1582 lines
54 KiB
C++
1582 lines
54 KiB
C++
/**
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* GTSAM Wrap Module Definition
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*
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* These are the current classes available through the matlab toolbox interface,
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* add more functions/classes as they are available.
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*
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* Requirements:
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* Classes must start with an uppercase letter
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* - Can wrap a typedef
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* Only one Method/Constructor per line, though methods/constructors can extend across multiple lines
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* Methods can return
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* - Eigen types: Matrix, Vector
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* - C/C++ basic types: string, bool, size_t, size_t, double, char, unsigned char
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* - void
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* - Any class with which be copied with boost::make_shared()
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* - boost::shared_ptr of any object type
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* Constructors
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* - Overloads are supported
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* - A class with no constructors can be returned from other functions but not allocated directly in MATLAB
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* Methods
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* - Constness has no effect
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* - Specify by-value (not reference) return types, even if C++ method returns reference
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* - Must start with a lowercase letter
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* - Overloads are supported
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* Static methods
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* - Must start with a letter (upper or lowercase) and use the "static" keyword
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* - The first letter will be made uppercase in the generated MATLAB interface
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* - Overloads are supported
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* Arguments to functions any of
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* - Eigen types: Matrix, Vector
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* - Eigen types and classes as an optionally const reference
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* - C/C++ basic types: string, bool, size_t, size_t, double, char, unsigned char
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* - Any class with which be copied with boost::make_shared() (except Eigen)
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* - boost::shared_ptr of any object type (except Eigen)
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* Comments can use either C++ or C style, with multiple lines
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* Namespace definitions
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* - Names of namespaces must start with a lowercase letter
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* - start a namespace with "namespace {"
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* - end a namespace with exactly "}///\namespace [namespace_name]", optionally adding the name of the namespace
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* - This ending is not C++ standard, and must contain "}///\namespace" to parse
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* - Namespaces can be nested
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* Namespace usage
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* - Namespaces can be specified for classes in arguments and return values
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* - In each case, the namespace must be fully specified, e.g., "namespace1::namespace2::ClassName"
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* Using namespace: FIXME: this functionality is currently broken
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* - To use a namespace (e.g., generate a "using namespace x" line in cpp files), add "using namespace x;"
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* - This declaration applies to all classes *after* the declaration, regardless of brackets
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* Includes in C++ wrappers
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* - By default, the include will be <[classname].h>
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* - All namespaces must have angle brackets: <path>
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* - To override, add a full include statement just before the class statement
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* - An override include can be added for a namespace by placing it just before the namespace statement
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* - Both classes and namespace accept exactly one namespace
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* Using classes defined in other modules
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* - If you are using a class 'OtherClass' not wrapped in this definition file, add "class OtherClass;" to avoid a dependency error
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* Virtual inheritance
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* - Specify fully-qualified base classes, i.e. "virtual class Derived : module::Base {"
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* - Mark with 'virtual' keyword, e.g. "virtual class Base {", and also "virtual class Derived : module::Base {"
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* - Forward declarations must also be marked virtual, e.g. "virtual class module::Base;" and
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* also "virtual class module::Derived;"
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* - Pure virtual (abstract) classes should list no constructors in this interface file
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* - Virtual classes must have a clone() function in C++ (though it does not have to be included
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* in the MATLAB interface). clone() will be called whenever an object copy is needed, instead
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* of using the copy constructor (which is used for non-virtual objects).
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* Templates
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* - Basic templates are supported either with an explicit list of types to instantiate,
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* e.g. template<T = {gtsam::Pose2, gtsam::Rot2, gtsam::Point3}> class Class1 { ... };
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* or with typedefs, e.g.
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* template<T, U> class Class2 { ... };
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* typedef Class2<Type1, Type2> MyInstantiatedClass;
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* - To create new instantiations in other modules, you must copy-and-paste the whole class definition
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* into the new module, but use only your new instantiation types.
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* - When forward-declaring template instantiations, use the generated/typedefed name, e.g.
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* class gtsam::Class1Pose2;
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* class gtsam::MyInstantiatedClass;
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*/
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/**
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* Status:
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* - TODO: global functions
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* - TODO: default values for arguments
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* - TODO: Handle gtsam::Rot3M conversions to quaternions
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*/
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namespace gtsam {
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//*************************************************************************
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// base
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//*************************************************************************
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virtual class Value {
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// No constructors because this is an abstract class
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// Testable
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void print(string s) const;
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// Manifold
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size_t dim() const;
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};
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virtual class LieVector : gtsam::Value {
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// Standard constructors
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LieVector();
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LieVector(Vector v);
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// Standard interface
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Vector vector() const;
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// Testable
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void print(string s) const;
