770 lines
29 KiB
OpenEdge ABL
770 lines
29 KiB
OpenEdge ABL
//*************************************************************************
|
|
// nonlinear
|
|
//*************************************************************************
|
|
|
|
namespace gtsam {
|
|
|
|
#include <gtsam/geometry/Cal3Bundler.h>
|
|
#include <gtsam/geometry/Cal3DS2.h>
|
|
#include <gtsam/geometry/Cal3Fisheye.h>
|
|
#include <gtsam/geometry/Cal3Unified.h>
|
|
#include <gtsam/geometry/Cal3_S2.h>
|
|
#include <gtsam/geometry/CalibratedCamera.h>
|
|
#include <gtsam/geometry/EssentialMatrix.h>
|
|
#include <gtsam/geometry/FundamentalMatrix.h>
|
|
#include <gtsam/geometry/PinholeCamera.h>
|
|
#include <gtsam/geometry/Point2.h>
|
|
#include <gtsam/geometry/Point3.h>
|
|
#include <gtsam/geometry/Pose2.h>
|
|
#include <gtsam/geometry/Pose3.h>
|
|
#include <gtsam/geometry/Similarity2.h>
|
|
#include <gtsam/geometry/Similarity3.h>
|
|
#include <gtsam/geometry/Rot2.h>
|
|
#include <gtsam/geometry/Rot3.h>
|
|
#include <gtsam/geometry/SO3.h>
|
|
#include <gtsam/geometry/SO4.h>
|
|
#include <gtsam/geometry/SOn.h>
|
|
#include <gtsam/geometry/StereoPoint2.h>
|
|
#include <gtsam/geometry/Unit3.h>
|
|
#include <gtsam/navigation/ImuBias.h>
|
|
#include <gtsam/navigation/NavState.h>
|
|
|
|
#include <gtsam/nonlinear/GraphvizFormatting.h>
|
|
class GraphvizFormatting : gtsam::DotWriter {
|
|
GraphvizFormatting();
|
|
|
|
enum Axis { X, Y, Z, NEGX, NEGY, NEGZ };
|
|
gtsam::GraphvizFormatting::Axis paperHorizontalAxis;
|
|
gtsam::GraphvizFormatting::Axis paperVerticalAxis;
|
|
|
|
double scale;
|
|
bool mergeSimilarFactors;
|
|
};
|
|
|
|
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
|
|
class NonlinearFactorGraph {
|
|
NonlinearFactorGraph();
|
|
NonlinearFactorGraph(const gtsam::NonlinearFactorGraph& graph);
|
|
|
|
// FactorGraph
|
|
void print(string s = "NonlinearFactorGraph: ",
|
|
const gtsam::KeyFormatter& keyFormatter =
|
|
gtsam::DefaultKeyFormatter) const;
|
|
bool equals(const gtsam::NonlinearFactorGraph& other, double tol) const;
|
|
size_t size() const;
|
|
bool empty() const;
|
|
void remove(size_t i);
|
|
void replace(size_t i, gtsam::NonlinearFactor* factors);
|
|
void resize(size_t size);
|
|
size_t nrFactors() const;
|
|
gtsam::NonlinearFactor* at(size_t idx) const;
|
|
void push_back(const gtsam::NonlinearFactorGraph& factors);
|
|
void push_back(gtsam::NonlinearFactor* factor);
|
|
void add(gtsam::NonlinearFactor* factor);
|
|
bool exists(size_t idx) const;
|
|
gtsam::KeySet keys() const;
|
|
gtsam::KeyVector keyVector() const;
|
|
|
|
template <T = {double,
|
|
gtsam::Vector,
|
|
gtsam::Point2,
|
|
gtsam::StereoPoint2,
|
|
gtsam::Point3,
|
|
gtsam::Rot2,
|
|
gtsam::SO3,
|
|
gtsam::SO4,
|
|
gtsam::Rot3,
|
|
gtsam::Pose2,
|
|
gtsam::Pose3,
|
|
gtsam::Similarity2,
|
|
gtsam::Similarity3,
|
|
gtsam::Cal3_S2,
|
|
gtsam::Cal3f,
|
|
gtsam::Cal3Bundler,
|
|
gtsam::Cal3Fisheye,
|
|
gtsam::Cal3Unified,
|
|
gtsam::CalibratedCamera,
|
|
gtsam::EssentialMatrix,
|
|
gtsam::FundamentalMatrix,
|
|
gtsam::SimpleFundamentalMatrix,
|
|
gtsam::PinholeCamera<gtsam::Cal3_S2>,
|
|
gtsam::PinholeCamera<gtsam::Cal3f>,
|
|
gtsam::PinholeCamera<gtsam::Cal3Bundler>,
|
|
gtsam::PinholeCamera<gtsam::Cal3Fisheye>,
|
|
gtsam::PinholeCamera<gtsam::Cal3Unified>,
|
|
gtsam::PinholeCamera<gtsam::CalibratedCamera>,
|
|
gtsam::imuBias::ConstantBias}>
|
|
void addPrior(size_t key, const T& prior,
|
|
const gtsam::noiseModel::Base* noiseModel);
|
|
|
|
// NonlinearFactorGraph
|
|
void printErrors(const gtsam::Values& values,
