MRPT  2.0.4
CKalmanFilterCapable.h
Go to the documentation of this file.
1 /* +------------------------------------------------------------------------+
2  | Mobile Robot Programming Toolkit (MRPT) |
3  | https://www.mrpt.org/ |
4  | |
5  | Copyright (c) 2005-2020, Individual contributors, see AUTHORS file |
6  | See: https://www.mrpt.org/Authors - All rights reserved. |
7  | Released under BSD License. See: https://www.mrpt.org/License |
8  +------------------------------------------------------------------------+ */
9 #pragma once
10 
14 #include <mrpt/containers/stl_containers_utils.h> // find_in_vector
16 #include <mrpt/math/CMatrixFixed.h>
17 #include <mrpt/math/CVectorFixed.h>
18 #include <mrpt/math/num_jacobian.h>
19 #include <mrpt/math/utils.h>
21 #include <mrpt/system/CTicTac.h>
25 #include <cstring> // memcpy
26 #include <vector>
27 
28 namespace mrpt
29 {
30 namespace bayes
31 {
32 /** The Kalman Filter algorithm to employ in bayes::CKalmanFilterCapable
33  * For further details on each algorithm see the tutorial:
34  * https://www.mrpt.org/Kalman_Filters
35  * \sa bayes::CKalmanFilterCapable::KF_options
36  * \ingroup mrpt_bayes_grp
37  */
39 {
44 };
45 
46 // Forward declaration:
47 template <
48  size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE,
49  typename KFTYPE>
51 
52 /** Generic options for the Kalman Filter algorithm in itself.
53  * \ingroup mrpt_bayes_grp
54  */
56 {
58  : verbosity_level(verb_level_ref)
59  {
60  }
61 
64  const std::string& section) override
65  {
66  method = iniFile.read_enum<TKFMethod>(section, "method", method);
68  section, "verbosity_level", verbosity_level);
79  section);
80  }
81 
82  /** This method must display clearly all the contents of the structure in
83  * textual form, sending it to a CStream. */
84  void dumpToTextStream(std::ostream& out) const override
85  {
86  out << "\n----------- [TKF_options] ------------ \n\n";
87  out << mrpt::format(
88  "method = %s\n",
90  out << mrpt::format(
91  "verbosity_level = %s\n",
94  .c_str());
95  out << mrpt::format(
96  "IKF_iterations = %i\n", IKF_iterations);
97  out << mrpt::format(
98  "enable_profiler = %c\n",
99  enable_profiler ? 'Y' : 'N');
100  out << "\n";
101  }
102 
103  /** The method to employ (default: kfEKFNaive) */
106  /** Number of refinement iterations, only for the IKF method. */
108  /** If enabled (default=false), detailed timing information will be dumped
109  * to the console thru a CTimerLog at the end of the execution. */
110  bool enable_profiler{false};
111  /** (default=true) If true, OnTransitionJacobian will be called; otherwise,
112  * the Jacobian will be estimated from a numeric approximation by calling
113  * several times to OnTransitionModel. */
115  /** (default=true) If true, OnObservationJacobians will be called;
116  * otherwise, the Jacobian will be estimated from a numeric approximation by
117  * calling several times to OnObservationModel. */
119  /** (default=false) If true, will compute all the Jacobians numerically and
120  * compare them to the analytical ones, throwing an exception on mismatch.
121  */
123  /** (default-1e-2) Sets the threshold for the difference between the
124  * analytic and the numerical jacobians */
126 };
127 
128 /** Auxiliary functions, for internal usage of MRPT classes */
129 namespace detail
130 {
131 // Auxiliary functions.
