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<title>mrpt::bayes::CKalmanFilterCapable Class Reference</title>
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<div class="title">mrpt::bayes::CKalmanFilterCapable Class Reference<div class="ingroups"><a class="el" href="group__mrpt__bayes__grp.html">[mrpt-bayes]</a></div></div> </div>
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<!-- doxytag: class="mrpt::bayes::CKalmanFilterCapable" --><!-- doxytag: inherits="mrpt::utils::CDebugOutputCapable" --><hr/><a name="details" id="details"></a><h2>Detailed Description</h2>
<div class="textblock"><p>Virtual base for Kalman Filter (EKF,IEKF,UKF) implementations. </p>
<p>This base class stores the state vector and covariance matrix of the system. It has virtual methods that must be completed by derived classes to address a given filtering problem. The main entry point of the algorithm is <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#aeebd325f91acbf7d27134c8c7388649e" title="The main entry point, executes one complete step: prediction + update.">CKalmanFilterCapable::runOneKalmanIteration</a>, which should be called AFTER setting the desired filter options in KF_options, as well as any options in the derived class. Note that the main entry point is protected, so derived classes must offer another method more specific to a given problem which, internally, calls runOneKalmanIteration.</p>
<p>For further details and examples, check out the tutorial: <a href="http://www.mrpt.org/Kalman_Filters">http://www.mrpt.org/Kalman_Filters</a></p>
<p>The Kalman filter algorithms are generic, but this implementation is biased to ease the implementation of SLAM-like problems. However, it can be also applied to many generic problems not related to robotics or SLAM.</p>
<p>The meaning of the template parameters is:</p>
<ul>
<li>VEH_SIZE: The dimension of the "vehicle state": either the full state vector or the "vehicle" part if applicable.</li>
<li>OBS_SIZE: The dimension of each observation (eg, 2 for pixel coordinates, 3 for 3D coordinates,etc).</li>
<li>FEAT_SIZE: The dimension of the features in the system state (the "map"), or 0 if not applicable (the default if not implemented).</li>
<li>ACT_SIZE: The dimension of each "action" u_k (or 0 if not applicable).</li>
<li>KFTYPE: The numeric type of the matrices (default: double)</li>
</ul>
<p>Revisions:</p>
<ul>
<li>2007: Antonio J. Ortiz de Galisteo (AJOGD)</li>
<li>2008/FEB: All KF classes corrected, reorganized, and rewritten (JLBC).</li>
<li>2008/MAR: Implemented IKF (JLBC).</li>
<li>2009/DEC: Totally rewritten as a generic template using fixed-size matrices where possible (JLBC).</li>
</ul>
<dl class="see"><dt><b>See also:</b></dt><dd><a class="el" href="classmrpt_1_1slam_1_1_c_range_bearing_k_f_s_l_a_m.html" title="An implementation of EKF-based SLAM with range-bearing sensors, odometry, a full 6D robot pose...">mrpt::slam::CRangeBearingKFSLAM</a>, <a class="el" href="classmrpt_1_1slam_1_1_c_range_bearing_k_f_s_l_a_m2_d.html" title="An implementation of EKF-based SLAM with range-bearing sensors, odometry, and a 2D (+heading) robot p...">mrpt::slam::CRangeBearingKFSLAM2D</a> </dd></dl>
</div>
<p><code>#include <<a class="el" href="_c_kalman_filter_capable_8h_source.html">mrpt/bayes/CKalmanFilterCapable.h</a>></code></p>
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Inheritance diagram for mrpt::bayes::CKalmanFilterCapable:</div>
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<map name="mrpt_1_1bayes_1_1_c_kalman_filter_capable_inherit__map" id="mrpt_1_1bayes_1_1_c_kalman_filter_capable_inherit__map">
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<center><span class="legend">[<a href="graph_legend.html">legend</a>]</span></center></div>
<p><a href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable-members.html">List of all members.</a></p>
<table class="memberdecls">
<tr><td colspan="2"><h2><a name="pub-types"></a>
Public Types</h2></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">typedef KFTYPE </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad66228d2cc13705900669ccbab41f3d9">kftype</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">The numeric type used in the Kalman Filter (default=double) <a href="#ad66228d2cc13705900669ccbab41f3d9"></a><br/></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">typedef <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html">CKalmanFilterCapable</a><br class="typebreak"/>
< VEH_SIZE, OBS_SIZE, <br class="typebreak"/>
FEAT_SIZE, ACT_SIZE, KFTYPE > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6195a8efec17869133a1fd31105d6a64">KFCLASS</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">My class, in a shorter name! <a href="#a6195a8efec17869133a1fd31105d6a64"></a><br/></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">typedef <br class="typebreak"/>
