIS = {gtsam::FourierBasis, gtsam::Chebyshev1Basis,
gtsam::Chebyshev2Basis, gtsam::Chebyshev2}>
virtual class VectorEvaluationFactor : gtsam::NoiseModelFactor {
VectorEvaluationFactor();
VectorEvaluationFactor(gtsam::Key key, const Vector& z,
const gtsam::noiseModel::Base* model, const size_t N,
double x);
const gtsam::noiseModel::Base* model, const size_t M,
const size_t N, double x);
VectorEvaluationFactor(gtsam::Key key, const Vector& z,
const gtsam::noiseModel::Base* model, const size_t N,
double x, double a, double b);
const gtsam::noiseModel::Base* model, const size_t M,
const size_t N, double x, double a, double b);
};
// TODO(Varun) Better way to support arbitrary dimensions?
// Especially if users mainly do `pip install gtsam` for the Python wrapper.
typedef gtsam::VectorEvaluationFactor<gtsam::Chebyshev2, 3>
VectorEvaluationFactorChebyshev2D3;
typedef gtsam::VectorEvaluationFactor<gtsam::Chebyshev2, 4>
VectorEvaluationFactorChebyshev2D4;
typedef gtsam::VectorEvaluationFactor<gtsam::Chebyshev2, 12>
VectorEvaluationFactorChebyshev2D12;
template <BASIS, P>
template <BASIS = {gtsam::FourierBasis, gtsam::Chebyshev1Basis,
gtsam::Chebyshev2Basis, gtsam::Chebyshev2}>
virtual class VectorComponentFactor : gtsam::NoiseModelFactor {
VectorComponentFactor();
VectorComponentFactor(gtsam::Key key, const double z,
const gtsam::noiseModel::Base* model, const size_t N,
size_t i, double x);
const gtsam::noiseModel::Base* model, const size_t M,
const size_t N, size_t i, double x);
VectorComponentFactor(gtsam::Key key, const double z,
const gtsam::noiseModel::Base* model, const size_t N,
size_t i, double x, double a, double b);
const gtsam::noiseModel::Base* model, const size_t M,
const size_t N, size_t i, double x, double a, double b);
};
typedef gtsam::VectorComponentFactor<gtsam::Chebyshev2, 3>
VectorComponentFactorChebyshev2D3;
typedef gtsam::VectorComponentFactor<gtsam::Chebyshev2, 4>
VectorComponentFactorChebyshev2D4;
typedef gtsam::VectorComponentFactor<gtsam::Chebyshev2, 12>
VectorComponentFactorChebyshev2D12;
#include <gtsam/geometry/Pose2.h>
#include <gtsam/geometry/Pose3.h>
template <BASIS, T>
template <BASIS = {gtsam::FourierBasis, gtsam::Chebyshev1Basis,
gtsam::Chebyshev2Basis, gtsam::Chebyshev2},
T = {gtsam::Rot2, gtsam::Rot3, gtsam::Pose2, gtsam::Pose3}>
virtual class ManifoldEvaluationFactor : gtsam::NoiseModelFactor {
ManifoldEvaluationFactor();
ManifoldEvaluationFactor(gtsam::Key key, const T& z,
@ -121,8 +100,42 @@ virtual class ManifoldEvaluationFactor : gtsam::NoiseModelFactor {
double x, double a, double b);
};
// TODO(gerry): Add `DerivativeFactor`, `VectorDerivativeFactor`, and
// `ComponentDerivativeFactor`
template <BASIS = {gtsam::FourierBasis, gtsam::Chebyshev1Basis,
gtsam::Chebyshev2Basis, gtsam::Chebyshev2}>
virtual class DerivativeFactor : gtsam::NoiseModelFactor {
DerivativeFactor();
DerivativeFactor(gtsam::Key key, const double z,
const gtsam::noiseModel::Base* model, const size_t N,
double x);
DerivativeFactor(gtsam::Key key, const double z,
const gtsam::noiseModel::Base* model, const size_t N,
double x, double a, double b);
};
template <BASIS = {gtsam::FourierBasis, gtsam::Chebyshev1Basis,
gtsam::Chebyshev2Basis, gtsam::Chebyshev2}>
virtual class VectorDerivativeFactor : gtsam::NoiseModelFactor {
VectorDerivativeFactor();
VectorDerivativeFactor(gtsam::Key key, const Vector& z,
const gtsam::noiseModel::Base* model, const size_t M,
const size_t N, double x);
VectorDerivativeFactor(gtsam::Key key, const Vector& z,
const gtsam::noiseModel::Base* model, const size_t M,
const size_t N, double x, double a, double b);
};
template <BASIS = {gtsam::FourierBasis, gtsam::Chebyshev1Basis,
gtsam::Chebyshev2Basis, gtsam::Chebyshev2}>
virtual class ComponentDerivativeFactor : gtsam::NoiseModelFactor {
ComponentDerivativeFactor();
ComponentDerivativeFactor(gtsam::Key key, const double z,
const gtsam::noiseModel::Base* model,
const size_t P, const size_t N, size_t i, double x);
ComponentDerivativeFactor(gtsam::Key key, const double z,
const gtsam::noiseModel::Base* model,
const size_t P, const size_t N, size_t i, double x,
double a, double b);
};
#include <gtsam/basis/FitBasis.h>
template <BASIS = {gtsam::FourierBasis, gtsam::Chebyshev1Basis,

62
gtsam/basis/tests/testBasisFactors.cpp
View File

@ -17,30 +17,28 @@
* @brief unit tests for factors in BasisFactors.h
*/
#include <CppUnitLite/TestHarness.h>
#include <gtsam/base/Testable.h>
#include <gtsam/base/TestableAssertions.h>
#include <gtsam/base/Vector.h>
#include <gtsam/basis/Basis.h>
#include <gtsam/basis/BasisFactors.h>
#include <gtsam/basis/Chebyshev2.h>
#include <gtsam/geometry/Pose2.h>
#include <gtsam/inference/Symbol.h>
#include <gtsam/nonlinear/FunctorizedFactor.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/nonlinear/factorTesting.h>
#include <gtsam/inference/Symbol.h>
#include <gtsam/base/Testable.h>
#include <gtsam/base/TestableAssertions.h>
#include <gtsam/base/Vector.h>
#include <CppUnitLite/TestHarness.h>
using gtsam::noiseModel::Isotropic;
using gtsam::Pose2;
using gtsam::Vector;
using gtsam::Values;
using gtsam::Chebyshev2;
using gtsam::ParameterMatrix;
using gtsam::LevenbergMarquardtParams;
using gtsam::LevenbergMarquardtOptimizer;
using gtsam::LevenbergMarquardtParams;
using gtsam::NonlinearFactorGraph;
using gtsam::NonlinearOptimizerParams;
using gtsam::Pose2;
using gtsam::Values;
using gtsam::Vector;
using gtsam::noiseModel::Isotropic;
constexpr size_t N = 2;
@ -81,15 +79,15 @@ TEST(BasisFactors, VectorEvaluationFactor) {
const Vector measured = Vector::Zero(M);
auto model = Isotropic::Sigma(M, 1.0);
VectorEvaluationFactor<Chebyshev2, M> factor(key, measured, model, N, 0);
VectorEvaluationFactor<Chebyshev2> factor(key, measured, model, M, N, 0);
NonlinearFactorGraph graph;
graph.add(factor);
ParameterMatrix<M> stateMatrix(N);
gtsam::Matrix stateMatrix = gtsam::Matrix::Zero(M, N);
Values initial;
initial.insert<ParameterMatrix<M>>(key, stateMatrix);
initial.insert<gtsam::Matrix>(key, stateMatrix);
LevenbergMarquardtParams parameters;
parameters.setMaxIterations(20);
@ -107,7 +105,7 @@ TEST(BasisFactors, Print) {
const Vector measured = Vector::Ones(M) * 42;
auto model = Isotropic::Sigma(M, 1.0);
VectorEvaluationFactor<Chebyshev2, M> factor(key, measured, model, N, 0);
VectorEvaluationFactor<Chebyshev2> factor(key, measured, model, M, N, 0);
std::string expected =
" keys = { X0 }\n"
@ -128,16 +126,16 @@ TEST(BasisFactors, VectorComponentFactor) {
const size_t i = 2;
const double measured = 0.0, t = 3.0, a = 2.0, b = 4.0;
auto model = Isotropic::Sigma(1, 1.0);
VectorComponentFactor<Chebyshev2, P> factor(key, measured, model, N, i,
t, a, b);
VectorComponentFactor<Chebyshev2> factor(key, measured, model, P, N, i, t, a,
b);
NonlinearFactorGraph graph;
graph.add(factor);
ParameterMatrix<P> stateMatrix(N);
gtsam::Matrix stateMatrix = gtsam::Matrix::Zero(P, N);
Values initial;
initial.insert<ParameterMatrix<P>>(key, stateMatrix);
initial.insert<gtsam::Matrix>(key, stateMatrix);
LevenbergMarquardtParams parameters;
parameters.setMaxIterations(20);
@ -153,16 +151,16 @@ TEST(BasisFactors, ManifoldEvaluationFactor) {
const Pose2 measured;
const double t = 3.0, a = 2.0, b = 4.0;
auto model = Isotropic::Sigma(3, 1.0);
ManifoldEvaluationFactor<Chebyshev2, Pose2> factor(key, measured, model, N,
t, a, b);
ManifoldEvaluationFactor<Chebyshev2, Pose2> factor(key, measured, model, N, t,
a, b);
NonlinearFactorGraph graph;
graph.add(factor);
ParameterMatrix<3> stateMatrix(N);
gtsam::Matrix stateMatrix = gtsam::Matrix::Zero(3, N);
Values initial;
initial.insert<ParameterMatrix<3>>(key, stateMatrix);
initial.insert<gtsam::Matrix>(key, stateMatrix);
LevenbergMarquardtParams parameters;
parameters.setMaxIterations(20);
@ -170,6 +168,8 @@ TEST(BasisFactors, ManifoldEvaluationFactor) {
LevenbergMarquardtOptimizer(graph, initial, parameters).optimize();
EXPECT_DOUBLES_EQUAL(0, graph.error(result), 1e-9);
// Check Jacobians
EXPECT_CORRECT_FACTOR_JACOBIANS(factor, initial, 1e-7, 1e-5);
}
//******************************************************************************
@ -179,15 +179,15 @@ TEST(BasisFactors, VecDerivativePrior) {
const Vector measured = Vector::Zero(M);
auto model = Isotropic::Sigma(M, 1.0);
VectorDerivativeFactor<Chebyshev2, M> vecDPrior(key, measured, model, N, 0);
VectorDerivativeFactor<Chebyshev2> vecDPrior(key, measured, model, M, N, 0);
NonlinearFactorGraph graph;
graph.add(vecDPrior);
ParameterMatrix<M> stateMatrix(N);
gtsam::Matrix stateMatrix = gtsam::Matrix::Zero(M, N);
Values initial;
initial.insert<ParameterMatrix<M>>(key, stateMatrix);
initial.insert<gtsam::Matrix>(key, stateMatrix);
LevenbergMarquardtParams parameters;
parameters.setMaxIterations(20);
@ -204,15 +204,15 @@ TEST(BasisFactors, ComponentDerivativeFactor) {
double measured = 0;
auto model = Isotropic::Sigma(1, 1.0);
ComponentDerivativeFactor<Chebyshev2, M> controlDPrior(key, measured, model,
N, 0, 0);
ComponentDerivativeFactor<Chebyshev2> controlDPrior(key, measured, model, M,
N, 0, 0);
NonlinearFactorGraph graph;
graph.add(controlDPrior);
Values initial;
ParameterMatrix<M> stateMatrix(N);
initial.insert<ParameterMatrix<M>>(key, stateMatrix);
gtsam::Matrix stateMatrix = gtsam::Matrix::Zero(M, N);
initial.insert<gtsam::Matrix>(key, stateMatrix);
LevenbergMarquardtParams parameters;
parameters.setMaxIterations(20);