132 template <
133  size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE,
134  typename KFTYPE>
135 inline size_t getNumberOfLandmarksInMap(
137  obj);
138 // Specialization:
139 template <size_t VEH_SIZE, size_t OBS_SIZE, size_t ACT_SIZE, typename KFTYPE>
140 inline size_t getNumberOfLandmarksInMap(
141  const CKalmanFilterCapable<
142  VEH_SIZE, OBS_SIZE, 0 /*FEAT_SIZE*/, ACT_SIZE, KFTYPE>& obj);
143 
144 template <
145  size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE,
146  typename KFTYPE>
147 inline bool isMapEmpty(
149  obj);
150 // Specialization:
151 template <size_t VEH_SIZE, size_t OBS_SIZE, size_t ACT_SIZE, typename KFTYPE>
152 inline bool isMapEmpty(
153  const CKalmanFilterCapable<
154  VEH_SIZE, OBS_SIZE, 0 /*FEAT_SIZE*/, ACT_SIZE, KFTYPE>& obj);
155 
156 template <
157  size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE,
158  typename KFTYPE>
159 void addNewLandmarks(
161  const typename CKalmanFilterCapable<
162  VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE>::vector_KFArray_OBS& Z,
163  const std::vector<int>& data_association,
164  const typename CKalmanFilterCapable<
165  VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE>::KFMatrix_OxO& R);
166 template <size_t VEH_SIZE, size_t OBS_SIZE, size_t ACT_SIZE, typename KFTYPE>
167 void addNewLandmarks(
169  VEH_SIZE, OBS_SIZE, 0 /* FEAT_SIZE=0 */, ACT_SIZE, KFTYPE>& obj,
170  const typename CKalmanFilterCapable<
171  VEH_SIZE, OBS_SIZE, 0 /* FEAT_SIZE=0 */, ACT_SIZE,
172  KFTYPE>::vector_KFArray_OBS& Z,
173  const std::vector<int>& data_association,
174  const typename CKalmanFilterCapable<
175  VEH_SIZE, OBS_SIZE, 0 /* FEAT_SIZE=0 */, ACT_SIZE,
176  KFTYPE>::KFMatrix_OxO& R);
177 } // namespace detail
178 
179 /** Virtual base for Kalman Filter (EKF,IEKF,UKF) implementations.
180  * This base class stores the state vector and covariance matrix of the
181  *system. It has virtual methods that must be completed
182  * by derived classes to address a given filtering problem. The main entry
183  *point of the algorithm is CKalmanFilterCapable::runOneKalmanIteration, which
184  * should be called AFTER setting the desired filter options in KF_options,
185  *as well as any options in the derived class.
186  * Note that the main entry point is protected, so derived classes must offer
187  *another method more specific to a given problem which, internally, calls
188  *runOneKalmanIteration.
189  *
190  * For further details and examples, check out the tutorial:
191  *http://www.mrpt.org/Kalman_Filters
192  *
193  * The Kalman filter algorithms are generic, but this implementation is biased
194  *to ease the implementation
195  * of SLAM-like problems. However, it can be also applied to many generic
196  *problems not related to robotics or SLAM.
197  *
198  * The meaning of the template parameters is:
199  * - VEH_SIZE: The dimension of the "vehicle state": either the full state
200  *vector or the "vehicle" part if applicable.
201  * - OBS_SIZE: The dimension of each observation (eg, 2 for pixel coordinates,
202  *3 for 3D coordinates,etc).
203  * - FEAT_SIZE: The dimension of the features in the system state (the "map"),
204  *or 0 if not applicable (the default if not implemented).
205  * - ACT_SIZE: The dimension of each "action" u_k (or 0 if not applicable).
206  * - KFTYPE: The numeric type of the matrices (default: double)
207  *
208  * Revisions:
209  * - 2007: Antonio J. Ortiz de Galisteo (AJOGD)
210  * - 2008/FEB: All KF classes corrected, reorganized, and rewritten (JLBC).
211  * - 2008/MAR: Implemented IKF (JLBC).
212  * - 2009/DEC: Totally rewritten as a generic template using fixed-size
213  *matrices where possible (JLBC).
214  *
215  * \sa mrpt::slam::CRangeBearingKFSLAM, mrpt::slam::CRangeBearingKFSLAM2D
216  * \ingroup mrpt_bayes_grp
217  */
218 template <
219  size_t VEH_SIZE, size_t OBS_SIZE, size_t FEAT_SIZE, size_t ACT_SIZE,
220  typename KFTYPE = double>
222 {
223  public:
224  static constexpr size_t get_vehicle_size() { return VEH_SIZE; }
225  static constexpr size_t get_observation_size() { return OBS_SIZE; }
226  static constexpr size_t get_feature_size() { return FEAT_SIZE; }
227  static constexpr size_t get_action_size() { return ACT_SIZE; }
228  inline size_t getNumberOfLandmarksInTheMap() const
229  {
230  return detail::getNumberOfLandmarksInMap(*this);
231  }
232  inline bool isMapEmpty() const { return detail::isMapEmpty(*this); }
233  /** The numeric type used in the Kalman Filter (default=double) */
234  using kftype = KFTYPE;
235  /** My class, in a shorter name! */
236  using KFCLASS =
238 
239  // ---------- Many useful typedefs to short the notation a bit... --------
242 
247 
254 
258  using vector_KFArray_OBS = std::vector<KFArray_OBS>;
260 
261  inline size_t getStateVectorLength() const { return m_xkk.size(); }
262  inline KFVector& internal_getXkk() { return m_xkk; }
263  inline KFMatrix& internal_getPkk() { return m_pkk; }
264  /** Returns the mean of the estimated value of the idx'th landmark (not
265  * applicable to non-SLAM problems).