<a class="el" href="structmrpt_1_1dynamicsize__vector.html">mrpt::dynamicsize_vector</a><br class="typebreak"/>
< KFTYPE > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a9a120b286d286048985b059aa721e0c1">KFVector</a></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">typedef <a class="el" href="classmrpt_1_1math_1_1_c_matrix_template_numeric.html">CMatrixTemplateNumeric</a><br class="typebreak"/>
< KFTYPE > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad214112622ea8531c9a7c052f93600bd">KFMatrix</a></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">typedef <a class="el" href="classmrpt_1_1math_1_1_c_matrix_fixed_numeric.html">CMatrixFixedNumeric</a><br class="typebreak"/>
< KFTYPE, VEH_SIZE, VEH_SIZE > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a8dd3e63dd847fd98ef59b472e46cad2c">KFMatrix_VxV</a></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">typedef <a class="el" href="classmrpt_1_1math_1_1_c_matrix_fixed_numeric.html">CMatrixFixedNumeric</a><br class="typebreak"/>
< KFTYPE, OBS_SIZE, OBS_SIZE > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a521a75ae040b78365ebf57f97967bc84">KFMatrix_OxO</a></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">typedef <a class="el" href="classmrpt_1_1math_1_1_c_matrix_fixed_numeric.html">CMatrixFixedNumeric</a><br class="typebreak"/>
< KFTYPE, FEAT_SIZE, FEAT_SIZE > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad8a2b4b92969f736bf6c147804b50614">KFMatrix_FxF</a></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">typedef <a class="el" href="classmrpt_1_1math_1_1_c_matrix_fixed_numeric.html">CMatrixFixedNumeric</a><br class="typebreak"/>
< KFTYPE, ACT_SIZE, ACT_SIZE > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#af20d435cc094724e00949ea137aa832b">KFMatrix_AxA</a></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">typedef <a class="el" href="classmrpt_1_1math_1_1_c_matrix_fixed_numeric.html">CMatrixFixedNumeric</a><br class="typebreak"/>
< KFTYPE, VEH_SIZE, OBS_SIZE > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a60912a7a33cd8e77605e4761848609b2">KFMatrix_VxO</a></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">typedef <a class="el" href="classmrpt_1_1math_1_1_c_matrix_fixed_numeric.html">CMatrixFixedNumeric</a><br class="typebreak"/>
< KFTYPE, VEH_SIZE, FEAT_SIZE > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a7277a24dd3aae42b96708b4669e74a12">KFMatrix_VxF</a></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">typedef <a class="el" href="classmrpt_1_1math_1_1_c_matrix_fixed_numeric.html">CMatrixFixedNumeric</a><br class="typebreak"/>
< KFTYPE, FEAT_SIZE, VEH_SIZE > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#affb2aa897e6434f6572d48ff1be1988e">KFMatrix_FxV</a></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">typedef <a class="el" href="classmrpt_1_1math_1_1_c_matrix_fixed_numeric.html">CMatrixFixedNumeric</a><br class="typebreak"/>
< KFTYPE, FEAT_SIZE, OBS_SIZE > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a253bf3d53d25e20ec0592868efdba607">KFMatrix_FxO</a></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">typedef <a class="el" href="classmrpt_1_1math_1_1_c_matrix_fixed_numeric.html">CMatrixFixedNumeric</a><br class="typebreak"/>
< KFTYPE, OBS_SIZE, FEAT_SIZE > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ada78f649a58aa64c3db6c8b436b5ceba">KFMatrix_OxF</a></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">typedef <a class="el" href="classmrpt_1_1math_1_1_c_matrix_fixed_numeric.html">CMatrixFixedNumeric</a><br class="typebreak"/>
< KFTYPE, OBS_SIZE, VEH_SIZE > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ab0ddb67c05616e7fc1fcabe9cc04c753">KFMatrix_OxV</a></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">typedef <a class="el" href="classmrpt_1_1math_1_1_c_array_numeric.html">CArrayNumeric</a>< KFTYPE, <br class="typebreak"/>
VEH_SIZE > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad4f1667845ca7553925160142821cff0">KFArray_VEH</a></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">typedef <a class="el" href="classmrpt_1_1math_1_1_c_array_numeric.html">CArrayNumeric</a>< KFTYPE, <br class="typebreak"/>
ACT_SIZE > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6454c68e3c45da92e2bbb3e8aa8f6f70">KFArray_ACT</a></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">typedef <a class="el" href="classmrpt_1_1math_1_1_c_array_numeric.html">CArrayNumeric</a>< KFTYPE, <br class="typebreak"/>
OBS_SIZE > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6c1b24a2c7b35e77411888a06de4e59d">KFArray_OBS</a></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">typedef <br class="typebreak"/>