96
gtsam/basis/tests/testChebyshev2.cpp
View File

@ -17,14 +17,15 @@
* methods
*/
#include <cstddef>
#include <CppUnitLite/TestHarness.h>
#include <gtsam/base/Testable.h>
#include <gtsam/basis/Chebyshev2.h>
#include <gtsam/basis/FitBasis.h>
#include <gtsam/geometry/Pose2.h>
#include <gtsam/geometry/Pose3.h>
#include <gtsam/nonlinear/factorTesting.h>
#include <gtsam/base/Testable.h>
#include <CppUnitLite/TestHarness.h>
#include <cstddef>
#include <functional>
using namespace std;
@ -107,7 +108,7 @@ TEST(Chebyshev2, InterpolateVector) {
double t = 30, a = 0, b = 100;
const size_t N = 3;
// Create 2x3 matrix with Vectors at Chebyshev points
ParameterMatrix<2> X(N);
Matrix X = Matrix::Zero(2, N);
X.row(0) = Chebyshev2::Points(N, a, b); // slope 1 ramp
// Check value
@ -115,14 +116,15 @@ TEST(Chebyshev2, InterpolateVector) {
expected << t, 0;
Eigen::Matrix<double, /*2x2N*/ -1, -1> actualH(2, 2 * N);
Chebyshev2::VectorEvaluationFunctor<2> fx(N, t, a, b);
Chebyshev2::VectorEvaluationFunctor fx(2, N, t, a, b);
EXPECT(assert_equal(expected, fx(X, actualH), 1e-9));
// Check derivative
std::function<Vector2(ParameterMatrix<2>)> f = std::bind(
&Chebyshev2::VectorEvaluationFunctor<2>::operator(), fx, std::placeholders::_1, nullptr);
std::function<Vector2(Matrix)> f =
std::bind(&Chebyshev2::VectorEvaluationFunctor::operator(), fx,
std::placeholders::_1, nullptr);
Matrix numericalH =
numericalDerivative11<Vector2, ParameterMatrix<2>, 2 * N>(f, X);
numericalDerivative11<Vector2, Matrix, 2 * N>(f, X);
EXPECT(assert_equal(numericalH, actualH, 1e-9));
}
@ -131,25 +133,66 @@ TEST(Chebyshev2, InterpolateVector) {
TEST(Chebyshev2, InterpolatePose2) {
double t = 30, a = 0, b = 100;
ParameterMatrix<3> X(N);
Matrix X(3, N);
X.row(0) = Chebyshev2::Points(N, a, b); // slope 1 ramp
X.row(1) = Vector::Zero(N);
X.row(2) = 0.1 * Vector::Ones(N);
Vector xi(3);
xi << t, 0, 0.1;
Eigen::Matrix<double, /*3x3N*/ -1, -1> actualH(3, 3 * N);
Chebyshev2::ManifoldEvaluationFunctor<Pose2> fx(N, t, a, b);
// We use xi as canonical coordinates via exponential map
Pose2 expected = Pose2::ChartAtOrigin::Retract(xi);
EXPECT(assert_equal(expected, fx(X)));
EXPECT(assert_equal(expected, fx(X, actualH)));
// Check derivative
std::function<Pose2(Matrix)> f =
std::bind(&Chebyshev2::ManifoldEvaluationFunctor<Pose2>::operator(), fx,
std::placeholders::_1, nullptr);
Matrix numericalH =
numericalDerivative11<Pose2, Matrix, 3 * N>(f, X);
EXPECT(assert_equal(numericalH, actualH, 1e-9));
}
#ifdef GTSAM_POSE3_EXPMAP
//******************************************************************************
// Interpolating poses using the exponential map
TEST(Chebyshev2, InterpolatePose3) {
double a = 10, b = 100;
double t = Chebyshev2::Points(N, a, b)(11);
Rot3 R = Rot3::Ypr(-2.21366492e-05, -9.35353636e-03, -5.90463598e-04);
Pose3 pose(R, Point3(1, 2, 3));
Vector6 xi = Pose3::ChartAtOrigin::Local(pose);
Eigen::Matrix<double, /*6x6N*/ -1, -1> actualH(6, 6 * N);
Matrix X = Matrix::Zero(6, N);
X.col(11) = xi;
Chebyshev2::ManifoldEvaluationFunctor<Pose3> fx(N, t, a, b);
// We use xi as canonical coordinates via exponential map
Pose3 expected = Pose3::ChartAtOrigin::Retract(xi);
EXPECT(assert_equal(expected, fx(X, actualH)));
// Check derivative
std::function<Pose3(Matrix)> f =
std::bind(&Chebyshev2::ManifoldEvaluationFunctor<Pose3>::operator(), fx,
std::placeholders::_1, nullptr);
Matrix numericalH =
numericalDerivative11<Pose3, Matrix, 6 * N>(f, X);
EXPECT(assert_equal(numericalH, actualH, 1e-8));
}
#endif
//******************************************************************************
TEST(Chebyshev2, Decomposition) {
// Create example sequence
Sequence sequence;
for (size_t i = 0; i < 16; i++) {
double x = (1.0/ 16)*i - 0.99, y = x;
double x = (1.0 / 16) * i - 0.99, y = x;
sequence[x] = y;
}
@ -370,14 +413,14 @@ TEST(Chebyshev2, Derivative6_03) {
TEST(Chebyshev2, VectorDerivativeFunctor) {
const size_t N = 3, M = 2;
const double x = 0.2;
using VecD = Chebyshev2::VectorDerivativeFunctor<M>;
VecD fx(N, x, 0, 3);
ParameterMatrix<M> X(N);
using VecD = Chebyshev2::VectorDerivativeFunctor;
VecD fx(M, N, x, 0, 3);
Matrix X = Matrix::Zero(M, N);
Matrix actualH(M, M * N);
EXPECT(assert_equal(Vector::Zero(M), (Vector)fx(X, actualH), 1e-8));
// Test Jacobian
Matrix expectedH = numericalDerivative11<Vector2, ParameterMatrix<M>, M * N>(
Matrix expectedH = numericalDerivative11<Vector2, Matrix, M * N>(
std::bind(&VecD::operator(), fx, std::placeholders::_1, nullptr), X);
EXPECT(assert_equal(expectedH, actualH, 1e-7));
}
@ -386,30 +429,29 @@ TEST(Chebyshev2, VectorDerivativeFunctor) {
// Test VectorDerivativeFunctor with polynomial function
TEST(Chebyshev2, VectorDerivativeFunctor2) {
const size_t N = 64, M = 1, T = 15;
using VecD = Chebyshev2::VectorDerivativeFunctor<M>;
using VecD = Chebyshev2::VectorDerivativeFunctor;
const Vector points = Chebyshev2::Points(N, 0, T);
// Assign the parameter matrix
Vector values(N);
// Assign the parameter matrix 1xN
Matrix X(1, N);
for (size_t i = 0; i < N; ++i) {
values(i) = f(points(i));
X(i) = f(points(i));
}
ParameterMatrix<M> X(values);
// Evaluate the derivative at the chebyshev points using
// VectorDerivativeFunctor.
for (size_t i = 0; i < N; ++i) {
VecD d(N, points(i), 0, T);
VecD d(M, N, points(i), 0, T);
Vector1 Dx = d(X);
EXPECT_DOUBLES_EQUAL(fprime(points(i)), Dx(0), 1e-6);
}
// Test Jacobian at the first chebyshev point.
Matrix actualH(M, M * N);
VecD vecd(N, points(0), 0, T);
VecD vecd(M, N, points(0), 0, T);
vecd(X, actualH);
Matrix expectedH = numericalDerivative11<Vector1, ParameterMatrix<M>, M * N>(
Matrix expectedH = numericalDerivative11<Vector1, Matrix, M * N>(
std::bind(&VecD::operator(), vecd, std::placeholders::_1, nullptr), X);
EXPECT(assert_equal(expectedH, actualH, 1e-6));
}
@ -419,14 +461,14 @@ TEST(Chebyshev2, VectorDerivativeFunctor2) {
TEST(Chebyshev2, ComponentDerivativeFunctor) {
const size_t N = 6, M = 2;
const double x = 0.2;
using CompFunc = Chebyshev2::ComponentDerivativeFunctor<M>;
using CompFunc = Chebyshev2::ComponentDerivativeFunctor;
size_t row = 1;
CompFunc fx(N, row, x, 0, 3);
ParameterMatrix<M> X(N);
CompFunc fx(M, N, row, x, 0, 3);
Matrix X = Matrix::Zero(M, N);
Matrix actualH(1, M * N);
EXPECT_DOUBLES_EQUAL(0, fx(X, actualH), 1e-8);
Matrix expectedH = numericalDerivative11<double, ParameterMatrix<M>, M * N>(
Matrix expectedH = numericalDerivative11<double, Matrix, M * N>(
std::bind(&CompFunc::operator(), fx, std::placeholders::_1, nullptr), X);
EXPECT(assert_equal(expectedH, actualH, 1e-7));
}