|
|
const string& str = "NonlinearFactorGraph: ",
|
|
const gtsam::KeyFormatter& keyFormatter =
|
|
gtsam::DefaultKeyFormatter) const;
|
|
double error(const gtsam::Values& values) const;
|
|
double probPrime(const gtsam::Values& values) const;
|
|
gtsam::Ordering orderingCOLAMD() const;
|
|
// Ordering* orderingCOLAMDConstrained(const gtsam::Values& c, const
|
|
// std::map<gtsam::Key,int>& constraints) const;
|
|
gtsam::GaussianFactorGraph* linearize(const gtsam::Values& linearizationPoint) const;
|
|
gtsam::NonlinearFactorGraph clone() const;
|
|
|
|
string dot(
|
|
const gtsam::Values& values,
|
|
const gtsam::KeyFormatter& keyFormatter = gtsam::DefaultKeyFormatter,
|
|
const gtsam::GraphvizFormatting& writer = gtsam::GraphvizFormatting());
|
|
void saveGraph(
|
|
const string& s, const gtsam::Values& values,
|
|
const gtsam::KeyFormatter& keyFormatter = gtsam::DefaultKeyFormatter,
|
|
const gtsam::GraphvizFormatting& writer = gtsam::GraphvizFormatting()) const;
|
|
|
|
// enabling serialization functionality
|
|
void serialize() const;
|
|
};
|
|
|
|
#include <gtsam/nonlinear/NonlinearFactor.h>
|
|
virtual class NonlinearFactor : gtsam::Factor {
|
|
// Factor base class
|
|
void print(string s = "", const gtsam::KeyFormatter& keyFormatter =
|
|
gtsam::DefaultKeyFormatter) const;
|
|
// NonlinearFactor
|
|
bool equals(const gtsam::NonlinearFactor& f, double tol) const;
|
|
double error(const gtsam::Values& c) const;
|
|
double error(const gtsam::HybridValues& c) const;
|
|
size_t dim() const;
|
|
bool active(const gtsam::Values& c) const;
|
|
gtsam::GaussianFactor* linearize(const gtsam::Values& c) const;
|
|
gtsam::NonlinearFactor* clone() const;
|
|
gtsam::NonlinearFactor* rekey(const gtsam::KeyVector& newKeys) const;
|
|
};
|
|
|
|
#include <gtsam/nonlinear/NonlinearFactor.h>
|
|
virtual class NoiseModelFactor : gtsam::NonlinearFactor {
|
|
bool equals(const gtsam::NoiseModelFactor& f, double tol) const;
|
|
gtsam::noiseModel::Base* noiseModel() const;
|
|
gtsam::NoiseModelFactor* cloneWithNewNoiseModel(gtsam::noiseModel::Base* newNoise) const;
|
|
gtsam::Vector unwhitenedError(const gtsam::Values& x) const;
|
|
gtsam::Vector whitenedError(const gtsam::Values& c) const;
|
|
};
|
|
|
|
#include <gtsam/nonlinear/Marginals.h>
|
|
class Marginals {
|
|
Marginals(const gtsam::NonlinearFactorGraph& graph,
|
|
const gtsam::Values& solution);
|
|
Marginals(const gtsam::GaussianFactorGraph& gfgraph,
|
|
const gtsam::Values& solution);
|
|
Marginals(const gtsam::GaussianFactorGraph& gfgraph,
|
|
const gtsam::VectorValues& solutionvec);
|
|
|
|
void print(string s = "Marginals: ", const gtsam::KeyFormatter& keyFormatter =
|
|
gtsam::DefaultKeyFormatter) const;
|
|
gtsam::Matrix marginalCovariance(size_t variable) const;
|
|
gtsam::Matrix marginalInformation(size_t variable) const;
|
|
gtsam::JointMarginal jointMarginalCovariance(
|
|
const gtsam::KeyVector& variables) const;
|
|
gtsam::JointMarginal jointMarginalInformation(
|
|
const gtsam::KeyVector& variables) const;
|
|
};
|
|
|
|
class JointMarginal {
|
|
gtsam::Matrix at(size_t iVariable, size_t jVariable) const;
|
|
gtsam::Matrix fullMatrix() const;
|
|
void print(string s = "", gtsam::KeyFormatter keyFormatter =
|
|
gtsam::DefaultKeyFormatter) const;
|
|
};
|
|
|
|
#include <gtsam/nonlinear/LinearContainerFactor.h>
|
|
virtual class LinearContainerFactor : gtsam::NonlinearFactor {
|
|
LinearContainerFactor(gtsam::GaussianFactor* factor,
|
|
const gtsam::Values& linearizationPoint);