<a class="el" href="structmrpt_1_1aligned__containers.html">mrpt::aligned_containers</a><br class="typebreak"/>
< <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6c1b24a2c7b35e77411888a06de4e59d">KFArray_OBS</a> >::vector_t </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#aa0e71d2a45e5d935eba18d817bb31cf9">vector_KFArray_OBS</a></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">typedef <a class="el" href="classmrpt_1_1math_1_1_c_array_numeric.html">CArrayNumeric</a>< KFTYPE, <br class="typebreak"/>
FEAT_SIZE > </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a0b5b72d68126a91918ce085789da06bd">KFArray_FEAT</a></td></tr>
<tr><td colspan="2"><h2><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">size_t </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a1ac5b0adbbd3a0e5122ebb148ff3e7ad">getNumberOfLandmarksInTheMap</a> () const </td></tr>
<tr><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a48adbb9852e76624b16e7fa115c19e65">isMapEmpty</a> () const </td></tr>
<tr><td class="memItemLeft" align="right" valign="top">size_t </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#aabe244779fd4694bc5e6d336874396de">getStateVectorLength</a> () const </td></tr>
<tr><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a4cc933fc0dacdd126b08b08591c617f4">getLandmarkMean</a> (size_t idx, <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a0b5b72d68126a91918ce085789da06bd">KFArray_FEAT</a> &feat) const </td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">Returns the mean of the estimated value of the idx'th landmark (not applicable to non-SLAM problems). <a href="#a4cc933fc0dacdd126b08b08591c617f4"></a><br/></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ae32eeb6c71cb7ea1acf21ae09ac19827">getLandmarkCov</a> (size_t idx, <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad8a2b4b92969f736bf6c147804b50614">KFMatrix_FxF</a> &feat_cov) const </td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">Returns the covariance of the idx'th landmark (not applicable to non-SLAM problems). <a href="#ae32eeb6c71cb7ea1acf21ae09ac19827"></a><br/></td></tr>
<tr><td class="memItemLeft" align="right" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a44fae14dfa9b912420b6465fb7e8f7c5">CKalmanFilterCapable</a> ()</td></tr>
<tr><td class="memItemLeft" align="right" valign="top">virtual </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a19b3a038e3ba1d3994eab0a08db733b5">~CKalmanFilterCapable</a> ()</td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">Destructor. <a href="#a19b3a038e3ba1d3994eab0a08db733b5"></a><br/></td></tr>
<tr><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmrpt_1_1utils_1_1_c_time_logger.html">mrpt::utils::CTimeLogger</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a4c2d71f07ef0501d157988ebccf9f3c0">getProfiler</a> ()</td></tr>
<tr><td colspan="2"><h2><a name="pub-static-methods"></a>
Static Public Member Functions</h2></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">static size_t </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6229386413a59a767a5197fca67e56cf">get_vehicle_size</a> ()</td></tr>
<tr><td class="memItemLeft" align="right" valign="top">static size_t </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ae1bc73d658a9a0e1ca3615a523624434">get_observation_size</a> ()</td></tr>
<tr><td class="memItemLeft" align="right" valign="top">static size_t </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad19146ca0b12bed3c2aace9329ece0fd">get_feature_size</a> ()</td></tr>
<tr><td class="memItemLeft" align="right" valign="top">static size_t </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#abaeadbd68b3a68d35e530a29b7c193b2">get_action_size</a> ()</td></tr>
<tr><td class="memItemLeft" align="right" valign="top">static void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1utils_1_1_c_debug_output_capable.html#ab78281b5d70d6e295a8527a10fea66de">printf_debug</a> (const char *frmt,...)</td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">Sends a formated text to "debugOut" if not NULL, or to cout otherwise. <a href="#ab78281b5d70d6e295a8527a10fea66de"></a><br/></td></tr>
<tr><td colspan="2"><h2><a name="pub-attribs"></a>
Public Attributes</h2></td></tr>
<tr><td class="memItemLeft" align="right" valign="top"><a class="el" href="structmrpt_1_1bayes_1_1_t_k_f__options.html">TKF_options</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a88651951bad5ea93ae84d848aeed6414">KF_options</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">Generic options for the Kalman Filter algorithm itself. <a href="#a88651951bad5ea93ae84d848aeed6414"></a><br/></td></tr>