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bool equals(const gtsam::LieVector& expected, double tol) const;
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// Group
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static gtsam::LieVector identity();
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gtsam::LieVector inverse() const;
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gtsam::LieVector compose(const gtsam::LieVector& p) const;
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gtsam::LieVector between(const gtsam::LieVector& l2) const;
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// Manifold
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size_t dim() const;
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gtsam::LieVector retract(Vector v) const;
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Vector localCoordinates(const gtsam::LieVector& t2) const;
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// Lie group
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static gtsam::LieVector Expmap(Vector v);
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static Vector Logmap(const gtsam::LieVector& p);
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};
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//*************************************************************************
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// geometry
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//*************************************************************************
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virtual class Point2 : gtsam::Value {
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// Standard Constructors
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Point2();
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Point2(double x, double y);
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Point2(Vector v);
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// Testable
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void print(string s) const;
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bool equals(const gtsam::Point2& pose, double tol) const;
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// Group
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static gtsam::Point2 identity();
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gtsam::Point2 inverse() const;
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gtsam::Point2 compose(const gtsam::Point2& p2) const;
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gtsam::Point2 between(const gtsam::Point2& p2) const;
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// Manifold
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static size_t Dim();
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size_t dim() const;
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gtsam::Point2 retract(Vector v) const;
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Vector localCoordinates(const gtsam::Point2& p) const;
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// Lie Group
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static gtsam::Point2 Expmap(Vector v);
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static Vector Logmap(const gtsam::Point2& p);
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// Standard Interface
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double x() const;
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double y() const;
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Vector vector() const;
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};
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virtual class StereoPoint2 : gtsam::Value {
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// Standard Constructors
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StereoPoint2();
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StereoPoint2(double uL, double uR, double v);
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// Testable
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void print(string s) const;
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bool equals(const gtsam::StereoPoint2& point, double tol) const;
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// Group
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static gtsam::StereoPoint2 identity();
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gtsam::StereoPoint2 inverse() const;
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gtsam::StereoPoint2 compose(const gtsam::StereoPoint2& p2) const;
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gtsam::StereoPoint2 between(const gtsam::StereoPoint2& p2) const;
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// Manifold
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static size_t Dim();
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size_t dim() const;
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gtsam::StereoPoint2 retract(Vector v) const;
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Vector localCoordinates(const gtsam::StereoPoint2& p) const;
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// Lie Group
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static gtsam::StereoPoint2 Expmap(Vector v);
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static Vector Logmap(const gtsam::StereoPoint2& p);
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// Standard Interface
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Vector vector() const;
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};
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virtual class Point3 : gtsam::Value {
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// Standard Constructors
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Point3();
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Point3(double x, double y, double z);
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Point3(Vector v);
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// Testable
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void print(string s) const;
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bool equals(const gtsam::Point3& p, double tol) const;
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// Group
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static gtsam::Point3 identity();
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gtsam::Point3 inverse() const;
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gtsam::Point3 compose(const gtsam::Point3& p2) const;
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gtsam::Point3 between(const gtsam::Point3& p2) const;
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// Manifold
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static size_t Dim();
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size_t dim() const;
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gtsam::Point3 retract(Vector v) const;
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Vector localCoordinates(const gtsam::Point3& p) const;
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// Lie Group
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static gtsam::Point3 Expmap(Vector v);
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static Vector Logmap(const gtsam::Point3& p);
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// Standard Interface
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Vector vector() const;
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double x() const;
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double y() const;
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double z() const;
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};
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virtual class Rot2 : gtsam::Value {
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// Standard Constructors and Named Constructors
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Rot2();
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Rot2(double theta);
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static gtsam::Rot2 fromAngle(double theta);
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static gtsam::Rot2 fromDegrees(double theta);
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static gtsam::Rot2 fromCosSin(double c, double s);
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// Testable
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void print(string s) const;
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bool equals(const gtsam::Rot2& rot, double tol) const;
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// Group
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static gtsam::Rot2 identity();
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gtsam::Rot2 inverse();
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gtsam::Rot2 compose(const gtsam::Rot2& p2) const;
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gtsam::Rot2 between(const gtsam::Rot2& p2) const;
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// Manifold
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static size_t Dim();
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size_t dim() const;
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gtsam::Rot2 retract(Vector v) const;
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Vector localCoordinates(const gtsam::Rot2& p) const;
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// Lie Group
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static gtsam::Rot2 Expmap(Vector v);
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static Vector Logmap(const gtsam::Rot2& p);
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// Group Action on Point2
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gtsam::Point2 rotate(const gtsam::Point2& point) const;
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gtsam::Point2 unrotate(const gtsam::Point2& point) const;
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// Standard Interface