|
|
LinearContainerFactor(gtsam::GaussianFactor* factor);
|
|
|
|
gtsam::GaussianFactor* factor() const;
|
|
// const std::optional<Values>& linearizationPoint() const;
|
|
|
|
bool isJacobian() const;
|
|
gtsam::JacobianFactor* toJacobian() const;
|
|
gtsam::HessianFactor* toHessian() const;
|
|
|
|
static gtsam::NonlinearFactorGraph ConvertLinearGraph(
|
|
const gtsam::GaussianFactorGraph& linear_graph,
|
|
const gtsam::Values& linearizationPoint);
|
|
|
|
static gtsam::NonlinearFactorGraph ConvertLinearGraph(
|
|
const gtsam::GaussianFactorGraph& linear_graph);
|
|
|
|
// enabling serialization functionality
|
|
void serializable() const;
|
|
}; // \class LinearContainerFactor
|
|
|
|
// Summarization functionality
|
|
//#include <gtsam/nonlinear/summarization.h>
|
|
//
|
|
//// Uses partial QR approach by default
|
|
// gtsam::GaussianFactorGraph summarize(
|
|
// const gtsam::NonlinearFactorGraph& graph, const gtsam::Values& values,
|
|
// const gtsam::KeySet& saved_keys);
|
|
//
|
|
// gtsam::NonlinearFactorGraph summarizeAsNonlinearContainer(
|
|
// const gtsam::NonlinearFactorGraph& graph, const gtsam::Values& values,
|
|
// const gtsam::KeySet& saved_keys);
|
|
|
|
//*************************************************************************
|
|
// Nonlinear optimizers
|
|
//*************************************************************************
|
|
#include <gtsam/nonlinear/NonlinearOptimizerParams.h>
|
|
virtual class NonlinearOptimizerParams {
|
|
NonlinearOptimizerParams();
|
|
void print(string str = "") const;
|
|
|
|
int getMaxIterations() const;
|
|
double getRelativeErrorTol() const;
|
|
double getAbsoluteErrorTol() const;
|
|
double getErrorTol() const;
|
|
string getVerbosity() const;
|
|
|
|
void setMaxIterations(int value);
|
|
void setRelativeErrorTol(double value);
|
|
void setAbsoluteErrorTol(double value);
|
|
void setErrorTol(double value);
|
|
void setVerbosity(string src);
|
|
|
|
string getLinearSolverType() const;
|
|
void setLinearSolverType(string solver);
|
|
|
|
void setIterativeParams(gtsam::IterativeOptimizationParameters* params);
|
|
void setOrdering(const gtsam::Ordering& ordering);
|
|
string getOrderingType() const;
|
|
void setOrderingType(string ordering);
|
|
|
|
bool isMultifrontal() const;
|
|
bool isSequential() const;
|
|
bool isCholmod() const;
|
|
bool isIterative() const;
|
|
|
|
// This only applies to python since matlab does not have lambda machinery.
|
|
gtsam::NonlinearOptimizerParams::IterationHook iterationHook;
|
|
};
|
|
|
|
bool checkConvergence(double relativeErrorTreshold,
|
|
double absoluteErrorTreshold, double errorThreshold,
|
|
double currentError, double newError);
|
|
bool checkConvergence(const gtsam::NonlinearOptimizerParams& params,
|
|
double currentError, double newError);
|
|
|
|
#include <gtsam/nonlinear/GaussNewtonOptimizer.h>
|
|
virtual class GaussNewtonParams : gtsam::NonlinearOptimizerParams {
|
|
GaussNewtonParams();
|
|
};
|
|
|
|
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
|
|
virtual class LevenbergMarquardtParams : gtsam::NonlinearOptimizerParams {
|
|
LevenbergMarquardtParams();
|
|
|
|
bool getDiagonalDamping() const;
|
|
double getlambdaFactor() const;
|
|
double getlambdaInitial() const;
|
|
double getlambdaLowerBound() const;
|
|
double getlambdaUpperBound() const;
|
|
bool getUseFixedLambdaFactor();
|
|
string getLogFile() const;
|
|
string getVerbosityLM() const;
|
|
|
|
void setDiagonalDamping(bool flag);
|
|
void setlambdaFactor(double value);
