BayesFilters 0.10.0 (2022-11-21)

CMake

• Minor version increases since API compatibility is broken.

Filtering utilities

• Improve documentation of methods bfl::utils::quaternion_to_rotation_vector(), bfl::utils::rotation_vector_to_quaternion(), bfl::utils::sum_quaternion_rotation_vector(), bfl::utils::diff_quaternion(),bfl::utils::mean_quaternion().
• Implemented class VectorDescription which describes the size of the inner components of a state/measurement vector (linear, rotational and noise parts).
• Updated the FunctionEvaluation alias within sigma_point.h in order to use a VectorDescription as one of the outputs.
• Updated implementation of variants of the unscented_transform() transforms within sigma_point.h taking into account the new definition of the FunctionEvaluation alias.
• Added a constructor to the UTWeight struct such that it can be constructed starting from a VectorDescription description.
• Added support for quaternions in bfl::sigma_point::sigma_point() method.
• Added support for quaternions in bfl::sigma_point::unscented_transform() method.
• Implemented method VectorDescription::dof_size() providing the number of degrees of freedom associated to the vector (which might be different from VectorDescription::total_size()).
• Fixed implementation of bfl::sigma_points::UTWeight constructor such that it uses the number of degrees of fredom of the vector to decide the number of sigma points to be initialized.
• Added support for arguments inheriting from Eigen::MatrixBase<T> with different instances of T within the constructors of methods bfl::utils::sum_quaternion_rotation_vector(), bfl::utils::diff_quaternion and bfl::utils::mean_quaternion().

Filtering functions

• Substituted ExogenousProcess::getOutputSize() with ExogenousProcess::getStateDescription() which uses the VectorDescription class to describe the size of the output of the exogenous process.
• Substituted StateProcess::getOutputSize() with pure virtual StateProcess::getStateDescription() which uses the VectorDescription class to describe the size of the state of the state model.
• Added pure virtual method StateProcess::getInputDescription() which uses the VectorDescription class to describe the size of the input to the state model.
• Substituted MeasurementModel::getOutputSize() with MeasuremnetModel::getMeasurementDescription() which uses the VectorDescription class to describe the size of the measurement of the measurement model. This method is not mandatory and a default implementation (raising an exception) is provided as not all measurements are provided with a vectorial description.
• Added method MeasurementModel::getInputDescription() which uses the VectorDescription class to describe the size of the input to the measurement model. This method is not mandatory and a default implementation (raising an exception) is provided as not all measurements are provided with a vectorial description.
• Updated implementation of class WhiteNoiseAcceleration to take into account changes in the StateModel interface.
• Updated implementation of class AdditiveStateModel to take into account changes in the StateModel interface.
• Updated implementation of class SimulatedLinearSensor to take into account changes in the StateModel and MeasurementModel interfaces.
• Added method StateModel::setSamplingTime() that sets the sampling time of the state model.

Filtering algorithms

• Updated implementation of SUKFCorrection in order to extract relevant information on the measurement size using the VectorDescription of the adopted measurement model.
• Updated implementation of UKFCorrection in order to extract relevant information on the measurement size using the VectorDescription of the adopted measurement model.
• Updated implementation of UKFPrediction in order to extract relevant information on state size using the VectorDescription of the adopted state model.
• Added support for measurement models having a time-varying input VectorDescription in UKFCorrection. The feature is enabled using a boolean argument in the constructor.

Test

• Updated tests test_Gaussian_Density_UVR, test_UKF, test_UPF, test_UPF_MAP, test_mixed_KF_SUKF, test_mixed_KF_UKF and test_mixed_UKF_KF to account for the changes in the interface of the AdditiveMeasurementModel, UKFPrediction, UKFCorrection and SUKFCorrection classes.

BayesFilters 0.9.0 (2021-06-14)

CMake

• Minor version increases since API compatibility is broken.

Bugfix

• Fixed missing self-assignment check in move assignment operators.

General improvements

• Changed any::any default pointer value to nullptr.