<tr><td colspan="2"><h2><a name="pro-methods"></a>
Protected Member Functions</h2></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#aeebd325f91acbf7d27134c8c7388649e">runOneKalmanIteration</a> ()</td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">The main entry point, executes one complete step: prediction + update. <a href="#aeebd325f91acbf7d27134c8c7388649e"></a><br/></td></tr>
<tr><td colspan="2"><div class="groupHeader">Virtual methods for Kalman Filter implementation</div></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a987165d6a5082835f2645d32a62b43cd">OnGetAction</a> (<a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6454c68e3c45da92e2bbb3e8aa8f6f70">KFArray_ACT</a> &out_u) const =0</td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">Must return the action vector u. <a href="#a987165d6a5082835f2645d32a62b43cd"></a><br/></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a006b2619c480312a774ba3443efbe0a5">OnTransitionModel</a> (const <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6454c68e3c45da92e2bbb3e8aa8f6f70">KFArray_ACT</a> &in_u, <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad4f1667845ca7553925160142821cff0">KFArray_VEH</a> &inout_x, bool &out_skipPrediction) const =0</td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">Implements the transition model <img class="formulaInl" alt="$ \hat{x}_{k|k-1} = f( \hat{x}_{k-1|k-1}, u_k ) $" src="form_86.png"/>. <a href="#a006b2619c480312a774ba3443efbe0a5"></a><br/></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a9102af2605891c4b76ef1216013da46d">OnTransitionJacobian</a> (<a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a8dd3e63dd847fd98ef59b472e46cad2c">KFMatrix_VxV</a> &out_F) const </td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">Implements the transition Jacobian <img class="formulaInl" alt="$ \frac{\partial f}{\partial x} $" src="form_29.png"/>. <a href="#a9102af2605891c4b76ef1216013da46d"></a><br/></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6546398d63e8298f0b0a59a86e2395a7">OnTransitionJacobianNumericGetIncrements</a> (<a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad4f1667845ca7553925160142821cff0">KFArray_VEH</a> &out_increments) const </td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">Only called if using a numeric approximation of the transition Jacobian, this method must return the increments in each dimension of the vehicle state vector while estimating the Jacobian. <a href="#a6546398d63e8298f0b0a59a86e2395a7"></a><br/></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a77cdfd45b0173035594248d605b49306">OnTransitionNoise</a> (<a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a8dd3e63dd847fd98ef59b472e46cad2c">KFMatrix_VxV</a> &out_Q) const =0</td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">Implements the transition noise covariance <img class="formulaInl" alt="$ Q_k $" src="form_90.png"/>. <a href="#a77cdfd45b0173035594248d605b49306"></a><br/></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a505fa1e6eb8a300e0e1171a26b4f5b15">OnPreComputingPredictions</a> (const <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#aa0e71d2a45e5d935eba18d817bb31cf9">vector_KFArray_OBS</a> &in_all_prediction_means, <a class="el" href="namespacemrpt.html#ad4d2b1efd37ed750302c76ebbcfc310d">mrpt::vector_size_t</a> &out_LM_indices_to_predict) const </td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">This will be called before OnGetObservationsAndDataAssociation to allow the application to reduce the number of covariance landmark predictions to be made. <a href="#a505fa1e6eb8a300e0e1171a26b4f5b15"></a><br/></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a644a7ca4a46f46b704c123671a0b6bc7">OnGetObservationNoise</a> (<a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a521a75ae040b78365ebf57f97967bc84">KFMatrix_OxO</a> &out_R) const =0</td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">Return the observation NOISE covariance matrix, that is, the model of the Gaussian additive noise of the sensor. <a href="#a644a7ca4a46f46b704c123671a0b6bc7"></a><br/></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a74490d79d5d8fbf499021af5f66d593b">OnGetObservationsAndDataAssociation</a> (<a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#aa0e71d2a45e5d935eba18d817bb31cf9">vector_KFArray_OBS</a> &out_z, <a class="el" href="namespacemrpt.html#ac2e04951e7bd82f53b6ecaa0fd8a2662">mrpt::vector_int</a> &out_data_association, const <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#aa0e71d2a45e5d935eba18d817bb31cf9">vector_KFArray_OBS</a> &in_all_predictions, const <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad214112622ea8531c9a7c052f93600bd">KFMatrix</a> &in_S, const <a class="el" href="namespacemrpt.html#ad4d2b1efd37ed750302c76ebbcfc310d">vector_size_t</a> &in_lm_indices_in_S, const <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a521a75ae040b78365ebf57f97967bc84">KFMatrix_OxO</a> &in_R)=0</td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">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. <a href="#a74490d79d5d8fbf499021af5f66d593b"></a><br/></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a27de563ddb767f0cf75ff7a53e532b1a">OnObservationModel</a> (const <a class="el" href="namespacemrpt.html#ad4d2b1efd37ed750302c76ebbcfc310d">mrpt::vector_size_t</a> &idx_landmarks_to_predict, <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#aa0e71d2a45e5d935eba18d817bb31cf9">vector_KFArray_OBS</a> &out_predictions) const =0</td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">Implements the observation prediction <img class="formulaInl" alt="$ h_i(x) $" src="form_91.png"/>. <a href="#a27de563ddb767f0cf75ff7a53e532b1a"></a><br/></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a45e3d11533732ae6fdc6f0d8a0c6a16e">OnObservationJacobians</a> (const size_t &idx_landmark_to_predict, <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ab0ddb67c05616e7fc1fcabe9cc04c753">KFMatrix_OxV</a> &Hx, <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ada78f649a58aa64c3db6c8b436b5ceba">KFMatrix_OxF</a> &Hy) const </td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">Implements the observation Jacobians <img class="formulaInl" alt="$ \frac{\partial h_i}{\partial x} $" src="form_92.png"/> and (when applicable) <img class="formulaInl" alt="$ \frac{\partial h_i}{\partial y_i} $" src="form_93.png"/>. <a href="#a45e3d11533732ae6fdc6f0d8a0c6a16e"></a><br/></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#acbba7768aa0513f67e30628787e429c2">OnObservationJacobiansNumericGetIncrements</a> (<a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad4f1667845ca7553925160142821cff0">KFArray_VEH</a> &out_veh_increments, <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a0b5b72d68126a91918ce085789da06bd">KFArray_FEAT</a> &out_feat_increments) const </td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">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. <a href="#acbba7768aa0513f67e30628787e429c2"></a><br/></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a2875f05e00d512746e6a39244fb28561">OnSubstractObservationVectors</a> (<a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6c1b24a2c7b35e77411888a06de4e59d">KFArray_OBS</a> &A, const <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6c1b24a2c7b35e77411888a06de4e59d">KFArray_OBS</a> &B) const </td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">Computes A=A-B, which may need to be re-implemented depending on the topology of the individual scalar components (eg, angles). <a href="#a2875f05e00d512746e6a39244fb28561"></a><br/></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a647a12e0844df2ead68949d5ed33967e">OnInverseObservationModel</a> (const <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6c1b24a2c7b35e77411888a06de4e59d">KFArray_OBS</a> &in_z, <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a0b5b72d68126a91918ce085789da06bd">KFArray_FEAT</a> &out_yn, <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#affb2aa897e6434f6572d48ff1be1988e">KFMatrix_FxV</a> &out_dyn_dxv, <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a253bf3d53d25e20ec0592868efdba607">KFMatrix_FxO</a> &out_dyn_dhn) const </td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">If applicable to the given problem, this method implements the inverse observation model needed to extend the "map" with a new "element". <a href="#a647a12e0844df2ead68949d5ed33967e"></a><br/></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#af7c70d63832d8d45d8c5de0ed6041109">OnInverseObservationModel</a> (const <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6c1b24a2c7b35e77411888a06de4e59d">KFArray_OBS</a> &in_z, <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a0b5b72d68126a91918ce085789da06bd">KFArray_FEAT</a> &out_yn, <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#affb2aa897e6434f6572d48ff1be1988e">KFMatrix_FxV</a> &out_dyn_dxv, <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a253bf3d53d25e20ec0592868efdba607">KFMatrix_FxO</a> &out_dyn_dhn, <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad8a2b4b92969f736bf6c147804b50614">KFMatrix_FxF</a> &out_dyn_dhn_R_dyn_dhnT, bool &out_use_dyn_dhn_jacobian) const </td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">If applicable to the given problem, this method implements the inverse observation model needed to extend the "map" with a new "element". <a