Class CRangeBearingKFSLAM2D

Nested Relationships

Nested Types

Inheritance Relationships

Base Type

  • public bayes:: CKalmanFilterCapable< 3, 2, 2, 3 >

Class Documentation

class CRangeBearingKFSLAM2D : public bayes::CKalmanFilterCapable<3, 2, 2, 3>

An implementation of EKF-based SLAM with range-bearing sensors, odometry, SE(2) robot pose, and 2D landmarks. The main method is processActionObservation() which processes pairs of actions/observations.

The following front-end applications are based on this class:

See also

CRangeBearingKFSLAM

Virtual methods for Kalman Filter implementation

void OnGetAction(KFArray_ACT &out_u) const override

Must return the action vector u.

Parameters:

out_u – The action vector which will be passed to OnTransitionModel

void OnTransitionModel(const KFArray_ACT &in_u, KFArray_VEH &inout_x, bool &out_skipPrediction) const override

Implements the transition model \( \hat{x}_{k|k-1} = f( \hat{x}_{k-1|k-1}, u_k ) \)

Parameters:

  • in_u – The vector returned by OnGetAction.

  • inout_x – At input has

    \[ \hat{x}_{k-1|k-1} \]
    , at output must have \( \hat{x}_{k|k-1} \) .

  • out_skip – Set this to true if for some reason you want to skip the prediction step (to do not modify either the vector or the covariance). Default:false

void OnTransitionJacobian(KFMatrix_VxV &out_F) const override

Implements the transition Jacobian \( \frac{\partial f}{\partial x} \)

Parameters:

out_F – Must return the Jacobian. The returned matrix must be \(V \times V\) with V being either the size of the whole state vector (for non-SLAM problems) or VEH_SIZE (for SLAM problems).

void OnTransitionJacobianNumericGetIncrements(KFArray_VEH &out_increments) const override

Only called if using a numeric approximation of the transition Jacobian, this method must return the increments in each dimension of the vehicle state vector while estimating the Jacobian.

void OnTransitionNoise(KFMatrix_VxV &out_Q) const override

Implements the transition noise covariance \( Q_k \)

Parameters:

out_Q – Must return the covariance matrix. The returned matrix must be of the same size than the jacobian from OnTransitionJacobian

void OnGetObservationsAndDataAssociation(vector_KFArray_OBS &out_z, std::vector<int> &out_data_association, const vector_KFArray_OBS &in_all_predictions, const KFMatrix &in_S, const std::vector<size_t> &in_lm_indices_in_S, const KFMatrix_OxO &in_R) override

This is called between the KF prediction step and the update step, and the application must return the observations and, when applicable, the data association between these observations and the current map.

This method will be called just once for each complete KF iteration.

Note

It is assumed that the observations are independent, i.e. there are NO cross-covariances between them.

Parameters:

  • out_z – N vectors, each for one “observation” of length OBS_SIZE, N being the number of “observations”: how many observed landmarks for a map, or just one if not applicable.

  • out_data_association – An empty vector or, where applicable, a vector where the i’th element corresponds to the position of the observation in the i’th row of out_z within the system state vector (in the range [0,getNumberOfLandmarksInTheMap()-1]), or -1 if it is a new map element and we want to insert it at the end of this KF iteration.

  • in_S – The full covariance matrix of the observation predictions (i.e. the “innovation covariance matrix”). This is a M*O x M*O matrix with M=length of “in_lm_indices_in_S”.

  • in_lm_indices_in_S – The indices of the map landmarks (range [0,getNumberOfLandmarksInTheMap()-1]) that can be found in the matrix in_S.

void OnObservationModel(const std::vector<size_t> &idx_landmarks_to_predict, vector_KFArray_OBS &out_predictions) const override
void OnObservationJacobians(size_t idx_landmark_to_predict, KFMatrix_OxV &Hx, KFMatrix_OxF &Hy) const override

Implements the observation Jacobians \( \frac{\partial h_i}{\partial x} \) and (when applicable) \( \frac{\partial h_i}{\partial y_i} \).

Parameters:

  • idx_landmark_to_predict – The index of the landmark in the map whose prediction is expected as output. For non SLAM-like problems, this will be zero and the expected output is for the whole state vector.

  • Hx – The output Jacobian \( \frac{\partial h_i}{\partial x} \).