|
|
void setlambdaInitial(double value);
|
|
void setlambdaLowerBound(double value);
|
|
void setlambdaUpperBound(double value);
|
|
void setUseFixedLambdaFactor(bool flag);
|
|
void setLogFile(string s);
|
|
void setVerbosityLM(string s);
|
|
|
|
static gtsam::LevenbergMarquardtParams LegacyDefaults();
|
|
static gtsam::LevenbergMarquardtParams CeresDefaults();
|
|
|
|
static gtsam::LevenbergMarquardtParams EnsureHasOrdering(
|
|
gtsam::LevenbergMarquardtParams params,
|
|
const gtsam::NonlinearFactorGraph& graph);
|
|
static gtsam::LevenbergMarquardtParams ReplaceOrdering(
|
|
gtsam::LevenbergMarquardtParams params, const gtsam::Ordering& ordering);
|
|
};
|
|
|
|
#include <gtsam/nonlinear/DoglegOptimizer.h>
|
|
virtual class DoglegParams : gtsam::NonlinearOptimizerParams {
|
|
DoglegParams();
|
|
|
|
double getDeltaInitial() const;
|
|
string getVerbosityDL() const;
|
|
|
|
void setDeltaInitial(double deltaInitial) const;
|
|
void setVerbosityDL(string verbosityDL) const;
|
|
};
|
|
|
|
#include <gtsam/nonlinear/GncParams.h>
|
|
enum GncLossType {
|
|
GM /*Geman McClure*/,
|
|
TLS /*Truncated least squares*/
|
|
};
|
|
|
|
template<PARAMS>
|
|
virtual class GncParams {
|
|
GncParams(const PARAMS& baseOptimizerParams);
|
|
GncParams();
|
|
PARAMS baseOptimizerParams;
|
|
gtsam::GncLossType lossType;
|
|
size_t maxIterations;
|
|
double muStep;
|
|
double relativeCostTol;
|
|
double weightsTol;
|
|
gtsam::This::Verbosity verbosity;
|
|
gtsam::This::IndexVector knownInliers;
|
|
gtsam::This::IndexVector knownOutliers;
|
|
|
|
void setLossType(const gtsam::GncLossType type);
|
|
void setMaxIterations(const size_t maxIter);
|
|
void setMuStep(const double step);
|
|
void setRelativeCostTol(double value);
|
|
void setWeightsTol(double value);
|
|
void setVerbosityGNC(const gtsam::This::Verbosity value);
|
|
void setKnownInliers(const gtsam::This::IndexVector& knownIn);
|
|
void setKnownOutliers(const gtsam::This::IndexVector& knownOut);
|
|
void print(const string& str = "GncParams: ") const;
|
|
|
|
enum Verbosity {
|
|
SILENT,
|
|
SUMMARY,
|
|
MU,
|
|
WEIGHTS,
|
|
VALUES
|
|
};
|
|
};
|
|
|
|
typedef gtsam::GncParams<gtsam::GaussNewtonParams> GncGaussNewtonParams;
|
|
typedef gtsam::GncParams<gtsam::LevenbergMarquardtParams> GncLMParams;
|
|
|
|
#include <gtsam/nonlinear/NonlinearOptimizer.h>
|
|
virtual class NonlinearOptimizer {
|
|
gtsam::Values optimize();
|
|
gtsam::Values optimizeSafely();
|
|
double error() const;
|
|
int iterations() const;
|
|
gtsam::Values values() const;
|
|
gtsam::NonlinearFactorGraph graph() const;
|
|
gtsam::GaussianFactorGraph* iterate() const;
|
|
};
|
|
|
|
#include <gtsam/nonlinear/GaussNewtonOptimizer.h>
|
|
virtual class GaussNewtonOptimizer : gtsam::NonlinearOptimizer {
|
|
GaussNewtonOptimizer(const gtsam::NonlinearFactorGraph& graph,
|
|
const gtsam::Values& initialValues);
|
|
GaussNewtonOptimizer(const gtsam::NonlinearFactorGraph& graph,
|
|
const gtsam::Values& initialValues,
|
|
const gtsam::GaussNewtonParams& params);
|
|
};
|
|
|
|
#include <gtsam/nonlinear/DoglegOptimizer.h>
|
|
virtual class DoglegOptimizer : gtsam::NonlinearOptimizer {
|
|
DoglegOptimizer(const gtsam::NonlinearFactorGraph& graph,
|
|
const gtsam::Values& initialValues);
|
|
DoglegOptimizer(const gtsam::NonlinearFactorGraph& graph,
|
|
const gtsam::Values& initialValues,
|
|
const gtsam::DoglegParams& params);
|
|
double getDelta() const;
|
|
};
|
|
|
|
// TODO(dellaert): This will only work when GTSAM_USE_BOOST_FEATURES is true.