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static gtsam::Rot2 relativeBearing(const gtsam::Point2& d); // Ignoring derivative
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static gtsam::Rot2 atan2(double y, double x);
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double theta() const;
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double degrees() const;
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double c() const;
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double s() const;
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Matrix matrix() const;
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};
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virtual class Rot3 : gtsam::Value {
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// Standard Constructors and Named Constructors
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Rot3();
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Rot3(Matrix R);
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static gtsam::Rot3 Rx(double t);
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static gtsam::Rot3 Ry(double t);
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static gtsam::Rot3 Rz(double t);
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static gtsam::Rot3 RzRyRx(double x, double y, double z); // FIXME: overloaded functions don't work yet
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static gtsam::Rot3 RzRyRx(Vector xyz);
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static gtsam::Rot3 yaw(double t); // positive yaw is to right (as in aircraft heading)
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static gtsam::Rot3 pitch(double t); // positive pitch is up (increasing aircraft altitude)
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static gtsam::Rot3 roll(double t); // positive roll is to right (increasing yaw in aircraft)
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static gtsam::Rot3 ypr(double y, double p, double r);
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static gtsam::Rot3 quaternion(double w, double x, double y, double z);
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static gtsam::Rot3 rodriguez(Vector v);
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// Testable
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void print(string s) const;
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bool equals(const gtsam::Rot3& rot, double tol) const;
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// Group
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static gtsam::Rot3 identity();
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gtsam::Rot3 inverse() const;
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gtsam::Rot3 compose(const gtsam::Rot3& p2) const;
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gtsam::Rot3 between(const gtsam::Rot3& p2) const;
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// Manifold
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static size_t Dim();
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size_t dim() const;
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gtsam::Rot3 retractCayley(Vector v) const; // FIXME, does not exist in both Matrix and Quaternion options
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gtsam::Rot3 retract(Vector v) const;
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Vector localCoordinates(const gtsam::Rot3& p) const;
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// Group Action on Point3
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gtsam::Point3 rotate(const gtsam::Point3& p) const;
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gtsam::Point3 unrotate(const gtsam::Point3& p) const;
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// Standard Interface
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static gtsam::Rot3 Expmap(Vector v);
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static Vector Logmap(const gtsam::Rot3& p);
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Matrix matrix() const;
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Matrix transpose() const;
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gtsam::Point3 column(size_t index) const;
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Vector xyz() const;
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Vector ypr() const;
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Vector rpy() const;
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double roll() const;
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double pitch() const;
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double yaw() const;
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// Vector toQuaternion() const; // FIXME: Can't cast to Vector properly
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};
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virtual class Pose2 : gtsam::Value {
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// Standard Constructor
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Pose2();
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Pose2(double x, double y, double theta);
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Pose2(double theta, const gtsam::Point2& t);
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Pose2(const gtsam::Rot2& r, const gtsam::Point2& t);
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Pose2(Vector v);
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// Testable
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void print(string s) const;
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bool equals(const gtsam::Pose2& pose, double tol) const;
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// Group
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static gtsam::Pose2 identity();
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gtsam::Pose2 inverse() const;
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gtsam::Pose2 compose(const gtsam::Pose2& p2) const;
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gtsam::Pose2 between(const gtsam::Pose2& p2) const;
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// Manifold
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static size_t Dim();
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size_t dim() const;
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gtsam::Pose2 retract(Vector v) const;
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Vector localCoordinates(const gtsam::Pose2& p) const;
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// Lie Group
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static gtsam::Pose2 Expmap(Vector v);
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static Vector Logmap(const gtsam::Pose2& p);
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Matrix adjointMap() const;
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Vector adjoint(const Vector& xi) const;
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static Matrix wedge(double vx, double vy, double w);
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// Group Actions on Point2
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gtsam::Point2 transform_from(const gtsam::Point2& p) const;
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gtsam::Point2 transform_to(const gtsam::Point2& p) const;
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// Standard Interface
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double x() const;
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double y() const;
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double theta() const;
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gtsam::Rot2 bearing(const gtsam::Point2& point) const;
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double range(const gtsam::Point2& point) const;
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gtsam::Point2 translation() const;
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gtsam::Rot2 rotation() const;
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Matrix matrix() const;
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};
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virtual class Pose3 : gtsam::Value {
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// Standard Constructors
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Pose3();
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Pose3(const gtsam::Pose3& pose);
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Pose3(const gtsam::Rot3& r, const gtsam::Point3& t);
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Pose3(const gtsam::Pose2& pose2); // FIXME: shadows Pose3(Pose3 pose)
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Pose3(Matrix t);
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// Testable
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void print(string s) const;
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bool equals(const gtsam::Pose3& pose, double tol) const;
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// Group
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static gtsam::Pose3 identity();
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gtsam::Pose3 inverse() const;
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gtsam::Pose3 compose(const gtsam::Pose3& p2) const;
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gtsam::Pose3 between(const gtsam::Pose3& p2) const;
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// Manifold
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static size_t Dim();
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size_t dim() const;
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gtsam::Pose3 retract(Vector v) const;
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gtsam::Pose3 retractFirstOrder(Vector v) const;
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Vector localCoordinates(const gtsam::Pose3& T2) const;
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// Lie Group
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static gtsam::Pose3 Expmap(Vector v);
