diff --git a/.github/workflows/build-windows.yml b/.github/workflows/build-windows.yml index 3d4bf3faf..0434577c1 100644 --- a/.github/workflows/build-windows.yml +++ b/.github/workflows/build-windows.yml @@ -21,24 +21,15 @@ jobs: # Github Actions requires a single row to be added to the build matrix. # See https://help.github.com/en/articles/workflow-syntax-for-github-actions. name: [ - #TODO This build fails, need to understand why. - # windows-2016-cl, windows-2019-cl, ] build_type: [ Debug, - #TODO(Varun) The release build takes over 2.5 hours, need to figure out why. - # Release + Release ] build_unstable: [ON] include: - #TODO This build fails, need to understand why. - # - name: windows-2016-cl - # os: windows-2016 - # compiler: cl - # platform: 32 - - name: windows-2019-cl os: windows-2019 compiler: cl @@ -125,4 +116,3 @@ jobs: # Run GTSAM_UNSTABLE tests #cmake --build build -j 4 --config ${{ matrix.build_type }} --target check.base_unstable - diff --git a/gtsam/base/base.i b/gtsam/base/base.i index 9b9f351ce..31d09cb3d 100644 --- a/gtsam/base/base.i +++ b/gtsam/base/base.i @@ -41,6 +41,7 @@ class DSFMap { std::map sets(); }; +// Used in Matlab wrapper class IndexPairSet { IndexPairSet(); // common STL methods @@ -54,6 +55,7 @@ class IndexPairSet { bool count(gtsam::IndexPair key) const; // returns true if value exists }; +// Used in Matlab wrapper class IndexPairVector { IndexPairVector(); IndexPairVector(const gtsam::IndexPairVector& other); @@ -70,6 +72,7 @@ class IndexPairVector { gtsam::IndexPairVector IndexPairSetAsArray(gtsam::IndexPairSet& set); +// Used in Matlab wrapper class IndexPairSetMap { IndexPairSetMap(); // common STL methods diff --git a/gtsam/basis/Basis.cpp b/gtsam/basis/Basis.cpp new file mode 100644 index 000000000..3e684b197 --- /dev/null +++ b/gtsam/basis/Basis.cpp @@ -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 + +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 diff --git a/gtsam/basis/Basis.h b/gtsam/basis/Basis.h index 97704dab4..41cdeeaaa 100644 --- a/gtsam/basis/Basis.h +++ b/gtsam/basis/Basis.h @@ -20,7 +20,6 @@ #include #include -#include #include @@ -81,16 +80,7 @@ using Weights = Eigen::Matrix; /* 1xN vector */ * * @ingroup basis */ -template -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. + * 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 class VectorEvaluationFunctor : protected EvaluationFunctor { protected: - using VectorM = Eigen::Matrix; - using Jacobian = Eigen::Matrix; + using Jacobian = Eigen::Matrix; 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(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& P, - OptionalJacobian H = {}) const { + Vector apply(const Matrix& P, + OptionalJacobian H = {}) const { if (H) *H = H_; return P.matrix() * this->weights_.transpose(); } /// c++ sugar - VectorM operator()(const ParameterMatrix& P, - OptionalJacobian H = {}) const { + Vector operator()(const Matrix& P, + OptionalJacobian H = {}) const { return apply(P, H); } }; @@ -231,13 +222,14 @@ class Basis { * * This component is specified by the row index i, with 0 class VectorComponentFunctor : public EvaluationFunctor { protected: using Jacobian = Eigen::Matrix; - 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& P, + double apply(const Matrix& P, OptionalJacobian H = {}) const { if (H) *H = H_; return P.row(rowIndex_) * EvaluationFunctor::weights_.transpose(); } /// c++ sugar - double operator()(const ParameterMatrix& P, + double operator()(const Matrix& P, OptionalJacobian H = {}) const { return apply(P, H); } }; /** - * Manifold EvaluationFunctor at a given x, applied to ParameterMatrix. + * 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 ManifoldEvaluationFunctor - : public VectorEvaluationFunctor::dimension> { + class ManifoldEvaluationFunctor : public VectorEvaluationFunctor { enum { M = traits::dimension }; - using Base = VectorEvaluationFunctor; + 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& P, - OptionalJacobian H = {}) const { + T apply(const Matrix& P, OptionalJacobian H = {}) const { // Interpolate the M-dimensional vector to yield a vector in tangent space Eigen::Matrix xi = Base::operator()(P, H); @@ -333,7 +324,7 @@ class Basis { } /// c++ sugar - T operator()(const ParameterMatrix& P, + T operator()(const Matrix& P, OptionalJacobian 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. + * 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 class VectorDerivativeFunctor : protected DerivativeFunctorBase { protected: - using VectorM = Eigen::Matrix; - using Jacobian = Eigen::Matrix; + using Jacobian = Eigen::Matrix; 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(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& P, - OptionalJacobian H = {}) const { + Vector apply(const Matrix& P, + OptionalJacobian H = {}) const { if (H) *H = H_; return P.matrix() * this->weights_.transpose(); } /// c++ sugar - VectorM operator()( - const ParameterMatrix& P, - OptionalJacobian H = {}) const { + Vector operator()(const Matrix& P, + OptionalJacobian H = {}) const { return apply(P, H); } }; @@ -452,13 +443,14 @@ class Basis { * * This component is specified by the row index i, with 0 class ComponentDerivativeFunctor : protected DerivativeFunctorBase { protected: using Jacobian = Eigen::Matrix; - 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& P, + double apply(const Matrix& P, OptionalJacobian H = {}) const { if (H) *H = H_; return P.row(rowIndex_) * this->weights_.transpose(); } /// c++ sugar - double operator()(const ParameterMatrix& P, + double operator()(const Matrix& P, OptionalJacobian H = {}) const { return apply(P, H); } }; - }; } // namespace gtsam diff --git a/gtsam/basis/BasisFactors.h b/gtsam/basis/BasisFactors.h index 0e42a8866..ad92ad672 100644 --- a/gtsam/basis/BasisFactors.h +++ b/gtsam/basis/BasisFactors.h @@ -75,7 +75,7 @@ class EvaluationFactor : public FunctorizedFactor { }; /** - * 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 { * 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 VectorEvaluationFactor - : public FunctorizedFactor> { +template +class VectorEvaluationFactor : public FunctorizedFactor { private: - using Base = FunctorizedFactor>; + using Base = FunctorizedFactor; 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(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(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 controlPrior(key, measured, model, - * N, i, t, a, b); + * VectorComponentFactor controlPrior(key, measured, model, + * N, i, t, a, b); * where N is the degree and i is the component index. * * @ingroup basis */ -template -class VectorComponentFactor - : public FunctorizedFactor> { +template +class VectorComponentFactor : public FunctorizedFactor { private: - using Base = FunctorizedFactor>; + using Base = FunctorizedFactor; 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

(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

(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 ManifoldEvaluationFactor - : public FunctorizedFactor::dimension>> { +class ManifoldEvaluationFactor : public FunctorizedFactor { private: - using Base = FunctorizedFactor::dimension>>; + using Base = FunctorizedFactor; 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 VectorDerivativeFactor - : public FunctorizedFactor> { +template +class VectorDerivativeFactor : public FunctorizedFactor { private: - using Base = FunctorizedFactor>; - using Func = typename BASIS::template VectorDerivativeFunctor; + using Base = FunctorizedFactor; + 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 ComponentDerivativeFactor - : public FunctorizedFactor> { +template +class ComponentDerivativeFactor : public FunctorizedFactor { private: - using Base = FunctorizedFactor>; - using Func = typename BASIS::template ComponentDerivativeFunctor

; + using Base = FunctorizedFactor; + 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() {} }; diff --git a/gtsam/basis/ParameterMatrix.h b/gtsam/basis/ParameterMatrix.h deleted file mode 100644 index 81686e046..000000000 --- a/gtsam/basis/ParameterMatrix.h +++ /dev/null @@ -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 -#include -#include - -#include - -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 -class ParameterMatrix { - using MatrixType = Eigen::Matrix; - - 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 transpose() const { return matrix_.transpose(); } - - /** - * Get the matrix row specified by `index`. - * @param index: The row index to retrieve. - */ - Eigen::Matrix 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 { - return matrix_.row(index); - } - - /** - * Get the matrix column specified by `index`. - * @param index: The column index to retrieve. - */ - Eigen::Matrix 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 { - 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 operator+(const ParameterMatrix& other) const { - return ParameterMatrix(matrix_ + other.matrix()); - } - - /** - * Add a MxN-sized vector to the ParameterMatrix. - * @param other: Vector which is reshaped and added. - */ - ParameterMatrix operator+( - const Eigen::Matrix& other) const { - // This form avoids a deep copy and instead typecasts `other`. - Eigen::Map other_(other.data(), M, cols()); - return ParameterMatrix(matrix_ + other_); - } - - /** - * Subtract a ParameterMatrix from another. - * @param other: ParameterMatrix to subtract. - */ - ParameterMatrix operator-(const ParameterMatrix& other) const { - return ParameterMatrix(matrix_ - other.matrix()); - } - - /** - * Subtract a MxN-sized vector from the ParameterMatrix. - * @param other: Vector which is reshaped and subracted. - */ - ParameterMatrix operator-( - const Eigen::Matrix& other) const { - Eigen::Map other_(other.data(), M, cols()); - return ParameterMatrix(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& 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& 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; - Vector result(matrix_.size()); - Eigen::Map(&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 -struct traits> - : public internal::VectorSpace> {}; - -/* ************************************************************************* */ -// Stream operator that takes a ParameterMatrix. Used for printing. -template -inline std::ostream& operator<<(std::ostream& os, - const ParameterMatrix& parameterMatrix) { - os << parameterMatrix.matrix(); - return os; -} - -} // namespace gtsam \ No newline at end of file diff --git a/gtsam/basis/basis.i b/gtsam/basis/basis.i index 0cd87ba65..f8cea70f8 100644 --- a/gtsam/basis/basis.i +++ b/gtsam/basis/basis.i @@ -46,18 +46,6 @@ class Chebyshev2 { static Matrix DifferentiationMatrix(size_t N, double a, double b); }; -#include - -template -class ParameterMatrix { - ParameterMatrix(const size_t N); - ParameterMatrix(const Matrix& matrix); - - Matrix matrix() const; - - void print(const string& s = "") const; -}; - #include template +template 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 - VectorEvaluationFactorChebyshev2D3; -typedef gtsam::VectorEvaluationFactor - VectorEvaluationFactorChebyshev2D4; -typedef gtsam::VectorEvaluationFactor - VectorEvaluationFactorChebyshev2D12; - -template +template 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 - VectorComponentFactorChebyshev2D3; -typedef gtsam::VectorComponentFactor - VectorComponentFactorChebyshev2D4; -typedef gtsam::VectorComponentFactor - VectorComponentFactorChebyshev2D12; +#include +#include -template +template 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 +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 +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 +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 template +#include +#include +#include #include #include #include #include +#include #include #include #include -#include -#include -#include -#include -#include - -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 factor(key, measured, model, N, 0); + VectorEvaluationFactor factor(key, measured, model, M, N, 0); NonlinearFactorGraph graph; graph.add(factor); - ParameterMatrix stateMatrix(N); + gtsam::Matrix stateMatrix = gtsam::Matrix::Zero(M, N); Values initial; - initial.insert>(key, stateMatrix); + initial.insert(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 factor(key, measured, model, N, 0); + VectorEvaluationFactor 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 factor(key, measured, model, N, i, - t, a, b); + VectorComponentFactor factor(key, measured, model, P, N, i, t, a, + b); NonlinearFactorGraph graph; graph.add(factor); - ParameterMatrix

stateMatrix(N); + gtsam::Matrix stateMatrix = gtsam::Matrix::Zero(P, N); Values initial; - initial.insert>(key, stateMatrix); + initial.insert(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 factor(key, measured, model, N, - t, a, b); + ManifoldEvaluationFactor 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>(key, stateMatrix); + initial.insert(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 vecDPrior(key, measured, model, N, 0); + VectorDerivativeFactor vecDPrior(key, measured, model, M, N, 0); NonlinearFactorGraph graph; graph.add(vecDPrior); - ParameterMatrix stateMatrix(N); + gtsam::Matrix stateMatrix = gtsam::Matrix::Zero(M, N); Values initial; - initial.insert>(key, stateMatrix); + initial.insert(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 controlDPrior(key, measured, model, - N, 0, 0); + ComponentDerivativeFactor controlDPrior(key, measured, model, M, + N, 0, 0); NonlinearFactorGraph graph; graph.add(controlDPrior); Values initial; - ParameterMatrix stateMatrix(N); - initial.insert>(key, stateMatrix); + gtsam::Matrix stateMatrix = gtsam::Matrix::Zero(M, N); + initial.insert(key, stateMatrix); LevenbergMarquardtParams parameters; parameters.setMaxIterations(20); diff --git a/gtsam/basis/tests/testChebyshev2.cpp b/gtsam/basis/tests/testChebyshev2.cpp index 56a79a38a..780714936 100644 --- a/gtsam/basis/tests/testChebyshev2.cpp +++ b/gtsam/basis/tests/testChebyshev2.cpp @@ -17,14 +17,15 @@ * methods */ -#include +#include +#include #include #include #include +#include #include -#include -#include +#include #include 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 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)> f = std::bind( - &Chebyshev2::VectorEvaluationFunctor<2>::operator(), fx, std::placeholders::_1, nullptr); + std::function f = + std::bind(&Chebyshev2::VectorEvaluationFunctor::operator(), fx, + std::placeholders::_1, nullptr); Matrix numericalH = - numericalDerivative11, 2 * N>(f, X); + numericalDerivative11(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 actualH(3, 3 * N); + Chebyshev2::ManifoldEvaluationFunctor 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 f = + std::bind(&Chebyshev2::ManifoldEvaluationFunctor::operator(), fx, + std::placeholders::_1, nullptr); + Matrix numericalH = + numericalDerivative11(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 actualH(6, 6 * N); + + Matrix X = Matrix::Zero(6, N); + X.col(11) = xi; + + Chebyshev2::ManifoldEvaluationFunctor 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 f = + std::bind(&Chebyshev2::ManifoldEvaluationFunctor::operator(), fx, + std::placeholders::_1, nullptr); + Matrix numericalH = + numericalDerivative11(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; - VecD fx(N, x, 0, 3); - ParameterMatrix 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, M * N>( + Matrix expectedH = numericalDerivative11( 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; + 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 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, M * N>( + Matrix expectedH = numericalDerivative11( 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; + using CompFunc = Chebyshev2::ComponentDerivativeFunctor; size_t row = 1; - CompFunc fx(N, row, x, 0, 3); - ParameterMatrix 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, M * N>( + Matrix expectedH = numericalDerivative11( std::bind(&CompFunc::operator(), fx, std::placeholders::_1, nullptr), X); EXPECT(assert_equal(expectedH, actualH, 1e-7)); } diff --git a/gtsam/basis/tests/testFourier.cpp b/gtsam/basis/tests/testFourier.cpp index 3a147a9f6..2995e8c45 100644 --- a/gtsam/basis/tests/testFourier.cpp +++ b/gtsam/basis/tests/testFourier.cpp @@ -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)); } diff --git a/gtsam/basis/tests/testParameterMatrix.cpp b/gtsam/basis/tests/testParameterMatrix.cpp deleted file mode 100644 index ae2e8e111..000000000 --- a/gtsam/basis/tests/testParameterMatrix.cpp +++ /dev/null @@ -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 -#include -#include -#include -#include -#include - -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 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 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 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 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 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 params(Matrix::Ones(M, N)); - ParameterMatrix expected(2 * Matrix::Ones(M, N)); - - // Add vector - EXPECT(assert_equal(expected, params + Vector::Ones(M * N))); - // Add another ParameterMatrix - ParameterMatrix actual = params + ParameterMatrix(Matrix::Ones(M, N)); - EXPECT(assert_equal(expected, actual)); -} - -//****************************************************************************** -TEST(ParameterMatrix, Subtraction) { - ParameterMatrix params(2 * Matrix::Ones(M, N)); - ParameterMatrix expected(Matrix::Ones(M, N)); - - // Subtract vector - EXPECT(assert_equal(expected, params - Vector::Ones(M * N))); - // Subtract another ParameterMatrix - ParameterMatrix actual = params - ParameterMatrix(Matrix::Ones(M, N)); - EXPECT(assert_equal(expected, actual)); -} - -//****************************************************************************** -TEST(ParameterMatrix, Multiplication) { - ParameterMatrix 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 params(Matrix::Ones(M, N)); - // vector - EXPECT(assert_equal(Vector::Ones(M * N), params.vector())); - // identity - EXPECT(assert_equal(ParameterMatrix::Identity(), - ParameterMatrix(Matrix::Zero(M, 0)))); -} - -//****************************************************************************** -int main() { - TestResult tr; - return TestRegistry::runAllTests(tr); -} -//****************************************************************************** diff --git a/gtsam/geometry/Pose2.h b/gtsam/geometry/Pose2.h index c3f588dcc..f1b38c5a6 100644 --- a/gtsam/geometry/Pose2.h +++ b/gtsam/geometry/Pose2.h @@ -258,10 +258,19 @@ public: inline const Rot2& r() const { return r_; } /// translation - inline const Point2& translation() const { return t_; } + inline const Point2& translation(OptionalJacobian<2, 3> Hself={}) const { + if (Hself) { + *Hself = Matrix::Zero(2, 3); + (*Hself).block<2, 2>(0, 0) = rotation().matrix(); + } + return t_; + } /// rotation - inline const Rot2& rotation() const { return r_; } + inline const Rot2& rotation(OptionalJacobian<1, 3> Hself={}) const { + if (Hself) *Hself << 0, 0, 1; + return r_; + } //// return transformation matrix GTSAM_EXPORT Matrix3 matrix() const; diff --git a/gtsam/geometry/Rot3.h b/gtsam/geometry/Rot3.h index 0b3798baa..2b9c5a45a 100644 --- a/gtsam/geometry/Rot3.h +++ b/gtsam/geometry/Rot3.h @@ -560,8 +560,8 @@ class GTSAM_EXPORT Rot3 : public LieGroup { #endif }; - /// std::vector of Rot3s, mainly for wrapper - using Rot3Vector = std::vector >; + /// std::vector of Rot3s, used in Matlab wrapper + using Rot3Vector = std::vector>; /** * [RQ] receives a 3 by 3 matrix and returns an upper triangular matrix R diff --git a/gtsam/geometry/geometry.i b/gtsam/geometry/geometry.i index 0710959bc..46172b774 100644 --- a/gtsam/geometry/geometry.i +++ b/gtsam/geometry/geometry.i @@ -28,7 +28,8 @@ class Point2 { // enabling serialization functionality void serialize() const; }; - + +// Used in Matlab wrapper class Point2Pairs { Point2Pairs(); size_t size() const; @@ -38,6 +39,7 @@ class Point2Pairs { }; // std::vector +// Used in Matlab wrapper class Point2Vector { // Constructors Point2Vector(); @@ -131,6 +133,7 @@ class Point3 { gtsam::Point3 normalize(const gtsam::Point3 &p, Eigen::Ref H) const; }; +// Used in Matlab wrapper class Point3Pairs { Point3Pairs(); size_t size() const; @@ -431,7 +434,9 @@ class Pose2 { gtsam::Rot2 bearing(const gtsam::Point2& point) const; double range(const gtsam::Point2& point) const; gtsam::Point2 translation() const; + gtsam::Point2 translation(Eigen::Ref Hself) const; gtsam::Rot2 rotation() const; + gtsam::Rot2 rotation(Eigen::Ref Hself) const; Matrix matrix() const; // enabling serialization functionality @@ -542,6 +547,7 @@ class Pose3 { void serialize() const; }; +// Used in Matlab wrapper class Pose3Pairs { Pose3Pairs(); size_t size() const; @@ -550,6 +556,7 @@ class Pose3Pairs { void push_back(const gtsam::Pose3Pair& pose_pair); }; +// Used in Matlab wrapper class Pose3Vector { Pose3Vector(); size_t size() const; @@ -1174,8 +1181,8 @@ class TriangulationParameters { const gtsam::SharedNoiseModel& noiseModel = nullptr); }; -// Templates appear not yet supported for free functions - issue raised at -// borglab/wrap#14 to add support +// Can be templated but overloaded for convenience. +// We can now call `triangulatePoint3` with any template type. // Cal3_S2 versions gtsam::Point3 triangulatePoint3(const gtsam::Pose3Vector& poses, diff --git a/gtsam/geometry/tests/testPose2.cpp b/gtsam/geometry/tests/testPose2.cpp index 8c1bdccc0..b52a6e590 100644 --- a/gtsam/geometry/tests/testPose2.cpp +++ b/gtsam/geometry/tests/testPose2.cpp @@ -474,6 +474,33 @@ TEST( Pose2, compose_matrix ) EXPECT(assert_equal(gM1*_1M2,matrix(gT1.compose(_1T2)))); // RIGHT DOES NOT } + + +/* ************************************************************************* */ +TEST( Pose2, translation ) { + Pose2 pose(3.5, -8.2, 4.2); + + Matrix actualH; + EXPECT(assert_equal((Vector2() << 3.5, -8.2).finished(), pose.translation(actualH), 1e-8)); + + std::function f = [](const Pose2& T) { return T.translation(); }; + Matrix numericalH = numericalDerivative11(f, pose); + EXPECT(assert_equal(numericalH, actualH, 1e-6)); +} + +/* ************************************************************************* */ +TEST( Pose2, rotation ) { + Pose2 pose(3.5, -8.2, 4.2); + + Matrix actualH(4, 3); + EXPECT(assert_equal(Rot2(4.2), pose.rotation(actualH), 1e-8)); + + std::function f = [](const Pose2& T) { return T.rotation(); }; + Matrix numericalH = numericalDerivative11(f, pose); + EXPECT(assert_equal(numericalH, actualH, 1e-6)); +} + + /* ************************************************************************* */ TEST( Pose2, between ) { diff --git a/gtsam/geometry/triangulation.cpp b/gtsam/geometry/triangulation.cpp index 06fb8fafe..039dfd74d 100644 --- a/gtsam/geometry/triangulation.cpp +++ b/gtsam/geometry/triangulation.cpp @@ -85,7 +85,8 @@ Vector4 triangulateHomogeneousDLT( Point3 triangulateLOST(const std::vector& poses, const Point3Vector& calibratedMeasurements, - const SharedIsotropic& measurementNoise) { + const SharedIsotropic& measurementNoise, + double rank_tol) { size_t m = calibratedMeasurements.size(); assert(m == poses.size()); @@ -96,17 +97,38 @@ Point3 triangulateLOST(const std::vector& poses, for (size_t i = 0; i < m; i++) { const Pose3& wTi = poses[i]; // TODO(akshay-krishnan): are there better ways to select j? - const int j = (i + 1) % m; + int j = (i + 1) % m; const Pose3& wTj = poses[j]; - const Point3 d_ij = wTj.translation() - wTi.translation(); + Point3 d_ij = wTj.translation() - wTi.translation(); + Point3 wZi = wTi.rotation().rotate(calibratedMeasurements[i]); + Point3 wZj = wTj.rotation().rotate(calibratedMeasurements[j]); + double num_i = wZi.cross(wZj).norm(); + double den_i = d_ij.cross(wZj).norm(); - const Point3 wZi = wTi.rotation().rotate(calibratedMeasurements[i]); - const Point3 wZj = wTj.rotation().rotate(calibratedMeasurements[j]); + // Handle q_i = 0 (or NaN), which arises if the measurement vectors, wZi and + // wZj, coincide (or the baseline vector coincides with the jth measurement + // vector). + if (num_i == 0 || den_i == 0) { + bool success = false; + for (size_t k = 2; k < m; k++) { + j = (i + k) % m; + const Pose3& wTj = poses[j]; + + d_ij = wTj.translation() - wTi.translation(); + wZj = wTj.rotation().rotate(calibratedMeasurements[j]); + num_i = wZi.cross(wZj).norm(); + den_i = d_ij.cross(wZj).norm(); + if (num_i > 0 && den_i > 0) { + success = true; + break; + } + } + if (!success) throw(TriangulationUnderconstrainedException()); + } // Note: Setting q_i = 1.0 gives same results as DLT. - const double q_i = wZi.cross(wZj).norm() / - (measurementNoise->sigma() * d_ij.cross(wZj).norm()); + const double q_i = num_i / (measurementNoise->sigma() * den_i); const Matrix23 coefficientMat = q_i * skewSymmetric(calibratedMeasurements[i]).topLeftCorner(2, 3) * @@ -115,7 +137,13 @@ Point3 triangulateLOST(const std::vector& poses, A.block<2, 3>(2 * i, 0) << coefficientMat; b.block<2, 1>(2 * i, 0) << coefficientMat * wTi.translation(); } - return A.colPivHouseholderQr().solve(b); + + Eigen::ColPivHouseholderQR A_Qr = A.colPivHouseholderQr(); + A_Qr.setThreshold(rank_tol); + + if (A_Qr.rank() < 3) throw(TriangulationUnderconstrainedException()); + + return A_Qr.solve(b); } Point3 triangulateDLT( diff --git a/gtsam/geometry/triangulation.h b/gtsam/geometry/triangulation.h index 7e58cee2d..3a8398804 100644 --- a/gtsam/geometry/triangulation.h +++ b/gtsam/geometry/triangulation.h @@ -110,7 +110,8 @@ GTSAM_EXPORT Point3 triangulateDLT( */ GTSAM_EXPORT Point3 triangulateLOST(const std::vector& poses, const Point3Vector& calibratedMeasurements, - const SharedIsotropic& measurementNoise); + const SharedIsotropic& measurementNoise, + double rank_tol = 1e-9); /** * Create a factor graph with projection factors from poses and one calibration @@ -439,7 +440,8 @@ Point3 triangulatePoint3(const std::vector& poses, auto calibratedMeasurements = calibrateMeasurementsShared(*sharedCal, measurements); - point = triangulateLOST(poses, calibratedMeasurements, measurementNoise); + point = triangulateLOST(poses, calibratedMeasurements, measurementNoise, + rank_tol); } else { // construct projection matrices from poses & calibration auto projection_matrices = projectionMatricesFromPoses(poses, sharedCal); @@ -512,7 +514,8 @@ Point3 triangulatePoint3(const CameraSet& cameras, auto calibratedMeasurements = calibrateMeasurements(cameras, measurements); - point = triangulateLOST(poses, calibratedMeasurements, measurementNoise); + point = triangulateLOST(poses, calibratedMeasurements, measurementNoise, + rank_tol); } else { // construct projection matrices from poses & calibration auto projection_matrices = projectionMatricesFromCameras(cameras); @@ -750,4 +753,3 @@ using CameraSetCal3Fisheye = CameraSet>; using CameraSetCal3Unified = CameraSet>; using CameraSetSpherical = CameraSet; } // \namespace gtsam - diff --git a/gtsam/gtsam.i b/gtsam/gtsam.i index 00b4d05f8..834d5a147 100644 --- a/gtsam/gtsam.i +++ b/gtsam/gtsam.i @@ -105,6 +105,7 @@ class KeyGroupMap { }; // Actually a FastSet +// Used in Matlab wrapper class FactorIndexSet { FactorIndexSet(); FactorIndexSet(const gtsam::FactorIndexSet& set); @@ -121,6 +122,7 @@ class FactorIndexSet { }; // Actually a vector +// Used in Matlab wrapper class FactorIndices { FactorIndices(); FactorIndices(const gtsam::FactorIndices& other); diff --git a/gtsam/inference/inference.i b/gtsam/inference/inference.i index d4c90e356..f9696258a 100644 --- a/gtsam/inference/inference.i +++ b/gtsam/inference/inference.i @@ -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, @@ -176,13 +177,9 @@ class DotWriter { class VariableIndex { // Standard Constructors and Named Constructors VariableIndex(); - // TODO: Templetize constructor when wrap supports it - // template - // VariableIndex(const T& factorGraph, size_t nVariables); - // VariableIndex(const T& factorGraph); - VariableIndex(const gtsam::SymbolicFactorGraph& sfg); - VariableIndex(const gtsam::GaussianFactorGraph& gfg); - VariableIndex(const gtsam::NonlinearFactorGraph& fg); + template + VariableIndex(const T& factorGraph); VariableIndex(const gtsam::VariableIndex& other); // Testable diff --git a/gtsam/nonlinear/CustomFactor.cpp b/gtsam/nonlinear/CustomFactor.cpp index bb0a77078..a6415df20 100644 --- a/gtsam/nonlinear/CustomFactor.cpp +++ b/gtsam/nonlinear/CustomFactor.cpp @@ -28,7 +28,7 @@ Vector CustomFactor::unwhitenedError(const Values& x, OptionalMatrixVecType H) c if(H) { /* * In this case, we pass the raw pointer to the `std::vector` object directly to pybind. - * As the type `std::vector` has been marked as opaque in `preamble.h`, any changes in + * As the type `std::vector` has been marked as opaque in `preamble/custom.h`, any changes in * Python will be immediately reflected on the C++ side. * * Example: diff --git a/gtsam/nonlinear/GncParams.h b/gtsam/nonlinear/GncParams.h index 10ac80663..d05e72ee2 100644 --- a/gtsam/nonlinear/GncParams.h +++ b/gtsam/nonlinear/GncParams.h @@ -73,8 +73,7 @@ class GncParams { double weightsTol = 1e-4; ///< If the weights are within weightsTol from being binary, stop iterating (only for TLS) Verbosity verbosity = SILENT; ///< Verbosity level - //TODO(Varun) replace IndexVector with vector once pybind11/stl.h is globally enabled. - /// Use IndexVector for inliers and outliers since it is fast + wrapping + /// Use IndexVector for inliers and outliers since it is fast using IndexVector = FastVector; ///< Slots in the factor graph corresponding to measurements that we know are inliers IndexVector knownInliers = IndexVector(); diff --git a/gtsam/nonlinear/nonlinear.i b/gtsam/nonlinear/nonlinear.i index 19f4ae588..6d101af98 100644 --- a/gtsam/nonlinear/nonlinear.i +++ b/gtsam/nonlinear/nonlinear.i @@ -25,7 +25,6 @@ namespace gtsam { #include #include #include -#include #include class GraphvizFormatting : gtsam::DotWriter { @@ -305,8 +304,8 @@ virtual class GncParams { double relativeCostTol; double weightsTol; gtsam::This::Verbosity verbosity; - gtsam::KeyVector knownInliers; - gtsam::KeyVector knownOutliers; + gtsam::This::IndexVector