|
|
#include <gtsam/nonlinear/GncOptimizer.h>
|
|
template<PARAMS>
|
|
virtual class GncOptimizer {
|
|
GncOptimizer(const gtsam::NonlinearFactorGraph& graph,
|
|
const gtsam::Values& initialValues,
|
|
const PARAMS& params);
|
|
void setInlierCostThresholds(const double inth);
|
|
const gtsam::Vector& getInlierCostThresholds();
|
|
void setInlierCostThresholdsAtProbability(const double alpha);
|
|
void setWeights(const gtsam::Vector w);
|
|
const gtsam::Vector& getWeights();
|
|
gtsam::Values optimize();
|
|
};
|
|
|
|
typedef gtsam::GncOptimizer<gtsam::GncParams<gtsam::GaussNewtonParams>> GncGaussNewtonOptimizer;
|
|
typedef gtsam::GncOptimizer<gtsam::GncParams<gtsam::LevenbergMarquardtParams>> GncLMOptimizer;
|
|
|
|
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
|
|
virtual class LevenbergMarquardtOptimizer : gtsam::NonlinearOptimizer {
|
|
LevenbergMarquardtOptimizer(const gtsam::NonlinearFactorGraph& graph,
|
|
const gtsam::Values& initialValues,
|
|
const gtsam::LevenbergMarquardtParams& params =
|
|
gtsam::LevenbergMarquardtParams());
|
|
LevenbergMarquardtOptimizer(const gtsam::NonlinearFactorGraph& graph,
|
|
const gtsam::Values& initialValues,
|
|
const gtsam::Ordering& ordering,
|
|
const gtsam::LevenbergMarquardtParams& params =
|
|
gtsam::LevenbergMarquardtParams());
|
|
|
|
double lambda() const;
|
|
void print(string str = "") const;
|
|
};
|
|
|
|
#include <gtsam/nonlinear/ISAM2.h>
|
|
class ISAM2GaussNewtonParams {
|
|
ISAM2GaussNewtonParams(double _wildfireThreshold = 0.001);
|
|
|
|
void print(string str = "") const;
|
|
|
|
/** Getters and Setters for all properties */
|
|
double getWildfireThreshold() const;
|
|
void setWildfireThreshold(double wildfireThreshold);
|
|
};
|
|
|
|
class ISAM2DoglegParams {
|
|
ISAM2DoglegParams();
|
|
|
|
void print(string str = "") const;
|
|
|
|
/** Getters and Setters for all properties */
|
|
double getWildfireThreshold() const;
|
|
void setWildfireThreshold(double wildfireThreshold);
|
|
double getInitialDelta() const;
|
|
void setInitialDelta(double initialDelta);
|
|
string getAdaptationMode() const;
|
|
void setAdaptationMode(string adaptationMode);
|
|
bool isVerbose() const;
|
|
void setVerbose(bool verbose);
|
|
};
|
|
|
|
class ISAM2ThresholdMapValue {
|
|
ISAM2ThresholdMapValue(char c, gtsam::Vector thresholds);
|
|
ISAM2ThresholdMapValue(const gtsam::ISAM2ThresholdMapValue& other);
|
|
};
|
|
|
|
class ISAM2ThresholdMap {
|
|
ISAM2ThresholdMap();
|
|
ISAM2ThresholdMap(const gtsam::ISAM2ThresholdMap& other);
|
|
|
|
// Note: no print function
|
|
|
|
// common STL methods
|
|
size_t size() const;
|
|
bool empty() const;
|
|
void clear();
|
|
|
|
// structure specific methods
|
|
void insert(const gtsam::ISAM2ThresholdMapValue& value) const;
|
|
};
|
|
|
|
class ISAM2Params {
|
|
ISAM2Params();
|
|
|
|
void print(string str = "") const;
|
|
|
|
/** Getters and Setters for all properties */
|
|
void setOptimizationParams(
|
|
const gtsam::ISAM2GaussNewtonParams& gauss_newton__params);
|
|
void setOptimizationParams(const gtsam::ISAM2DoglegParams& optimizationParams);
|
|
void setRelinearizeThreshold(double relinearizeThreshold);
|
|
void setRelinearizeThreshold(const gtsam::ISAM2ThresholdMap& threshold_map);
|
|
string getFactorization() const;
|
|
void setFactorization(string factorization);
|
|
|
|
int relinearizeSkip;
|
|
bool enableRelinearization;
|
|
bool evaluateNonlinearError;
|
|
bool cacheLinearizedFactors;
|
|
bool enableDetailedResults;
|
|
bool enablePartialRelinearizationCheck;
|
|
bool findUnusedFactorSlots;
|
|
|
|
enum Factorization { CHOLESKY, QR };
|
|