Filtering utilities

• Constructor EstimatesExtraction::EstimatesExtraction() takes the state size, both linear and circular.
• Class EstimatesExtraction does not assume that the state is a 7-vector containing cartesian position and axis/angle representation of orientation anymore.
• Implemented MAP (maximum a posteriori) estimate extraction within method EstimatesExtraction::map().
• Implemented overloaded version of EstimatesExtraction::extract() taking extra arguments (particle weights at the previous time step, likelihoods at the current step and matrix of Markov transition probabilities between previous and current states) required to expose MAP extraction utiliy to the user.
• Implemented method GaussianMixture::resize().
• Implemented method GaussianMixture::augmentWithNoise().
• Implemented method Gaussian::resize().
• Implemented method ParticleSet::resize().
• Added support for quaternions in classes Gaussian, GaussianMixture and ParticleSet.
• Implemented evaluation of a multivariate Gaussian probability density function in method utils::multivariate_gaussian_density.
• Implemented evaluation of the logarithm of a multivariate Gaussian probability density function in method utils::multivariate_gaussian_log_density.
• Implemented evaluation of a multivariate Gaussian probability density function with UVR factorized covariance matrix in method utils::multivariate_gaussian_density_UVR().
• Implemented evaluation of the logarithm of a multivariate Gaussian probability density function with UVR factorized covariance matrix in method utils::multivariate_gaussian_log_density_UVR().
• Methods EstimatesExtraction::extract() return a std::pair containing a boolean indicating if the estimate is valid and the estracted estimate.
• Methods EstimatesExtraction::extract() assume that particle weights are in the log space.
• Constructor HistoryBuffer::HistoryBuffer() takes the state size.
• Renamed Logger::log_filenames() in Logger::log_file_names().
• Renamed Logger::get_prefix_path() in Logger::get_folder_path().
• Renamed Logger::get_prefix_name() in Logger::get_file_name_prefix().
• Logger::enable_log() now requires absolute or relative paths as input.
• Added method bfl::utils::quaternion_to_rotation_vector() that takes the logarithm of a unitary quaternion and return its imaginary part belonging to the tangent space, i.e. it is a rotation vector.
• Added method bfl::utils::rotation_vector_to_quaternion() that takes the exponential of rotation vector, i.e. the associated unitary quaternion.
• Added method bfl::utils::sum_quaternion_rotation_vector() that evaluates the colwise sum between a unitary quaternion and a set of rotation vectors. The i-th sum is obtained as the quaternion product between the exponential of the i-th rotation vector and the quaternion.
• Added method bfl::utils::diff_quaternion() that evaluates the colwise difference between a set of quaternions and a given unitary quaternion (right operand). The i-th difference is obtained as the logarithm of the quaternion product between the i-th quaternion and the conjugated right operand, i.e it is a rotation vector.
• Added method bfl::utils::mean_quaternion() that evaluates the weighted mean of a set of unitary quaternions.
• utils.h is now a template header-only utility file.
• Add Skipper interface class to model skipping functionalities.
• Add Skippable interface class to model a functionality that can be skipped on command.
• Rename Skippable::getSkipState() in Skippable::is_skipping().