knownInliers; + gtsam::This::IndexVector knownOutliers; void setLossType(const gtsam::GncLossType type); void setMaxIterations(const size_t maxIter); @@ -314,8 +313,8 @@ virtual class GncParams { void setRelativeCostTol(double value); void setWeightsTol(double value); void setVerbosityGNC(const gtsam::This::Verbosity value); - void setKnownInliers(const gtsam::KeyVector& knownIn); - void setKnownOutliers(const gtsam::KeyVector& knownOut); + void setKnownInliers(const gtsam::This::IndexVector& knownIn); + void setKnownOutliers(const gtsam::This::IndexVector& knownOut); void print(const string& str = "GncParams: ") const; enum Verbosity { diff --git a/gtsam/nonlinear/values.i b/gtsam/nonlinear/values.i index 680cafc31..e36b1cf1c 100644 --- a/gtsam/nonlinear/values.i +++ b/gtsam/nonlinear/values.i @@ -25,7 +25,6 @@ namespace gtsam { #include #include #include -#include #include @@ -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 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 , - 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); }; diff --git a/gtsam/sfm/sfm.i b/gtsam/sfm/sfm.i index c57e03174..930a0dd46 100644 --- a/gtsam/sfm/sfm.i +++ b/gtsam/sfm/sfm.i @@ -6,15 +6,14 @@ namespace gtsam { #include class SfmTrack2d { - std::vector> measurements; + std::vector measurements; SfmTrack2d(); SfmTrack2d(const std::vector& measurements); size_t numberMeasurements() const; - pair measurement(size_t idx) const; + gtsam::SfmMeasurement measurement(size_t idx) const; pair siftIndex(size_t idx) const; void addMeasurement(size_t idx, const gtsam::Point2& m); - gtsam::SfmMeasurement measurement(size_t idx) const; bool hasUniqueCameras() const; Eigen::MatrixX2d measurementMatrix() const; Eigen::VectorXi indexVector() const; @@ -100,6 +99,7 @@ typedef gtsam::BinaryMeasurement BinaryMeasurementUnit3; typedef gtsam::BinaryMeasurement BinaryMeasurementRot3; typedef gtsam::BinaryMeasurement BinaryMeasurementPoint3; +// Used in Matlab wrapper class BinaryMeasurementsUnit3 { BinaryMeasurementsUnit3(); size_t size() const; @@ -107,6 +107,7 @@ class BinaryMeasurementsUnit3 { void push_back(const gtsam::BinaryMeasurement& measurement); }; +// Used in Matlab wrapper class BinaryMeasurementsPoint3 { BinaryMeasurementsPoint3(); size_t size() const; @@ -114,6 +115,7 @@ class BinaryMeasurementsPoint3 { void push_back(const gtsam::BinaryMeasurement& measurement); }; +// Used in Matlab wrapper class BinaryMeasurementsRot3 { BinaryMeasurementsRot3(); size_t size() const; @@ -121,41 +123,19 @@ class BinaryMeasurementsRot3 { void push_back(const gtsam::BinaryMeasurement& measurement); }; +#include #include -// TODO(frank): copy/pasta below until we have integer template parameters in -// wrap! - -class ShonanAveragingParameters2 { - ShonanAveragingParameters2(const gtsam::LevenbergMarquardtParams& lm); - ShonanAveragingParameters2(const gtsam::LevenbergMarquardtParams& lm, +template +class ShonanAveragingParameters { + ShonanAveragingParameters(const gtsam::LevenbergMarquardtParams& lm); + ShonanAveragingParameters(const gtsam::LevenbergMarquardtParams& lm, string method); gtsam::LevenbergMarquardtParams getLMParams() const; void setOptimalityThreshold(double value); double getOptimalityThreshold() const; - void setAnchor(size_t index, const gtsam::Rot2& value); - pair getAnchor(); - void setAnchorWeight(double value); - double getAnchorWeight() const; - void setKarcherWeight(double value); - double getKarcherWeight() const; - void setGaugesWeight(double value); - double getGaugesWeight() const; - void setUseHuber(bool value); - bool getUseHuber() const; - void setCertifyOptimality(bool value); - bool getCertifyOptimality() const; -}; - -class ShonanAveragingParameters3 { - ShonanAveragingParameters3(const gtsam::LevenbergMarquardtParams& lm); - ShonanAveragingParameters3(const gtsam::LevenbergMarquardtParams& lm, - string method); - gtsam::LevenbergMarquardtParams getLMParams() const; - void setOptimalityThreshold(double value); - double getOptimalityThreshold() const; - void setAnchor(size_t index, const gtsam::Rot3& value); - pair getAnchor(); + void setAnchor(size_t index, const gtsam::This::Rot& value); + pair getAnchor(); void setAnchorWeight(double value); double getAnchorWeight() const; void setKarcherWeight(double value); @@ -168,6 +148,7 @@ class ShonanAveragingParameters3 { bool getCertifyOptimality() const; }; +// NOTE(Varun): Not templated because each class has specializations defined. class ShonanAveraging2 { ShonanAveraging2(string g2oFile); ShonanAveraging2(string g2oFile, @@ -214,18 +195,16 @@ class ShonanAveraging2 { }; class ShonanAveraging3 { - ShonanAveraging3( - const std::vector>& measurements, - const gtsam::ShonanAveragingParameters3& parameters = - gtsam::ShonanAveragingParameters3()); + ShonanAveraging3(const gtsam::This::Measurements& measurements, + const gtsam::ShonanAveragingParameters3& parameters = + gtsam::ShonanAveragingParameters3()); ShonanAveraging3(string g2oFile); ShonanAveraging3(string g2oFile, const gtsam::ShonanAveragingParameters3& parameters); - // TODO(frank): deprecate once we land pybind wrapper - ShonanAveraging3(const gtsam::BetweenFactorPose3s& factors); ShonanAveraging3(const gtsam::BetweenFactorPose3s& factors, - const gtsam::ShonanAveragingParameters3& parameters); + const gtsam::ShonanAveragingParameters3& parameters = + gtsam::ShonanAveragingParameters3()); // Query properties size_t nrUnknowns() const; @@ -267,6 +246,7 @@ class ShonanAveraging3 { #include +// Used in Matlab wrapper class KeyPairDoubleMap { KeyPairDoubleMap(); KeyPairDoubleMap(const gtsam::KeyPairDoubleMap& other); @@ -310,12 +290,9 @@ class TranslationRecovery { const gtsam::BinaryMeasurementsUnit3& relativeTranslations, const double scale, const gtsam::BinaryMeasurementsPoint3& betweenTranslations) const; - // default empty betweenTranslations - gtsam::Values run(const gtsam::BinaryMeasurementsUnit3& relativeTranslations, - const double scale) const; // default scale = 1.0, empty betweenTranslations - gtsam::Values run( - const gtsam::BinaryMeasurementsUnit3& relativeTranslations) const; + gtsam::Values run(const gtsam::BinaryMeasurementsUnit3& relativeTranslations, + const double scale = 1.0) const; }; namespace gtsfm { diff --git a/gtsam/slam/slam.i b/gtsam/slam/slam.i index 8e1e06d5b..d35a87eee 100644 --- a/gtsam/slam/slam.i +++ b/gtsam/slam/slam.i @@ -251,7 +251,7 @@ void save2D(const gtsam::NonlinearFactorGraph& graph, string filename); // std::vector::shared_ptr> -// Ignored by pybind -> will be List[BetweenFactorPose2] +// Used in Matlab wrapper class BetweenFactorPose2s { BetweenFactorPose2s(); size_t size() const; @@ -261,13 +261,14 @@ class BetweenFactorPose2s { gtsam::BetweenFactorPose2s parse2DFactors(string filename); // std::vector::shared_ptr> -// Ignored by pybind -> will be List[BetweenFactorPose3] +// Used in Matlab wrapper class BetweenFactorPose3s { BetweenFactorPose3s(); size_t size() const; gtsam::BetweenFactor* at(size_t i) const; void push_back(const gtsam::BetweenFactor* factor); }; + gtsam::BetweenFactorPose3s parse3DFactors(string filename); pair load3D(string filename); diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt index 2557da237..2b2abf507 100644 --- a/python/CMakeLists.txt +++ b/python/CMakeLists.txt @@ -48,10 +48,12 @@ set(ignore gtsam::ISAM2ThresholdMapValue gtsam::FactorIndices gtsam::FactorIndexSet + gtsam::IndexPairSet gtsam::IndexPairSetMap gtsam::IndexPairVector gtsam::BetweenFactorPose2s gtsam::BetweenFactorPose3s + gtsam::FixedLagSmootherKeyTimestampMap gtsam::FixedLagSmootherKeyTimestampMapValue gtsam::Point2Vector gtsam::Point2Pairs diff --git a/python/gtsam/__init__.py b/python/gtsam/__init__.py index d00e47b65..904afd2e0 100644 --- a/python/gtsam/__init__.py +++ b/python/gtsam/__init__.py @@ -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(): diff --git a/python/gtsam/examples/FixedLagSmootherExample.py b/python/gtsam/examples/FixedLagSmootherExample.py index 99af0edcf..c56ebbe07 100644 --- a/python/gtsam/examples/FixedLagSmootherExample.py +++ b/python/gtsam/examples/FixedLagSmootherExample.py @@ -16,6 +16,7 @@ import numpy as np import gtsam import gtsam_unstable + def BatchFixedLagSmootherExample(): """ Runs a batch fixed smoother on an agent with two odometry @@ -31,7 +32,7 @@ def BatchFixedLagSmootherExample(): # that will be sent to the smoothers new_factors = gtsam.NonlinearFactorGraph() new_values = gtsam.Values() - new_timestamps = gtsam.FixedLagSmootherKeyTimestampMap() + new_timestamps = {} # Create a prior on the first pose, placing it at the origin prior_mean = gtsam.Pose2(0, 0, 0) @@ -39,7 +40,7 @@ def BatchFixedLagSmootherExample(): 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)) + new_timestamps[X1] = 0.0 delta_time = 0.25 time = 0.25 @@ -49,7 +50,7 @@ def BatchFixedLagSmootherExample(): current_key = int(1000 * time) # assign current key to the current timestamp - new_timestamps.insert((current_key, time)) + new_timestamps[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] diff --git a/python/gtsam/examples/TranslationAveragingExample.py b/python/gtsam/examples/TranslationAveragingExample.py index 92a7d04e3..ea1cab88d 100644 --- a/python/gtsam/examples/TranslationAveragingExample.py +++ b/python/gtsam/examples/TranslationAveragingExample.py @@ -17,9 +17,8 @@ Date: September 2020 from collections import defaultdict from typing import List, Tuple -import numpy as np - import gtsam +import numpy as np from gtsam.examples import SFMdata # Hyperparameters for 1dsfm, values used from Kyle Wilson's code. @@ -59,7 +58,7 @@ def get_data() -> Tuple[gtsam.Values, List[gtsam.BinaryMeasurementUnit3]]: return wRc_values, i_iZj_list -def filter_outliers(w_iZj_list: gtsam.BinaryMeasurementsUnit3) -> gtsam.BinaryMeasurementsUnit3: +def filter_outliers(w_iZj_list: List[gtsam.BinaryMeasurementUnit3]) -> List[gtsam.BinaryMeasurementUnit3]: """Removes outliers from a list of Unit3 measurements that are the translation directions from camera i to camera j in the world frame.""" @@ -89,14 +88,14 @@ def