href="#af7c70d63832d8d45d8c5de0ed6041109"></a><br/></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a41be072d4693b55225b484c249db5fc2">OnNewLandmarkAddedToMap</a> (const size_t in_obsIdx, const size_t in_idxNewFeat)</td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">If applicable to the given problem, do here any special handling of adding a new landmark to the map. <a href="#a41be072d4693b55225b484c249db5fc2"></a><br/></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a8a5246ad413ba2f6121b9f7f261283b8">OnNormalizeStateVector</a> ()</td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">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. <a href="#a8a5246ad413ba2f6121b9f7f261283b8"></a><br/></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">virtual void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a5a3514fd81d6f81b8ff7b2c72dfae33e">OnPostIteration</a> ()</td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">This method is called after finishing one KF iteration and before returning from <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#aeebd325f91acbf7d27134c8c7388649e" title="The main entry point, executes one complete step: prediction + update.">runOneKalmanIteration()</a>. <a href="#a5a3514fd81d6f81b8ff7b2c72dfae33e"></a><br/></td></tr>
<tr><td colspan="2"><h2><a name="pro-attribs"></a>
Protected Attributes</h2></td></tr>
<tr><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmrpt_1_1utils_1_1_c_time_logger.html">mrpt::utils::CTimeLogger</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a3eff55c428065c52bd2f9418e3944e48">m_timLogger</a></td></tr>
<tr><td colspan="2"><div class="groupHeader">Kalman filter state</div></td></tr>
<tr><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a9a120b286d286048985b059aa721e0c1">KFVector</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a7e8948c9d209c23517c45c6494b46147">m_xkk</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">The system state vector. <a href="#a7e8948c9d209c23517c45c6494b46147"></a><br/></td></tr>
<tr><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad214112622ea8531c9a7c052f93600bd">KFMatrix</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a15c291ca9eb40996381dc0e60f01d533">m_pkk</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">The system full covariance matrix. <a href="#a15c291ca9eb40996381dc0e60f01d533"></a><br/></td></tr>
<tr><td colspan="2"><h2><a name="pri-static-methods"></a>
Static Private Member Functions</h2></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">static void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a713c4352da9cc10f827a52ed852a9e6a">KF_aux_estimate_trans_jacobian</a> (const <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad4f1667845ca7553925160142821cff0">KFArray_VEH</a> &x, const std::pair< <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6195a8efec17869133a1fd31105d6a64">KFCLASS</a> *, <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6454c68e3c45da92e2bbb3e8aa8f6f70">KFArray_ACT</a> > &dat, <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad4f1667845ca7553925160142821cff0">KFArray_VEH</a> &out_x)</td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">Auxiliary functions for Jacobian numeric estimation. <a href="#a713c4352da9cc10f827a52ed852a9e6a"></a><br/></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">static void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a8fb02067b7c9ff1210085bbd8ba25204">KF_aux_estimate_obs_Hx_jacobian</a> (const <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad4f1667845ca7553925160142821cff0">KFArray_VEH</a> &x, const std::pair< <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6195a8efec17869133a1fd31105d6a64">KFCLASS</a> *, size_t > &dat, <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6c1b24a2c7b35e77411888a06de4e59d">KFArray_OBS</a> &out_x)</td></tr>
<tr><td class="memItemLeft" align="right" valign="top">static void </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a07208df3280ab8f80b4952ab09bfd55a">KF_aux_estimate_obs_Hy_jacobian</a> (const <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a0b5b72d68126a91918ce085789da06bd">KFArray_FEAT</a> &x, const std::pair< <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6195a8efec17869133a1fd31105d6a64">KFCLASS</a> *, size_t > &dat, <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6c1b24a2c7b35e77411888a06de4e59d">KFArray_OBS</a> &out_x)</td></tr>
<tr><td colspan="2"><h2><a name="pri-attribs"></a>
Private Attributes</h2></td></tr>
<tr><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#aa0e71d2a45e5d935eba18d817bb31cf9">vector_KFArray_OBS</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a90762ef91865e4f300226f31cb187828">all_predictions</a></td></tr>