11
gtsam/basis/tests/testFourier.cpp
View File

@ -180,17 +180,16 @@ TEST(Basis, Derivative7) {
//******************************************************************************
TEST(Basis, VecDerivativeFunctor) {
using DotShape = typename FourierBasis::VectorDerivativeFunctor<2>;
using DotShape = typename FourierBasis::VectorDerivativeFunctor;
const size_t N = 3;
// MATLAB example, Dec 27 2019, commit 014eded5
double h = 2 * M_PI / 16;
Vector2 dotShape(0.5556, -0.8315); // at h/2
DotShape dotShapeFunction(N, h / 2);
Matrix23 theta_mat = (Matrix32() << 0, 0, 0.7071, 0.7071, 0.7071, -0.7071)
.finished()
.transpose();
ParameterMatrix<2> theta(theta_mat);
DotShape dotShapeFunction(2, N, h / 2);
Matrix theta = (Matrix32() << 0, 0, 0.7071, 0.7071, 0.7071, -0.7071)
.finished()
.transpose();
EXPECT(assert_equal(Vector(dotShape), dotShapeFunction(theta), 1e-4));
}

145
gtsam/basis/tests/testParameterMatrix.cpp
View File

@ -1,145 +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 testParameterMatrix.cpp
* @date Sep 22, 2020
* @author Varun Agrawal, Frank Dellaert
* @brief Unit tests for ParameterMatrix.h
*/
#include <CppUnitLite/TestHarness.h>
#include <gtsam/base/Testable.h>
#include <gtsam/basis/BasisFactors.h>
#include <gtsam/basis/Chebyshev2.h>
#include <gtsam/basis/ParameterMatrix.h>
#include <gtsam/inference/Symbol.h>
using namespace std;
using namespace gtsam;
using Parameters = Chebyshev2::Parameters;
const size_t M = 2, N = 5;
//******************************************************************************
TEST(ParameterMatrix, Constructor) {
ParameterMatrix<2> actual1(3);
ParameterMatrix<2> expected1(Matrix::Zero(2, 3));
EXPECT(assert_equal(expected1, actual1));
ParameterMatrix<2> actual2(Matrix::Ones(2, 3));
ParameterMatrix<2> expected2(Matrix::Ones(2, 3));
EXPECT(assert_equal(expected2, actual2));
EXPECT(assert_equal(expected2.matrix(), actual2.matrix()));
}
//******************************************************************************
TEST(ParameterMatrix, Dimensions) {
ParameterMatrix<M> params(N);
EXPECT_LONGS_EQUAL(params.rows(), M);
EXPECT_LONGS_EQUAL(params.cols(), N);
EXPECT_LONGS_EQUAL(params.dim(), M * N);
}
//******************************************************************************
TEST(ParameterMatrix, Getters) {
ParameterMatrix<M> params(N);
Matrix expectedMatrix = Matrix::Zero(2, 5);
EXPECT(assert_equal(expectedMatrix, params.matrix()));
Matrix expectedMatrixTranspose = Matrix::Zero(5, 2);
EXPECT(assert_equal(expectedMatrixTranspose, params.transpose()));
ParameterMatrix<M> p2(Matrix::Ones(M, N));
Vector expectedRowVector = Vector::Ones(N);
for (size_t r = 0; r < M; ++r) {
EXPECT(assert_equal(p2.row(r), expectedRowVector));
}
ParameterMatrix<M> p3(2 * Matrix::Ones(M, N));
Vector expectedColVector = 2 * Vector::Ones(M);
for (size_t c = 0; c < M; ++c) {
EXPECT(assert_equal(p3.col(c), expectedColVector));
}
}
//******************************************************************************
TEST(ParameterMatrix, Setters) {
ParameterMatrix<M> params(Matrix::Zero(M, N));
Matrix expected = Matrix::Zero(M, N);
// row
params.row(0) = Vector::Ones(N);
expected.row(0) = Vector::Ones(N);
EXPECT(assert_equal(expected, params.matrix()));
// col
params.col(2) = Vector::Ones(M);
expected.col(2) = Vector::Ones(M);
EXPECT(assert_equal(expected, params.matrix()));
// setZero
params.setZero();
expected.setZero();
EXPECT(assert_equal(expected, params.matrix()));
}
//******************************************************************************
TEST(ParameterMatrix, Addition) {
ParameterMatrix<M> params(Matrix::Ones(M, N));
ParameterMatrix<M> expected(2 * Matrix::Ones(M, N));
// Add vector
EXPECT(assert_equal(expected, params + Vector::Ones(M * N)));
// Add another ParameterMatrix
ParameterMatrix<M> actual = params + ParameterMatrix<M>(Matrix::Ones(M, N));
EXPECT(assert_equal(expected, actual));
}
//******************************************************************************
TEST(ParameterMatrix, Subtraction) {
ParameterMatrix<M> params(2 * Matrix::Ones(M, N));
ParameterMatrix<M> expected(Matrix::Ones(M, N));
// Subtract vector
EXPECT(assert_equal(expected, params - Vector::Ones(M * N)));
// Subtract another ParameterMatrix
ParameterMatrix<M> actual = params - ParameterMatrix<M>(Matrix::Ones(M, N));
EXPECT(assert_equal(expected, actual));
}
//******************************************************************************
TEST(ParameterMatrix, Multiplication) {
ParameterMatrix<M> params(Matrix::Ones(M, N));
Matrix multiplier = 2 * Matrix::Ones(N, 2);
Matrix expected = 2 * N * Matrix::Ones(M, 2);
EXPECT(assert_equal(expected, params * multiplier));
}
//******************************************************************************
TEST(ParameterMatrix, VectorSpace) {
ParameterMatrix<M> params(Matrix::Ones(M, N));
// vector
EXPECT(assert_equal(Vector::Ones(M * N), params.vector()));
// identity
EXPECT(assert_equal(ParameterMatrix<M>::Identity(),
ParameterMatrix<M>(Matrix::Zero(M, 0))));
}
//******************************************************************************
int main() {
TestResult tr;
return TestRegistry::runAllTests(tr);
}
//******************************************************************************

3
gtsam/discrete/TableFactor.cpp
View File

@ -21,7 +21,6 @@
#include <gtsam/discrete/TableFactor.h>
#include <gtsam/hybrid/HybridValues.h>
#include <boost/format.hpp>
#include <utility>
using namespace std;
@ -203,7 +202,7 @@ void TableFactor::print(const string& s, const KeyFormatter& formatter) const {
cout << s;
cout << " f[";
for (auto&& key : keys())
cout << boost::format(" (%1%,%2%),") % formatter(key) % cardinality(key);
cout << " (" << formatter(key) << "," << cardinality(key) << "),";
cout << " ]" << endl;
for (SparseIt it(sparse_table_); it; ++it) {
DiscreteValues assignment = findAssignments(it.index());

4
gtsam/geometry/StereoPoint2.h
View File

@ -46,7 +46,9 @@ public:
uL_(0), uR_(0), v_(0) {
}
/** constructor */
/** uL and uR represent the x-axis value of left and right frame coordinates respectively.
v represents the y coordinate value. The y-axis value should be the same under the
stereo constraint. */
StereoPoint2(double uL, double uR, double v) :
uL_(uL), uR_(uR), v_(v) {
}

1
gtsam/inference/inference.i
View File

@ -109,6 +109,7 @@ class Ordering {
FACTOR_GRAPH = {gtsam::NonlinearFactorGraph, gtsam::DiscreteFactorGraph,
gtsam::SymbolicFactorGraph, gtsam::GaussianFactorGraph, gtsam::HybridGaussianFactorGraph}>
static gtsam::Ordering Colamd(const FACTOR_GRAPH& graph);
static gtsam::Ordering Colamd(const gtsam::VariableIndex& variableIndex);
template <
FACTOR_GRAPH = {gtsam::NonlinearFactorGraph, gtsam::DiscreteFactorGraph,

1
gtsam/nonlinear/nonlinear.i
View File

@ -25,7 +25,6 @@ namespace gtsam {
#include <gtsam/geometry/Unit3.h>
#include <gtsam/navigation/ImuBias.h>
#include <gtsam/navigation/NavState.h>
#include <gtsam/basis/ParameterMatrix.h>
#include <gtsam/nonlinear/GraphvizFormatting.h>
class GraphvizFormatting : gtsam::DotWriter {