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static Vector Logmap(const gtsam::Pose3& p);
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Matrix adjointMap() const;
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Vector adjoint(const Vector& xi) const;
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static Matrix wedge(double wx, double wy, double wz, double vx, double vy, double vz);
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// Group Action on Point3
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gtsam::Point3 transform_from(const gtsam::Point3& p) const;
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gtsam::Point3 transform_to(const gtsam::Point3& p) const;
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// Standard Interface
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gtsam::Rot3 rotation() const;
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gtsam::Point3 translation() const;
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double x() const;
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double y() const;
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double z() const;
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Matrix matrix() const;
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gtsam::Pose3 transform_to(const gtsam::Pose3& pose) const; // FIXME: shadows other transform_to()
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double range(const gtsam::Point3& point);
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double range(const gtsam::Pose3& pose);
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};
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virtual class Cal3_S2 : gtsam::Value {
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// Standard Constructors
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Cal3_S2();
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Cal3_S2(double fx, double fy, double s, double u0, double v0);
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Cal3_S2(Vector v);
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// Testable
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void print(string s) const;
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bool equals(const gtsam::Cal3_S2& rhs, double tol) const;
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// Manifold
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static size_t Dim();
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size_t dim() const;
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gtsam::Cal3_S2 retract(Vector v) const;
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Vector localCoordinates(const gtsam::Cal3_S2& c) const;
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// Action on Point2
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gtsam::Point2 calibrate(const gtsam::Point2& p) const;
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gtsam::Point2 uncalibrate(const gtsam::Point2& p) const;
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// Standard Interface
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double fx() const;
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double fy() const;
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double skew() const;
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double px() const;
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double py() const;
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gtsam::Point2 principalPoint() const;
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Vector vector() const;
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Matrix matrix() const;
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Matrix matrix_inverse() const;
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};
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virtual class Cal3DS2 : gtsam::Value {
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// Standard Constructors
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Cal3DS2();
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Cal3DS2(double fx, double fy, double s, double u0, double v0, double k1, double k2, double k3, double k4);
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Cal3DS2(Vector v);
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// Testable
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void print(string s) const;
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bool equals(const gtsam::Cal3DS2& rhs, double tol) const;
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// Manifold
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static size_t Dim();
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size_t dim() const;
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gtsam::Cal3DS2 retract(Vector v) const;
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Vector localCoordinates(const gtsam::Cal3DS2& c) const;
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// Action on Point2
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gtsam::Point2 uncalibrate(const gtsam::Point2& p) const;
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// TODO: D2d functions that start with an uppercase letter
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// Standard Interface
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double fx() const;
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double fy() const;
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double skew() const;
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double px() const;
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double py() const;
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Vector vector() const;
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Vector k() const;
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//Matrix K() const; //FIXME: Uppercase
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};
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class Cal3_S2Stereo {
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// Standard Constructors
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Cal3_S2Stereo();
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Cal3_S2Stereo(double fx, double fy, double s, double u0, double v0, double b);
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// Testable
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void print(string s) const;
|
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bool equals(const gtsam::Cal3_S2Stereo& pose, double tol) const;
|
|
|
|
// Standard Interface
|
|
double fx() const;
|
|
double fy() const;
|
|
double skew() const;
|
|
double px() const;
|
|
double py() const;
|
|
gtsam::Point2 principalPoint() const;
|
|
double baseline() const;
|
|
};
|
|
|
|
virtual class CalibratedCamera : gtsam::Value {
|
|
// Standard Constructors and Named Constructors
|
|
CalibratedCamera();
|
|
CalibratedCamera(const gtsam::Pose3& pose);
|
|
CalibratedCamera(const Vector& v);
|
|
gtsam::CalibratedCamera level(const gtsam::Pose2& pose2, double height);
|
|
|
|
// Testable
|
|
void print(string s) const;
|
|
bool equals(const gtsam::CalibratedCamera& camera, double tol) const;
|
|
|
|
// Manifold
|
|
static size_t Dim();
|
|
size_t dim() const;
|
|
gtsam::CalibratedCamera retract(const Vector& d) const;
|
|
Vector localCoordinates(const gtsam::CalibratedCamera& T2) const;
|
|
|
|
// Group
|
|
gtsam::CalibratedCamera compose(const gtsam::CalibratedCamera& c) const;
|
|
gtsam::CalibratedCamera inverse() const;
|
|
|
|
// Action on Point3
|
|
gtsam::Point2 project(const gtsam::Point3& point) const;
|
|
static gtsam::Point2 project_to_camera(const gtsam::Point3& cameraPoint);
|
|
|
|
// Standard Interface
|
|
gtsam::Pose3 pose() const;
|
|
double range(const gtsam::Point3& p) const; // TODO: Other overloaded range methods
|
|
};
|
|
|
|
virtual class SimpleCamera : gtsam::Value {
|
|
// Standard Constructors and Named Constructors
|
|
SimpleCamera();
|
|
SimpleCamera(const gtsam::Pose3& pose);
|
|
SimpleCamera(const gtsam::Pose3& pose, const gtsam::Cal3_S2& K);
|
|
static gtsam::SimpleCamera level(const gtsam::Cal3_S2& K,
|
|
const gtsam::Pose2& pose, double height);
|
|
static gtsam::SimpleCamera level(const gtsam::Pose2& pose, double height); // FIXME overload
|
|
static gtsam::SimpleCamera lookat(const gtsam::Point3& eye,
|
|
const gtsam::Point3& target, const gtsam::Point3& upVector,
|
|
const gtsam::Cal3_S2& K);
|
|
|
|
// Testable
|
|
void print(string s) const;
|
|
bool equals(const gtsam::SimpleCamera& camera, double tol) const;
|
|
|
|
// Standard Interface
|
|
gtsam::Pose3 pose() const;
|
|
gtsam::Cal3_S2 calibration();
|
|
|
|
// Manifold
|
|
gtsam::SimpleCamera retract(const Vector& d) const;
|
|
Vector localCoordinates(const gtsam::SimpleCamera& T2) const;
|
|
size_t dim() const;
|
|
static size_t Dim();
|
|
|
|
// Transformations and measurement functions
|
|
static gtsam::Point2 project_to_camera(const gtsam::Point3& cameraPoint);
|
|
pair<gtsam::Point2,bool> projectSafe(const gtsam::Point3& pw) const;
|
|
gtsam::Point2 project(const gtsam::Point3& point);
|
|
gtsam::Point3 backproject(const gtsam::Point2& p, double depth) const;
|
|
double range(const gtsam::Point3& point);
|
|
double range(const gtsam::Pose3& point); // FIXME, overload
|
|
};
|
|
|
|
//*************************************************************************
|
|
// inference
|
|
//*************************************************************************
|
|
class Permutation {
|
|
// Standard Constructors and Named Constructors
|
|
Permutation();
|
|
Permutation(size_t nVars);
|
|
static gtsam::Permutation Identity(size_t nVars);
|
|
// FIXME: Cannot currently wrap std::vector
|
|
//static gtsam::Permutation PullToFront(const vector<size_t>& toFront, size_t size, bool filterDuplicates);
|
|
//static gtsam::Permutation PushToBack(const vector<size_t>& toBack, size_t size, bool filterDuplicates = false);
|
|
|
|
// Testable
|
|
void print(string s) const;
|
|
bool equals(const gtsam::Permutation& rhs, double tol) const;
|
|
|
|
// Standard interface
|
|
size_t at(size_t variable) const;
|
|