266  * \exception std::exception On idx>= getNumberOfLandmarksInTheMap()
267  */
268  inline void getLandmarkMean(size_t idx, KFArray_FEAT& feat) const
269  {
271  std::memcpy(
272  &feat[0], &m_xkk[VEH_SIZE + idx * FEAT_SIZE],
273  FEAT_SIZE * sizeof(m_xkk[0]));
274  }
275  /** Returns the covariance of the idx'th landmark (not applicable to
276  * non-SLAM problems).
277  * \exception std::exception On idx>= getNumberOfLandmarksInTheMap()
278  */
279  inline void getLandmarkCov(size_t idx, KFMatrix_FxF& feat_cov) const
280  {
281  feat_cov = m_pkk.blockCopy<FEAT_SIZE, FEAT_SIZE>(
282  VEH_SIZE + idx * FEAT_SIZE, VEH_SIZE + idx * FEAT_SIZE);
283  }
284 
285  protected:
286  /** @name Kalman filter state
287  @{ */
288 
289  /** The system state vector. */
291  /** The system full covariance matrix. */
293 
294  /** @} */
295 
297 
298  /** @name Virtual methods for Kalman Filter implementation
299  @{
300  */
301 
302  /** Must return the action vector u.
303  * \param out_u The action vector which will be passed to OnTransitionModel
304  */
305  virtual void OnGetAction(KFArray_ACT& out_u) const = 0;
306 
307  /** Implements the transition model \f$ \hat{x}_{k|k-1} = f(
308  * \hat{x}_{k-1|k-1}, u_k ) \f$
309  * \param in_u The vector returned by OnGetAction.
310  * \param inout_x At input has \f[ \hat{x}_{k-1|k-1} \f] , at output must
311  * have \f$ \hat{x}_{k|k-1} \f$ .
312  * \param out_skip Set this to true if for some reason you want to skip the
313  * prediction step (to do not modify either the vector or the covariance).
314  * Default:false
315  * \note Even if you return "out_skip=true", the value of "inout_x" MUST be
316  * updated as usual (this is to allow numeric approximation of Jacobians).
317  */
318  virtual void OnTransitionModel(
319  const KFArray_ACT& in_u, KFArray_VEH& inout_x,
320  bool& out_skipPrediction) const = 0;
321 
322  /** Implements the transition Jacobian \f$ \frac{\partial f}{\partial x} \f$
323  * \param out_F Must return the Jacobian.
324  * The returned matrix must be \f$V \times V\f$ with V being either the
325  * size of the whole state vector (for non-SLAM problems) or VEH_SIZE (for
326  * SLAM problems).
327  */
328  virtual void OnTransitionJacobian([
329  [maybe_unused]] KFMatrix_VxV& out_F) const
330  {
332  }
333 
334  /** Only called if using a numeric approximation of the transition Jacobian,
335  * this method must return the increments in each dimension of the vehicle
336  * state vector while estimating the Jacobian.
337  */
339  KFArray_VEH& out_increments) const
340  {
341  for (size_t i = 0; i < VEH_SIZE; i++) out_increments[i] = 1e-6;
342  }
343 
344  /** Implements the transition noise covariance \f$ Q_k \f$
345  * \param out_Q Must return the covariance matrix.
346  * The returned matrix must be of the same size than the jacobian from
347  * OnTransitionJacobian
348  */
349  virtual void OnTransitionNoise(KFMatrix_VxV& out_Q) const = 0;
350 
351  /** This will be called before OnGetObservationsAndDataAssociation to allow
352  * the application to reduce the number of covariance landmark predictions
353  * to be made.
354  * For example, features which are known to be "out of sight" shouldn't be
355  * added to the output list to speed up the calculations.
356  * \param in_all_prediction_means The mean of each landmark predictions;
357  * the computation or not of the corresponding covariances is what we're
358  * trying to determined with this method.
359  * \param out_LM_indices_to_predict The list of landmark indices in the map
360  * [0,getNumberOfLandmarksInTheMap()-1] that should be predicted.
361  * \note This is not a pure virtual method, so it should be implemented
362  * only if desired. The default implementation returns a vector with all the
363  * landmarks in the map.
364  * \sa OnGetObservations, OnDataAssociation
365  */
367  [[maybe_unused]] const vector_KFArray_OBS& in_all_prediction_means,
368  std::vector<size_t>& out_LM_indices_to_predict) const
369  {
370  // Default: all of them:
371  const size_t N = this->getNumberOfLandmarksInTheMap();
372  out_LM_indices_to_predict.resize(N);
373  for (size_t i = 0; i < N; i++) out_LM_indices_to_predict[i] = i;
374  }
375 
376  /** Return the observation NOISE covariance matrix, that is, the model of
377  * the Gaussian additive noise of the sensor.