Filtering functions

• Added pure public virtual method GaussianPrediction::getStateModel() (required to properly implement GPFPrediction::getStateModel()).
• Implemented method KFPrediction::getStateModel().
• Implemented method KFCorrection::getLikelihood().
• Implemented method UKFPrediction::getStateModel().
• Implemented method UKFCorrection::getLikelihood().
• Implemented method SUKFCorrection::getLikelihood().
• Changed implementation of GaussianLikelihood::likelihood().
• Changed implementation of GPFPrediction::getStateModel().
• Changed implementation of GPFCorrection::getLikelihood().
• Changed implementation of GPFCorrection::evaluateProposal().
• Changed implementation of WhiteNoiseAcceleration.
• Fixed missing const(s) keywords in signature of method StateModel::getTransitionProbability().
• Fixed missing const(s) keywords in signature of method WhiteNoiseAcceleration::getTransitionProbability().
• SUKFCorrection::getNoiseCovarianceMatrix() is now virtual.
• Method MeasurementModel::freeze() takes an input argument of type Data.
• PFCorrection::correct() now does not perform measurements freeze before calling PFCorrection::correctStep().
• GaussianCorrection::correct() now does not perform measurements freeze before calling GaussianCorrection::correctStep().
• GPFPrediction::predictStep() calls predict() instead of predictStep() on the underlying GaussianPrediction.
• GPFPrediction::correctStep() calls correct() instead of correctStep() on the underlying GaussianCorrection.
• GaussianPrediction is not a friend of GPFPrediction anymore.
• GaussianCorrection is not a friend of GPFCorrection anymore.
• Removed decorator classes. Using decorator was an easy way of extending functionalities, but at the cost of writing erroneous behavior in the filters.
• Removed friendships from *Prediction and *Correction classes.
• Implemented freeze_measurments for *Correction classes.
• Made skip-related variable value in *Prediction classes coherent with assigned values.
• Removed setters from *Prediction and derived classes. All the required data to create an object are passed to the constructor.
• Removed setters from *Correction and derived classes. All the required data to create an object are passed to the constructor.
• Add StateProcess interface class to describe state model functionalities.
• Add ExogenousProcess interface class to describe exogenous model functionalities.
• Add GaussianMixturePrediction interface class to describe Gaussian mixture-based prediction functionalities.
• Move ExogenousModel inside StateModel. Consequently, the prediction classes only need to handle a StateModel that in turns will handle the ExogenousModel properly, if present.
• Add Filter interface class to describe Bayes filter functionalities.
• Rename FilteringAlgorithm::getFilteringStep() in FilteringAlgorithm::step_number().
• Rename FilteringAlgorithm::isRunning() in FilteringAlgorithm::is_running().
• Rename FilteringAlgorithm::filteringRecursion() in FilteringAlgorithm::filtering_recursion().
• Rename FilteringAlgorithm::initialization() in FilteringAlgorithm::initialization_step().
• Rename FilteringAlgorithm::filteringStep() in FilteringAlgorithm::filtering_step().
• Rename FilteringAlgorithm::runCondition() in FilteringAlgorithm::run_condition().
• Method PFCorrection::freeze_measurements() takes an input argument of type Data.
• Method GaussianCorrection::freeze_measurements() takes an input argument of type Data such that it can be passed to the underlying MeasurementModel::freeze(const bfl::Data&).
• LinearModel now inherits from LTIMeasurementModel.

Filtering algorithms

• SIS::filteringStep() performs measurements freeze before performing the actual correction. The correction is skipped if the freeze fails. The user might want to re-implement this method (or provide their own algorithm) if they need to handle the measurements freeze differently.
• Add method GaussianFilter::prediction() returning reference to the underlying GaussianPrediction prediction_ (now private).
• Add method GaussianFilter::correction() returning reference to the underlying GaussianCorrection correction_ (now private).
• Add method ParticleFilter::initialization() returning reference to the underlying ParticleSetInitialization initialization_.
• Add method ParticleFilter::prediction() returning reference to the underlying PFPrediction prediction_ (now private).
• Add method ParticleFilter::correction() returning reference to the underlying PFCorrection``correction_ (now private).
• Add method ParticleFilter::resampling() returning reference to the underlying Resampling resampling_.

Test

• Mean extraction is performed using EstimatesExtraction utilities in test_UPF.
• Add mean extraction and logging in test_SIS to simplify inspection of the algorithm outcome.
• Reduce number of particles in test_SIS to reduce testing computation time in Debug.
• Add testUPF_MAP testing MAP (maximum a posteriori) estimate extraction within a UPF particle filter.
• Add test_Gaussian_Density_UVR testing the method utils::multivariate_gaussian_density_UVR().
• Change test_Gaussian in order to test resizing, noise augmentation and support for quaternions.
• Update test_KF, test_UKF, test_mixed_KF_UKF, test_mixed_UKF_KF, test_mixed_KF_SUKF to account for the removed measurements freeze within GaussianCorrection::correct().
• Add test_QuaternionUtils testing quaterion utilities in the utils namespace

BayesFilters 0.8.0 (2018-12-18)

Dependencies

• Removed OpenCV dependency.