filter_outliers(w_iZj_list: gtsam.BinaryMeasurementsUnit3) -> gtsam.BinaryMe avg_outlier_weights[keypair] += weight / len(outlier_weights) # Remove w_iZj that have weight greater than threshold, these are outliers. - w_iZj_inliers = gtsam.BinaryMeasurementsUnit3() + w_iZj_inliers = [] [w_iZj_inliers.append(w_iZj) for w_iZj in w_iZj_list if avg_outlier_weights[( w_iZj.key1(), w_iZj.key2())] < OUTLIER_WEIGHT_THRESHOLD] return w_iZj_inliers -def estimate_poses(i_iZj_list: gtsam.BinaryMeasurementsUnit3, +def estimate_poses(i_iZj_list: List[gtsam.BinaryMeasurementUnit3], wRc_values: gtsam.Values) -> gtsam.Values: """Estimate poses given rotations and normalized translation directions between cameras. @@ -112,7 +111,7 @@ def estimate_poses(i_iZj_list: gtsam.BinaryMeasurementsUnit3, """ # Convert the translation direction measurements to world frame using the rotations. - w_iZj_list = gtsam.BinaryMeasurementsUnit3() + w_iZj_list = [] for i_iZj in i_iZj_list: w_iZj = gtsam.Unit3(wRc_values.atRot3(i_iZj.key1()) .rotate(i_iZj.measured().point3())) diff --git a/python/gtsam/gtsam.tpl b/python/gtsam/gtsam.tpl index d748fd65e..c72a216a2 100644 --- a/python/gtsam/gtsam.tpl +++ b/python/gtsam/gtsam.tpl @@ -11,6 +11,7 @@ #include #include +#include #include #include #include diff --git a/python/gtsam/preamble/base.h b/python/gtsam/preamble/base.h index 5cf633e65..d07a75f6f 100644 --- a/python/gtsam/preamble/base.h +++ b/python/gtsam/preamble/base.h @@ -10,9 +10,3 @@ * Without this they will be automatically converted to a Python object, and all * mutations on Python side will not be reflected on C++. */ - -PYBIND11_MAKE_OPAQUE(gtsam::IndexPairVector); - -PYBIND11_MAKE_OPAQUE(gtsam::IndexPairSet); - -PYBIND11_MAKE_OPAQUE(std::vector); // JacobianVector diff --git a/python/gtsam/preamble/basis.h b/python/gtsam/preamble/basis.h index 56a07cfdd..d07a75f6f 100644 --- a/python/gtsam/preamble/basis.h +++ b/python/gtsam/preamble/basis.h @@ -10,5 +10,3 @@ * Without this they will be automatically converted to a Python object, and all * mutations on Python side will not be reflected on C++. */ - -#include diff --git a/python/gtsam/preamble/custom.h b/python/gtsam/preamble/custom.h index d07a75f6f..d7bc6360f 100644 --- a/python/gtsam/preamble/custom.h +++ b/python/gtsam/preamble/custom.h @@ -10,3 +10,6 @@ * Without this they will be automatically converted to a Python object, and all * mutations on Python side will not be reflected on C++. */ + +// Added so that CustomFactor can pass the JacobianVector to the C++ side +PYBIND11_MAKE_OPAQUE(std::vector); // JacobianVector diff --git a/python/gtsam/preamble/discrete.h b/python/gtsam/preamble/discrete.h index 320e0ac71..598ab626d 100644 --- a/python/gtsam/preamble/discrete.h +++ b/python/gtsam/preamble/discrete.h @@ -11,5 +11,4 @@ * mutations on Python side will not be reflected on C++. */ -#include - +PYBIND11_MAKE_OPAQUE(gtsam::DiscreteKeys); diff --git a/python/gtsam/preamble/geometry.h b/python/gtsam/preamble/geometry.h index f36221d38..40b841abc 100644 --- a/python/gtsam/preamble/geometry.h +++ b/python/gtsam/preamble/geometry.h @@ -10,14 +10,8 @@ * Without this they will be automatically converted to a Python object, and all * mutations on Python side will not be reflected on C++. */ -#include -PYBIND11_MAKE_OPAQUE( - std::vector>); -PYBIND11_MAKE_OPAQUE(gtsam::Point2Pairs); -PYBIND11_MAKE_OPAQUE(gtsam::Point3Pairs); -PYBIND11_MAKE_OPAQUE(gtsam::Pose2Pairs); -PYBIND11_MAKE_OPAQUE(gtsam::Pose3Pairs); -PYBIND11_MAKE_OPAQUE(std::vector); -PYBIND11_MAKE_OPAQUE(gtsam::CameraSet>); PYBIND11_MAKE_OPAQUE(gtsam::CameraSet>); +PYBIND11_MAKE_OPAQUE(gtsam::CameraSet>); +PYBIND11_MAKE_OPAQUE(gtsam::CameraSet>); +PYBIND11_MAKE_OPAQUE(gtsam::CameraSet>); diff --git a/python/gtsam/preamble/gtsam.h b/python/gtsam/preamble/gtsam.h index ec39c410a..d07a75f6f 100644 --- a/python/gtsam/preamble/gtsam.h +++ b/python/gtsam/preamble/gtsam.h @@ -10,8 +10,3 @@ * Without this they will be automatically converted to a Python object, and all * mutations on Python side will not be reflected on C++. */ -#ifdef GTSAM_ALLOCATOR_TBB -PYBIND11_MAKE_OPAQUE(std::vector>); -#else -PYBIND11_MAKE_OPAQUE(std::vector); -#endif diff --git a/python/gtsam/preamble/hybrid.h b/python/gtsam/preamble/hybrid.h index 90a062d66..d07a75f6f 100644 --- a/python/gtsam/preamble/hybrid.h +++ b/python/gtsam/preamble/hybrid.h @@ -10,11 +10,3 @@ * Without this they will be automatically converted to a Python object, and all * mutations on Python side will not be reflected on C++. */ -#include - -// NOTE: Needed since we are including pybind11/stl.h. -#ifdef GTSAM_ALLOCATOR_TBB -PYBIND11_MAKE_OPAQUE(std::vector>); -#else -PYBIND11_MAKE_OPAQUE(std::vector); -#endif diff --git a/python/gtsam/preamble/sfm.h b/python/gtsam/preamble/sfm.h index 27a4e5de9..bd71c0817 100644 --- a/python/gtsam/preamble/sfm.h +++ b/python/gtsam/preamble/sfm.h @@ -11,17 +11,3 @@ * mutations on Python side will not be reflected on C++. */ -// Including can cause some serious hard-to-debug bugs!!! -// #include -#include - -PYBIND11_MAKE_OPAQUE(std::vector); -PYBIND11_MAKE_OPAQUE(std::vector); -PYBIND11_MAKE_OPAQUE(std::vector); -PYBIND11_MAKE_OPAQUE( - std::vector>); -PYBIND11_MAKE_OPAQUE( - std::vector>); -PYBIND11_MAKE_OPAQUE( - std::vector); -PYBIND11_MAKE_OPAQUE(gtsam::gtsfm::MatchIndicesMap); \ No newline at end of file diff --git a/python/gtsam/preamble/slam.h b/python/gtsam/preamble/slam.h index 6788bd3c9..d07a75f6f 100644 --- a/python/gtsam/preamble/slam.h +++ b/python/gtsam/preamble/slam.h @@ -10,9 +10,3 @@ * Without this they will be automatically converted to a Python object, and all * mutations on Python side will not be reflected on C++. */ - -PYBIND11_MAKE_OPAQUE( - std::vector > >); -PYBIND11_MAKE_OPAQUE( - std::vector > >); -PYBIND11_MAKE_OPAQUE(gtsam::Rot3Vector); diff --git a/python/gtsam/specializations/base.h b/python/gtsam/specializations/base.h index 9eefdeca8..22fe3beff 100644 --- a/python/gtsam/specializations/base.h +++ b/python/gtsam/specializations/base.h @@ -11,7 +11,3 @@ * and saves one copy operation. */ -py::bind_map(m_, "IndexPairSetMap"); -py::bind_vector(m_, "IndexPairVector"); - -py::bind_vector >(m_, "JacobianVector"); diff --git a/python/gtsam/specializations/custom.h b/python/gtsam/specializations/custom.h index d46ccdc66..cffb5115f 100644 --- a/python/gtsam/specializations/custom.h +++ b/python/gtsam/specializations/custom.h @@ -9,4 +9,7 @@ * interface, but without the `` copying mechanism. Combined * with `PYBIND11_MAKE_OPAQUE` this allows the types to be modified with Python, * and saves one copy operation. - */ \ No newline at end of file + */ + +// Added so that CustomFactor can pass the JacobianVector to the C++ side +py::bind_vector >(m_, "JacobianVector"); diff --git a/python/gtsam/specializations/geometry.h b/python/gtsam/specializations/geometry.h index 99f40253f..4aea1b5cf 100644 --- a/python/gtsam/specializations/geometry.h +++ b/python/gtsam/specializations/geometry.h @@ -11,14 +11,6 @@ * and saves one copy operation. */ -py::bind_vector< - std::vector>>( - m_, "Point2Vector"); -py::bind_vector>(m_, "Point2Pairs"); -py::bind_vector>(m_, "Point3Pairs"); -py::bind_vector>(m_, "Pose2Pairs"); -py::bind_vector>(m_, "Pose3Pairs"); -py::bind_vector>(m_, "Pose3Vector"); py::bind_vector>>( m_, "CameraSetCal3_S2"); py::bind_vector>>( diff --git a/python/gtsam/specializations/gtsam.h b/python/gtsam/specializations/gtsam.h index 490d71902..da8842eaf 100644 --- a/python/gtsam/specializations/gtsam.h +++ b/python/gtsam/specializations/gtsam.h @@ -10,11 +10,3 @@ * with `PYBIND11_MAKE_OPAQUE` this allows the types to be modified with Python, * and saves one copy operation. */ - -#ifdef GTSAM_ALLOCATOR_TBB -py::bind_vector > >(m_, "KeyVector"); -py::implicitly_convertible > >(); -#else -py::bind_vector >(m_, "KeyVector"); -py::implicitly_convertible >(); -#endif diff --git a/python/gtsam/specializations/inference.h b/python/gtsam/specializations/inference.h index 9e23444ea..da8842eaf 100644 --- a/python/gtsam/specializations/inference.h +++ b/python/gtsam/specializations/inference.h @@ -10,5 +10,3 @@ * with `PYBIND11_MAKE_OPAQUE` this allows the types to be modified with Python, * and saves one copy operation. */ - -py::bind_map>(m_, "__MapCharDouble"); diff --git a/python/gtsam/specializations/sfm.h b/python/gtsam/specializations/sfm.h index c4817f555..22fe3beff 100644 --- a/python/gtsam/specializations/sfm.h +++ b/python/gtsam/specializations/sfm.h @@ -11,18 +11,3 @@ * and saves one copy operation. */ -py::bind_vector > >( - m_, "BinaryMeasurementsPoint3"); -py::bind_vector > >( - m_, "BinaryMeasurementsUnit3"); -py::bind_vector > >( - m_, "BinaryMeasurementsRot3"); -py::bind_map(m_, "KeyPairDoubleMap"); -py::bind_vector>(m_, "SfmTrack2dVector"); -py::bind_vector>(m_, "SfmTracks"); -py::bind_vector>(m_, "SfmCameras"); -py::bind_vector>>( - m_, "SfmMeasurementVector"); - -py::bind_map(m_, "MatchIndicesMap"); -py::bind_vector>(m_, "KeypointsVector"); diff --git a/python/gtsam/specializations/slam.h b/python/gtsam/specializations/slam.h index af579d9df..da8842eaf 100644 --- a/python/gtsam/specializations/slam.h +++ b/python/gtsam/specializations/slam.h @@ -10,11 +10,3 @@ * with `PYBIND11_MAKE_OPAQUE` this allows the types to be modified with Python, * and saves one copy operation. */ - -py::bind_vector< - std::vector>>>( - m_, "BetweenFactorPose3s"); -py::bind_vector< - std::vector>>>( - m_, "BetweenFactorPose2s"); -py::bind_vector(m_, "Rot3Vector"); diff --git a/python/gtsam/symbol_shorthand.py b/python/gtsam/symbol_shorthand.py index 748d36558..5f7866d18 100644 --- a/python/gtsam/symbol_shorthand.py +++ b/python/gtsam/symbol_shorthand.py @@ -1,4 +1,4 @@ # This trick is to allow direct import of sub-modules # without this, we can only do `from gtsam.gtsam.symbol_shorthand import X` # with this trick, we can do `from gtsam.symbol_shorthand import X` -from .gtsam.symbol_shorthand import * \ No newline at end of file +from .gtsam.symbol_shorthand import * diff --git a/python/gtsam/tests/test_Cal3Fisheye.py