378  * \param out_R The noise covariance matrix. It might be non diagonal, but
379  * it'll usually be.
380  * \note Upon call, it can be assumed that the previous contents of out_R
381  * are all zeros.
382  */
383  virtual void OnGetObservationNoise(KFMatrix_OxO& out_R) const = 0;
384 
385  /** This is called between the KF prediction step and the update step, and
386  * the application must return the observations and, when applicable, the
387  * data association between these observations and the current map.
388  *
389  * \param out_z N vectors, each for one "observation" of length OBS_SIZE, N
390  * being the number of "observations": how many observed landmarks for a
391  * map, or just one if not applicable.
392  * \param out_data_association An empty vector or, where applicable, a
393  * vector where the i'th element corresponds to the position of the
394  * observation in the i'th row of out_z within the system state vector (in
395  * the range [0,getNumberOfLandmarksInTheMap()-1]), or -1 if it is a new map
396  * element and we want to insert it at the end of this KF iteration.
397  * \param in_all_predictions A vector with the prediction of ALL the
398  * landmarks in the map. Note that, in contrast, in_S only comprises a
399  * subset of all the landmarks.
400  * \param in_S The full covariance matrix of the observation predictions
401  * (i.e. the "innovation covariance matrix"). This is a M*O x M*O matrix
402  * with M=length of "in_lm_indices_in_S".
403  * \param in_lm_indices_in_S The indices of the map landmarks (range
404  * [0,getNumberOfLandmarksInTheMap()-1]) that can be found in the matrix
405  * in_S.
406  *
407  * This method will be called just once for each complete KF iteration.
408  * \note It is assumed that the observations are independent, i.e. there
409  * are NO cross-covariances between them.
410  */
412  vector_KFArray_OBS& out_z, std::vector<int>& out_data_association,
413  const vector_KFArray_OBS& in_all_predictions, const KFMatrix& in_S,
414  const std::vector<size_t>& in_lm_indices_in_S,
415  const KFMatrix_OxO& in_R) = 0;
416 
417  /** Implements the observation prediction \f$ h_i(x) \f$.
418  * \param idx_landmark_to_predict The indices of the landmarks in the map
419  * whose predictions are expected as output. For non SLAM-like problems,
420  * this input value is undefined and the application should just generate
421  * one observation for the given problem.
422  * \param out_predictions The predicted observations.
423  */
424  virtual void OnObservationModel(
425  const std::vector<size_t>& idx_landmarks_to_predict,
426  vector_KFArray_OBS& out_predictions) const = 0;
427 
428  /** Implements the observation Jacobians \f$ \frac{\partial h_i}{\partial x}
429  * \f$ and (when applicable) \f$ \frac{\partial h_i}{\partial y_i} \f$.
430  * \param idx_landmark_to_predict The index of the landmark in the map
431  * whose prediction is expected as output. For non SLAM-like problems, this
432  * will be zero and the expected output is for the whole state vector.
433  * \param Hx The output Jacobian \f$ \frac{\partial h_i}{\partial x} \f$.
434  * \param Hy The output Jacobian \f$ \frac{\partial h_i}{\partial y_i}
435  * \f$.
436  */
438  [[maybe_unused]] size_t idx_landmark_to_predict,
439  [[maybe_unused]] KFMatrix_OxV& Hx,
440  [[maybe_unused]] KFMatrix_OxF& Hy) const
441  {
443  }
444 
445  /** Only called if using a numeric approximation of the observation
446  * Jacobians, this method must return the increments in each dimension of
447  * the vehicle state vector while estimating the Jacobian.
448  */
450  KFArray_VEH& out_veh_increments,
451  KFArray_FEAT& out_feat_increments) const
452  {
453  for (size_t i = 0; i < VEH_SIZE; i++) out_veh_increments[i] = 1e-6;
454  for (size_t i = 0; i < FEAT_SIZE; i++) out_feat_increments[i] = 1e-6;
455  }
456 
457  /** Computes A=A-B, which may need to be re-implemented depending on the
458  * topology of the individual scalar components (eg, angles).
459  */
461  KFArray_OBS& A, const KFArray_OBS& B) const
462  {
463  A -= B;
464  }
465 
466  public:
467  /** If applicable to the given problem, this method implements the inverse
468  * observation model needed to extend the "map" with a new "element".
469  * \param in_z The observation vector whose inverse sensor model is to be
470  * computed. This is actually one of the vector<> returned by
471  * OnGetObservationsAndDataAssociation().