gtsam::ISAM2Params::Factorization factorization;
|
|
};
|
|
|
|
class ISAM2Clique {
|
|
// Constructors
|
|
ISAM2Clique();
|
|
|
|
// Standard Interface
|
|
gtsam::Vector gradientContribution() const;
|
|
void print(string s = "",
|
|
gtsam::KeyFormatter keyFormatter = gtsam::DefaultKeyFormatter);
|
|
};
|
|
|
|
class ISAM2Result {
|
|
ISAM2Result();
|
|
|
|
void print(string str = "") const;
|
|
|
|
/** Getters and Setters for all properties */
|
|
size_t getVariablesRelinearized() const;
|
|
size_t getVariablesReeliminated() const;
|
|
gtsam::FactorIndices getNewFactorsIndices() const;
|
|
size_t getCliques() const;
|
|
double getErrorBefore() const;
|
|
double getErrorAfter() const;
|
|
};
|
|
|
|
class ISAM2 {
|
|
ISAM2();
|
|
ISAM2(const gtsam::ISAM2Params& params);
|
|
ISAM2(const gtsam::ISAM2& other);
|
|
|
|
bool equals(const gtsam::ISAM2& other, double tol) const;
|
|
void print(string s = "", const gtsam::KeyFormatter& keyFormatter =
|
|
gtsam::DefaultKeyFormatter) const;
|
|
void printStats() const;
|
|
void saveGraph(string s) const;
|
|
|
|
gtsam::ISAM2Result update();
|
|
gtsam::ISAM2Result update(const gtsam::NonlinearFactorGraph& newFactors,
|
|
const gtsam::Values& newTheta);
|
|
gtsam::ISAM2Result update(const gtsam::NonlinearFactorGraph& newFactors,
|
|
const gtsam::Values& newTheta,
|
|
const gtsam::FactorIndices& removeFactorIndices);
|
|
gtsam::ISAM2Result update(const gtsam::NonlinearFactorGraph& newFactors,
|
|
const gtsam::Values& newTheta,
|
|
const gtsam::FactorIndices& removeFactorIndices,
|
|
const gtsam::KeyGroupMap& constrainedKeys);
|
|
gtsam::ISAM2Result update(const gtsam::NonlinearFactorGraph& newFactors,
|
|
const gtsam::Values& newTheta,
|
|
const gtsam::FactorIndices& removeFactorIndices,
|
|
const gtsam::KeyGroupMap& constrainedKeys,
|
|
const gtsam::KeyList& noRelinKeys);
|
|
gtsam::ISAM2Result update(const gtsam::NonlinearFactorGraph& newFactors,
|
|
const gtsam::Values& newTheta,
|
|
const gtsam::FactorIndices& removeFactorIndices,
|
|
gtsam::KeyGroupMap& constrainedKeys,
|
|
const gtsam::KeyList& noRelinKeys,
|
|
const gtsam::KeyList& extraReelimKeys,
|
|
bool force_relinearize = false);
|
|
|
|
gtsam::ISAM2Result update(const gtsam::NonlinearFactorGraph& newFactors,
|
|
const gtsam::Values& newTheta,
|
|
const gtsam::ISAM2UpdateParams& updateParams);
|
|
|
|
double error(const gtsam::VectorValues& x) const;
|
|
|
|
gtsam::Values getLinearizationPoint() const;
|
|
bool valueExists(gtsam::Key key) const;
|
|
gtsam::Values calculateEstimate() const;
|
|
template <VALUE = {gtsam::Point2, gtsam::Rot2, gtsam::Pose2, gtsam::Point3,
|
|
gtsam::Rot3, gtsam::Pose3, gtsam::Similarity2, gtsam::Similarity3, gtsam::Cal3_S2, gtsam::Cal3DS2,
|
|
gtsam::Cal3f, gtsam::Cal3Bundler, gtsam::imuBias::ConstantBias,
|
|
gtsam::EssentialMatrix, gtsam::FundamentalMatrix, gtsam::SimpleFundamentalMatrix,
|
|
gtsam::PinholeCamera<gtsam::Cal3_S2>,
|
|
gtsam::PinholeCamera<gtsam::Cal3Bundler>,
|
|
gtsam::PinholeCamera<gtsam::Cal3Fisheye>,
|
|
gtsam::PinholeCamera<gtsam::Cal3Unified>, gtsam::Vector, gtsam::Matrix}>
|
|
VALUE calculateEstimate(size_t key) const;
|
|
gtsam::Matrix marginalCovariance(size_t key) const;
|
|
gtsam::Values calculateBestEstimate() const;
|
|
gtsam::VectorValues getDelta() const;
|
|
double error(const gtsam::VectorValues& x) const;
|
|
gtsam::NonlinearFactorGraph getFactorsUnsafe() const;
|
|
gtsam::VariableIndex getVariableIndex() const;
|
|
const gtsam::KeySet& getFixedVariables() const;
|
|
gtsam::ISAM2Params params() const;
|
|
|
|
void printStats() const;
|
|
gtsam::VectorValues gradientAtZero() const;