  • Hy – The output Jacobian \( \frac{\partial h_i}{\partial y_i} \).

void OnObservationJacobiansNumericGetIncrements(KFArray_VEH &out_veh_increments, KFArray_FEAT &out_feat_increments) const override

Only called if using a numeric approximation of the observation Jacobians, this method must return the increments in each dimension of the vehicle state vector while estimating the Jacobian.

void OnSubstractObservationVectors(KFArray_OBS &A, const KFArray_OBS &B) const override

Computes A=A-B, which may need to be re-implemented depending on the topology of the individual scalar components (eg, angles).

void OnGetObservationNoise(KFMatrix_OxO &out_R) const override

Return the observation NOISE covariance matrix, that is, the model of the Gaussian additive noise of the sensor.

Parameters:

out_R – The noise covariance matrix. It might be non diagonal, but it’ll usually be.

void OnPreComputingPredictions(const vector_KFArray_OBS &in_all_prediction_means, std::vector<size_t> &out_LM_indices_to_predict) const override

This will be called before OnGetObservationsAndDataAssociation to allow the application to reduce the number of covariance landmark predictions to be made. For example, features which are known to be “out of sight” shouldn’t be added to the output list to speed up the calculations.

See also

OnGetObservations, OnDataAssociation

Note

This is not a pure virtual method, so it should be implemented only if desired. The default implementation returns a vector with all the landmarks in the map.

Parameters:

  • in_all_prediction_means – The mean of each landmark predictions; the computation or not of the corresponding covariances is what we’re trying to determined with this method.

  • out_LM_indices_to_predict – The list of landmark indices in the map [0,getNumberOfLandmarksInTheMap()-1] that should be predicted.

void OnInverseObservationModel(const KFArray_OBS &in_z, KFArray_FEAT &out_yn, KFMatrix_FxV &out_dyn_dxv, KFMatrix_FxO &out_dyn_dhn) const override

If applicable to the given problem, this method implements the inverse observation model needed to extend the “map” with a new “element”.

  • O: OBS_SIZE

  • V: VEH_SIZE

  • F: FEAT_SIZE

Note

OnNewLandmarkAddedToMap will be also called after calling this method if a landmark is actually being added to the map.

Parameters:

  • in_z – The observation vector whose inverse sensor model is to be computed. This is actually one of the vector<> returned by OnGetObservations().

  • out_yn – The F-length vector with the inverse observation model \( y_n=y(x,z_n) \).

  • out_dyn_dxv – The \(F \times V\) Jacobian of the inv. sensor model wrt the robot pose \( \frac{\partial y_n}{\partial x_v} \).

  • out_dyn_dhn – The \(F \times O\) Jacobian of the inv. sensor model wrt the observation vector \( \frac{\partial y_n}{\partial h_n} \).

void OnNewLandmarkAddedToMap(size_t in_obsIdx, size_t in_idxNewFeat) override

If applicable to the given problem, do here any special handling of adding a new landmark to the map.

See also

OnInverseObservationModel

Parameters:

  • in_obsIndex – The index of the observation whose inverse sensor is to be computed. It corresponds to the row in in_z where the observation can be found.

  • in_idxNewFeat – The index that this new feature will have in the state vector (0:just after the vehicle state, 1: after that,…). Save this number so data association can be done according to these indices.

void OnNormalizeStateVector() override

This method is called after the prediction and after the update, to give the user an opportunity to normalize the state vector (eg, keep angles within -pi,pi range) if the application requires it.

Public Types

using landmark_point_t = mrpt::math::TPoint2D

Either mrpt::math::TPoint2D or mrpt::math::TPoint3D

Public Functions

CRangeBearingKFSLAM2D()

Default constructor

~CRangeBearingKFSLAM2D() override

Destructor

void reset()

Reset the state of the SLAM filter: The map is emptied and the robot put back to (0,0,0).

void processActionObservation(mrpt::obs::CActionCollection::Ptr &action, mrpt::obs::CSensoryFrame::Ptr &SF)

Process one new action and observations to update the map and robot pose estimate. See the description of the class at the top of this page.

Parameters:

  • action – May contain odometry

  • SF – The set of observations, must contain at least one CObservationBearingRange

void getCurrentState(mrpt::poses::CPosePDFGaussian &out_robotPose, std::vector<mrpt::math::TPoint2D> &out_landmarksPositions, std::map<unsigned int, mrpt::maps::CLandmark::TLandmarkID> &out_landmarkIDs, mrpt::math::CVectorDouble &out_fullState, mrpt::math::CMatrixDouble &out_fullCovariance) const

Returns the complete mean and cov.