472  * \param out_yn The F-length vector with the inverse observation model \f$
473  * y_n=y(x,z_n) \f$.
474  * \param out_dyn_dxv The \f$F \times V\f$ Jacobian of the inv. sensor
475  * model wrt the robot pose \f$ \frac{\partial y_n}{\partial x_v} \f$.
476  * \param out_dyn_dhn The \f$F \times O\f$ Jacobian of the inv. sensor
477  * model wrt the observation vector \f$ \frac{\partial y_n}{\partial h_n}
478  * \f$.
479  *
480  * - O: OBS_SIZE
481  * - V: VEH_SIZE
482  * - F: FEAT_SIZE
483  *
484  * \note OnNewLandmarkAddedToMap will be also called after calling this
485  * method if a landmark is actually being added to the map.
486  * \deprecated This version of the method is deprecated. The alternative
487  * method is preferred to allow a greater flexibility.
488  */
490  [[maybe_unused]] const KFArray_OBS& in_z,
491  [[maybe_unused]] KFArray_FEAT& out_yn,
492  [[maybe_unused]] KFMatrix_FxV& out_dyn_dxv,
493  [[maybe_unused]] KFMatrix_FxO& out_dyn_dhn) const
494  {
495  MRPT_START
497  "Inverse sensor model required but not implemented in derived "
498  "class.");
499  MRPT_END
500  }
501 
502  /** If applicable to the given problem, this method implements the inverse
503  * observation model needed to extend the "map" with a new "element".
504  * The uncertainty in the new map feature comes from two parts: one from
505  * the vehicle uncertainty (through the out_dyn_dxv Jacobian),
506  * and another from the uncertainty in the observation itself. By
507  * default, out_use_dyn_dhn_jacobian=true on call, and if it's left at
508  * "true",
509  * the base KalmanFilter class will compute the uncertainty of the
510  * landmark relative position from out_dyn_dhn.
511  * Only in some problems (e.g. MonoSLAM), it'll be needed for the
512  * application to directly return the covariance matrix \a
513  * out_dyn_dhn_R_dyn_dhnT, which is the equivalent to:
514  *
515  * \f$ \frac{\partial y_n}{\partial h_n} R \frac{\partial
516  * y_n}{\partial h_n}^\top \f$.
517  *
518  * but may be computed from additional terms, or whatever needed by the
519  * user.
520  *
521  * \param in_z The observation vector whose inverse sensor model is to be
522  * computed. This is actually one of the vector<> returned by
523  * OnGetObservationsAndDataAssociation().
524  * \param out_yn The F-length vector with the inverse observation model \f$
525  * y_n=y(x,z_n) \f$.
526  * \param out_dyn_dxv The \f$F \times V\f$ Jacobian of the inv. sensor
527  * model wrt the robot pose \f$ \frac{\partial y_n}{\partial x_v} \f$.
528  * \param out_dyn_dhn The \f$F \times O\f$ Jacobian of the inv. sensor
529  * model wrt the observation vector \f$ \frac{\partial y_n}{\partial h_n}
530  * \f$.
531  * \param out_dyn_dhn_R_dyn_dhnT See the discussion above.
532  *
533  * - O: OBS_SIZE
534  * - V: VEH_SIZE
535  * - F: FEAT_SIZE
536  *
537  * \note OnNewLandmarkAddedToMap will be also called after calling this
538  * method if a landmark is actually being added to the map.
539  */
541  const KFArray_OBS& in_z, KFArray_FEAT& out_yn,
542  KFMatrix_FxV& out_dyn_dxv, KFMatrix_FxO& out_dyn_dhn,
543  [[maybe_unused]] KFMatrix_FxF& out_dyn_dhn_R_dyn_dhnT,
544  bool& out_use_dyn_dhn_jacobian) const
545  {
546  MRPT_START
547  OnInverseObservationModel(in_z, out_yn, out_dyn_dxv, out_dyn_dhn);
548  out_use_dyn_dhn_jacobian = true;
549  MRPT_END
550  }
551 
552  /** If applicable to the given problem, do here any special handling of
553  * adding a new landmark to the map.
554  * \param in_obsIndex The index of the observation whose inverse sensor is
555  * to be computed. It corresponds to the row in in_z where the observation
556  * can be found.
557  * \param in_idxNewFeat The index that this new feature will have in the
558  * state vector (0:just after the vehicle state, 1: after that,...). Save
559  * this number so data association can be done according to these indices.
560  * \sa OnInverseObservationModel
561  */
563  [[maybe_unused]] const size_t in_obsIdx,
564  [[maybe_unused]] const size_t in_idxNewFeat)
565  {
566  // Do nothing in this base class.