|
|
|
|
string dot(const gtsam::KeyFormatter& keyFormatter =
|
|
gtsam::DefaultKeyFormatter) const;
|
|
void saveGraph(string s, const gtsam::KeyFormatter& keyFormatter =
|
|
gtsam::DefaultKeyFormatter) const;
|
|
};
|
|
|
|
#include <gtsam/nonlinear/NonlinearISAM.h>
|
|
class NonlinearISAM {
|
|
NonlinearISAM();
|
|
NonlinearISAM(int reorderInterval);
|
|
void print(string s = "", const gtsam::KeyFormatter& keyFormatter =
|
|
gtsam::DefaultKeyFormatter) const;
|
|
void printStats() const;
|
|
void saveGraph(string s) const;
|
|
gtsam::Values estimate() const;
|
|
gtsam::Matrix marginalCovariance(size_t key) const;
|
|
int reorderInterval() const;
|
|
int reorderCounter() const;
|
|
void update(const gtsam::NonlinearFactorGraph& newFactors,
|
|
const gtsam::Values& initialValues);
|
|
void reorder_relinearize();
|
|
|
|
// These might be expensive as instead of a reference the wrapper will make a
|
|
// copy
|
|
gtsam::GaussianISAM bayesTree() const;
|
|
gtsam::Values getLinearizationPoint() const;
|
|
gtsam::NonlinearFactorGraph getFactorsUnsafe() const;
|
|
};
|
|
|
|
//*************************************************************************
|
|
// Nonlinear factor types
|
|
//*************************************************************************
|
|
#include <gtsam/nonlinear/PriorFactor.h>
|
|
template <T = {double,
|
|
gtsam::Vector,
|
|
gtsam::Point2,
|
|
gtsam::StereoPoint2,
|
|
gtsam::Point3,
|
|
gtsam::Rot2,
|
|
gtsam::SO3,
|
|
gtsam::SO4,
|
|
gtsam::SOn,
|
|
gtsam::Rot3,
|
|
gtsam::Pose2,
|
|
gtsam::Pose3,
|
|
gtsam::Similarity2,
|
|
gtsam::Similarity3,
|
|
gtsam::Unit3,
|
|
gtsam::Cal3_S2,
|
|
gtsam::Cal3DS2,
|
|
gtsam::Cal3Bundler,
|
|
gtsam::Cal3Fisheye,
|
|
gtsam::Cal3Unified,
|
|
gtsam::CalibratedCamera,
|
|
gtsam::PinholeCamera<gtsam::Cal3_S2>,
|
|
gtsam::PinholeCamera<gtsam::Cal3Bundler>,
|
|
gtsam::PinholeCamera<gtsam::Cal3Fisheye>,
|
|
gtsam::PinholeCamera<gtsam::Cal3Unified>,
|
|
gtsam::NavState,
|
|
gtsam::imuBias::ConstantBias}>
|
|
virtual class PriorFactor : gtsam::NoiseModelFactor {
|
|
PriorFactor(size_t key, const T& prior,
|
|
const gtsam::noiseModel::Base* noiseModel);
|
|
T prior() const;
|
|
|
|
// enabling serialization functionality
|
|
void serialize() const;
|
|
};
|
|
|
|
#include <gtsam/nonlinear/NonlinearEquality.h>
|
|
template <T = {gtsam::Point2, gtsam::StereoPoint2, gtsam::Point3, gtsam::Rot2,
|
|
gtsam::SO3, gtsam::SO4, gtsam::SOn, gtsam::Rot3, gtsam::Pose2,
|
|
gtsam::Pose3, gtsam::Similarity2, gtsam::Similarity3, gtsam::Cal3_S2, gtsam::CalibratedCamera,
|
|
gtsam::PinholeCamera<gtsam::Cal3_S2>,
|
|
gtsam::PinholeCamera<gtsam::Cal3Bundler>,
|
|
gtsam::PinholeCamera<gtsam::Cal3Fisheye>,
|
|
gtsam::PinholeCamera<gtsam::Cal3Unified>,
|
|
gtsam::imuBias::ConstantBias}>
|
|
virtual class NonlinearEquality : gtsam::NoiseModelFactor {
|
|
// 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);
|
|
|
|
// enabling serialization functionality
|
|
void serialize() const;
|
|
};
|
|
|
|
template <T = {gtsam::Point2, gtsam::StereoPoint2, gtsam::Point3, gtsam::Rot2,
|
|
gtsam::SO3, gtsam::SO4, gtsam::SOn, gtsam::Rot3, gtsam::Pose2,
|
|
gtsam::Pose3, gtsam::Similarity2, gtsam::Similarity3, gtsam::Cal3_S2, gtsam::CalibratedCamera,
|
|
gtsam::PinholeCamera<gtsam::Cal3_S2>,
|
|
gtsam::PinholeCamera<gtsam::Cal3Bundler>,
|
|
gtsam::PinholeCamera<gtsam::Cal3Fisheye>,
|
|
gtsam::PinholeCamera<gtsam::Cal3Unified>,
|
|
gtsam::imuBias::ConstantBias}>
|
|
virtual class NonlinearEquality2 : gtsam::NoiseModelFactor {
|
|
NonlinearEquality2(gtsam::Key key1, gtsam::Key key2, double mu = 1e4);
|
|