<tr><td class="memItemLeft" align="right" valign="top"><a class="el" href="namespacemrpt.html#ad4d2b1efd37ed750302c76ebbcfc310d">vector_size_t</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a87c0cfc640df569e33a97749951a6e06">predictLMidxs</a></td></tr>
<tr><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad214112622ea8531c9a7c052f93600bd">KFMatrix</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a581ce072177eef66789bf8024f3c2ed7">dh_dx</a></td></tr>
<tr><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad214112622ea8531c9a7c052f93600bd">KFMatrix</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6dcad04e08c1483c1ed5a0e5c7e5ef79">dh_dx_full</a></td></tr>
<tr><td class="memItemLeft" align="right" valign="top"><a class="el" href="namespacemrpt.html#ad4d2b1efd37ed750302c76ebbcfc310d">vector_size_t</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a099172f5abfc72517b79cf221200b9f2">idxs</a></td></tr>
<tr><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad214112622ea8531c9a7c052f93600bd">KFMatrix</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a65d721081bd1d54c1d05d88c4279fe42">S</a></td></tr>
<tr><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad214112622ea8531c9a7c052f93600bd">KFMatrix</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a65e1419380dea75082a0aa0a09f04332">Pkk_subset</a></td></tr>
<tr><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#aa0e71d2a45e5d935eba18d817bb31cf9">vector_KFArray_OBS</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#aa43fef4c1e7af3dee7ad10053132f47b">Z</a></td></tr>
<tr><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad214112622ea8531c9a7c052f93600bd">KFMatrix</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#aac0376b299cfa494e755b1ddfb512b7e">K</a></td></tr>
<tr><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad214112622ea8531c9a7c052f93600bd">KFMatrix</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a19e1025c17ff21c2714fd5bcb9d58a9b">S_1</a></td></tr>
<tr><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad214112622ea8531c9a7c052f93600bd">KFMatrix</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a8eca88f65c4032f3179b8e2e5d13250b">dh_dx_full_obs</a></td></tr>
<tr><td class="memItemLeft" align="right" valign="top"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad214112622ea8531c9a7c052f93600bd">KFMatrix</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad9c15457835641e93026e505b3c44ac7">aux_K_dh_dx</a></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ada33322b67cea44a95948712be1c131b">m_user_didnt_implement_jacobian</a></td></tr>
<tr><td colspan="2"><h2><a name="friends"></a>
Friends</h2></td></tr>
<tr><td class="memItemLeft" align="right" valign="top">struct </td><td class="memItemRight" valign="bottom"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a1f7c0688afe03ffd6f14c4e9f4e5a050">detail::CRunOneKalmanIteration_addNewLandmarks</a></td></tr>
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<hr/><h2>Member Typedef Documentation</h2>
<a class="anchor" id="a6454c68e3c45da92e2bbb3e8aa8f6f70"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::KFArray_ACT" ref="a6454c68e3c45da92e2bbb3e8aa8f6f70" args="" -->
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<td class="memname">typedef <a class="el" href="classmrpt_1_1math_1_1_c_array_numeric.html">CArrayNumeric</a><KFTYPE,ACT_SIZE> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6454c68e3c45da92e2bbb3e8aa8f6f70">mrpt::bayes::CKalmanFilterCapable::KFArray_ACT</a></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00179">179</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a0b5b72d68126a91918ce085789da06bd"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::KFArray_FEAT" ref="a0b5b72d68126a91918ce085789da06bd" args="" -->
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<td class="memname">typedef <a class="el" href="classmrpt_1_1math_1_1_c_array_numeric.html">CArrayNumeric</a><KFTYPE,FEAT_SIZE> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a0b5b72d68126a91918ce085789da06bd">mrpt::bayes::CKalmanFilterCapable::KFArray_FEAT</a></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00182">182</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a6c1b24a2c7b35e77411888a06de4e59d"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::KFArray_OBS" ref="a6c1b24a2c7b35e77411888a06de4e59d" args="" -->
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<td class="memname">typedef <a class="el" href="classmrpt_1_1math_1_1_c_array_numeric.html">CArrayNumeric</a><KFTYPE,OBS_SIZE> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6c1b24a2c7b35e77411888a06de4e59d">mrpt::bayes::CKalmanFilterCapable::KFArray_OBS</a></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00180">180</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="ad4f1667845ca7553925160142821cff0"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::KFArray_VEH" ref="ad4f1667845ca7553925160142821cff0" args="" -->