size_t size() const;
|
|
bool empty() const;
|
|
void resize(size_t newSize);
|
|
gtsam::Permutation* permute(const gtsam::Permutation& permutation) const;
|
|
gtsam::Permutation* inverse() const;
|
|
};
|
|
|
|
class IndexFactor {
|
|
// Standard Constructors and Named Constructors
|
|
IndexFactor();
|
|
IndexFactor(size_t j);
|
|
IndexFactor(size_t j1, size_t j2);
|
|
IndexFactor(size_t j1, size_t j2, size_t j3);
|
|
IndexFactor(size_t j1, size_t j2, size_t j3, size_t j4);
|
|
IndexFactor(size_t j1, size_t j2, size_t j3, size_t j4, size_t j5);
|
|
IndexFactor(size_t j1, size_t j2, size_t j3, size_t j4, size_t j5, size_t j6);
|
|
// FIXME: Must wrap std::set<Index> for this to work
|
|
//IndexFactor(const std::set<Index>& js);
|
|
|
|
// From Factor
|
|
size_t size() const;
|
|
void print(string s) const;
|
|
bool equals(const gtsam::IndexFactor& other, double tol) const;
|
|
// FIXME: Need to wrap std::vector<KeyType>
|
|
//std::vector<KeyType>& keys();
|
|
};
|
|
|
|
class IndexConditional {
|
|
// Standard Constructors and Named Constructors
|
|
IndexConditional();
|
|
IndexConditional(size_t key);
|
|
IndexConditional(size_t key, size_t parent);
|
|
IndexConditional(size_t key, size_t parent1, size_t parent2);
|
|
IndexConditional(size_t key, size_t parent1, size_t parent2, size_t parent3);
|
|
// FIXME: Must wrap std::vector<KeyType> for this to work
|
|
//IndexFactor(size_t key, const std::vector<KeyType>& parents);
|
|
//IndexConditional(const std::vector<Index>& keys, size_t nrFrontals);
|
|
//template<class KEYS> static shared_ptr FromKeys(const KEYS& keys, size_t nrFrontals);
|
|
|
|
// Testable
|
|
void print(string s) const;
|
|
bool equals(const gtsam::IndexConditional& other, double tol) const;
|
|
|
|
// Standard interface
|
|
size_t nrFrontals() const;
|
|
size_t nrParents() const;
|
|
gtsam::IndexFactor* toFactor() const;
|
|
};
|
|
|
|
#include <gtsam/inference/SymbolicFactorGraph.h>
|
|
class SymbolicBayesNet {
|
|
// Standard Constructors and Named Constructors
|
|
SymbolicBayesNet();
|
|
SymbolicBayesNet(const gtsam::SymbolicBayesNet& bn);
|
|
SymbolicBayesNet(const gtsam::IndexConditional* conditional);
|
|
|
|
// Testable
|
|
void print(string s) const;
|
|
bool equals(const gtsam::SymbolicBayesNet& other, double tol) const;
|
|
|
|
// Standard interface
|
|
size_t size() const;
|
|
void push_back(const gtsam::IndexConditional* conditional);
|
|
// FIXME: cannot overload functions
|
|
//void push_back(const SymbolicBayesNet bn);
|
|
void push_front(const gtsam::IndexConditional* conditional);
|
|
// FIXME: cannot overload functions
|
|
//void push_front(const SymbolicBayesNet bn);
|
|
void pop_front();
|
|
void permuteWithInverse(const gtsam::Permutation& inversePermutation);
|
|
bool permuteSeparatorWithInverse(const gtsam::Permutation& inversePermutation);
|
|
};
|
|
|
|
#include <gtsam/inference/SymbolicFactorGraph.h>
|
|
class SymbolicBayesTree {
|
|
// Standard Constructors and Named Constructors
|
|
SymbolicBayesTree();
|
|
SymbolicBayesTree(const gtsam::SymbolicBayesNet& bn);
|
|
SymbolicBayesTree(const gtsam::SymbolicBayesTree& other);
|
|
// FIXME: wrap needs to understand std::list
|
|
//SymbolicBayesTree(const gtsam::SymbolicBayesNet& bayesNet, std::list<gtsam::SymbolicBayesTree> subtrees);
|
|
|
|
// Testable
|
|
void print(string s) const;
|
|
bool equals(const gtsam::SymbolicBayesTree& other, double tol) const;
|
|
|
|
// Standard interface
|
|
size_t size() const;
|
|
void saveGraph(string s) const;
|
|
void clear();
|
|
// TODO: There are many other BayesTree member functions which might be of use
|
|
};
|
|
|
|
class SymbolicFactorGraph {
|
|
// Standard Constructors and Named Constructors
|
|
SymbolicFactorGraph();
|
|
SymbolicFactorGraph(const gtsam::SymbolicBayesNet& bayesNet);
|
|
SymbolicFactorGraph(const gtsam::SymbolicBayesTree& bayesTree);
|
|
|
|
// From FactorGraph
|
|
void push_back(gtsam::IndexFactor* factor);
|
|
void print(string s) const;
|
|
bool equals(const gtsam::SymbolicFactorGraph& rhs, double tol) const;
|
|
size_t size() const;
|
|
|
|
// Standard interface
|
|
// FIXME: Must wrap FastSet<Index> for this to work
|
|
//FastSet<Index> keys() const;
|
|
};
|
|
|
|
class SymbolicSequentialSolver {
|
|
// Standard Constructors and Named Constructors
|
|
SymbolicSequentialSolver(const gtsam::SymbolicFactorGraph& factorGraph);
|
|
SymbolicSequentialSolver(const gtsam::SymbolicFactorGraph* factorGraph, const gtsam::VariableIndex* variableIndex);
|
|
|
|
// Testable
|
|
void print(string s) const;
|
|
bool equals(const gtsam::SymbolicSequentialSolver& rhs, double tol) const;
|
|
|
|
// Standard interface
|
|
gtsam::SymbolicBayesNet* eliminate() const;
|
|
};
|
|
|
|
class SymbolicMultifrontalSolver {
|
|
// Standard Constructors and Named Constructors
|
|
SymbolicMultifrontalSolver(const gtsam::SymbolicFactorGraph& factorGraph);
|
|
SymbolicMultifrontalSolver(const gtsam::SymbolicFactorGraph* factorGraph, const gtsam::VariableIndex* variableIndex);
|
|
|
|
// Testable
|
|
void print(string s) const;
|
|
bool equals(const gtsam::SymbolicMultifrontalSolver& rhs, double tol) const;
|
|
|
|
// Standard interface
|
|
gtsam::SymbolicBayesTree* eliminate() const;
|
|
};
|
|
|
|
#include <gtsam/inference/SymbolicFactorGraph.h>
|
|
class VariableIndex {
|
|
// Standard Constructors and Named Constructors
|
|
VariableIndex();
|
|
// FIXME: Handle templates somehow
|
|
//template<class FactorGraph> VariableIndex(const FactorGraph& factorGraph, size_t nVariables);
|
|
//template<class FactorGraph> VariableIndex(const FactorGraph& factorGraph);
|
|
VariableIndex(const gtsam::SymbolicFactorGraph& factorGraph);
|
|
VariableIndex(const gtsam::SymbolicFactorGraph& factorGraph, size_t nVariables);
|
|
// VariableIndex(const gtsam::GaussianFactorGraph& factorGraph);
|
|
// VariableIndex(const gtsam::GaussianFactorGraph& factorGraph, size_t nVariables);
|
|
// VariableIndex(const gtsam::NonlinearFactorGraph& factorGraph);
|
|
// VariableIndex(const gtsam::NonlinearFactorGraph& factorGraph, size_t nVariables);
|
|
VariableIndex(const gtsam::VariableIndex& other);
|
|
|
|
// Testable
|
|
bool equals(const gtsam::VariableIndex& other, double tol) const;
|
|
void print(string s) const;
|
|
|
|
// Standard interface
|
|
size_t size() const;
|
|
size_t nFactors() const;
|
|
size_t nEntries() const;
|
|
void permuteInPlace(const gtsam::Permutation& permutation);
|
|
};
|
|
|
|
//*************************************************************************
|
|
// linear
|
|
//*************************************************************************
|
|
|
|
#include <gtsam/linear/NoiseModel.h>
|
|
namespace noiseModel {
|
|
virtual class Base {
|
|
};
|
|
|
|
virtual class Gaussian : gtsam::noiseModel::Base {
|
|
static gtsam::noiseModel::Gaussian* SqrtInformation(Matrix R);
|
|
static gtsam::noiseModel::Gaussian* Covariance(Matrix R);
|
|
// Matrix R() const; // FIXME: cannot parse!!!
|
|
void print(string s) const;
|
|
};
|
|
|
|
virtual class Diagonal : gtsam::noiseModel::Gaussian {
|
|
static gtsam::noiseModel::Diagonal* Sigmas(Vector sigmas);
|
|
static gtsam::noiseModel::Diagonal* Variances(Vector variances);
|
|
static gtsam::noiseModel::Diagonal* Precisions(Vector precisions);
|
|
// Matrix R() const; // FIXME: cannot parse!!!
|
|
void print(string s) const;
|
|
};
|
|
|
|
virtual class Isotropic : gtsam::noiseModel::Diagonal {
|
|
static gtsam::noiseModel::Isotropic* Sigma(size_t dim, double sigma);
|
|
static gtsam::noiseModel::Isotropic* Variance(size_t dim, double varianace);
|
|
static gtsam::noiseModel::Isotropic* Precision(size_t dim, double precision);
|
|
void print(string s) const;
|
|
};
|
|
|
|
virtual class Unit : gtsam::noiseModel::Isotropic {
|
|
static gtsam::noiseModel::Unit* Create(size_t dim);
|
|
void print(string s) const;
|
|
};
|
|
}///\namespace noiseModel
|
|
|
|
|
|
class Sampler {
|
|
Sampler(gtsam::noiseModel::Diagonal* model, int seed);
|
|
Sampler(Vector sigmas, int seed);
|
|
Sampler(int seed);
|
|
|
|
size_t dim() const;
|
|
Vector sigmas() const;
|
|
gtsam::noiseModel::Diagonal* model() const;
|
|
|
|
Vector sample();
|
|
Vector sampleNewModel(gtsam::noiseModel::Diagonal* model);
|
|
};
|
|
|
|
class VectorValues {
|
|
VectorValues();
|
|
VectorValues(size_t nVars, size_t varDim);
|
|
void print(string s) const;
|
|
bool equals(const gtsam::VectorValues& expected, double tol) const;
|
|
size_t size() const;
|
|
void insert(size_t j, Vector value);
|
|
};
|
|
|
|
class GaussianConditional {
|
|
GaussianConditional(size_t key, Vector d, Matrix R, Vector sigmas);
|
|
GaussianConditional(size_t key, Vector d, Matrix R, size_t name1, Matrix S,
|
|
Vector sigmas);
|
|
GaussianConditional(size_t key, Vector d, Matrix R, size_t name1, Matrix S,
|
|
size_t name2, Matrix T, Vector sigmas);
|
|
void print(string s) const;
|
|
bool equals(const gtsam::GaussianConditional &cg, double tol) const;
|
|
};
|
|
|
|
class GaussianDensity {
|
|
GaussianDensity(size_t key, Vector d, Matrix R, Vector sigmas);
|
|
void print(string s) const;
|
|
Vector mean() const;
|
|
Matrix information() const;
|
|
Matrix covariance() const;
|
|
};
|
|
|
|
class GaussianBayesNet {
|
|
GaussianBayesNet();
|
|
void print(string s) const;
|
|
bool equals(const gtsam::GaussianBayesNet& cbn, double tol) const;
|
|
void push_back(gtsam::GaussianConditional* conditional);
|
|
void push_front(gtsam::GaussianConditional* conditional);
|
|
};
|
|
|
|
virtual class GaussianFactor {
|
|
void print(string s) const;
|
|
bool equals(const gtsam::GaussianFactor& lf, double tol) const;
|
|
double error(const gtsam::VectorValues& c) const;
|
|
gtsam::GaussianFactor* negate() const;
|
|
size_t size() const;
|
|
};
|
|
|
|
virtual class JacobianFactor : gtsam::GaussianFactor {
|
|
JacobianFactor();
|
|
JacobianFactor(Vector b_in);
|
|
JacobianFactor(size_t i1, Matrix A1, Vector b,
|
|
const gtsam::noiseModel::Diagonal* model);
|
|
JacobianFactor(size_t i1, Matrix A1, size_t i2, Matrix A2, Vector b,
|
|
const gtsam::noiseModel::Diagonal* model);
|
|
JacobianFactor(size_t i1, Matrix A1, size_t i2, Matrix A2, size_t i3, Matrix A3,
|
|
Vector b, const gtsam::noiseModel::Diagonal* model);
|
|
JacobianFactor(const gtsam::GaussianFactor& factor);