gtsam::Vector evaluateError(const T& x1, const T& x2);
|
|
};
|
|
|
|
#include <gtsam/nonlinear/FixedLagSmoother.h>
|
|
class FixedLagSmootherKeyTimestampMapValue {
|
|
FixedLagSmootherKeyTimestampMapValue(size_t key, double timestamp);
|
|
FixedLagSmootherKeyTimestampMapValue(const gtsam::FixedLagSmootherKeyTimestampMapValue& other);
|
|
};
|
|
|
|
class FixedLagSmootherKeyTimestampMap {
|
|
FixedLagSmootherKeyTimestampMap();
|
|
FixedLagSmootherKeyTimestampMap(const gtsam::FixedLagSmootherKeyTimestampMap& other);
|
|
|
|
// Note: no print function
|
|
|
|
// common STL methods
|
|
size_t size() const;
|
|
bool empty() const;
|
|
void clear();
|
|
|
|
double at(const size_t key) const;
|
|
void insert(const gtsam::FixedLagSmootherKeyTimestampMapValue& value);
|
|
};
|
|
|
|
class FixedLagSmootherResult {
|
|
size_t getIterations() const;
|
|
size_t getNonlinearVariables() const;
|
|
size_t getLinearVariables() const;
|
|
double getError() const;
|
|
};
|
|
|
|
virtual class FixedLagSmoother {
|
|
void print(string s) const;
|
|
bool equals(const gtsam::FixedLagSmoother& rhs, double tol) const;
|
|
|
|
gtsam::FixedLagSmootherKeyTimestampMap timestamps() const;
|
|
double smootherLag() const;
|
|
|
|
gtsam::FixedLagSmootherResult update(const gtsam::NonlinearFactorGraph &newFactors,
|
|
const gtsam::Values &newTheta,
|
|
const gtsam::FixedLagSmootherKeyTimestampMap ×tamps);
|
|
gtsam::FixedLagSmootherResult update(const gtsam::NonlinearFactorGraph &newFactors,
|
|
const gtsam::Values &newTheta,
|
|
const gtsam::FixedLagSmootherKeyTimestampMap ×tamps,
|
|
const gtsam::FactorIndices &factorsToRemove);
|
|
gtsam::Values calculateEstimate() const;
|
|
};
|
|
|
|
#include <gtsam/nonlinear/BatchFixedLagSmoother.h>
|
|
virtual class BatchFixedLagSmoother : gtsam::FixedLagSmoother {
|
|
BatchFixedLagSmoother();
|
|
BatchFixedLagSmoother(double smootherLag);
|
|
BatchFixedLagSmoother(double smootherLag, const gtsam::LevenbergMarquardtParams& parameters);
|
|
|
|
void print(string s = "BatchFixedLagSmoother:\n") const;
|
|
|
|
gtsam::LevenbergMarquardtParams params() const;
|
|
|
|
gtsam::NonlinearFactorGraph getFactors() const;
|
|
|
|
template <VALUE = {gtsam::Point2, gtsam::Rot2, gtsam::Pose2, gtsam::Point3,
|
|
gtsam::Rot3, gtsam::Pose3, gtsam::Similarity2, gtsam::Similarity3, gtsam::Cal3_S2, gtsam::Cal3DS2,
|
|
gtsam::Vector, gtsam::Matrix}>
|
|
VALUE calculateEstimate(size_t key) const;
|
|
};
|
|
|
|
#include <gtsam/nonlinear/IncrementalFixedLagSmoother.h>
|
|
virtual class IncrementalFixedLagSmoother : gtsam::FixedLagSmoother {
|
|
IncrementalFixedLagSmoother();
|
|
IncrementalFixedLagSmoother(double smootherLag);
|
|
IncrementalFixedLagSmoother(double smootherLag, const gtsam::ISAM2Params& parameters);
|
|
|
|
void print(string s = "IncrementalFixedLagSmoother:\n") const;
|
|
|
|
gtsam::ISAM2Params params() const;
|
|
|
|
gtsam::NonlinearFactorGraph getFactors() const;
|
|
gtsam::ISAM2 getISAM2() const;
|
|
};
|
|
|
|
#include <gtsam/nonlinear/ExtendedKalmanFilter.h>
|
|
template <T = {gtsam::Point2,
|
|
gtsam::Point3,
|
|
gtsam::Rot2,
|
|
gtsam::Rot3,
|
|
gtsam::Pose2,
|
|
gtsam::Pose3,
|
|
gtsam::Similarity2,
|
|
gtsam::Similarity3,
|
|
gtsam::NavState,
|
|
gtsam::imuBias::ConstantBias}>
|
|
virtual class ExtendedKalmanFilter {
|
|
ExtendedKalmanFilter(gtsam::Key key_initial, const T& x_initial, const gtsam::noiseModel::Gaussian* P_initial);
|
|
|
|
T predict(const gtsam::NoiseModelFactor& motionFactor);
|
|
T update(const gtsam::NoiseModelFactor& measurementFactor);
|
|
|
|
gtsam::JacobianFactor::shared_ptr Density() const;
|
|
};
|
|
|
|
} // namespace gtsam
|