b/python/gtsam/tests/test_Cal3Fisheye.py index e54afc757..50a15dc6e 100644 --- a/python/gtsam/tests/test_Cal3Fisheye.py +++ b/python/gtsam/tests/test_Cal3Fisheye.py @@ -12,10 +12,10 @@ Refactored: Roderick Koehle import unittest import numpy as np +from gtsam.symbol_shorthand import K, L, P +from gtsam.utils.test_case import GtsamTestCase import gtsam -from gtsam.utils.test_case import GtsamTestCase -from gtsam.symbol_shorthand import K, L, P def ulp(ftype=np.float64): @@ -27,7 +27,7 @@ def ulp(ftype=np.float64): class TestCal3Fisheye(GtsamTestCase): - + @classmethod def setUpClass(cls): """ @@ -51,10 +51,10 @@ class TestCal3Fisheye(GtsamTestCase): camera1 = gtsam.PinholeCameraCal3Fisheye(pose1) camera2 = gtsam.PinholeCameraCal3Fisheye(pose2) cls.origin = np.array([0.0, 0.0, 0.0]) - cls.poses = gtsam.Pose3Vector([pose1, pose2]) - cls.cameras = gtsam.CameraSetCal3Fisheye([camera1, camera2]) - cls.measurements = gtsam.Point2Vector([k.project(cls.origin) for k in cls.cameras]) - + 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.) @@ -63,7 +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) @@ -167,7 +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() @@ -187,7 +187,7 @@ class TestCal3Fisheye(GtsamTestCase): def test_triangulation_rectify(self): """Estimate spatial point from image measurements using rectification""" - rectified = gtsam.Point2Vector([k.calibration().calibrate(pt) for k, pt in zip(self.cameras, self.measurements)]) + rectified = [k.calibration().calibrate(pt) for k, pt in zip(self.cameras, self.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) diff --git a/python/gtsam/tests/test_Cal3Unified.py b/python/gtsam/tests/test_Cal3Unified.py index 630109d66..fce741999 100644 --- a/python/gtsam/tests/test_Cal3Unified.py +++ b/python/gtsam/tests/test_Cal3Unified.py @@ -11,10 +11,10 @@ Author: Frank Dellaert & Duy Nguyen Ta (Python) import unittest import numpy as np +from gtsam.symbol_shorthand import K, L, P +from gtsam.utils.test_case import GtsamTestCase import gtsam -from gtsam.utils.test_case import GtsamTestCase -from gtsam.symbol_shorthand import K, L, P class TestCal3Unified(GtsamTestCase): @@ -48,9 +48,9 @@ class TestCal3Unified(GtsamTestCase): camera1 = gtsam.PinholeCameraCal3Unified(pose1, cls.stereographic) camera2 = gtsam.PinholeCameraCal3Unified(pose2, cls.stereographic) cls.origin = np.array([0.0, 0.0, 0.0]) - cls.poses = gtsam.Pose3Vector([pose1, pose2]) - cls.cameras = gtsam.CameraSetCal3Unified([camera1, camera2]) - cls.measurements = gtsam.Point2Vector([k.project(cls.origin) for k in cls.cameras]) + cls.poses = [pose1, pose2] + cls.cameras = [camera1, camera2] + cls.measurements = [k.project(cls.origin) for k in cls.cameras] def test_Cal3Unified(self): K = gtsam.Cal3Unified() @@ -107,7 +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()) @@ -142,7 +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.]])) @@ -158,7 +158,7 @@ class TestCal3Unified(GtsamTestCase): def test_triangulation_rectify(self): """Estimate spatial point from image measurements using rectification""" - rectified = gtsam.Point2Vector([k.calibration().calibrate(pt) for k, pt in zip(self.cameras, self.measurements)]) + rectified = [k.calibration().calibrate(pt) for k, pt in zip(self.cameras, self.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) diff --git a/python/gtsam/tests/test_DecisionTreeFactor.py b/python/gtsam/tests/test_DecisionTreeFactor.py index 0499e7215..1b8598953 100644 --- a/python/gtsam/tests/test_DecisionTreeFactor.py +++ b/python/gtsam/tests/test_DecisionTreeFactor.py @@ -13,7 +13,8 @@ Author: Frank Dellaert import unittest -from gtsam import DecisionTreeFactor, DiscreteValues, DiscreteDistribution, Ordering +from gtsam import (DecisionTreeFactor, DiscreteDistribution, DiscreteValues, + Ordering) from gtsam.utils.test_case import GtsamTestCase diff --git a/python/gtsam/tests/test_DsfTrackGenerator.py b/python/gtsam/tests/test_DsfTrackGenerator.py index 284c3f5cc..be6aa0796 100644 --- a/python/gtsam/tests/test_DsfTrackGenerator.py +++ b/python/gtsam/tests/test_DsfTrackGenerator.py @@ -5,13 +5,13 @@ Authors: John Lambert import unittest -import gtsam import numpy as np -from gtsam import (IndexPair, KeypointsVector, MatchIndicesMap, Point2, - SfmMeasurementVector, SfmTrack2d) from gtsam.gtsfm import Keypoints from gtsam.utils.test_case import GtsamTestCase +import gtsam +from gtsam import IndexPair, Point2, SfmTrack2d + class TestDsfTrackGenerator(GtsamTestCase): """Tests for DsfTrackGenerator.""" @@ -23,14 +23,14 @@ class TestDsfTrackGenerator(GtsamTestCase): kps_i1 = Keypoints(np.array([[50.0, 60], [70, 80], [90, 100]])) kps_i2 = Keypoints(np.array([[110.0, 120], [130, 140]])) - keypoints_list = KeypointsVector() + keypoints_list = [] 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 = MatchIndicesMap() + matches_dict = {} matches_dict[IndexPair(0, 1)] = np.array([[0, 0], [1, 1]]) matches_dict[IndexPair(1, 2)] = np.array([[2, 0], [1, 1]]) @@ -86,7 +86,7 @@ class TestSfmTrack2d(GtsamTestCase): def test_sfm_track_2d_constructor(self) -> None: """Test construction of 2D SfM track.""" - measurements = SfmMeasurementVector() + measurements = [] measurements.append((0, Point2(10, 20))) track = SfmTrack2d(measurements=measurements) track.measurement(0) diff --git a/python/gtsam/tests/test_FixedLagSmootherExample.py b/python/gtsam/tests/test_FixedLagSmootherExample.py index ddd2d49e4..64914ab38 100644 --- a/python/gtsam/tests/test_FixedLagSmootherExample.py +++ b/python/gtsam/tests/test_FixedLagSmootherExample.py @@ -11,10 +11,10 @@ Author: Frank Dellaert & Duy Nguyen Ta (Python) import unittest import numpy as np +from gtsam.utils.test_case import GtsamTestCase import gtsam -import gtsam_unstable -from gtsam.utils.test_case import GtsamTestCase + class TestFixedLagSmootherExample(GtsamTestCase): ''' @@ -36,7 +36,7 @@ class TestFixedLagSmootherExample(GtsamTestCase): # that will be sent to the smoothers new_factors = gtsam.NonlinearFactorGraph() new_values = gtsam.Values() - new_timestamps = gtsam.FixedLagSmootherKeyTimestampMap() + new_timestamps = {} # Create a prior on the first pose, placing it at the origin prior_mean = gtsam.Pose2(0, 0, 0) @@ -47,7 +47,7 @@ class TestFixedLagSmootherExample(GtsamTestCase): gtsam.PriorFactorPose2( X1, prior_mean, prior_noise)) new_values.insert(X1, prior_mean) - new_timestamps.insert((X1, 0.0)) + new_timestamps[X1] = 0.0 delta_time = 0.25 time = 0.25 @@ -77,7 +77,7 @@ class TestFixedLagSmootherExample(GtsamTestCase): current_key = int(1000 * time) # assign current key to the current timestamp - new_timestamps.insert((current_key, time)) + new_timestamps[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] * diff --git a/python/gtsam/tests/test_GaussianFactorGraph.py b/python/gtsam/tests/test_GaussianFactorGraph.py index 09ac4c564..326f62d5a 100644 --- a/python/gtsam/tests/test_GaussianFactorGraph.py +++ b/python/gtsam/tests/test_GaussianFactorGraph.py @@ -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 = gtsam.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__': diff --git a/python/gtsam/tests/test_HybridBayesNet.py b/python/gtsam/tests/test_HybridBayesNet.py index 01e1c5a5d..bc685dec6 100644 --- a/python/gtsam/tests/test_HybridBayesNet.py +++ b/python/gtsam/tests/test_HybridBayesNet.py @@ -17,8 +17,9 @@ import numpy as np from gtsam.symbol_shorthand import A, X from gtsam.utils.test_case import GtsamTestCase -from gtsam import (DiscreteConditional, DiscreteKeys, DiscreteValues, GaussianConditional, - GaussianMixture, HybridBayesNet, HybridValues, noiseModel, VectorValues) +from gtsam import (DiscreteConditional, DiscreteKeys, DiscreteValues, + GaussianConditional, GaussianMixture, HybridBayesNet, + HybridValues, VectorValues, noiseModel) class TestHybridBayesNet(GtsamTestCase): @@ -31,8 +32,8 @@ class TestHybridBayesNet(GtsamTestCase): # Create the continuous conditional I_1x1 = np.eye(1) - conditional = GaussianConditional.FromMeanAndStddev(X(0), 2 * I_1x1, X(1), [-4], - 5.0) + conditional = GaussianConditional.FromMeanAndStddev( + X(0), 2 * I_1x1, X(1), [-4], 5.0) # Create the noise models model0 = noiseModel.Diagonal.Sigmas([2.0]) @@ -47,8 +48,9 @@ class TestHybridBayesNet(GtsamTestCase): # Create hybrid Bayes net. bayesNet = HybridBayesNet() bayesNet.push_back(conditional) - bayesNet.push_back(GaussianMixture( - [X(1)], [], discrete_keys, [conditional0, conditional1])) + bayesNet.push_back( + GaussianMixture([X(1)], [], discrete_keys, + [conditional0, conditional1])) bayesNet.push_back(DiscreteConditional(Asia, "99/1")) # Create values at which to evaluate. @@ -76,7 +78,7 @@ class TestHybridBayesNet(GtsamTestCase): self.check_invariance(bayesNet.at(0).asGaussian(), continuous) self.check_invariance(bayesNet.at(0).asGaussian(), values) self.check_invariance(bayesNet.at(0), values) - + self.check_invariance(bayesNet.at(1), values) self.check_invariance(bayesNet.at(2).asDiscrete(), discrete) @@ -89,7 +91,8 @@ class TestHybridBayesNet(GtsamTestCase): self.assertTrue(probability >= 0.0) logProb = conditional.logProbability(values) self.assertAlmostEqual(probability, np.exp(logProb)) - expected = conditional.logNormalizationConstant() - conditional.error(values) + expected = conditional.logNormalizationConstant() - \ + conditional.error(values) self.assertAlmostEqual(logProb, expected) diff --git a/python/gtsam/tests/test_KarcherMeanFactor.py b/python/gtsam/tests/test_KarcherMeanFactor.py index f4ec64283..0bc942341 100644 --- a/python/gtsam/tests/test_KarcherMeanFactor.py +++ b/python/gtsam/tests/test_KarcherMeanFactor.py @@ -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])) @@ -29,9 +29,11 @@ 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. - rotations = gtsam.Rot3Vector([R, R.inverse()]) + """ + Check that optimizing for Karcher mean (which minimizes Between distance) + gets correct result. + """ + rotations = [R, R.inverse()] expected = Rot3() actual = gtsam.FindKarcherMean(rotations) self.gtsamAssertEquals(expected, actual) @@ -42,7 +44,7 @@ class TestKarcherMean(GtsamTestCase): a2Rb2 = Rot3() a3Rb3 = Rot3() - aRb_list = gtsam.Rot3Vector([a1Rb1, a2Rb2, a3Rb3]) + aRb_list = [a1Rb1, a2Rb2, a3Rb3] aRb_expected = Rot3() aRb = gtsam.FindKarcherMean(aRb_list) @@ -58,9 +60,7 @@ class TestKarcherMean(GtsamTestCase): 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) + keys = [1, 2] graph.add(gtsam.KarcherMeanFactorRot3(keys)) initial = gtsam.Values() @@ -69,11 +69,9 @@ class TestKarcherMean(GtsamTestCase): expected = Rot3() result = gtsam.GaussNewtonOptimizer(graph, initial).optimize() - actual = gtsam.FindKarcherMean( - gtsam.Rot3Vector([result.atRot3(1), result.atRot3(2)])) + 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__": diff --git a/python/gtsam/tests/test_Pose2.py b/python/gtsam/tests/test_Pose2.py index d3a51d638..d29448194 100644 --- a/python/gtsam/tests/test_Pose2.py +++ b/python/gtsam/tests/test_Pose2.py @@ -11,14 +11,15 @@ Author: Frank Dellaert & Duy Nguyen Ta & John Lambert import math import unittest -import gtsam import numpy as np -from gtsam import Point2, Point2Pairs, 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]) @@ -28,7 +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) @@ -42,7 +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) @@ -83,7 +84,7 @@ class TestPose2(GtsamTestCase): ] # fmt: on - ab_pairs = Point2Pairs(list(zip(pts_a, pts_b))) + ab_pairs = list(zip(pts_a, pts_b)) aTb = Pose2.Align(ab_pairs) self.assertIsNotNone(aTb) diff --git a/python/gtsam/tests/test_Pose3.py b/python/gtsam/tests/test_Pose3.py index 4464e8d47..32b3ad47f 100644 --- a/python/gtsam/tests/test_Pose3.py +++ b/python/gtsam/tests/test_Pose3.py @@ -13,10 +13,10 @@ import math import unittest import numpy as np +from gtsam.utils.test_case import GtsamTestCase import gtsam -from gtsam import Point3, Pose3, Rot3, Point3Pairs -from gtsam.utils.test_case import GtsamTestCase +from gtsam import Point3, Pose3, Rot3 def numerical_derivative_pose(pose, method, delta=1e-5): @@ -223,7 +223,7 @@ class TestPose3(GtsamTestCase): sTt = Pose3(Rot3.Rodrigues(0, 0, -math.pi), Point3(2, 4, 0)) transformed = sTt.transformTo(square) - st_pairs = Point3Pairs() + st_pairs = [] for j in range(4): st_pairs.append((square[:,j], transformed[:,j])) @@ -237,4 +237,4 @@ class TestPose3(GtsamTestCase): if __name__ == "__main__": - unittest.main() \ No newline at end of file + unittest.main() diff --git a/python/gtsam/tests/test_ShonanAveraging.py b/python/gtsam/tests/test_ShonanAveraging.py index 19b9f8dc1..845f6cf1c 100644 --- a/python/gtsam/tests/test_ShonanAveraging.py +++ b/python/gtsam/tests/test_ShonanAveraging.py @@ -12,20 +12,14 @@ Author: Frank Dellaert import unittest -import gtsam import numpy as np -from gtsam import ( - BetweenFactorPose2, - LevenbergMarquardtParams, - Rot2, - Pose2, - ShonanAveraging2, - ShonanAveragingParameters2, - ShonanAveraging3, - ShonanAveragingParameters3, -) from gtsam.utils.test_case import GtsamTestCase +import gtsam +from gtsam import (BetweenFactorPose2, LevenbergMarquardtParams, Pose2, Rot2, + ShonanAveraging2, ShonanAveraging3, + ShonanAveragingParameters2, ShonanAveragingParameters3) + DEFAULT_PARAMS = ShonanAveragingParameters3( gtsam.LevenbergMarquardtParams.CeresDefaults() ) @@ -146,7 +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. @@ -183,7 +176,7 @@ class TestShonanAveraging(GtsamTestCase): shonan_params.setCertifyOptimality(True) noise_model = gtsam.noiseModel.Unit.Create(3) - between_factors = gtsam.BetweenFactorPose2s() + between_factors = [] for (i1, i2), i2Ri1 in i2Ri1_dict.items(): i2Ti1 = Pose2(i2Ri1, np.zeros(2)) between_factors.append( @@ -196,11 +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) diff --git a/python/gtsam/tests/test_Sim2.py b/python/gtsam/tests/test_Sim2.py index ea809b965..5501322c3 100644 --- a/python/gtsam/tests/test_Sim2.py +++ b/python/gtsam/tests/test_Sim2.py @@ -12,15 +12,16 @@ Author: John Lambert import unittest import numpy as np -from gtsam import Pose2, Pose2Pairs, Rot2, Similarity2 from gtsam.utils.test_case import GtsamTestCase +from gtsam import Pose2, Rot2, Similarity2 + class TestSim2(GtsamTestCase): """Test selected Sim2 methods.""" def test_align_poses_along_straight_line(self) -> None: - """Test Align Pose2Pairs method. + """Test Align of list of Pose2Pair. Scenario: 3 object poses @@ -46,7 +47,7 @@ class TestSim2(GtsamTestCase): wToi_list = [wTo0, wTo1, wTo2] - we_pairs = Pose2Pairs(list(zip(wToi_list, eToi_list))) + we_pairs = list(zip(wToi_list, eToi_list)) # Recover the transformation wSe (i.e. world_S_egovehicle) wSe = Similarity2.Align(we_pairs) @@ -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 @@ -81,7 +82,7 @@ class TestSim2(GtsamTestCase): wToi_list = [wTo0, wTo1, wTo2] - we_pairs = Pose2Pairs(list(zip(wToi_list, eToi_list))) + we_pairs = list(zip(wToi_list, eToi_list)) # Recover the transformation wSe (i.e. world_S_egovehicle) wSe = Similarity2.Align(we_pairs) @@ -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). @@ -119,7 +120,7 @@ class TestSim2(GtsamTestCase): bTi_list = [bTi0, bTi1, bTi2, bTi3] - ab_pairs = Pose2Pairs(list(zip(aTi_list, bTi_list))) + ab_pairs = list(zip(aTi_list, bTi_list)) # Recover the transformation wSe (i.e. world_S_egovehicle) aSb = Similarity2.Align(ab_pairs) diff --git a/python/gtsam/tests/test_Sim3.py b/python/gtsam/tests/test_Sim3.py index c00a36435..7723738c8 100644 --- a/python/gtsam/tests/test_Sim3.py +++ b/python/gtsam/tests/test_Sim3.py @@ -13,17 +13,17 @@ import unittest from typing import List, Optional import numpy as np +from gtsam.utils.test_case import GtsamTestCase import gtsam -from gtsam import Point3, Pose3, Pose3Pairs, Rot3, Similarity3 -from gtsam.utils.test_case import GtsamTestCase +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 @@ -49,7 +49,7 @@ class TestSim3(GtsamTestCase): wToi_list = [wTo0, wTo1, wTo2] - we_pairs = Pose3Pairs(list(zip(wToi_list, eToi_list))) + we_pairs = list(zip(wToi_list, eToi_list)) # Recover the transformation wSe (i.e. world_S_egovehicle) wSe = Similarity3.Align(we_pairs) @@ -58,7 +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 @@ -84,7 +84,7 @@ class TestSim3(GtsamTestCase): wToi_list = [wTo0, wTo1, wTo2] - we_pairs = Pose3Pairs(list(zip(wToi_list, eToi_list))) + we_pairs = list(zip(wToi_list, eToi_list)) # Recover the transformation wSe (i.e. world_S_egovehicle) wSe = Similarity3.Align(we_pairs) @@ -93,7 +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). @@ -122,7 +122,7 @@ class TestSim3(GtsamTestCase): bTi_list = [bTi0, bTi1, bTi2, bTi3] - ab_pairs = Pose3Pairs(list(zip(aTi_list, bTi_list))) + ab_pairs = list(zip(aTi_list, bTi_list)) # Recover the transformation wSe (i.e. world_S_egovehicle) aSb = Similarity3.Align(ab_pairs) @@ -689,7 +689,7 @@ def align_poses_sim3(aTi_list: List[Pose3], bTi_list: List[Pose3]) -> Similarity assert len(aTi_list) == len(bTi_list) assert n_to_align >= 2, "SIM(3) alignment uses at least 2 frames" - ab_pairs = Pose3Pairs(list(zip(aTi_list, bTi_list))) + ab_pairs = list(zip(aTi_list, bTi_list)) aSb = Similarity3.Align(ab_pairs) diff --git a/python/gtsam/tests/test_TranslationRecovery.py b/python/gtsam/tests/test_TranslationRecovery.py index 99fbce89e..d8e061435 100644 --- a/python/gtsam/tests/test_TranslationRecovery.py +++ b/python/gtsam/tests/test_TranslationRecovery.py @@ -1,25 +1,25 @@ -from __future__ import print_function - -import numpy as np import unittest import gtsam +import numpy as np + -""" Returns example pose values of 3 points A, B and C in the world frame """ def ExampleValues(): + """ Returns example pose values of 3 points A, B and C in the world frame """ T = [] T.append(gtsam.Point3(np.array([3.14, 1.59, 2.65]))) T.append(gtsam.Point3(np.array([-1.0590e+00, -3.6017e-02, -1.5720e+00]))) T.append(gtsam.Point3(np.array([8.5034e+00, 6.7499e+00, -3.6383e+00]))) - + data = gtsam.Values() - for i in range(len(T)): - data.insert(i, gtsam.Pose3(gtsam.Rot3(), T[i])) + for i, t in enumerate(T): + data.insert(i, gtsam.Pose3(gtsam.Rot3(), t)) return data -""" Returns binary measurements for the points in the given edges.""" + def SimulateMeasurements(gt_poses, graph_edges): - measurements = gtsam.BinaryMeasurementsUnit3() + """ Returns binary measurements for the points in the given edges.""" + measurements = [] for edge in graph_edges: Ta = gt_poses.atPose3(edge[0]).translation() Tb = gt_poses.atPose3(edge[1]).translation() @@ -28,18 +28,20 @@ def SimulateMeasurements(gt_poses, graph_edges): gtsam.noiseModel.Isotropic.Sigma(3, 0.01))) return measurements -""" Tests for the translation recovery class """ + class TestTranslationRecovery(unittest.TestCase): - """Test selected Translation Recovery methods.""" + """ Tests for the translation recovery class.""" def test_constructor(self): """Construct from binary measurements.""" algorithm = gtsam.TranslationRecovery() self.assertIsInstance(algorithm, gtsam.TranslationRecovery) - algorithm_params = gtsam.TranslationRecovery(gtsam.LevenbergMarquardtParams()) + algorithm_params = gtsam.TranslationRecovery( + gtsam.LevenbergMarquardtParams()) self.assertIsInstance(algorithm_params, gtsam.TranslationRecovery) def test_run(self): + """Test selected Translation Recovery methods.""" gt_poses = ExampleValues() measurements = SimulateMeasurements(gt_poses, [[0, 1], [0, 2], [1, 2]]) @@ -51,15 +53,23 @@ class TestTranslationRecovery(unittest.TestCase): scale = 2.0 result = algorithm.run(measurements, scale) - w_aTc = gt_poses.atPose3(2).translation() - gt_poses.atPose3(0).translation() - w_aTb = gt_poses.atPose3(1).translation() - gt_poses.atPose3(0).translation() - w_aTc_expected = w_aTc*scale/np.linalg.norm(w_aTb) - w_aTb_expected = w_aTb*scale/np.linalg.norm(w_aTb) + w_aTc = gt_poses.atPose3(2).translation() - gt_poses.atPose3( + 0).translation() + w_aTb = gt_poses.atPose3(1).translation() - gt_poses.atPose3( + 0).translation() + w_aTc_expected = w_aTc * scale / np.linalg.norm(w_aTb) + w_aTb_expected = w_aTb * scale / np.linalg.norm(w_aTb) + + np.testing.assert_array_almost_equal(result.atPoint3(0), + np.array([0, 0, 0]), 6, + "Origin result is