|
|
void print(string s) const;
|
|
bool equals(const gtsam::GaussianFactor& lf, double tol) const;
|
|
bool empty() const;
|
|
size_t size() const;
|
|
Vector getb() const;
|
|
double error(const gtsam::VectorValues& c) const;
|
|
gtsam::GaussianConditional* eliminateFirst();
|
|
gtsam::GaussianFactor* negate() const;
|
|
};
|
|
|
|
virtual class HessianFactor : gtsam::GaussianFactor {
|
|
HessianFactor(const gtsam::HessianFactor& gf);
|
|
HessianFactor();
|
|
HessianFactor(size_t j, Matrix G, Vector g, double f);
|
|
HessianFactor(size_t j, Vector mu, Matrix Sigma);
|
|
HessianFactor(size_t j1, size_t j2, Matrix G11, Matrix G12, Vector g1, Matrix G22,
|
|
Vector g2, double f);
|
|
HessianFactor(size_t j1, size_t j2, size_t j3, Matrix G11, Matrix G12, Matrix G13,
|
|
Vector g1, Matrix G22, Matrix G23, Vector g2, Matrix G33, Vector g3,
|
|
double f);
|
|
HessianFactor(const gtsam::GaussianConditional& cg);
|
|
HessianFactor(const gtsam::GaussianFactor& factor);
|
|
size_t size() const;
|
|
void print(string s) const;
|
|
bool equals(const gtsam::GaussianFactor& lf, double tol) const;
|
|
double error(const gtsam::VectorValues& c) const;
|
|
gtsam::GaussianFactor* negate() const;
|
|
};
|
|
|
|
class GaussianFactorGraph {
|
|
GaussianFactorGraph();
|
|
GaussianFactorGraph(const gtsam::GaussianBayesNet& CBN);
|
|
|
|
// From FactorGraph
|
|
void print(string s) const;
|
|
bool equals(const gtsam::GaussianFactorGraph& lfgraph, double tol) const;
|
|
size_t size() const;
|
|
gtsam::GaussianFactor* at(size_t idx) const;
|
|
|
|
// Building the graph
|
|
void push_back(gtsam::GaussianFactor* factor);
|
|
void add(Vector b);
|
|
void add(size_t key1, Matrix A1, Vector b, const gtsam::noiseModel::Diagonal* model);
|
|
void add(size_t key1, Matrix A1, size_t key2, Matrix A2, Vector b,
|
|
const gtsam::noiseModel::Diagonal* model);
|
|
void add(size_t key1, Matrix A1, size_t key2, Matrix A2, size_t key3, Matrix A3,
|
|
Vector b, const gtsam::noiseModel::Diagonal* model);
|
|
|
|
// error and probability
|
|
double error(const gtsam::VectorValues& c) const;
|
|
double probPrime(const gtsam::VectorValues& c) const;
|
|
|
|
// combining
|
|
static gtsam::GaussianFactorGraph combine2(
|
|
const gtsam::GaussianFactorGraph& lfg1,
|
|
const gtsam::GaussianFactorGraph& lfg2);
|
|
void combine(const gtsam::GaussianFactorGraph& lfg);
|
|
|
|
// Conversion to matrices
|
|
Matrix sparseJacobian_() const;
|
|
Matrix denseJacobian() const;
|
|
Matrix denseHessian() const;
|
|
};
|
|
|
|
class GaussianISAM {
|
|
GaussianISAM();
|
|
void saveGraph(string s) const;
|
|
gtsam::GaussianFactor* marginalFactor(size_t j) const;
|
|
gtsam::GaussianBayesNet* marginalBayesNet(size_t key) const;
|
|
Matrix marginalCovariance(size_t key) const;
|
|
gtsam::GaussianBayesNet* jointBayesNet(size_t key1, size_t key2) const;
|
|
};
|
|
|
|
class GaussianSequentialSolver {
|
|
GaussianSequentialSolver(const gtsam::GaussianFactorGraph& graph,
|
|
bool useQR);
|
|
gtsam::GaussianBayesNet* eliminate() const;
|
|
gtsam::VectorValues* optimize() const;
|
|
gtsam::GaussianFactor* marginalFactor(size_t j) const;
|
|
Matrix marginalCovariance(size_t j) const;
|
|
};
|
|
|
|
class KalmanFilter {
|
|
KalmanFilter(size_t n);
|
|
// gtsam::GaussianDensity* init(Vector x0, const gtsam::SharedDiagonal& P0);
|
|
gtsam::GaussianDensity* init(Vector x0, Matrix P0);
|
|
void print(string s) const;
|
|
static size_t step(gtsam::GaussianDensity* p);
|
|
gtsam::GaussianDensity* predict(gtsam::GaussianDensity* p, Matrix F,
|
|
Matrix B, Vector u, const gtsam::noiseModel::Diagonal* modelQ);
|
|
gtsam::GaussianDensity* predictQ(gtsam::GaussianDensity* p, Matrix F,
|
|
Matrix B, Vector u, Matrix Q);
|
|
gtsam::GaussianDensity* predict2(gtsam::GaussianDensity* p, Matrix A0,
|
|
Matrix A1, Vector b, const gtsam::noiseModel::Diagonal* model);
|
|
gtsam::GaussianDensity* update(gtsam::GaussianDensity* p, Matrix H,
|
|
Vector z, const gtsam::noiseModel::Diagonal* model);
|
|
gtsam::GaussianDensity* updateQ(gtsam::GaussianDensity* p, Matrix H,
|
|
Vector z, Matrix Q);
|
|
};
|
|
|
|
//*************************************************************************
|
|
// nonlinear
|
|
//*************************************************************************
|
|
|
|
class Symbol {
|
|
Symbol(char c, size_t j);
|
|
Symbol(size_t k);
|
|
void print(string s) const;
|
|
size_t key() const;
|
|
size_t index() const;
|
|
char chr() const;
|
|
};
|
|
|
|
class Ordering {
|
|
// Standard Constructors and Named Constructors
|
|
Ordering();
|
|
|
|
// Testable
|
|
void print(string s) const;
|
|
bool equals(const gtsam::Ordering& ord, double tol) const;
|
|
|
|
// Standard interface
|
|
size_t nVars() const;
|
|
size_t size() const;
|
|
size_t at(size_t key) const;
|
|
bool exists(size_t key) const;
|
|
void insert(size_t key, size_t order);
|
|
void push_back(size_t key);
|
|
void permuteWithInverse(const gtsam::Permutation& inversePermutation);
|
|
gtsam::InvertedOrdering invert() const;
|
|
};
|
|
|
|
#include <gtsam/nonlinear/Ordering.h>
|
|
class InvertedOrdering {
|
|
InvertedOrdering();
|
|
|
|
// FIXME: add bracket operator overload
|
|
|
|
bool empty() const;
|
|
size_t size() const;
|
|
bool count(size_t index) const; // Use as a boolean function with implicit cast
|
|
|
|
void clear();
|
|
};
|
|
|
|
class NonlinearFactorGraph {
|
|
NonlinearFactorGraph();
|
|
void print(string s) const;
|
|
double error(const gtsam::Values& c) const;
|
|
double probPrime(const gtsam::Values& c) const;
|
|
gtsam::NonlinearFactor* at(size_t i) const;
|
|
void add(const gtsam::NonlinearFactor* factor);
|
|
gtsam::Ordering* orderingCOLAMD(const gtsam::Values& c) const;
|
|
// Ordering* orderingCOLAMDConstrained(const gtsam::Values& c, const std::map<gtsam::Key,int>& constraints) const;
|
|
gtsam::GaussianFactorGraph* linearize(const gtsam::Values& c, const gtsam::Ordering& ordering) const;
|
|
gtsam::NonlinearFactorGraph clone() const;
|
|
};
|
|
|
|
virtual class NonlinearFactor {
|
|
void print(string s) const;
|
|
void equals(const gtsam::NonlinearFactor& other, double tol) const;
|
|
gtsam::KeyVector keys() const;
|
|
size_t size() const;
|
|
size_t dim() const;
|
|
double error(const gtsam::Values& c) const;
|
|
bool active(const gtsam::Values& c) const;
|
|
gtsam::GaussianFactor* linearize(const gtsam::Values& c, const gtsam::Ordering& ordering) const;
|
|
gtsam::NonlinearFactor* clone() const;
|
|
// gtsam::NonlinearFactor* rekey(const gtsam::KeyVector& newKeys) const; //FIXME: Conversion from KeyVector to std::vector does not happen
|
|
};
|
|
|
|
class Values {
|
|
Values();
|
|
size_t size() const;
|
|
void print(string s) const;
|
|
void insert(size_t j, const gtsam::Value& value);
|
|
bool exists(size_t j) const;
|
|
gtsam::Value at(size_t j) const;
|
|
};
|
|
|
|
// Actually a FastList<Key>
|
|
#include <gtsam/nonlinear/Key.h>
|
|
class KeyList {
|
|
KeyList();
|
|
KeyList(const gtsam::KeyList& other);
|
|
|
|
// Note: no print function
|
|
|
|
// common STL methods
|
|
size_t size() const;
|
|
bool empty() const;
|
|
void clear();
|
|
|
|
// structure specific methods
|
|
size_t front() const;
|
|
size_t back() const;
|
|
void push_back(size_t key);
|
|
void push_front(size_t key);
|
|
void sort();
|
|
void remove(size_t key);
|
|
};
|
|
|
|
// Actually a FastSet<Key>
|
|
#include <gtsam/nonlinear/Key.h>
|
|
class KeySet {
|
|
KeySet();
|
|
KeySet(const gtsam::KeySet& other);
|
|
|
|
// Testable
|
|
void print(string s) const;
|
|
bool equals(const gtsam::KeySet& other) const;
|
|
|
|
// common STL methods
|
|
size_t size() const;
|
|
bool empty() const;
|
|
void clear();
|
|
|
|
// structure specific methods
|
|
void insert(size_t key);
|
|
bool erase(size_t key); // returns true if value was removed
|
|
bool count(size_t key) const; // returns true if value exists
|
|
};
|
|
|
|
// Actually a FastVector<Key>
|
|
#include <gtsam/nonlinear/Key.h>
|
|
class KeyVector {
|
|
KeyVector();
|
|
KeyVector(const gtsam::KeyVector& other);
|
|
KeyVector(const gtsam::KeySet& other);
|
|
KeyVector(const gtsam::KeyList& other);
|
|
|
|
// Note: no print function
|
|
|
|
// common STL methods
|
|
size_t size() const;
|
|
bool empty() const;
|
|
void clear();
|
|
|
|
// structure specific methods
|
|
size_t at(size_t i) const;
|
|
size_t front() const;
|
|
size_t back() const;
|
|
void push_back(size_t key) const;
|
|
};
|
|
|
|
class Marginals {
|
|
Marginals(const gtsam::NonlinearFactorGraph& graph,
|
|
const gtsam::Values& solution);
|
|
void print(string s) const;
|
|
Matrix marginalCovariance(size_t variable) const;
|
|
Matrix marginalInformation(size_t variable) const;
|
|
};
|
|
|
|
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
|
|
class LevenbergMarquardtParams {
|
|
LevenbergMarquardtParams();
|
|
void print(string s) const;
|
|
|
|
size_t getMaxIterations() const;
|
|
double getRelativeErrorTol() const;
|
|
double getAbsoluteErrorTol() const;
|
|
double getErrorTol() const;
|
|
string getVerbosity() const;
|
|
|
|
void setMaxIterations(size_t value);
|
|
void setRelativeErrorTol(double value);
|
|
void setAbsoluteErrorTol(double value);
|
|
void setErrorTol(double value);
|
|
void setVerbosity(string s);
|
|
|
|
bool isMultifrontal() const;
|
|
bool isSequential() const;
|
|
bool isCholmod() const;
|
|
bool isCG() const;
|
|
|
|
double getlambdaInitial() const ;
|
|
double getlambdaFactor() const ;
|
|
double getlambdaUpperBound() const;
|
|
string getVerbosityLM() const ;
|
|
|
|
void setlambdaInitial(double value);
|
|
void setlambdaFactor(double value);
|
|
void setlambdaUpperBound(double value);
|
|
void setVerbosityLM(string s);
|
|
};
|
|
|
|
//*************************************************************************
|
|
// Nonlinear factor types
|
|
//*************************************************************************
|
|
template<T = {gtsam::LieVector, gtsam::Point2, gtsam::StereoPoint2, gtsam::Point3, gtsam::Rot2, gtsam::Rot3, gtsam::Pose2, gtsam::Pose3, gtsam::Cal3_S2, gtsam::CalibratedCamera, gtsam::SimpleCamera}>
|
|
virtual class PriorFactor : gtsam::NonlinearFactor {
|
|
PriorFactor(size_t key, const T& prior, const gtsam::noiseModel::Base* noiseModel);
|
|
};
|
|
|
|
|
|