CMake

• Devel branch will now have +100 on the patch number to differentiate from master branch.
• Fourth number of the project version has been removed to be compliant with SemVer system.
• Third number of SemVer increases since API compatibility is broken.

Data types

• Using only double data type within the whole library.

Filtering algorithms

• Added logging capabilities to FilteringAlgorithm.
• Removed SIS::setInitialization(), SIS::setPrediction(), SIS::setCorrection and SIS:setResampling methods. The associated unique_ptr classes are now mandatory arguments of ParticleFilter constructor.
• Constructor SIS::SIS takes the state size, both linear and circular, and the filtering step classes as arguments to initialize, respectively, the ParticleSet and the ParticleFilter classes.
• Method SIS::filteringStep uses VectorXi instead of VectorXf to represent particle parents.
• Method SIS::filteringStep uses particle weights in log space instead of linear space.
• Moved default logging facilities of class SIS from method SIS::filteringStep to overridden method Logger::log.
• Re-implemented class KalmanFilter, a general Gaussian filtering algorithm using a GaussianPrediction and a GaussianCorrection.
• Renamed class KalmanFilter to GaussianFilter.
• Added call to virtual method Logger::log in method GaussianFilter::filteringStep.

Filtering functions

• Renamed UpdateParticles in BootstrapCorrection.
• Removed VisualParticleFilter class.
• Removed PFVisualCorrection and derived classes.
• Added LikelihoodModel interface class.
• Added GaussianLikelihood class.
• Added class GaussianPrediction, a generic Gaussian prediction step.
• Added class GaussianCorrection, a generic Gaussian correction step.
• Added class KFPrediction, a (Gaussian) Kalman prediction step for LinearStateModel models.
• Added class KFCorrection, a (Gaussian) Kalman correction step for LinearMeasurementModel models.
• Added class UKFPrediction, a (Gaussian) unscented Kalman prediction step for StateModel and AdditiveStateModel models.
• Added class UKFCorrection, a (Gaussian) unscented Kalman correction step for MeasurementModel and AdditiveMeasurementModel models.
• Added class SUKFCorrection, a (Gaussian) unscented Kalman correction step for AdditiveMeasurementModel models.
• Added class GPFPrediction, a particle filter prediction step that propagates the Gaussian belief associated to particles.
• Added class GPFCorrection, a particle filter correction step that propagates the Gaussian belief associated to particles and weights them according to a Gaussian proposal distribution.
• PFCorrection::getLikelihood() method is now pure virtual.
• Used new ParticleSet class within classes PFPrediction, PFPredictionDecorator, PFCorrection, PFCorrectionDecorator, DrawParticles, BootstrapCorrection, Resampling, ResamplingWithPrior, ParticleSetInitialization, InitSurveillanceAreaGrid and SIS.
• Method ResamplingWithPrior::resample heavily changed (due to use of ParticleSet).
• Methods Resampling::resample and ResamplingWithPrior::resample use VectorXi instead of VectorXf to represent particle parents.
• Methods Resampling::resample and Resampling::neff uses particle weights in log space instead of linear space.
• Methods ResamplingWithPrior::resample uses particle weights in log space instead of linear space.
• PFCorrection now performs measurements freeze before calling PFCorrection::correctStep and does not call it if measurements freeze fails.
• BootstrapCorrection::correctStep now does not freeze measurements anymore.
• BootstrapCorrection::correctStep uses particle weights in log space instead of linear space.
• InitSurveillanceAreaGrid::initialize uses particle weights in log space instead of linear space.

State models

• Added SimulatedStateModel class to simulate kinematic or dynamic models using StateModel classes.
• Added non-pure virtual method StateModel::getTransitionProbability.
• Added non-pure virtual method StateModel::getJacobian.
• Added pure virtual method StateModel::getOutputSize
• Added pure virtual method ExogenousModel::getOutputSize
• Implemented method StateModelDecorator::getOutputSize
• Implemented AdditiveStateModel class inheriting from StateModel.
• Implemented LinearStateModel class inheriting from AdditiveStateModel.
• Implemented LTIStateModel class inheriting from LinearStateModel.
• WhiteNoiseAcceleration class now inherits from LinearStateModel.
• Implemented method WhiteNoiseAcceleration::getOutputSize
• Implemented method WhiteNoiseAcceleration::getTransitionProbability.