63
gtsam/nonlinear/values.i
View File

@ -25,7 +25,6 @@ namespace gtsam {
#include <gtsam/geometry/Unit3.h>
#include <gtsam/navigation/ImuBias.h>
#include <gtsam/navigation/NavState.h>
#include <gtsam/basis/ParameterMatrix.h>
#include <gtsam/linear/VectorValues.h>
@ -96,21 +95,6 @@ class Values {
void insert(size_t j, const gtsam::imuBias::ConstantBias& constant_bias);
void insert(size_t j, const gtsam::NavState& nav_state);
void insert(size_t j, double c);
void insert(size_t j, const gtsam::ParameterMatrix<1>& X);
void insert(size_t j, const gtsam::ParameterMatrix<2>& X);
void insert(size_t j, const gtsam::ParameterMatrix<3>& X);
void insert(size_t j, const gtsam::ParameterMatrix<4>& X);
void insert(size_t j, const gtsam::ParameterMatrix<5>& X);
void insert(size_t j, const gtsam::ParameterMatrix<6>& X);
void insert(size_t j, const gtsam::ParameterMatrix<7>& X);
void insert(size_t j, const gtsam::ParameterMatrix<8>& X);
void insert(size_t j, const gtsam::ParameterMatrix<9>& X);
void insert(size_t j, const gtsam::ParameterMatrix<10>& X);
void insert(size_t j, const gtsam::ParameterMatrix<11>& X);
void insert(size_t j, const gtsam::ParameterMatrix<12>& X);
void insert(size_t j, const gtsam::ParameterMatrix<13>& X);
void insert(size_t j, const gtsam::ParameterMatrix<14>& X);
void insert(size_t j, const gtsam::ParameterMatrix<15>& X);
template <T = {gtsam::Point2, gtsam::Point3}>
void insert(size_t j, const T& val);
@ -144,21 +128,6 @@ class Values {
void update(size_t j, Vector vector);
void update(size_t j, Matrix matrix);
void update(size_t j, double c);
void update(size_t j, const gtsam::ParameterMatrix<1>& X);
void update(size_t j, const gtsam::ParameterMatrix<2>& X);
void update(size_t j, const gtsam::ParameterMatrix<3>& X);
void update(size_t j, const gtsam::ParameterMatrix<4>& X);
void update(size_t j, const gtsam::ParameterMatrix<5>& X);
void update(size_t j, const gtsam::ParameterMatrix<6>& X);
void update(size_t j, const gtsam::ParameterMatrix<7>& X);
void update(size_t j, const gtsam::ParameterMatrix<8>& X);
void update(size_t j, const gtsam::ParameterMatrix<9>& X);
void update(size_t j, const gtsam::ParameterMatrix<10>& X);
void update(size_t j, const gtsam::ParameterMatrix<11>& X);
void update(size_t j, const gtsam::ParameterMatrix<12>& X);
void update(size_t j, const gtsam::ParameterMatrix<13>& X);
void update(size_t j, const gtsam::ParameterMatrix<14>& X);
void update(size_t j, const gtsam::ParameterMatrix<15>& X);
void insert_or_assign(size_t j, const gtsam::Point2& point2);
void insert_or_assign(size_t j, const gtsam::Point3& point3);
@ -199,21 +168,6 @@ class Values {
void insert_or_assign(size_t j, Vector vector);
void insert_or_assign(size_t j, Matrix matrix);
void insert_or_assign(size_t j, double c);
void insert_or_assign(size_t j, const gtsam::ParameterMatrix<1>& X);
void insert_or_assign(size_t j, const gtsam::ParameterMatrix<2>& X);
void insert_or_assign(size_t j, const gtsam::ParameterMatrix<3>& X);
void insert_or_assign(size_t j, const gtsam::ParameterMatrix<4>& X);
void insert_or_assign(size_t j, const gtsam::ParameterMatrix<5>& X);
void insert_or_assign(size_t j, const gtsam::ParameterMatrix<6>& X);
void insert_or_assign(size_t j, const gtsam::ParameterMatrix<7>& X);
void insert_or_assign(size_t j, const gtsam::ParameterMatrix<8>& X);
void insert_or_assign(size_t j, const gtsam::ParameterMatrix<9>& X);
void insert_or_assign(size_t j, const gtsam::ParameterMatrix<10>& X);
void insert_or_assign(size_t j, const gtsam::ParameterMatrix<11>& X);
void insert_or_assign(size_t j, const gtsam::ParameterMatrix<12>& X);
void insert_or_assign(size_t j, const gtsam::ParameterMatrix<13>& X);
void insert_or_assign(size_t j, const gtsam::ParameterMatrix<14>& X);
void insert_or_assign(size_t j, const gtsam::ParameterMatrix<15>& X);
template <T = {gtsam::Point2,
gtsam::Point3,
@ -243,22 +197,7 @@ class Values {
gtsam::NavState,
Vector,
Matrix,
double,
gtsam::ParameterMatrix<1>,
gtsam::ParameterMatrix<2>,
gtsam::ParameterMatrix<3>,
gtsam::ParameterMatrix<4>,
gtsam::ParameterMatrix<5>,
gtsam::ParameterMatrix<6>,
gtsam::ParameterMatrix<7>,
gtsam::ParameterMatrix<8>,
gtsam::ParameterMatrix<9>,
gtsam::ParameterMatrix<10>,
gtsam::ParameterMatrix<11>,
gtsam::ParameterMatrix<12>,
gtsam::ParameterMatrix<13>,
gtsam::ParameterMatrix<14>,
gtsam::ParameterMatrix<15>}>
double}>
T at(size_t j);
};

42
python/gtsam/__init__.py
View File

@ -4,9 +4,49 @@
import sys
from gtsam.utils import findExampleDataFile
from gtsam import gtsam, utils
from gtsam.gtsam import *
from gtsam.utils import findExampleDataFile
#### Typedefs to allow for backwards compatibility
#TODO(Varun) deprecate in future release
# gtsam
KeyVector = list
# base
IndexPairSetMap = dict
IndexPairVector = list
# geometry
Point2Vector = list
Pose3Vector = list
Rot3Vector = list
Point2Pairs = list
Point3Pairs = list
Pose2Pairs = list
Pose3Pairs = list
# sfm
BinaryMeasurementsPoint3 = list
BinaryMeasurementsUnit3 = list
BinaryMeasurementsRot3 = list
KeyPairDoubleMap = dict
SfmTrack2dVector = list
SfmTracks = list
SfmCameras = list
SfmMeasurementVector = list
MatchIndicesMap = dict
KeypointsVector = list
# slam
BetweenFactorPose3s = list
BetweenFactorPose2s = list
class FixedLagSmootherKeyTimestampMap(dict):
"""Class to provide backwards compatibility"""
def insert(self, key_value):
self[key_value[0]] = key_value[1]
#### End typedefs
def _init():

11
python/gtsam/tests/test_Cal3Fisheye.py
View File

@ -11,11 +11,12 @@ Refactored: Roderick Koehle
"""
import unittest
import gtsam
import numpy as np
from gtsam.symbol_shorthand import K, L, P
from gtsam.utils.test_case import GtsamTestCase
import gtsam
def ulp(ftype=np.float64):
"""
@ -26,7 +27,7 @@ def ulp(ftype=np.float64):
class TestCal3Fisheye(GtsamTestCase):
@classmethod
def setUpClass(cls):
"""
@ -53,7 +54,7 @@ class TestCal3Fisheye(GtsamTestCase):
cls.poses = [pose1, pose2]
cls.cameras = [camera1, camera2]
cls.measurements = [k.project(cls.origin) for k in cls.cameras]
def test_Cal3Fisheye(self):
K = gtsam.Cal3Fisheye()
self.assertEqual(K.fx(), 1.)
@ -62,7 +63,7 @@ class TestCal3Fisheye(GtsamTestCase):
def test_distortion(self):
"""Fisheye distortion and rectification"""
equidistant = gtsam.Cal3Fisheye()
perspective_pt = self.obj_point[0:2]/self.obj_point[2]
perspective_pt = self.obj_point[0:2] / self.obj_point[2]
distorted_pt = equidistant.uncalibrate(perspective_pt)
rectified_pt = equidistant.calibrate(distorted_pt)
self.gtsamAssertEquals(distorted_pt, self.img_point)
@ -166,7 +167,7 @@ class TestCal3Fisheye(GtsamTestCase):
pose = gtsam.Pose3()
camera = gtsam.Cal3Fisheye()
state = gtsam.Values()
camera_key, pose_key, landmark_key = K(0), P(0), L(0)
pose_key, landmark_key = P(0), L(0)
state.insert_point3(landmark_key, obj_point)
state.insert_pose3(pose_key, pose)
g = gtsam.NonlinearFactorGraph()

7
python/gtsam/tests/test_Cal3Unified.py
View File

@ -10,11 +10,12 @@ Author: Frank Dellaert & Duy Nguyen Ta (Python)
"""
import unittest
import gtsam
import numpy as np
from gtsam.symbol_shorthand import K, L, P
from gtsam.utils.test_case import GtsamTestCase
import gtsam
class TestCal3Unified(GtsamTestCase):
@ -106,7 +107,7 @@ class TestCal3Unified(GtsamTestCase):
state = gtsam.Values()
measured = self.img_point
noise_model = gtsam.noiseModel.Isotropic.Sigma(2, 1)
camera_key, pose_key, landmark_key = K(0), P(0), L(0)
camera_key, pose_key, landmark_key = K(0), P(0), L(0)
k = self.stereographic
state.insert_cal3unified(camera_key, k)
state.insert_pose3(pose_key, gtsam.Pose3())
@ -141,7 +142,7 @@ class TestCal3Unified(GtsamTestCase):
Dcal = np.zeros((2, 10), order='F')
Dp = np.zeros((2, 2), order='F')
camera.calibrate(img_point, Dcal, Dp)
self.gtsamAssertEquals(Dcal, np.array(
[[ 0., 0., 0., -1., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., -1., 0., 0., 0., 0., 0.]]))

1
python/gtsam/tests/test_FixedLagSmootherExample.py
View File

@ -14,7 +14,6 @@ import numpy as np
from gtsam.utils.test_case import GtsamTestCase
import gtsam
import gtsam_unstable
class TestFixedLagSmootherExample(GtsamTestCase):

15
python/gtsam/tests/test_GaussianFactorGraph.py
View File

@ -14,11 +14,12 @@ from __future__ import print_function
import unittest
import gtsam
import numpy as np
from gtsam.symbol_shorthand import X
from gtsam.utils.test_case import GtsamTestCase
import gtsam
def create_graph():
"""Create a basic linear factor graph for testing"""
@ -40,6 +41,7 @@ def create_graph():
class TestGaussianFactorGraph(GtsamTestCase):
"""Tests for Gaussian Factor Graphs."""
def test_fg(self):
"""Test solving a linear factor graph"""
graph, X = create_graph()
@ -98,12 +100,11 @@ class TestGaussianFactorGraph(GtsamTestCase):
bn = gfg.eliminateSequential(ordering)
self.assertEqual(bn.size(), 3)
keyVector = []
keyVector.append(keys[2])
#TODO(Varun) Below code causes segfault in Debug config
# ordering = gtsam.Ordering.ColamdConstrainedLastGaussianFactorGraph(gfg, keyVector)
# bn = gfg.eliminateSequential(ordering)
# self.assertEqual(bn.size(), 3)
keyVector = [keys[2]]
ordering = gtsam.Ordering.ColamdConstrainedLastGaussianFactorGraph(
gfg, keyVector)
bn = gfg.eliminateSequential(ordering)
self.assertEqual(bn.size(), 3)
if __name__ == '__main__':

15
python/gtsam/tests/test_KarcherMeanFactor.py
View File

@ -13,15 +13,15 @@ Author: Frank Dellaert
import unittest
import gtsam
import numpy as np
from gtsam import Rot3
from gtsam.utils.test_case import GtsamTestCase
import gtsam
from gtsam import Rot3
KEY = 0
MODEL = gtsam.noiseModel.Unit.Create(3)
# Rot3 version
R = Rot3.Expmap(np.array([0.1, 0, 0]))
@ -29,8 +29,10 @@ R = Rot3.Expmap(np.array([0.1, 0, 0]))
class TestKarcherMean(GtsamTestCase):
def test_find(self):
# Check that optimizing for Karcher mean (which minimizes Between distance)
# gets correct result.
"""
Check that optimizing for Karcher mean (which minimizes Between distance)
gets correct result.
"""
rotations = [R, R.inverse()]
expected = Rot3()
actual = gtsam.FindKarcherMean(rotations)
@ -69,8 +71,7 @@ class TestKarcherMean(GtsamTestCase):
result = gtsam.GaussNewtonOptimizer(graph, initial).optimize()
actual = gtsam.FindKarcherMean([result.atRot3(1), result.atRot3(2)])
self.gtsamAssertEquals(expected, actual)
self.gtsamAssertEquals(
R12, result.atRot3(1).between(result.atRot3(2)))
self.gtsamAssertEquals(R12, result.atRot3(1).between(result.atRot3(2)))
if __name__ == "__main__":