incorrect.") + np.testing.assert_array_almost_equal(result.atPoint3(1), + w_aTb_expected, 6, + "Point B result is incorrect.") + np.testing.assert_array_almost_equal(result.atPoint3(2), + w_aTc_expected, 6, + "Point C result is incorrect.") - np.testing.assert_array_almost_equal(result.atPoint3(0), np.array([0,0,0]), 6, "Origin result is incorrect.") - np.testing.assert_array_almost_equal(result.atPoint3(1), w_aTb_expected, 6, "Point B result is incorrect.") - np.testing.assert_array_almost_equal(result.atPoint3(2), w_aTc_expected, 6, "Point C result is incorrect.") if __name__ == "__main__": unittest.main() - diff --git a/python/gtsam/tests/test_Triangulation.py b/python/gtsam/tests/test_Triangulation.py index 8630e1da7..8e245093b 100644 --- a/python/gtsam/tests/test_Triangulation.py +++ b/python/gtsam/tests/test_Triangulation.py @@ -11,15 +11,15 @@ Authors: Frank Dellaert & Fan Jiang (Python) & Sushmita Warrier & John Lambert # pylint: disable=no-name-in-module, invalid-name, no-member import unittest from typing import Iterable, List, Optional, Tuple, Union + 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, Point2Vector, Point3, - Pose3, Pose3Vector, Rot3, TriangulationParameters, - TriangulationResult) -from gtsam.utils.test_case import GtsamTestCase + PinholeCameraCal3Bundler, Point2, Point3, Pose3, Rot3, + TriangulationParameters, TriangulationResult) UPRIGHT = Rot3.Ypr(-np.pi / 2, 0.0, -np.pi / 2) @@ -35,9 +35,7 @@ class TestTriangulationExample(GtsamTestCase): # create second camera 1 meter to the right of first camera pose2 = pose1.compose(Pose3(Rot3(), Point3(1, 0, 0))) # twoPoses - self.poses = Pose3Vector() - self.poses.append(pose1) - self.poses.append(pose2) + self.poses = [pose1, pose2] # landmark ~5 meters infront of camera self.landmark = Point3(5, 0.5, 1.2) @@ -49,7 +47,7 @@ class TestTriangulationExample(GtsamTestCase): cal_params: Iterable[Iterable[Union[int, float]]], camera_set: Optional[Union[CameraSetCal3Bundler, CameraSetCal3_S2]] = None, - ) -> Tuple[Point2Vector, Union[CameraSetCal3Bundler, CameraSetCal3_S2, + ) -> Tuple[List[Point2], Union[CameraSetCal3Bundler, CameraSetCal3_S2, List[Cal3Bundler], List[Cal3_S2]]]: """ Generate vector of measurements for given calibration and camera model. @@ -67,7 +65,7 @@ class TestTriangulationExample(GtsamTestCase): cameras = camera_set() else: cameras = [] - measurements = Point2Vector() + measurements = [] for k, pose in zip(cal_params, self.poses): K = calibration(*k) @@ -96,7 +94,7 @@ class TestTriangulationExample(GtsamTestCase): 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 = Point2Vector() + measurements_noisy = [] measurements_noisy.append(measurements[0] - np.array([0.1, 0.5])) measurements_noisy.append(measurements[1] - np.array([-0.2, 0.3])) @@ -163,8 +161,8 @@ class TestTriangulationExample(GtsamTestCase): z2: Point2 = camera2.project(landmark) z3: Point2 = camera3.project(landmark) - poses = gtsam.Pose3Vector([pose1, pose2, pose3]) - measurements = Point2Vector([z1, z2, z3]) + poses = [pose1, pose2, pose3] + measurements = [z1, z2, z3] # noise free, so should give exactly the landmark actual = gtsam.triangulatePoint3(poses, @@ -226,15 +224,15 @@ class TestTriangulationExample(GtsamTestCase): z2 = camera2.project(self.landmark) cameras = CameraSetCal3_S2() - measurements = Point2Vector() - cameras.append(camera1) cameras.append(camera2) + + measurements = [] measurements.append(z1) measurements.append(z2) landmarkDistanceThreshold = 10 # landmark is closer than that - # all default except landmarkDistanceThreshold: + # all default except landmarkDistanceThreshold: params = TriangulationParameters(1.0, False, landmarkDistanceThreshold) actual: TriangulationResult = gtsam.triangulateSafe( cameras, measurements, params) @@ -242,8 +240,8 @@ class TestTriangulationExample(GtsamTestCase): self.assertTrue(actual.valid()) landmarkDistanceThreshold = 4 # landmark is farther than that - params2 = TriangulationParameters( - 1.0, False, landmarkDistanceThreshold) + params2 = TriangulationParameters(1.0, False, + landmarkDistanceThreshold) actual = gtsam.triangulateSafe(cameras, measurements, params2) self.assertTrue(actual.farPoint()) @@ -258,15 +256,17 @@ class TestTriangulationExample(GtsamTestCase): 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 = 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, + params4 = TriangulationParameters(1.0, False, + landmarkDistanceThreshold, outlierThreshold) actual = gtsam.triangulateSafe(cameras, measurements, params4) self.assertTrue(actual.outlier()) diff --git a/python/gtsam/tests/test_backwards_compatibility.py b/python/gtsam/tests/test_backwards_compatibility.py new file mode 100644 index 000000000..414b65e8c --- /dev/null +++ b/python/gtsam/tests/test_backwards_compatibility.py @@ -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() diff --git a/python/gtsam/tests/test_custom_factor.py b/python/gtsam/tests/test_custom_factor.py index 4f0f33361..d19706c49 100644 --- a/python/gtsam/tests/test_custom_factor.py +++ b/python/gtsam/tests/test_custom_factor.py @@ -8,31 +8,34 @@ See LICENSE for the license information CustomFactor unit tests. Author: Fan Jiang """ -from typing import List import unittest -from gtsam import Values, Pose2, CustomFactor +from typing import List import numpy as np +from gtsam.utils.test_case import GtsamTestCase import gtsam -from gtsam.utils.test_case import GtsamTestCase +from gtsam import CustomFactor, Pose2, Values class TestCustomFactor(GtsamTestCase): + def test_new(self): """Test the creation of a new CustomFactor""" - def error_func(this: CustomFactor, v: gtsam.Values, H: List[np.ndarray]): + def error_func(this: CustomFactor, v: gtsam.Values, + H: List[np.ndarray]): """Minimal error function stub""" - return np.array([1, 0, 0]) + return np.array([1, 0, 0]), H noise_model = gtsam.noiseModel.Unit.Create(3) - cf = CustomFactor(noise_model, gtsam.KeyVector([0]), error_func) + cf = CustomFactor(noise_model, [0], error_func) def test_new_keylist(self): """Test the creation of a new CustomFactor""" - def error_func(this: CustomFactor, v: gtsam.Values, H: List[np.ndarray]): + def error_func(this: CustomFactor, v: gtsam.Values, + H: List[np.ndarray]): """Minimal error function stub""" return np.array([1, 0, 0]) @@ -43,7 +46,8 @@ class TestCustomFactor(GtsamTestCase): """Test if calling the factor works (only error)""" expected_pose = Pose2(1, 1, 0) - def error_func(this: CustomFactor, v: gtsam.Values, H: List[np.ndarray]) -> np.ndarray: + def error_func(this: CustomFactor, v: gtsam.Values, + H: List[np.ndarray]) -> np.ndarray: """Minimal error function with no Jacobian""" key0 = this.keys()[0] error = -v.atPose2(key0).localCoordinates(expected_pose) @@ -65,7 +69,8 @@ class TestCustomFactor(GtsamTestCase): expected = Pose2(2, 2, np.pi / 2) - def error_func(this: CustomFactor, v: gtsam.Values, H: List[np.ndarray]): + def error_func(this: CustomFactor, v: gtsam.Values, + H: List[np.ndarray]): """ the custom error function. One can freely use variables captured from the outside scope. Or the variables can be acquired by indexing `v`. @@ -84,7 +89,7 @@ class TestCustomFactor(GtsamTestCase): return error noise_model = gtsam.noiseModel.Unit.Create(3) - cf = CustomFactor(noise_model, gtsam.KeyVector([0, 1]), error_func) + cf = CustomFactor(noise_model, [0, 1], error_func) v = Values() v.insert(0, gT1) v.insert(1, gT2) @@ -104,7 +109,8 @@ class TestCustomFactor(GtsamTestCase): gT1 = Pose2(1, 2, np.pi / 2) gT2 = Pose2(-1, 4, np.pi) - def error_func(this: CustomFactor, v: gtsam.Values, _: List[np.ndarray]): + def error_func(this: CustomFactor, v: gtsam.Values, + _: List[np.ndarray]): """Minimal error function stub""" return np.array([1, 0, 0]) @@ -125,7 +131,8 @@ class TestCustomFactor(GtsamTestCase): expected = Pose2(2, 2, np.pi / 2) - def error_func(this: CustomFactor, v: gtsam.Values, H: List[np.ndarray]): + def error_func(this: CustomFactor, v: gtsam.Values, + H: List[np.ndarray]): """ Error function that mimics a BetweenFactor :param this: reference to the current CustomFactor being evaluated @@ -138,7 +145,8 @@ class TestCustomFactor(GtsamTestCase): gT1, gT2 = v.atPose2(key0), v.atPose2(key1) error = expected.localCoordinates(gT1.between(gT2)) - self.assertTrue(H is None) # Should be true if we only request the error + self.assertTrue( + H is None) # Should be true if we only request the error if H is not None: result = gT1.between(gT2) @@ -147,7 +155,7 @@ class TestCustomFactor(GtsamTestCase): return error noise_model = gtsam.noiseModel.Unit.Create(3) - cf = CustomFactor(noise_model, gtsam.KeyVector([0, 1]), error_func) + cf = CustomFactor(noise_model, [0, 1], error_func) v = Values() v.insert(0, gT1) v.insert(1, gT2) @@ -165,7 +173,8 @@ class TestCustomFactor(GtsamTestCase): expected = Pose2(2, 2, np.pi / 2) - def error_func(this: CustomFactor, v: gtsam.Values, H: List[np.ndarray]): + def error_func(this: CustomFactor, v: gtsam.Values, + H: List[np.ndarray]): """ Error function that mimics a BetweenFactor :param this: reference to the current CustomFactor being evaluated diff --git a/python/gtsam/utils/plot.py b/python/gtsam/utils/plot.py index 880c436e8..fe067e787 100644 --- a/python/gtsam/utils/plot.py +++ b/python/gtsam/utils/plot.py @@ -12,7 +12,6 @@ from mpl_toolkits.mplot3d import Axes3D # pylint: disable=unused-import import gtsam from gtsam import Marginals, Point2, Point3, Pose2, Pose3, Values - # For translation between a scaling of the uncertainty ellipse and the # percentage of inliers see discussion in # [PR 1067](https://github.com/borglab/gtsam/pull/1067) @@ -557,7 +556,7 @@ def plot_incremental_trajectory(fignum: int, axes = fig.axes[0] poses = gtsam.utilities.allPose3s(values) - keys = gtsam.KeyVector(poses.keys()) + keys = poses.keys() for key in keys[start:]: if values.exists(key): diff --git a/timing/timeTest.cpp b/timing/timeTest.cpp index 9da3cfb2e..c450b9cf0 100644 --- a/timing/timeTest.cpp +++ b/timing/timeTest.cpp @@ -21,7 +21,6 @@ using namespace gtsam; int main(int argc, char *argv[]) { - // FIXME: ticPush_ does not exist { gttic_(top1); gttic_(sub1);