567  }
568 
569  /** This method is called after the prediction and after the update, to give
570  * the user an opportunity to normalize the state vector (eg, keep angles
571  * within -pi,pi range) if the application requires it.
572  */
573  virtual void OnNormalizeStateVector()
574  {
575  // Do nothing in this base class.
576  }
577 
578  /** This method is called after finishing one KF iteration and before
579  * returning from runOneKalmanIteration().
580  */
581  virtual void OnPostIteration()
582  {
583  // Do nothing in this base class.
584  }
585 
586  /** @}
587  */
588 
589  public:
591  : mrpt::system::COutputLogger("CKalmanFilterCapable"),
593  {
594  }
595  /** Destructor */
596  ~CKalmanFilterCapable() override = default;
598  /** Generic options for the Kalman Filter algorithm itself. */
600 
601  private:
602  // "Local" variables to runOneKalmanIteration, declared here to avoid
603  // allocating them over and over again with each call.
604  // (The variables that go into the stack remains in the function body)
606  std::vector<size_t> m_predictLMidxs;
607  /** The vector of all partial Jacobians dh[i]_dx for each prediction */
608  std::vector<KFMatrix_OxV> m_Hxs;
609  /** The vector of all partial Jacobians dh[i]_dy[i] for each prediction */
610  std::vector<KFMatrix_OxF> m_Hys;
612  vector_KFArray_OBS m_Z; // Each entry is one observation:
613  KFMatrix m_K; // Kalman gain
614  KFMatrix m_S_1; // Inverse of m_S
617 
618  protected:
619  /** The main entry point, executes one complete step: prediction + update.
620  * It is protected since derived classes must provide a problem-specific
621  * entry point for users.
622  * The exact order in which this method calls the virtual method is
623  * explained in https://www.mrpt.org/Kalman_Filters
624  */
625  void runOneKalmanIteration();
626 
627  private:
628  mutable bool m_user_didnt_implement_jacobian{true};
629 
630  /** Auxiliary functions for Jacobian numeric estimation */
631  static void KF_aux_estimate_trans_jacobian(
632  const KFArray_VEH& x, const std::pair<KFCLASS*, KFArray_ACT>& dat,
633  KFArray_VEH& out_x);
635  const KFArray_VEH& x, const std::pair<KFCLASS*, size_t>& dat,
636  KFArray_OBS& out_x);
638  const KFArray_FEAT& x, const std::pair<KFCLASS*, size_t>& dat,
639  KFArray_OBS& out_x);
640 
641  template <
642  size_t VEH_SIZEb, size_t OBS_SIZEb, size_t FEAT_SIZEb, size_t ACT_SIZEb,
643  typename KFTYPEb>
644  friend void detail::addNewLandmarks(
646  VEH_SIZEb, OBS_SIZEb, FEAT_SIZEb, ACT_SIZEb, KFTYPEb>& obj,
647  const typename CKalmanFilterCapable<
648  VEH_SIZEb, OBS_SIZEb, FEAT_SIZEb, ACT_SIZEb,
649  KFTYPEb>::vector_KFArray_OBS& Z,
650  const std::vector<int>& data_association,
651  const typename CKalmanFilterCapable<
652  VEH_SIZEb, OBS_SIZEb, FEAT_SIZEb, ACT_SIZEb, KFTYPEb>::KFMatrix_OxO&
653  R);
654 }; // end class
655 
656 } // namespace bayes
657 } // namespace mrpt
658 
660 using namespace mrpt::bayes;
666 
667 // Template implementation:
668 #define CKalmanFilterCapable_H
CMatrixFixed< Scalar, BLOCK_ROWS, BLOCK_COLS > blockCopy(int start_row=0, int start_col=0) const
const blockCopy(): Returns a copy of the given block
Definition: MatrixBase.h:233
A compile-time fixed-size numeric matrix container.
Definition: CMatrixFixed.h:33
bool use_analytic_observation_jacobian
(default=true) If true, OnObservationJacobians will be called; otherwise, the Jacobian will be estima...
#define MRPT_START
Definition: exceptions.h:241
VerbosityLevel
Enumeration of available verbosity levels.
MRPT_FILL_ENUM(kfEKFNaive)
This is a virtual base class for sets of options than can be loaded from and/or saved to configuratio...
virtual void OnTransitionNoise(KFMatrix_VxV &out_Q) const =0
Implements the transition noise covariance .
virtual void OnNormalizeStateVector()
This method is called after the prediction and after the update, to give the user an opportunity to n...
mrpt::math::CMatrixFixed< KFTYPE, VEH_SIZE, VEH_SIZE > KFMatrix_VxV
~CKalmanFilterCapable() override=default
Destructor.