8
python/gtsam/tests/test_Pose2.py
View File

@ -12,12 +12,14 @@ import math
import unittest
import numpy as np
from gtsam import Point2, Pose2
from gtsam.utils.test_case import GtsamTestCase
from gtsam import Point2, Point2Pairs, Pose2
class TestPose2(GtsamTestCase):
"""Test selected Pose2 methods."""
def test_adjoint(self) -> None:
"""Test adjoint method."""
xi = np.array([1, 2, 3])
@ -27,7 +29,7 @@ class TestPose2(GtsamTestCase):
def test_transformTo(self):
"""Test transformTo method."""
pose = Pose2(2, 4, -math.pi/2)
pose = Pose2(2, 4, -math.pi / 2)
actual = pose.transformTo(Point2(3, 2))
expected = Point2(2, 1)
self.gtsamAssertEquals(actual, expected, 1e-6)
@ -41,7 +43,7 @@ class TestPose2(GtsamTestCase):
def test_transformFrom(self):
"""Test transformFrom method."""
pose = Pose2(2, 4, -math.pi/2)
pose = Pose2(2, 4, -math.pi / 2)
actual = pose.transformFrom(Point2(2, 1))
expected = Point2(3, 2)
self.gtsamAssertEquals(actual, expected, 1e-6)

5
python/gtsam/tests/test_Pose3.py
View File

@ -12,11 +12,12 @@ Author: Frank Dellaert, Duy Nguyen Ta
import math
import unittest
import gtsam
import numpy as np
from gtsam import Point3, Pose3, Rot3
from gtsam.utils.test_case import GtsamTestCase
import gtsam
from gtsam import Point3, Pose3, Rot3
def numerical_derivative_pose(pose, method, delta=1e-5):
jacobian = np.zeros((6, 6))

10
python/gtsam/tests/test_ShonanAveraging.py
View File

@ -12,12 +12,13 @@ Author: Frank Dellaert
import unittest
import gtsam
import numpy as np
from gtsam.utils.test_case import GtsamTestCase
import gtsam
from gtsam import (BetweenFactorPose2, LevenbergMarquardtParams, Pose2, Rot2,
ShonanAveraging2, ShonanAveraging3,
ShonanAveragingParameters2, ShonanAveragingParameters3)
from gtsam.utils.test_case import GtsamTestCase
DEFAULT_PARAMS = ShonanAveragingParameters3(
gtsam.LevenbergMarquardtParams.CeresDefaults()
@ -139,7 +140,6 @@ class TestShonanAveraging(GtsamTestCase):
self.assertAlmostEqual(3.0756, shonan.cost(initial), places=3)
result, _lambdaMin = shonan.run(initial, 3, 3)
self.assertAlmostEqual(0.0015, shonan.cost(result), places=3)
def test_constructorBetweenFactorPose2s(self) -> None:
"""Check if ShonanAveraging2 constructor works when not initialized from g2o file.
@ -189,11 +189,11 @@ class TestShonanAveraging(GtsamTestCase):
wRi_list = [result_values.atRot2(i) for i in range(num_images)]
thetas_deg = np.array([wRi.degrees() for wRi in wRi_list])
# map all angles to [0,360)
thetas_deg = thetas_deg % 360
thetas_deg -= thetas_deg[0]
expected_thetas_deg = np.array([0.0, 90.0, 0.0])
np.testing.assert_allclose(thetas_deg, expected_thetas_deg, atol=0.1)

7
python/gtsam/tests/test_Sim2.py
View File

@ -12,9 +12,10 @@ Author: John Lambert
import unittest
import numpy as np
from gtsam import Pose2, Rot2, Similarity2
from gtsam.utils.test_case import GtsamTestCase
from gtsam import Pose2, Rot2, Similarity2
class TestSim2(GtsamTestCase):
"""Test selected Sim2 methods."""
@ -55,7 +56,7 @@ class TestSim2(GtsamTestCase):
self.gtsamAssertEquals(wToi, wSe.transformFrom(eToi))
def test_align_poses_along_straight_line_gauge(self):
"""Test if Align Pose3Pairs method can account for gauge ambiguity.
"""Test if Pose2 Align method can account for gauge ambiguity.
Scenario:
3 object poses
@ -90,7 +91,7 @@ class TestSim2(GtsamTestCase):
self.gtsamAssertEquals(wToi, wSe.transformFrom(eToi))
def test_align_poses_scaled_squares(self):
"""Test if Align Pose2Pairs method can account for gauge ambiguity.
"""Test if Align method can account for gauge ambiguity.
Make sure a big and small square can be aligned.
The u's represent a big square (10x10), and v's represents a small square (4x4).

11
python/gtsam/tests/test_Sim3.py
View File

@ -12,17 +12,18 @@ Author: John Lambert
import unittest
from typing import List, Optional
import gtsam
import numpy as np
from gtsam import Point3, Pose3, Rot3, Similarity3
from gtsam.utils.test_case import GtsamTestCase
import gtsam
from gtsam import Point3, Pose3, Rot3, Similarity3
class TestSim3(GtsamTestCase):
"""Test selected Sim3 methods."""
def test_align_poses_along_straight_line(self):
"""Test Align Pose3Pairs method.
"""Test Pose3 Align method.
Scenario:
3 object poses
@ -57,7 +58,7 @@ class TestSim3(GtsamTestCase):
self.gtsamAssertEquals(wToi, wSe.transformFrom(eToi))
def test_align_poses_along_straight_line_gauge(self):
"""Test if Align Pose3Pairs method can account for gauge ambiguity.
"""Test if Pose3 Align method can account for gauge ambiguity.
Scenario:
3 object poses
@ -92,7 +93,7 @@ class TestSim3(GtsamTestCase):
self.gtsamAssertEquals(wToi, wSe.transformFrom(eToi))
def test_align_poses_scaled_squares(self):
"""Test if Align Pose3Pairs method can account for gauge ambiguity.
"""Test if Pose3 Align method can account for gauge ambiguity.
Make sure a big and small square can be aligned.
The u's represent a big square (10x10), and v's represents a small square (4x4).

5
python/gtsam/tests/test_Triangulation.py
View File

@ -12,13 +12,14 @@ Authors: Frank Dellaert & Fan Jiang (Python) & Sushmita Warrier & John Lambert
import unittest
from typing import Iterable, List, Optional, Tuple, Union
import gtsam
import numpy as np
from gtsam.utils.test_case import GtsamTestCase
import gtsam
from gtsam import (Cal3_S2, Cal3Bundler, CameraSetCal3_S2,
CameraSetCal3Bundler, PinholeCameraCal3_S2,
PinholeCameraCal3Bundler, Point2, Point3, Pose3, Rot3,
TriangulationParameters, TriangulationResult)
from gtsam.utils.test_case import GtsamTestCase
UPRIGHT = Rot3.Ypr(-np.pi / 2, 0.0, -np.pi / 2)