See also

getCurrentRobotPose

Parameters:

  • out_robotPose – The mean & 3x3 covariance matrix of the robot 2D pose

  • out_landmarksPositions – One entry for each of the M landmark positions (2D).

  • out_landmarkIDs – Each element[index] (for indices of out_landmarksPositions) gives the corresponding landmark ID.

  • out_fullState – The complete state vector (3+2M).

  • out_fullCovariance – The full (3+2M)x(3+2M) covariance matrix of the filter.

void getCurrentRobotPose(mrpt::poses::CPosePDFGaussian &out_robotPose) const

Returns the mean & 3x3 covariance matrix of the robot 2D pose.

See also

getCurrentState

void getAs3DObject(mrpt::viz::CSetOfObjects::Ptr &outObj) const

Returns a 3D representation of the landmarks in the map and the robot 3D position according to the current filter state.

Parameters:

out_objects –

void loadOptions(const mrpt::config::CConfigFileBase &ini)

Load options from a ini-like file/text

void saveMapAndPath2DRepresentationAsMATLABFile(const std::string &fil, float stdCount = 3.0f, const std::string &styleLandmarks = std::string("b"), const std::string &stylePath = std::string("r"), const std::string &styleRobot = std::string("r")) const

Save the current state of the filter (robot pose & map) to a MATLAB script which displays all the elements in 2D

inline const TDataAssocInfo &getLastDataAssociation() const

Returns a read-only reference to the information on the last data-association

Public Members

TOptions options

The options for the algorithm

Protected Functions

inline void getLandmarkIDsFromIndexInStateVector(std::map<unsigned int, mrpt::maps::CLandmark::TLandmarkID> &out_id2index) const

Protected Attributes

mrpt::obs::CActionCollection::Ptr m_action

Set up by processActionObservation

mrpt::obs::CSensoryFrame::Ptr m_SF

Set up by processActionObservation

mrpt::containers::bimap<mrpt::maps::CLandmark::TLandmarkID, unsigned int> m_IDs

The mapping between landmark IDs and indexes in the Pkk cov. matrix:

mrpt::maps::CSimpleMap m_SFs

The sequence of all the observations and the robot path (kept for debugging, statistics,etc)

TDataAssocInfo m_last_data_association

Last data association

struct TDataAssocInfo

Information for data-association:

See also

getLastDataAssociation

Public Functions

inline TDataAssocInfo()
inline void clear()

Public Members

mrpt::math::CMatrixDynamic<kftype> Y_pred_means
mrpt::math::CMatrixDynamic<kftype> Y_pred_covs
std::vector<size_t> predictions_IDs
std::map<size_t, size_t> newly_inserted_landmarks

Map from the 0-based index within the last observation and the landmark 0-based index in the map (the robot-map state vector) Only used for stats and so.

TDataAssociationResults results
struct TOptions : public mrpt::config::CLoadableOptions

The options for the algorithm

Public Functions

TOptions()

Default values

void loadFromConfigFile(const mrpt::config::CConfigFileBase &source, const std::string &section) override
void dumpToTextStream(std::ostream &out) const override

Public Members

mrpt::math::CVectorFloat stds_Q_no_odo

A 3-length vector with the std. deviation of the transition model in (x,y,phi) used only when there is no odometry (if there is odo, its uncertainty values will be used instead); x y: In meters, phi: radians (but in degrees when loading from a configuration ini-file!)

float std_sensor_range = {0.1f}

The std. deviation of the sensor (for the matrix R in the kalman filters), in meters and radians.

float std_sensor_yaw
float quantiles_3D_representation = {3}

Default = 3

bool create_simplemap = {false}

Whether to fill m_SFs (default=false)

TDataAssociationMethod data_assoc_method = {assocNN}
TDataAssociationMetric data_assoc_metric = {metricMaha}
double data_assoc_IC_chi2_thres = {0.99}

Threshold in [0,1] for the chi2square test for individual compatibility between predictions and observations (default: 0.99)

TDataAssociationMetric data_assoc_IC_metric = {metricMaha}

Whether to use mahalanobis (->chi2 criterion) vs. Matching likelihood.

double data_assoc_IC_ml_threshold = {0.0}

Only if data_assoc_IC_metric==ML, the log-ML threshold (Default=0.0)