#define THROW_EXCEPTION(msg)
Definition: exceptions.h:67
virtual void OnObservationModel(const std::vector< size_t > &idx_landmarks_to_predict, vector_KFArray_OBS &out_predictions) const =0
Implements the observation prediction .
std::string std::string format(std::string_view fmt, ARGS &&... args)
Definition: format.h:26
void loadFromConfigFile(const mrpt::config::CConfigFileBase &iniFile, const std::string &section) override
This method load the options from a ".ini"-like file or memory-stored string list.
virtual 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)=0
This is called between the KF prediction step and the update step, and the application must return th...
static constexpr size_t get_action_size()
The namespace for Bayesian filtering algorithm: different particle filters and Kalman filter algorith...
bool enable_profiler
If enabled (default=false), detailed timing information will be dumped to the console thru a CTimerLo...
static void KF_aux_estimate_obs_Hx_jacobian(const KFArray_VEH &x, const std::pair< KFCLASS *, size_t > &dat, KFArray_OBS &out_x)
size_type size() const
Get a 2-vector with [NROWS NCOLS] (as in MATLAB command size(x))
virtual void OnGetObservationNoise(KFMatrix_OxO &out_R) const =0
Return the observation NOISE covariance matrix, that is, the model of the Gaussian additive noise of ...
double debug_verify_analytic_jacobians_threshold
(default-1e-2) Sets the threshold for the difference between the analytic and the numerical jacobians...
void getLandmarkCov(size_t idx, KFMatrix_FxF &feat_cov) const
Returns the covariance of the idx&#39;th landmark (not applicable to non-SLAM problems).
void runOneKalmanIteration()
The main entry point, executes one complete step: prediction + update.
static constexpr size_t get_feature_size()
mrpt::math::CVectorFixed< KFTYPE, VEH_SIZE > KFArray_VEH
KFMatrix m_pkk
The system full covariance matrix.
virtual void OnTransitionModel(const KFArray_ACT &in_u, KFArray_VEH &inout_x, bool &out_skipPrediction) const =0
Implements the transition model .
virtual void OnObservationJacobiansNumericGetIncrements(KFArray_VEH &out_veh_increments, KFArray_FEAT &out_feat_increments) const
Only called if using a numeric approximation of the observation Jacobians, this method must return th...
std::vector< KFMatrix_OxF > m_Hys
The vector of all partial Jacobians dh[i]_dy[i] for each prediction.
virtual void OnInverseObservationModel(const KFArray_OBS &in_z, KFArray_FEAT &out_yn, KFMatrix_FxV &out_dyn_dxv, KFMatrix_FxO &out_dyn_dhn, [[maybe_unused]] KFMatrix_FxF &out_dyn_dhn_R_dyn_dhnT, bool &out_use_dyn_dhn_jacobian) const
If applicable to the given problem, this method implements the inverse observation model needed to ex...
virtual void OnSubstractObservationVectors(KFArray_OBS &A, const KFArray_OBS &B) const
Computes A=A-B, which may need to be re-implemented depending on the topology of the individual scala...
#define ASSERT_(f)
Defines an assertion mechanism.
Definition: exceptions.h:120
void dumpToTextStream(std::ostream &out) const override
This method must display clearly all the contents of the structure in textual form, sending it to a CStream.
This class allows loading and storing values and vectors of different types from a configuration text...
mrpt::math::CMatrixDynamic< KFTYPE > KFMatrix
virtual void OnPreComputingPredictions([[maybe_unused]] const vector_KFArray_OBS &in_all_prediction_means, std::vector< size_t > &out_LM_indices_to_predict) const
This will be called before OnGetObservationsAndDataAssociation to allow the application to reduce the...
mrpt::system::VerbosityLevel & verbosity_level
TKF_options KF_options
Generic options for the Kalman Filter algorithm itself.
A helper class that can convert an enum value into its textual representation, and viceversa...
void addNewLandmarks(CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE > &obj, const typename CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::vector_KFArray_OBS &Z, const std::vector< int > &data_association, const typename CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE >::KFMatrix_OxO &R)
Versatile class for consistent logging and management of output messages.
void getLandmarkMean(size_t idx, KFArray_FEAT &feat) const
Returns the mean of the estimated value of the idx&#39;th landmark (not applicable to non-SLAM problems)...
Virtual base for Kalman Filter (EKF,IEKF,UKF) implementations.
size_t getNumberOfLandmarksInMap(const CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE > &obj)
string iniFile(myDataDir+string("benchmark-options.ini"))
static void KF_aux_estimate_trans_jacobian(const KFArray_VEH &x, const std::pair< KFCLASS *, KFArray_ACT > &dat, KFArray_VEH &out_x)
Auxiliary functions for Jacobian numeric estimation.