897
python/gtsam/tests/test_backwards_compatibility.py Normal file
View File

@ -0,0 +1,897 @@
"""
GTSAM Copyright 2010-2019, Georgia Tech Research Corporation,
Atlanta, Georgia 30332-0415
All Rights Reserved
See LICENSE for the license information
Unit tests to ensure backwards compatibility of the Python wrapper.
Author: Varun Agrawal
"""
import unittest
from typing import Iterable, List, Optional, Tuple, Union
import numpy as np
from gtsam.gtsfm import Keypoints
from gtsam.symbol_shorthand import X
from gtsam.utils.test_case import GtsamTestCase
import gtsam
from gtsam import (BetweenFactorPose2, Cal3_S2, Cal3Bundler, CameraSetCal3_S2,
CameraSetCal3Bundler, IndexPair, LevenbergMarquardtParams,
PinholeCameraCal3_S2, PinholeCameraCal3Bundler, Point2,
Point2Pairs, Point3, Pose2, Pose2Pairs, Pose3, Rot2, Rot3,
SfmTrack2d, ShonanAveraging2, ShonanAveragingParameters2,
Similarity2, Similarity3, TriangulationParameters,
TriangulationResult)
UPRIGHT = Rot3.Ypr(-np.pi / 2, 0.0, -np.pi / 2)
class TestBackwardsCompatibility(GtsamTestCase):
"""Tests for the backwards compatibility for the Python wrapper."""
def setUp(self):
"""Setup test fixtures"""
p1 = [-1.0, 0.0, -1.0]
p2 = [1.0, 0.0, -1.0]
q1 = Rot3(1.0, 0.0, 0.0, 0.0)
q2 = Rot3(1.0, 0.0, 0.0, 0.0)
pose1 = Pose3(q1, p1)
pose2 = Pose3(q2, p2)
camera1 = gtsam.PinholeCameraCal3Fisheye(pose1)
camera2 = gtsam.PinholeCameraCal3Fisheye(pose2)
self.origin = np.array([0.0, 0.0, 0.0])
self.poses = gtsam.Pose3Vector([pose1, pose2])
self.fisheye_cameras = gtsam.CameraSetCal3Fisheye([camera1, camera2])
self.fisheye_measurements = gtsam.Point2Vector(
[k.project(self.origin) for k in self.fisheye_cameras])
fx, fy, s, u0, v0 = 2, 2, 0, 0, 0
k1, k2, p1, p2 = 0, 0, 0, 0
xi = 1
self.stereographic = gtsam.Cal3Unified(fx, fy, s, u0, v0, k1, k2, p1,
p2, xi)
camera1 = gtsam.PinholeCameraCal3Unified(pose1, self.stereographic)
camera2 = gtsam.PinholeCameraCal3Unified(pose2, self.stereographic)
self.unified_cameras = gtsam.CameraSetCal3Unified([camera1, camera2])
self.unified_measurements = gtsam.Point2Vector(
[k.project(self.origin) for k in self.unified_cameras])
## Set up two camera poses
# Looking along X-axis, 1 meter above ground plane (x-y)
pose1 = Pose3(UPRIGHT, Point3(0, 0, 1))
# create second camera 1 meter to the right of first camera
pose2 = pose1.compose(Pose3(Rot3(), Point3(1, 0, 0)))
# twoPoses
self.triangulation_poses = gtsam.Pose3Vector()
self.triangulation_poses.append(pose1)
self.triangulation_poses.append(pose2)
# landmark ~5 meters infront of camera
self.landmark = Point3(5, 0.5, 1.2)
def test_Cal3Fisheye_triangulation_rectify(self):
"""
Estimate spatial point from image measurements using
rectification from a Cal3Fisheye camera model.
"""
rectified = gtsam.Point2Vector([
k.calibration().calibrate(pt)
for k, pt in zip(self.fisheye_cameras, self.fisheye_measurements)
])
shared_cal = gtsam.Cal3_S2()
triangulated = gtsam.triangulatePoint3(self.poses,
shared_cal,
rectified,
rank_tol=1e-9,
optimize=False)
self.gtsamAssertEquals(triangulated, self.origin)
def test_Cal3Unified_triangulation_rectify(self):
"""
Estimate spatial point from image measurements using
rectification from a Cal3Unified camera model.
"""
rectified = gtsam.Point2Vector([
k.calibration().calibrate(pt)
for k, pt in zip(self.unified_cameras, self.unified_measurements)
])
shared_cal = gtsam.Cal3_S2()
triangulated = gtsam.triangulatePoint3(self.poses,
shared_cal,
rectified,
rank_tol=1e-9,
optimize=False)
self.gtsamAssertEquals(triangulated, self.origin)
def test_track_generation(self) -> None:
"""Ensures that DSF generates three tracks from measurements
in 3 images (H=200,W=400)."""
kps_i0 = Keypoints(np.array([[10.0, 20], [30, 40]]))
kps_i1 = Keypoints(np.array([[50.0, 60], [70, 80], [90, 100]]))
kps_i2 = Keypoints(np.array([[110.0, 120], [130, 140]]))
keypoints_list = gtsam.KeypointsVector()
keypoints_list.append(kps_i0)
keypoints_list.append(kps_i1)
keypoints_list.append(kps_i2)
# For each image pair (i1,i2), we provide a (K,2) matrix
# of corresponding image indices (k1,k2).
matches_dict = gtsam.MatchIndicesMap()
matches_dict[IndexPair(0, 1)] = np.array([[0, 0], [1, 1]])
matches_dict[IndexPair(1, 2)] = np.array([[2, 0], [1, 1]])
tracks = gtsam.gtsfm.tracksFromPairwiseMatches(
matches_dict,
keypoints_list,
verbose=False,
)
assert len(tracks) == 3
# Verify track 0.
track0 = tracks[0]
assert track0.numberMeasurements() == 2
np.testing.assert_allclose(track0.measurements[0][1], Point2(10, 20))
np.testing.assert_allclose(track0.measurements[1][1], Point2(50, 60))
assert track0.measurements[0][0] == 0
assert track0.measurements[1][0] == 1
np.testing.assert_allclose(
track0.measurementMatrix(),
[
[10, 20],
[50, 60],
],
)
np.testing.assert_allclose(track0.indexVector(), [0, 1])
# Verify track 1.
track1 = tracks[1]
np.testing.assert_allclose(
track1.measurementMatrix(),
[
[30, 40],
[70, 80],
[130, 140],
],
)
np.testing.assert_allclose(track1.indexVector(), [0, 1, 2])
# Verify track 2.
track2 = tracks[2]
np.testing.assert_allclose(
track2.measurementMatrix(),
[
[90, 100],
[110, 120],
],
)
np.testing.assert_allclose(track2.indexVector(), [1, 2])
def test_sfm_track_2d_constructor(self) -> None:
"""Test construction of 2D SfM track."""
measurements = gtsam.SfmMeasurementVector()
measurements.append((0, Point2(10, 20)))
track = SfmTrack2d(measurements=measurements)
track.measurement(0)
assert track.numberMeasurements() == 1
def test_FixedLagSmootherExample(self):
'''
Simple test that checks for equality between C++ example
file and the Python implementation. See
gtsam_unstable/examples/FixedLagSmootherExample.cpp
'''
# Define a batch fixed lag smoother, which uses
# Levenberg-Marquardt to perform the nonlinear optimization
lag = 2.0
smoother_batch = gtsam.BatchFixedLagSmoother(lag)
# Create containers to store the factors and linearization points
# that will be sent to the smoothers
new_factors = gtsam.NonlinearFactorGraph()
new_values = gtsam.Values()
new_timestamps = gtsam.FixedLagSmootherKeyTimestampMap()
# Create a prior on the first pose, placing it at the origin
prior_mean = Pose2(0, 0, 0)
prior_noise = gtsam.noiseModel.Diagonal.Sigmas(
np.array([0.3, 0.3, 0.1]))
X1 = 0
new_factors.push_back(
gtsam.PriorFactorPose2(X1, prior_mean, prior_noise))
new_values.insert(X1, prior_mean)
new_timestamps.insert((X1, 0.0))
delta_time = 0.25
time = 0.25
i = 0
ground_truth = [
Pose2(0.995821, 0.0231012, 0.0300001),
Pose2(1.49284, 0.0457247, 0.045),
Pose2(1.98981, 0.0758879, 0.06),
Pose2(2.48627, 0.113502, 0.075),
Pose2(2.98211, 0.158558, 0.09),
Pose2(3.47722, 0.211047, 0.105),
Pose2(3.97149, 0.270956, 0.12),
Pose2(4.4648, 0.338272, 0.135),
Pose2(4.95705, 0.41298, 0.15),
Pose2(5.44812, 0.495063, 0.165),
Pose2(5.9379, 0.584503, 0.18),
]
# Iterates from 0.25s to 3.0s, adding 0.25s each loop
# In each iteration, the agent moves at a constant speed
# and its two odometers measure the change. The smoothed
# result is then compared to the ground truth
while time <= 3.0:
previous_key = int(1000 * (time - delta_time))
current_key = int(1000 * time)
# assign current key to the current timestamp
new_timestamps.insert((current_key, time))
# Add a guess for this pose to the new values
# Assume that the robot moves at 2 m/s. Position is time[s] *
# 2[m/s]
current_pose = Pose2(time * 2, 0, 0)
new_values.insert(current_key, current_pose)
# Add odometry factors from two different sources with different
# error stats
odometry_measurement_1 = Pose2(0.61, -0.08, 0.02)
odometry_noise_1 = gtsam.noiseModel.Diagonal.Sigmas(
np.array([0.1, 0.1, 0.05]))
new_factors.push_back(
gtsam.BetweenFactorPose2(previous_key, current_key,
odometry_measurement_1,
odometry_noise_1))
odometry_measurement_2 = Pose2(0.47, 0.03, 0.01)
odometry_noise_2 = gtsam.noiseModel.Diagonal.Sigmas(
np.array([0.05, 0.05, 0.05]))
new_factors.push_back(
gtsam.BetweenFactorPose2(previous_key, current_key,
odometry_measurement_2,
odometry_noise_2))
# Update the smoothers with the new factors. In this case,
# one iteration must pass for Levenberg-Marquardt to accurately
# estimate
if time >= 0.50:
smoother_batch.update(new_factors, new_values, new_timestamps)
estimate = smoother_batch.calculateEstimatePose2(current_key)
self.assertTrue(estimate.equals(ground_truth[i], 1e-4))
i += 1
new_timestamps.clear()
new_values.clear()
new_factors.resize(0)
time += delta_time
def test_ordering(self):
"""Test ordering"""
gfg = gtsam.GaussianFactorGraph()
x0 = X(0)
x1 = X(1)
x2 = X(2)
BETWEEN_NOISE = gtsam.noiseModel.Diagonal.Sigmas(np.ones(1))
PRIOR_NOISE = gtsam.noiseModel.Diagonal.Sigmas(np.ones(1))
gfg.add(x1, np.eye(1), x0, -np.eye(1), np.ones(1), BETWEEN_NOISE)
gfg.add(x2, np.eye(1), x1, -np.eye(1), 2 * np.ones(1), BETWEEN_NOISE)
gfg.add(x0, np.eye(1), np.zeros(1), PRIOR_NOISE)
keys = (x0, x1, x2)
ordering = gtsam.Ordering()
for key in keys[::-1]:
ordering.push_back(key)
bn = gfg.eliminateSequential(ordering)
self.assertEqual(bn.size(), 3)
keyVector = gtsam.KeyVector()
keyVector.append(keys[2])