template<T = {gtsam::LieVector, gtsam::Point2, gtsam::Point3, gtsam::Rot2, gtsam::Rot3, gtsam::Pose2, gtsam::Pose3}>
|
|
virtual class BetweenFactor : gtsam::NonlinearFactor {
|
|
BetweenFactor(size_t key1, size_t key2, const T& relativePose, const gtsam::noiseModel::Base* noiseModel);
|
|
};
|
|
|
|
|
|
template<T = {gtsam::LieVector, gtsam::Point2, gtsam::StereoPoint2, gtsam::Point3, gtsam::Rot2, gtsam::Rot3, gtsam::Pose2, gtsam::Pose3, gtsam::Cal3_S2, gtsam::CalibratedCamera, gtsam::SimpleCamera}>
|
|
virtual class NonlinearEquality : gtsam::NonlinearFactor {
|
|
// Constructor - forces exact evaluation
|
|
NonlinearEquality(size_t j, const T& feasible);
|
|
// Constructor - allows inexact evaluation
|
|
NonlinearEquality(size_t j, const T& feasible, double error_gain);
|
|
};
|
|
|
|
|
|
template<POSE, POINT>
|
|
virtual class RangeFactor : gtsam::NonlinearFactor {
|
|
RangeFactor(size_t key1, size_t key2, double measured, const gtsam::noiseModel::Base* noiseModel);
|
|
};
|
|
|
|
typedef gtsam::RangeFactor<gtsam::Pose2, gtsam::Point2> RangeFactor2D;
|
|
typedef gtsam::RangeFactor<gtsam::Pose3, gtsam::Point3> RangeFactor3D;
|
|
typedef gtsam::RangeFactor<gtsam::CalibratedCamera, gtsam::Point3> RangeFactorCalibratedCamera;
|
|
typedef gtsam::RangeFactor<gtsam::SimpleCamera, gtsam::Point3> RangeFactorSimpleCamera;
|
|
|
|
template<POSE, POINT, ROT>
|
|
virtual class BearingFactor : gtsam::NonlinearFactor {
|
|
BearingFactor(size_t key1, size_t key2, const ROT& measured, const gtsam::noiseModel::Base* noiseModel);
|
|
};
|
|
|
|
typedef gtsam::BearingFactor<gtsam::Pose2, gtsam::Point2, gtsam::Rot2> BearingFactor2D;
|
|
|
|
|
|
#include <ProjectionFactor.h>
|
|
template<POSE, LANDMARK, CALIBRATION>
|
|
virtual class GenericProjectionFactor : gtsam::NonlinearFactor {
|
|
GenericProjectionFactor(const gtsam::Point2& measured, const gtsam::noiseModel::Base* noiseModel,
|
|
size_t poseKey, size_t pointKey, const CALIBRATION* k);
|
|
gtsam::Point2 measured() const;
|
|
CALIBRATION* calibration() const;
|
|
};
|
|
typedef gtsam::GenericProjectionFactor<gtsam::Pose3, gtsam::Point3, gtsam::Cal3_S2> GenericProjectionFactorCal3_S2;
|
|
typedef gtsam::GenericProjectionFactor<gtsam::Pose3, gtsam::Point3, gtsam::Cal3DS2> GenericProjectionFactorCal3DS2;
|
|
|
|
}///\namespace gtsam
|
|
|
|
//*************************************************************************
|
|
// Pose2SLAM
|
|
//*************************************************************************
|
|
|
|
#include <gtsam/slam/pose2SLAM.h>
|
|
namespace pose2SLAM {
|
|
|
|
class Values {
|
|
Values();
|
|
Values(const pose2SLAM::Values& values);
|
|
size_t size() const;
|
|
void print(string s) const;
|
|
bool exists(size_t key);
|
|
gtsam::KeyVector keys() const; // Note the switch to KeyVector, rather than KeyList
|
|
|
|
static pose2SLAM::Values Circle(size_t n, double R);
|
|
void insertPose(size_t key, const gtsam::Pose2& pose);
|
|
void updatePose(size_t key, const gtsam::Pose2& pose);
|
|
gtsam::Pose2 pose(size_t i);
|
|
Matrix poses() const;
|
|
};
|
|
|
|
class Graph {
|
|
Graph();
|
|
Graph(const gtsam::NonlinearFactorGraph& graph);
|
|
Graph(const pose2SLAM::Graph& graph);
|
|
|
|
// FactorGraph
|
|
void print(string s) const;
|
|
bool equals(const pose2SLAM::Graph& fg, double tol) const;
|
|
size_t size() const;
|
|
bool empty() const;
|
|
void remove(size_t i);
|
|
size_t nrFactors() const;
|
|
gtsam::NonlinearFactor* at(size_t i) const;
|
|
|
|
// NonlinearFactorGraph
|
|
double error(const pose2SLAM::Values& values) const;
|
|
double probPrime(const pose2SLAM::Values& values) const;
|
|
gtsam::Ordering* orderingCOLAMD(const pose2SLAM::Values& values) const;
|
|
gtsam::GaussianFactorGraph* linearize(const pose2SLAM::Values& values,
|
|
const gtsam::Ordering& ordering) const;
|
|
|
|
// pose2SLAM-specific
|
|
void addPoseConstraint(size_t key, const gtsam::Pose2& pose);
|
|
void addPosePrior(size_t key, const gtsam::Pose2& pose, const gtsam::noiseModel::Base* noiseModel);
|
|
void addRelativePose(size_t key1, size_t key2, const gtsam::Pose2& relativePoseMeasurement, const gtsam::noiseModel::Base* noiseModel);
|
|
pose2SLAM::Values optimize(const pose2SLAM::Values& initialEstimate, size_t verbosity) const;
|
|
pose2SLAM::Values optimizeSPCG(const pose2SLAM::Values& initialEstimate, size_t verbosity) const;
|
|
gtsam::Marginals marginals(const pose2SLAM::Values& solution) const;
|
|
};
|
|
|
|
}///\namespace pose2SLAM
|
|
|
|
//*************************************************************************
|
|
// Pose3SLAM
|
|
//*************************************************************************
|
|
|
|
#include <gtsam/slam/pose3SLAM.h>
|
|
namespace pose3SLAM {
|
|
|
|
class Values {
|
|
Values();
|
|
Values(const pose3SLAM::Values& values);
|
|
size_t size() const;
|
|
void print(string s) const;
|
|
bool exists(size_t key);
|
|
gtsam::KeyVector keys() const; // Note the switch to KeyVector, rather than KeyList
|
|
|
|
static pose3SLAM::Values Circle(size_t n, double R);
|
|
void insertPose(size_t key, const gtsam::Pose3& pose);
|
|
void updatePose(size_t key, const gtsam::Pose3& pose);
|
|
gtsam::Pose3 pose(size_t i);
|
|
Matrix translations() const;
|
|
};
|
|
|
|
class Graph {
|
|
Graph();
|
|
Graph(const gtsam::NonlinearFactorGraph& graph);
|
|
Graph(const pose3SLAM::Graph& graph);
|
|
|
|
// FactorGraph
|
|
void print(string s) const;
|
|
bool equals(const pose3SLAM::Graph& fg, double tol) const;
|
|
size_t size() const;
|
|
bool empty() const;
|
|
void remove(size_t i);
|
|
size_t nrFactors() const;
|
|
gtsam::NonlinearFactor* at(size_t i) const;
|
|
|
|
// NonlinearFactorGraph
|
|
double error(const pose3SLAM::Values& values) const;
|
|
double probPrime(const pose3SLAM::Values& values) const;
|
|
gtsam::Ordering* orderingCOLAMD(const pose3SLAM::Values& values) const;
|
|
gtsam::GaussianFactorGraph* linearize(const pose3SLAM::Values& values,
|
|
const gtsam::Ordering& ordering) const;
|
|
|
|
// pose3SLAM-specific
|
|
void addPoseConstraint(size_t i, const gtsam::Pose3& p);
|
|
void addPosePrior(size_t key, const gtsam::Pose3& p, const gtsam::noiseModel::Base* model);
|
|
void addRelativePose(size_t key1, size_t key2, const gtsam::Pose3& z, const gtsam::noiseModel::Base* model);
|
|
pose3SLAM::Values optimize(const pose3SLAM::Values& initialEstimate, size_t verbosity) const;
|
|
// FIXME gtsam::LevenbergMarquardtOptimizer optimizer(const pose3SLAM::Values& initialEstimate, const gtsam::LevenbergMarquardtParams& parameters) const;
|
|
gtsam::Marginals marginals(const pose3SLAM::Values& solution) const;
|
|
};
|
|
|
|
}///\namespace pose3SLAM
|
|
|
|
//*************************************************************************
|
|
// planarSLAM
|
|
//*************************************************************************
|
|
|
|
#include <gtsam/slam/planarSLAM.h>
|
|
namespace planarSLAM {
|
|
|
|
class Values {
|
|
Values();
|
|
Values(const planarSLAM::Values& values);
|
|
size_t size() const;
|
|
void print(string s) const;
|
|
bool exists(size_t key);
|
|
gtsam::KeyVector keys() const; // Note the switch to KeyVector, rather than KeyList
|
|
|
|
// inherited from pose2SLAM
|
|
static planarSLAM::Values Circle(size_t n, double R);
|
|
void insertPose(size_t key, const gtsam::Pose2& pose);
|
|
void updatePose(size_t key, const gtsam::Pose2& pose);
|
|
gtsam::Pose2 pose(size_t i);
|
|
Matrix poses() const;
|
|
|
|
// Access to poses
|
|
planarSLAM::Values allPoses() const;
|
|
size_t nrPoses() const;
|
|
gtsam::KeyVector poseKeys() const; // Note the switch to KeyVector, rather than KeyList
|
|
|
|
// Access to points
|
|
planarSLAM::Values allPoints() const;
|
|
size_t nrPoints() const;
|
|
gtsam::KeyVector pointKeys() const; // Note the switch to KeyVector, rather than KeyList
|
|
|
|
void insertPoint(size_t key, const gtsam::Point2& point);
|
|
void updatePoint(size_t key, const gtsam::Point2& point);
|
|
gtsam::Point2 point(size_t key) const;
|
|
Matrix points() const;
|
|
};
|
|
|
|
class Graph {
|
|
Graph();
|
|
Graph(const gtsam::NonlinearFactorGraph& graph);
|
|
Graph(const pose2SLAM::Graph& graph);
|
|
Graph(const planarSLAM::Graph& graph);
|
|
|
|
// FactorGraph
|
|
void print(string s) const;
|
|
bool equals(const planarSLAM::Graph& fg, double tol) const;
|
|
size_t size() const;
|
|
bool empty() const;
|
|
void remove(size_t i);
|
|
size_t nrFactors() const;
|
|
gtsam::NonlinearFactor* at(size_t i) const;
|
|
|
|
// NonlinearFactorGraph
|
|
double error(const planarSLAM::Values& values) const;
|
|
double probPrime(const planarSLAM::Values& values) const;
|
|
gtsam::Ordering* orderingCOLAMD(const planarSLAM::Values& values) const;
|
|
gtsam::GaussianFactorGraph* linearize(const planarSLAM::Values& values,
|
|
const gtsam::Ordering& ordering) const;
|
|
|
|
// pose2SLAM-inherited
|
|
void addPoseConstraint(size_t key, const gtsam::Pose2& pose);
|
|
void addPosePrior(size_t key, const gtsam::Pose2& pose, const gtsam::noiseModel::Base* noiseModel);
|
|
void addRelativePose(size_t key1, size_t key2, const gtsam::Pose2& relativePoseMeasurement, const gtsam::noiseModel::Base* noiseModel);
|
|
planarSLAM::Values optimize(const planarSLAM::Values& initialEstimate, size_t verbosity) const;
|
|
planarSLAM::Values optimizeSPCG(const planarSLAM::Values& initialEstimate, size_t verbosity) const;
|
|
gtsam::Marginals marginals(const planarSLAM::Values& solution) const;
|
|
|
|
// planarSLAM-specific
|
|
void addPointConstraint(size_t pointKey, const gtsam::Point2& p);
|
|
void addPointPrior(size_t pointKey, const gtsam::Point2& p, const gtsam::noiseModel::Base* model);
|
|
void addBearing(size_t poseKey, size_t pointKey, const gtsam::Rot2& bearing, const gtsam::noiseModel::Base* noiseModel);
|
|
void addRange(size_t poseKey, size_t pointKey, double range, const gtsam::noiseModel::Base* noiseModel);
|
|
void addBearingRange(size_t poseKey, size_t pointKey, const gtsam::Rot2& bearing,double range, const gtsam::noiseModel::Base* noiseModel);
|
|
};
|
|
|
|
class Odometry {
|
|
Odometry(size_t key1, size_t key2, const gtsam::Pose2& measured,
|
|
const gtsam::noiseModel::Base* model);
|
|
void print(string s) const;
|
|
gtsam::GaussianFactor* linearize(const planarSLAM::Values& center,