#define MRPT_ENUM_TYPE_END()
Definition: TEnumType.h:78
double kftype
The numeric type used in the Kalman Filter (default=double)
std::vector< KFMatrix_OxV > m_Hxs
The vector of all partial Jacobians dh[i]_dx for each prediction.
virtual void OnTransitionJacobianNumericGetIncrements(KFArray_VEH &out_increments) const
Only called if using a numeric approximation of the transition Jacobian, this method must return the ...
mrpt::math::CVectorFixed< KFTYPE, FEAT_SIZE > KFArray_FEAT
Generic options for the Kalman Filter algorithm in itself.
virtual void OnTransitionJacobian([[maybe_unused]] KFMatrix_VxV &out_F) const
Implements the transition Jacobian .
#define MRPT_LOAD_CONFIG_VAR( variableName, variableType, configFileObject, sectionNameStr)
An useful macro for loading variables stored in a INI-like file under a key with the same name that t...
static constexpr size_t get_observation_size()
This is the global namespace for all Mobile Robot Programming Toolkit (MRPT) libraries.
virtual void OnNewLandmarkAddedToMap([[maybe_unused]] const size_t in_obsIdx, [[maybe_unused]] const size_t in_idxNewFeat)
If applicable to the given problem, do here any special handling of adding a new landmark to the map...
A versatile "profiler" that logs the time spent within each pair of calls to enter(X)-leave(X), among other stats.
const float R
mrpt::vision::TStereoCalibResults out
bool debug_verify_analytic_jacobians
(default=false) If true, will compute all the Jacobians numerically and compare them to the analytica...
bool isMapEmpty(const CKalmanFilterCapable< VEH_SIZE, OBS_SIZE, FEAT_SIZE, ACT_SIZE, KFTYPE > &obj)
TKFMethod
The Kalman Filter algorithm to employ in bayes::CKalmanFilterCapable For further details on each algo...
COutputLogger()
Default class constructor.
#define MRPT_END
Definition: exceptions.h:245
virtual void OnObservationJacobians([[maybe_unused]] size_t idx_landmark_to_predict, [[maybe_unused]] KFMatrix_OxV &Hx, [[maybe_unused]] KFMatrix_OxF &Hy) const
Implements the observation Jacobians and (when applicable) .
virtual void OnInverseObservationModel([[maybe_unused]] const KFArray_OBS &in_z, [[maybe_unused]] KFArray_FEAT &out_yn, [[maybe_unused]] KFMatrix_FxV &out_dyn_dxv, [[maybe_unused]] KFMatrix_FxO &out_dyn_dhn) const
If applicable to the given problem, this method implements the inverse observation model needed to ex...
static constexpr size_t get_vehicle_size()
TKFMethod method
The method to employ (default: kfEKFNaive)
static void KF_aux_estimate_obs_Hy_jacobian(const KFArray_FEAT &x, const std::pair< KFCLASS *, size_t > &dat, KFArray_OBS &out_x)
virtual void OnGetAction(KFArray_ACT &out_u) const =0
Must return the action vector u.
mrpt::math::CVectorFixed< KFTYPE, OBS_SIZE > KFArray_OBS
VerbosityLevel m_min_verbosity_level
Provided messages with VerbosityLevel smaller than this value shall be ignored.
#define MRPT_ENUM_TYPE_BEGIN(_ENUM_TYPE_WITH_NS)
Definition: TEnumType.h:62
TKF_options(mrpt::system::VerbosityLevel &verb_level_ref)
mrpt::system::CTimeLogger & getProfiler()
KFVector m_xkk
The system state vector.
bool use_analytic_transition_jacobian
(default=true) If true, OnTransitionJacobian will be called; otherwise, the Jacobian will be estimate...
mrpt::system::CTimeLogger m_timLogger
int IKF_iterations
Number of refinement iterations, only for the IKF method.
mrpt::math::CMatrixFixed< KFTYPE, OBS_SIZE, OBS_SIZE > KFMatrix_OxO
virtual void OnPostIteration()
This method is called after finishing one KF iteration and before returning from runOneKalmanIteratio...
void memcpy(void *dest, size_t destSize, const void *src, size_t copyCount) noexcept
An OS and compiler independent version of "memcpy".



Page generated by Doxygen 1.8.14 for MRPT 2.0.4 Git: 7b5ddf9de Fri May 29 14:02:56 2020 +0200 at vie may 29 14:15:09 CEST 2020