ordering = gtsam.Ordering.ColamdConstrainedLastGaussianFactorGraph(
gfg, keyVector)
bn = gfg.eliminateSequential(ordering)
self.assertEqual(bn.size(), 3)
def test_find(self):
"""
Check that optimizing for Karcher mean (which minimizes Between distance)
gets correct result.
"""
R = Rot3.Expmap(np.array([0.1, 0, 0]))
rotations = gtsam.Rot3Vector([R, R.inverse()])
expected = Rot3()
actual = gtsam.FindKarcherMean(rotations)
self.gtsamAssertEquals(expected, actual)
def test_find_karcher_mean_identity(self):
"""Averaging 3 identity rotations should yield the identity."""
a1Rb1 = Rot3()
a2Rb2 = Rot3()
a3Rb3 = Rot3()
aRb_list = gtsam.Rot3Vector([a1Rb1, a2Rb2, a3Rb3])
aRb_expected = Rot3()
aRb = gtsam.FindKarcherMean(aRb_list)
self.gtsamAssertEquals(aRb, aRb_expected)
def test_factor(self):
"""Check that the InnerConstraint factor leaves the mean unchanged."""
# Make a graph with two variables, one between, and one InnerConstraint
# The optimal result should satisfy the between, while moving the other
# variable to make the mean the same as before.
# Mean of R and R' is identity. Let's make a BetweenFactor making R21 =
# R*R*R, i.e. geodesic length is 3 rather than 2.
R = Rot3.Expmap(np.array([0.1, 0, 0]))
MODEL = gtsam.noiseModel.Unit.Create(3)
graph = gtsam.NonlinearFactorGraph()
R12 = R.compose(R.compose(R))
graph.add(gtsam.BetweenFactorRot3(1, 2, R12, MODEL))
keys = gtsam.KeyVector()
keys.append(1)
keys.append(2)
graph.add(gtsam.KarcherMeanFactorRot3(keys))
initial = gtsam.Values()
initial.insert(1, R.inverse())
initial.insert(2, R)
expected = Rot3()
result = gtsam.GaussNewtonOptimizer(graph, initial).optimize()
actual = gtsam.FindKarcherMean(
gtsam.Rot3Vector([result.atRot3(1),
result.atRot3(2)]))
self.gtsamAssertEquals(expected, actual)
self.gtsamAssertEquals(R12, result.atRot3(1).between(result.atRot3(2)))
def test_align(self) -> None:
"""Ensure estimation of the Pose2 element to align two 2d point clouds succeeds.
Two point clouds represent horseshoe-shapes of the same size, just rotated and translated:
| X---X
| |
| X---X
------------------
|
|
O | O
| | |
O---O
"""
pts_a = [
Point2(1, -3),
Point2(1, -5),
Point2(-1, -5),
Point2(-1, -3),
]
pts_b = [
Point2(3, 1),
Point2(1, 1),
Point2(1, 3),
Point2(3, 3),
]
ab_pairs = Point2Pairs(list(zip(pts_a, pts_b)))
aTb = Pose2.Align(ab_pairs)
self.assertIsNotNone(aTb)
for pt_a, pt_b in zip(pts_a, pts_b):
pt_a_ = aTb.transformFrom(pt_b)
np.testing.assert_allclose(pt_a, pt_a_)
# Matrix version
A = np.array(pts_a).T
B = np.array(pts_b).T
aTb = Pose2.Align(A, B)
self.assertIsNotNone(aTb)
for pt_a, pt_b in zip(pts_a, pts_b):
pt_a_ = aTb.transformFrom(pt_b)
np.testing.assert_allclose(pt_a, pt_a_)
def test_align_squares(self):
"""Test if Align method can align 2 squares."""
square = np.array([[0, 0, 0], [0, 1, 0], [1, 1, 0], [1, 0, 0]],
float).T
sTt = Pose3(Rot3.Rodrigues(0, 0, -np.pi), gtsam.Point3(2, 4, 0))
transformed = sTt.transformTo(square)
st_pairs = gtsam.Point3Pairs()
for j in range(4):
st_pairs.append((square[:, j], transformed[:, j]))
# Recover the transformation sTt
estimated_sTt = Pose3.Align(st_pairs)
self.gtsamAssertEquals(estimated_sTt, sTt, 1e-10)
# Matrix version
estimated_sTt = Pose3.Align(square, transformed)
self.gtsamAssertEquals(estimated_sTt, sTt, 1e-10)
def test_constructorBetweenFactorPose2s(self) -> None:
"""Check if ShonanAveraging2 constructor works when not initialized from g2o file.
GT pose graph:
| cam 1 = (0,4)
--o
| .
. .
. .
| |
o-- ... o--
cam 0 cam 2 = (4,0)
(0,0)
"""
num_images = 3
wTi_list = [
Pose2(Rot2.fromDegrees(0), np.array([0, 0])),
Pose2(Rot2.fromDegrees(90), np.array([0, 4])),
Pose2(Rot2.fromDegrees(0), np.array([4, 0])),
]
edges = [(0, 1), (1, 2), (0, 2)]
i2Ri1_dict = {(i1, i2):
wTi_list[i2].inverse().compose(wTi_list[i1]).rotation()
for (i1, i2) in edges}
lm_params = LevenbergMarquardtParams.CeresDefaults()
shonan_params = ShonanAveragingParameters2(lm_params)
shonan_params.setUseHuber(False)
shonan_params.setCertifyOptimality(True)
noise_model = gtsam.noiseModel.Unit.Create(3)
between_factors = gtsam.BetweenFactorPose2s()
for (i1, i2), i2Ri1 in i2Ri1_dict.items():
i2Ti1 = Pose2(i2Ri1, np.zeros(2))
between_factors.append(
BetweenFactorPose2(i2, i1, i2Ti1, noise_model))
obj = ShonanAveraging2(between_factors, shonan_params)
initial = obj.initializeRandomly()
result_values, _ = obj.run(initial, min_p=2, max_p=100)
wRi_list = [result_values.atRot2(i) for i in range(num_images)]
thetas_deg = np.array([wRi.degrees() for wRi in wRi_list])
# map all angles to [0,360)
thetas_deg = thetas_deg % 360
thetas_deg -= thetas_deg[0]
expected_thetas_deg = np.array([0.0, 90.0, 0.0])
np.testing.assert_allclose(thetas_deg, expected_thetas_deg, atol=0.1)
def test_align_poses2_along_straight_line(self) -> None:
"""Test Align of list of Pose2Pair.
Scenario:
3 object poses
same scale (no gauge ambiguity)
world frame has poses rotated about 180 degrees.
world and egovehicle frame translated by 15 meters w.r.t. each other
"""
R180 = Rot2.fromDegrees(180)
# Create source poses (three objects o1, o2, o3 living in the egovehicle "e" frame)
# Suppose they are 3d cuboids detected by an onboard sensor in the egovehicle frame
eTo0 = Pose2(Rot2(), np.array([5, 0]))
eTo1 = Pose2(Rot2(), np.array([10, 0]))
eTo2 = Pose2(Rot2(), np.array([15, 0]))
eToi_list = [eTo0, eTo1, eTo2]
# Create destination poses
# (same three objects, but instead living in the world "w" frame)
wTo0 = Pose2(R180, np.array([-10, 0]))
wTo1 = Pose2(R180, np.array([-5, 0]))
wTo2 = Pose2(R180, np.array([0, 0]))
wToi_list = [wTo0, wTo1, wTo2]
we_pairs = Pose2Pairs(list(zip(wToi_list, eToi_list)))
# Recover the transformation wSe (i.e. world_S_egovehicle)
wSe = Similarity2.Align(we_pairs)
for wToi, eToi in zip(wToi_list, eToi_list):
self.gtsamAssertEquals(wToi, wSe.transformFrom(eToi))
def test_align_poses2_along_straight_line_gauge(self):
"""Test if Align Pose2Pairs method can account for gauge ambiguity.
Scenario:
3 object poses
with gauge ambiguity (2x scale)
world frame has poses rotated by 90 degrees.
world and egovehicle frame translated by 11 meters w.r.t. each other
"""
R90 = Rot2.fromDegrees(90)
# Create source poses (three objects o1, o2, o3 living in the egovehicle "e" frame)
# Suppose they are 3d cuboids detected by an onboard sensor in the egovehicle frame
eTo0 = Pose2(Rot2(), np.array([1, 0]))
eTo1 = Pose2(Rot2(), np.array([2, 0]))
eTo2 = Pose2(Rot2(), np.array([4, 0]))
eToi_list = [eTo0, eTo1, eTo2]
# Create destination poses
# (same three objects, but instead living in the world/city "w" frame)
wTo0 = Pose2(R90, np.array([0, 12]))
wTo1 = Pose2(R90, np.array([0, 14]))
wTo2 = Pose2(R90, np.array([0, 18]))
wToi_list = [wTo0, wTo1, wTo2]
we_pairs = Pose2Pairs(list(zip(wToi_list, eToi_list)))
# Recover the transformation wSe (i.e. world_S_egovehicle)
wSe = Similarity2.Align(we_pairs)
for wToi, eToi in zip(wToi_list, eToi_list):
self.gtsamAssertEquals(wToi, wSe.transformFrom(eToi))
def test_align_poses2_scaled_squares(self):
"""Test if Align Pose2Pairs method can account for gauge ambiguity.
Make sure a big and small square can be aligned.
The u's represent a big square (10x10), and v's represents a small square (4x4).
Scenario:
4 object poses
with gauge ambiguity (2.5x scale)
"""
# 0, 90, 180, and 270 degrees yaw
R0 = Rot2.fromDegrees(0)
R90 = Rot2.fromDegrees(90)
R180 = Rot2.fromDegrees(180)
R270 = Rot2.fromDegrees(270)
aTi0 = Pose2(R0, np.array([2, 3]))
aTi1 = Pose2(R90, np.array([12, 3]))
aTi2 = Pose2(R180, np.array([12, 13]))
aTi3 = Pose2(R270, np.array([2, 13]))
aTi_list = [aTi0, aTi1, aTi2, aTi3]
bTi0 = Pose2(R0, np.array([4, 3]))
bTi1 = Pose2(R90, np.array([8, 3]))
bTi2 = Pose2(R180, np.array([8, 7]))
bTi3 = Pose2(R270, np.array([4, 7]))
bTi_list = [bTi0, bTi1, bTi2, bTi3]
ab_pairs = Pose2Pairs(list(zip(aTi_list, bTi_list)))
# Recover the transformation wSe (i.e. world_S_egovehicle)
aSb = Similarity2.Align(ab_pairs)
for aTi, bTi in zip(aTi_list, bTi_list):
self.gtsamAssertEquals(aTi, aSb.transformFrom(bTi))
def test_align_poses3_along_straight_line(self):
"""Test Align Pose3Pairs method.
Scenario:
3 object poses
same scale (no gauge ambiguity)
world frame has poses rotated about x-axis (90 degree roll)
world and egovehicle frame translated by 15 meters w.r.t. each other
"""
Rx90 = Rot3.Rx(np.deg2rad(90))
# Create source poses (three objects o1, o2, o3 living in the egovehicle "e" frame)
# Suppose they are 3d cuboids detected by an onboard sensor in the egovehicle frame
eTo0 = Pose3(Rot3(), np.array([5, 0, 0]))
eTo1 = Pose3(Rot3(), np.array([10, 0, 0]))
eTo2 = Pose3(Rot3(), np.array([15, 0, 0]))
eToi_list = [eTo0, eTo1, eTo2]
# Create destination poses
# (same three objects, but instead living in the world/city "w" frame)
wTo0 = Pose3(Rx90, np.array([-10, 0, 0]))
wTo1 = Pose3(Rx90, np.array([-5, 0, 0]))
wTo2 = Pose3(Rx90, np.array([0, 0, 0]))
wToi_list = [wTo0, wTo1, wTo2]
we_pairs = gtsam.Pose3Pairs(list(zip(wToi_list, eToi_list)))
# Recover the transformation wSe (i.e. world_S_egovehicle)
wSe = Similarity3.Align(we_pairs)