Measurement models

• Added SimulatedLinearSensor class.
• Added MeasurementModelDecorator class.
• Added logging capabilities to MeasurementModel.
• Removed method MeasurementModel::getNoiseSample.
• Added pure virtual method MeasurementModel::getOutputSize
• Method MeasurementModel::measure replaces method MeasurementModel::getAgentMeasurements and does not take the state as input.
• Method MeasurementModel::freezeMeasurements replaces method MeasurementModel::bufferAgentData const.
• Method BootstrapCorrection::correctStep uses MeasurementModel::freezeMeasurements.
• Added class AdditiveMeasurementModel.
• Added class LinearMeasurementModel.
• Added class LTIMeasurementModel.
• Renamed LinearSensor to LinearModel.
• LinearModel class now inherits from LinearMeasurementModel.
• LinearModel class now does not implement MeasurementModel::measure.

Filtering utilities

• Added Data class in order to have a type for encapsulating data coming from any process.
• Added GaussianMixture, Gaussian and ParticleSet classes.
• Added directional_add(), directional_sub() and directional_mean() functions in directional_statistics.h/cpp.
• Added sigma_point(), unscented_weights() and unscented_transform() functions in sigma_point.h/cpp.
• Added UTWeight struct to store unscented transform weights in sigma_point.h/cpp.
• Added alias FunctionEvaluation in sigma_point.h/cpp.
• Added method utils::log_sum_exp to evaluate the logarithm of a sum of exponentials.

Test

• Removed test_ParticleFilter.
• Added test_DirectionalStatisticsUtils for directional_statistics.h/cpp.
• Added test_SigmaPointUtils for sigma_point.h/cpp.
• Added test_Gaussian for Gaussian and GaussianMixture classes.
• Added test_KF testing Gaussian filtering with KFPrediction and KFCorrection.
• Added test_UKF testing Gaussian filtering with UKFPrediction and UKFCorrection.
• Added test_mixed_KF_UKF testing Gaussian filtering with KFPrediction and UKFCorrection.
• Added test_mixed_UKF_KF testing Gaussian filtering with UKFPrediction and KFCorrection.
• Added test_mixed_KF_SUKF testing Gaussian filtering with KFPrediction and SUKFCorrection.
• Added test_UPF testing particle filtering with UKFPrediction and UKFCorrection.
• Updated test_SIS.
• Updated test_SIS_Decorators.

BayesFilters 0.7.1.0 (2018-06-27)

Bugfix

• Fix WhiteNoiseAcceleration implementation.

BayesFilters 0.7.0.0 (2018-06-07)

Filtering classes

• Remove PFVisualCorrection::getVisualObservationModel() and PFVisualCorrection::setVisualObservationModel().

Bugfix

• Fix PF prediction and correction move constructor (minor fix).

CMake

• Threads is now a dependency of the exported library. This will solve a CMake error during configuration in CMake projects linking BayesFilters.
• BayesFilters is now fully relocatable.

BayesFilters 0.6.2.0 (2018-01-24)

Filtering classes

• Add ResamplingWithPrior class.
• Add HistoryBuffer and EstimatesExtraction classes.

Filtering features

• Add 'all' option to skip() method of particle filters. This option is particularly useful to reset the internal status of the filters.

Dependencies

• Removed Eigen::NullaryExpr() calls. Now the library can be compiled with any Eigen 3.3 version.

Bugfix

• Windows is now fully supported and tested.

Test

• Add Appveyor and Travis yml files. Tests still need to be improved.

CMake

• Update CMake modules.

BayesFilters 0.6.1.0 (2017-11-20)

CMake

• Update installation helper files, which are updated from YCM commit f162fcb.

BayesFilters 0.6.0.1 (2017-11-08)

Bugfixes

• FilteringAlgorithm::filteringRecursion: fix the invocation position of initialization.

BayesFilters 0.6.0.0 (2017-11-06)

This is the first public release of the BayesFilters library.