|
|
const gtsam::Ordering& ordering) const;
|
|
};
|
|
|
|
}///\namespace planarSLAM
|
|
|
|
//*************************************************************************
|
|
// VisualSLAM
|
|
//*************************************************************************
|
|
|
|
#include <gtsam/slam/visualSLAM.h>
|
|
namespace visualSLAM {
|
|
|
|
class Values {
|
|
Values();
|
|
Values(const visualSLAM::Values& values);
|
|
size_t size() const;
|
|
void print(string s) const;
|
|
bool exists(size_t key);
|
|
gtsam::KeyVector keys() const; // Note the switch to KeyVector, rather than KeyList
|
|
|
|
// pose3SLAM inherited
|
|
static visualSLAM::Values Circle(size_t n, double R);
|
|
void insertPose(size_t key, const gtsam::Pose3& pose);
|
|
void updatePose(size_t key, const gtsam::Pose3& pose);
|
|
gtsam::Pose3 pose(size_t i);
|
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Matrix translations() const;
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// Access to poses
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visualSLAM::Values allPoses() const;
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size_t nrPoses() const;
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gtsam::KeyVector poseKeys() const; // Note the switch to KeyVector, rather than KeyList
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|
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// Access to points
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visualSLAM::Values allPoints() const;
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size_t nrPoints() const;
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gtsam::KeyVector pointKeys() const; // Note the switch to KeyVector, rather than KeyList
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|
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void insertPoint(size_t key, const gtsam::Point3& pose);
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void updatePoint(size_t key, const gtsam::Point3& pose);
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gtsam::Point3 point(size_t j);
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void insertBackprojections(const gtsam::SimpleCamera& c, Vector J, Matrix Z, double depth);
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void perturbPoints(double sigma, size_t seed);
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Matrix points() const;
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};
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|
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class Graph {
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Graph();
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Graph(const gtsam::NonlinearFactorGraph& graph);
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Graph(const pose3SLAM::Graph& graph);
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Graph(const visualSLAM::Graph& graph);
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|
|
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// FactorGraph
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void print(string s) const;
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bool equals(const visualSLAM::Graph& fg, double tol) const;
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size_t size() const;
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|
bool empty() const;
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void remove(size_t i);
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|
size_t nrFactors() const;
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|
gtsam::NonlinearFactor* at(size_t i) const;
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|
|
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double error(const visualSLAM::Values& values) const;
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|
gtsam::Ordering* orderingCOLAMD(const visualSLAM::Values& values) const;
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|
gtsam::GaussianFactorGraph* linearize(const visualSLAM::Values& values,
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const gtsam::Ordering& ordering) const;
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|
|
|
// pose3SLAM-inherited
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void addPoseConstraint(size_t i, const gtsam::Pose3& p);
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|
void addPosePrior(size_t key, const gtsam::Pose3& p, const gtsam::noiseModel::Base* model);
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|
void addRelativePose(size_t key1, size_t key2, const gtsam::Pose3& z, const gtsam::noiseModel::Base* model);
|
|
visualSLAM::Values optimize(const visualSLAM::Values& initialEstimate, size_t verbosity) const;
|
|
visualSLAM::LevenbergMarquardtOptimizer optimizer(const visualSLAM::Values& initialEstimate, const gtsam::LevenbergMarquardtParams& parameters) const;
|
|
gtsam::Marginals marginals(const visualSLAM::Values& solution) const;
|
|
|
|
// Priors and constraints
|
|
void addPointConstraint(size_t pointKey, const gtsam::Point3& p);
|
|
void addPointPrior(size_t pointKey, const gtsam::Point3& p, const gtsam::noiseModel::Base* model);
|
|
void addRangeFactor(size_t poseKey, size_t pointKey, double range, const gtsam::noiseModel::Base* model);
|
|
|
|
// Measurements
|
|
void addMeasurement(const gtsam::Point2& measured,
|
|
const gtsam::noiseModel::Base* model, size_t poseKey, size_t pointKey,
|
|
const gtsam::Cal3_S2* K);
|
|
void addMeasurements(size_t i, Vector J, Matrix Z,
|
|
const gtsam::noiseModel::Base* model, const gtsam::Cal3_S2* K);
|
|
void addStereoMeasurement(const gtsam::StereoPoint2& measured,
|
|
const gtsam::noiseModel::Base* model, size_t poseKey, size_t pointKey,
|
|
const gtsam::Cal3_S2Stereo* K);
|
|
|
|
// Information
|
|
Matrix reprojectionErrors(const visualSLAM::Values& values) const;
|
|
|
|
};
|
|
|
|
class ISAM {
|
|
ISAM();
|
|
ISAM(int reorderInterval);
|
|
void print(string s) const;
|
|
void printStats() const;
|
|
void saveGraph(string s) const;
|
|
visualSLAM::Values estimate() const;
|
|
Matrix marginalCovariance(size_t key) const;
|
|
int reorderInterval() const;
|
|
int reorderCounter() const;
|
|
void update(const visualSLAM::Graph& newFactors, const visualSLAM::Values& initialValues);
|
|
void reorder_relinearize();
|
|
void addKey(size_t key);
|
|
void setOrdering(const gtsam::Ordering& new_ordering);
|
|
|
|
// These might be expensive as instead of a reference the wrapper will make a copy
|
|
gtsam::GaussianISAM bayesTree() const;
|
|
visualSLAM::Values getLinearizationPoint() const;
|
|
gtsam::Ordering getOrdering() const;
|
|
gtsam::NonlinearFactorGraph getFactorsUnsafe() const;
|
|
};
|
|
|
|
class LevenbergMarquardtOptimizer {
|
|
double lambda() const;
|
|
void iterate();
|
|
double error() const;
|
|
size_t iterations() const;
|
|
visualSLAM::Values optimize();
|
|
visualSLAM::Values optimizeSafely();
|
|
visualSLAM::Values values() const;
|
|
};
|
|
|
|
}///\namespace visualSLAM
|
|
|
|
//************************************************************************
|
|
// sparse BA
|
|
//************************************************************************
|
|
|
|
#include <gtsam/slam/sparseBA.h>
|
|
namespace sparseBA {
|
|
|
|
class Values {
|
|
Values();
|
|
Values(const sparseBA::Values& values);
|
|
size_t size() const;
|
|
void print(string s) const;
|
|
bool exists(size_t key);
|
|
gtsam::KeyVector keys() const;
|
|
|
|
// Access to cameras
|
|
sparseBA::Values allSimpleCameras() const ;
|
|
size_t nrSimpleCameras() const ;
|
|
gtsam::KeyVector simpleCameraKeys() const ;
|
|
void insertSimpleCamera(size_t j, const gtsam::SimpleCamera& camera);
|
|
void updateSimpleCamera(size_t j, const gtsam::SimpleCamera& camera);
|
|
gtsam::SimpleCamera simpleCamera(size_t j) const;
|
|
|
|
// Access to points, inherited from visualSLAM
|
|
sparseBA::Values allPoints() const;
|
|
size_t nrPoints() const;
|
|
gtsam::KeyVector pointKeys() const; // Note the switch to KeyVector, rather than KeyList
|
|
void insertPoint(size_t key, const gtsam::Point3& pose);
|
|
void updatePoint(size_t key, const gtsam::Point3& pose);
|
|
gtsam::Point3 point(size_t j);
|
|
Matrix points() const;
|
|
};
|
|
|
|
class Graph {
|
|
Graph();
|
|
Graph(const gtsam::NonlinearFactorGraph& graph);
|
|
Graph(const sparseBA::Graph& graph);
|
|
|
|
// Information
|
|
Matrix reprojectionErrors(const sparseBA::Values& values) const;
|
|
|
|
// inherited from FactorGraph
|
|
void print(string s) const;
|
|
bool equals(const sparseBA::Graph& fg, double tol) const;
|
|
size_t size() const;
|
|
bool empty() const;
|
|
void remove(size_t i);
|
|
size_t nrFactors() const;
|
|
gtsam::NonlinearFactor* at(size_t i) const;
|
|
|
|
double error(const sparseBA::Values& values) const;
|
|
gtsam::Ordering* orderingCOLAMD(const sparseBA::Values& values) const;
|
|
gtsam::GaussianFactorGraph* linearize(const sparseBA::Values& values, const gtsam::Ordering& ordering) const;
|
|
|
|
sparseBA::Values optimize(const sparseBA::Values& initialEstimate, size_t verbosity) const;
|
|
sparseBA::LevenbergMarquardtOptimizer optimizer(const sparseBA::Values& initialEstimate, const gtsam::LevenbergMarquardtParams& parameters) const;
|
|
gtsam::Marginals marginals(const sparseBA::Values& solution) const;
|
|
|
|
// inherited from visualSLAM
|
|
void addPointConstraint(size_t pointKey, const gtsam::Point3& p);
|
|
void addPointPrior(size_t pointKey, const gtsam::Point3& p, const gtsam::noiseModel::Base* model);
|
|
|
|
// add factors
|
|
void addSimpleCameraPrior(size_t cameraKey, const gtsam::SimpleCamera &camera, gtsam::noiseModel::Base* model);
|
|
void addSimpleCameraConstraint(size_t cameraKey, const gtsam::SimpleCamera &camera);
|
|
void addSimpleCameraMeasurement(const gtsam::Point2 &z, gtsam::noiseModel::Base* model, size_t cameraKey, size_t pointKey);
|
|
};
|
|
|
|
class LevenbergMarquardtOptimizer {
|
|
double lambda() const;
|
|
void iterate();
|
|
double error() const;
|
|
size_t iterations() const;
|
|
sparseBA::Values optimize();
|
|
sparseBA::Values optimizeSafely();
|
|
sparseBA::Values values() const;
|
|
};
|
|
}///\namespace sparseBA
|
|
|