for wToi, eToi in zip(wToi_list, eToi_list):
self.gtsamAssertEquals(wToi, wSe.transformFrom(eToi))
def test_align_poses3_along_straight_line_gauge(self):
"""Test if Align Pose3Pairs method can account for gauge ambiguity.
Scenario:
3 object poses
with gauge ambiguity (2x scale)
world frame has poses rotated about z-axis (90 degree yaw)
world and egovehicle frame translated by 11 meters w.r.t. each other
"""
Rz90 = Rot3.Rz(np.deg2rad(90))
# Create source poses (three objects o1, o2, o3 living in the egovehicle "e" frame)
# Suppose they are 3d cuboids detected by an onboard sensor in the egovehicle frame
eTo0 = Pose3(Rot3(), np.array([1, 0, 0]))
eTo1 = Pose3(Rot3(), np.array([2, 0, 0]))
eTo2 = Pose3(Rot3(), np.array([4, 0, 0]))
eToi_list = [eTo0, eTo1, eTo2]
# Create destination poses
# (same three objects, but instead living in the world/city "w" frame)
wTo0 = Pose3(Rz90, np.array([0, 12, 0]))
wTo1 = Pose3(Rz90, np.array([0, 14, 0]))
wTo2 = Pose3(Rz90, np.array([0, 18, 0]))
wToi_list = [wTo0, wTo1, wTo2]
we_pairs = gtsam.Pose3Pairs(list(zip(wToi_list, eToi_list)))
# Recover the transformation wSe (i.e. world_S_egovehicle)
wSe = Similarity3.Align(we_pairs)
for wToi, eToi in zip(wToi_list, eToi_list):
self.gtsamAssertEquals(wToi, wSe.transformFrom(eToi))
def test_align_poses3_scaled_squares(self):
"""Test if Align Pose3Pairs method can account for gauge ambiguity.
Make sure a big and small square can be aligned.
The u's represent a big square (10x10), and v's represents a small square (4x4).
Scenario:
4 object poses
with gauge ambiguity (2.5x scale)
"""
# 0, 90, 180, and 270 degrees yaw
R0 = Rot3.Rz(np.deg2rad(0))
R90 = Rot3.Rz(np.deg2rad(90))
R180 = Rot3.Rz(np.deg2rad(180))
R270 = Rot3.Rz(np.deg2rad(270))
aTi0 = Pose3(R0, np.array([2, 3, 0]))
aTi1 = Pose3(R90, np.array([12, 3, 0]))
aTi2 = Pose3(R180, np.array([12, 13, 0]))
aTi3 = Pose3(R270, np.array([2, 13, 0]))
aTi_list = [aTi0, aTi1, aTi2, aTi3]
bTi0 = Pose3(R0, np.array([4, 3, 0]))
bTi1 = Pose3(R90, np.array([8, 3, 0]))
bTi2 = Pose3(R180, np.array([8, 7, 0]))
bTi3 = Pose3(R270, np.array([4, 7, 0]))
bTi_list = [bTi0, bTi1, bTi2, bTi3]
ab_pairs = gtsam.Pose3Pairs(list(zip(aTi_list, bTi_list)))
# Recover the transformation wSe (i.e. world_S_egovehicle)
aSb = Similarity3.Align(ab_pairs)
for aTi, bTi in zip(aTi_list, bTi_list):
self.gtsamAssertEquals(aTi, aSb.transformFrom(bTi))
def generate_measurements(
self,
calibration: Union[Cal3Bundler, Cal3_S2],
camera_model: Union[PinholeCameraCal3Bundler, PinholeCameraCal3_S2],
cal_params: Iterable[Iterable[Union[int, float]]],
camera_set: Optional[Union[CameraSetCal3Bundler,
CameraSetCal3_S2]] = None,
) -> Tuple[List[Point2], Union[CameraSetCal3Bundler, CameraSetCal3_S2,
List[Cal3Bundler], List[Cal3_S2]]]:
"""
Generate vector of measurements for given calibration and camera model.
Args:
calibration: Camera calibration e.g. Cal3_S2
camera_model: Camera model e.g. PinholeCameraCal3_S2
cal_params: Iterable of camera parameters for `calibration` e.g. [K1, K2]
camera_set: Cameraset object (for individual calibrations)
Returns:
list of measurements and list/CameraSet object for cameras
"""
if camera_set is not None:
cameras = camera_set()
else:
cameras = []
measurements = gtsam.Point2Vector()
for k, pose in zip(cal_params, self.triangulation_poses):
K = calibration(*k)
camera = camera_model(pose, K)
cameras.append(camera)
z = camera.project(self.landmark)
measurements.append(z)
return measurements, cameras
def test_TriangulationExample(self) -> None:
"""Tests triangulation with shared Cal3_S2 calibration"""
# Some common constants
sharedCal = (1500, 1200, 0, 640, 480)
measurements, _ = self.generate_measurements(
calibration=Cal3_S2,
camera_model=PinholeCameraCal3_S2,
cal_params=(sharedCal, sharedCal))
triangulated_landmark = gtsam.triangulatePoint3(
self.triangulation_poses,
Cal3_S2(sharedCal),
measurements,
rank_tol=1e-9,
optimize=True)
self.gtsamAssertEquals(self.landmark, triangulated_landmark, 1e-9)
# Add some noise and try again: result should be ~ (4.995, 0.499167, 1.19814)
measurements_noisy = gtsam.Point2Vector()
measurements_noisy.append(measurements[0] - np.array([0.1, 0.5]))
measurements_noisy.append(measurements[1] - np.array([-0.2, 0.3]))
triangulated_landmark = gtsam.triangulatePoint3(
self.triangulation_poses,
Cal3_S2(sharedCal),
measurements_noisy,
rank_tol=1e-9,
optimize=True)
self.gtsamAssertEquals(self.landmark, triangulated_landmark, 1e-2)
def test_triangulation_robust_three_poses(self) -> None:
"""Ensure triangulation with a robust model works."""
sharedCal = Cal3_S2(1500, 1200, 0, 640, 480)
# landmark ~5 meters infront of camera
landmark = Point3(5, 0.5, 1.2)
pose1 = Pose3(UPRIGHT, Point3(0, 0, 1))
pose2 = pose1 * Pose3(Rot3(), Point3(1, 0, 0))
pose3 = pose1 * Pose3(Rot3.Ypr(0.1, 0.2, 0.1), Point3(0.1, -2, -0.1))
camera1 = PinholeCameraCal3_S2(pose1, sharedCal)
camera2 = PinholeCameraCal3_S2(pose2, sharedCal)
camera3 = PinholeCameraCal3_S2(pose3, sharedCal)
z1: Point2 = camera1.project(landmark)
z2: Point2 = camera2.project(landmark)
z3: Point2 = camera3.project(landmark)
poses = gtsam.Pose3Vector([pose1, pose2, pose3])
measurements = gtsam.Point2Vector([z1, z2, z3])
# noise free, so should give exactly the landmark
actual = gtsam.triangulatePoint3(poses,
sharedCal,
measurements,
rank_tol=1e-9,
optimize=False)
self.assertTrue(np.allclose(landmark, actual, atol=1e-2))
# Add outlier
measurements[0] += Point2(100, 120) # very large pixel noise!
# now estimate does not match landmark
actual2 = gtsam.triangulatePoint3(poses,
sharedCal,
measurements,
rank_tol=1e-9,
optimize=False)
# DLT is surprisingly robust, but still off (actual error is around 0.26m)
self.assertTrue(np.linalg.norm(landmark - actual2) >= 0.2)
self.assertTrue(np.linalg.norm(landmark - actual2) <= 0.5)
# Again with nonlinear optimization
actual3 = gtsam.triangulatePoint3(poses,
sharedCal,
measurements,
rank_tol=1e-9,
optimize=True)
# result from nonlinear (but non-robust optimization) is close to DLT and still off
self.assertTrue(np.allclose(actual2, actual3, atol=0.1))
# Again with nonlinear optimization, this time with robust loss
model = gtsam.noiseModel.Robust.Create(
gtsam.noiseModel.mEstimator.Huber.Create(1.345),
gtsam.noiseModel.Unit.Create(2))
actual4 = gtsam.triangulatePoint3(poses,
sharedCal,
measurements,
rank_tol=1e-9,
optimize=True,
model=model)
# using the Huber loss we now have a quite small error!! nice!
self.assertTrue(np.allclose(landmark, actual4, atol=0.05))
def test_outliers_and_far_landmarks(self) -> None:
"""Check safe triangulation function."""
pose1, pose2 = self.poses
K1 = Cal3_S2(1500, 1200, 0, 640, 480)
# create first camera. Looking along X-axis, 1 meter above ground plane (x-y)
camera1 = PinholeCameraCal3_S2(pose1, K1)
# create second camera 1 meter to the right of first camera
K2 = Cal3_S2(1600, 1300, 0, 650, 440)
camera2 = PinholeCameraCal3_S2(pose2, K2)
# 1. Project two landmarks into two cameras and triangulate
z1 = camera1.project(self.landmark)
z2 = camera2.project(self.landmark)
cameras = CameraSetCal3_S2()
cameras.append(camera1)
cameras.append(camera2)
measurements = gtsam.Point2Vector()
measurements.append(z1)
measurements.append(z2)
landmarkDistanceThreshold = 10 # landmark is closer than that
# all default except landmarkDistanceThreshold:
params = TriangulationParameters(1.0, False, landmarkDistanceThreshold)
actual: TriangulationResult = gtsam.triangulateSafe(
cameras, measurements, params)
self.gtsamAssertEquals(actual.get(), self.landmark, 1e-2)
self.assertTrue(actual.valid())
landmarkDistanceThreshold = 4 # landmark is farther than that
params2 = TriangulationParameters(1.0, False,
landmarkDistanceThreshold)
actual = gtsam.triangulateSafe(cameras, measurements, params2)
self.assertTrue(actual.farPoint())
# 3. Add a slightly rotated third camera above with a wrong measurement
# (OUTLIER)
pose3 = pose1 * Pose3(Rot3.Ypr(0.1, 0.2, 0.1), Point3(0.1, -2, -.1))
K3 = Cal3_S2(700, 500, 0, 640, 480)
camera3 = PinholeCameraCal3_S2(pose3, K3)
z3 = camera3.project(self.landmark)
cameras.append(camera3)
measurements.append(z3 + Point2(10, -10))
landmarkDistanceThreshold = 10 # landmark is closer than that
outlierThreshold = 100 # loose, the outlier is going to pass
params3 = TriangulationParameters(1.0, False,
landmarkDistanceThreshold,
outlierThreshold)
actual = gtsam.triangulateSafe(cameras, measurements, params3)
self.assertTrue(actual.valid())
# now set stricter threshold for outlier rejection
outlierThreshold = 5 # tighter, the outlier is not going to pass
params4 = TriangulationParameters(1.0, False,
landmarkDistanceThreshold,
outlierThreshold)
actual = gtsam.triangulateSafe(cameras, measurements, params4)
self.assertTrue(actual.outlier())
if __name__ == "__main__":
unittest.main()

2
tests/CMakeLists.txt
View File

@ -13,7 +13,7 @@ if (NOT GTSAM_USE_BOOST_FEATURES)
endif()
if (NOT GTSAM_ENABLE_BOOST_SERIALIZATION)
list(APPEND excluded_tests "testSerializationSLAM.cpp")
list(APPEND excluded_tests "testSerializationSlam.cpp")
endif()
# Build tests