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<td class="memname">typedef <a class="el" href="classmrpt_1_1math_1_1_c_array_numeric.html">CArrayNumeric</a><KFTYPE,VEH_SIZE> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad4f1667845ca7553925160142821cff0">mrpt::bayes::CKalmanFilterCapable::KFArray_VEH</a></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00178">178</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a6195a8efec17869133a1fd31105d6a64"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::KFCLASS" ref="a6195a8efec17869133a1fd31105d6a64" args="" -->
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<td class="memname">typedef <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html">CKalmanFilterCapable</a><VEH_SIZE,OBS_SIZE,FEAT_SIZE,ACT_SIZE,KFTYPE> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6195a8efec17869133a1fd31105d6a64">mrpt::bayes::CKalmanFilterCapable::KFCLASS</a></td>
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<p>My class, in a shorter name! </p>
<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00158">158</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="ad214112622ea8531c9a7c052f93600bd"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::KFMatrix" ref="ad214112622ea8531c9a7c052f93600bd" args="" -->
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<td class="memname">typedef <a class="el" href="classmrpt_1_1math_1_1_c_matrix_template_numeric.html">CMatrixTemplateNumeric</a><KFTYPE> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad214112622ea8531c9a7c052f93600bd">mrpt::bayes::CKalmanFilterCapable::KFMatrix</a></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00162">162</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="af20d435cc094724e00949ea137aa832b"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::KFMatrix_AxA" ref="af20d435cc094724e00949ea137aa832b" args="" -->
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<td class="memname">typedef <a class="el" href="classmrpt_1_1math_1_1_c_matrix_fixed_numeric.html">CMatrixFixedNumeric</a><KFTYPE,ACT_SIZE,ACT_SIZE> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#af20d435cc094724e00949ea137aa832b">mrpt::bayes::CKalmanFilterCapable::KFMatrix_AxA</a></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00167">167</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="ad8a2b4b92969f736bf6c147804b50614"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::KFMatrix_FxF" ref="ad8a2b4b92969f736bf6c147804b50614" args="" -->
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<td class="memname">typedef <a class="el" href="classmrpt_1_1math_1_1_c_matrix_fixed_numeric.html">CMatrixFixedNumeric</a><KFTYPE,FEAT_SIZE,FEAT_SIZE> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad8a2b4b92969f736bf6c147804b50614">mrpt::bayes::CKalmanFilterCapable::KFMatrix_FxF</a></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00166">166</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a253bf3d53d25e20ec0592868efdba607"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::KFMatrix_FxO" ref="a253bf3d53d25e20ec0592868efdba607" args="" -->
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<td class="memname">typedef <a class="el" href="classmrpt_1_1math_1_1_c_matrix_fixed_numeric.html">CMatrixFixedNumeric</a><KFTYPE,FEAT_SIZE,OBS_SIZE> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a253bf3d53d25e20ec0592868efdba607">mrpt::bayes::CKalmanFilterCapable::KFMatrix_FxO</a></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00173">173</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="affb2aa897e6434f6572d48ff1be1988e"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::KFMatrix_FxV" ref="affb2aa897e6434f6572d48ff1be1988e" args="" -->
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<td class="memname">typedef <a class="el" href="classmrpt_1_1math_1_1_c_matrix_fixed_numeric.html">CMatrixFixedNumeric</a><KFTYPE,FEAT_SIZE,VEH_SIZE> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#affb2aa897e6434f6572d48ff1be1988e">mrpt::bayes::CKalmanFilterCapable::KFMatrix_FxV</a></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00172">172</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="ada78f649a58aa64c3db6c8b436b5ceba"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::KFMatrix_OxF" ref="ada78f649a58aa64c3db6c8b436b5ceba" args="" -->
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<td class="memname">typedef <a class="el" href="classmrpt_1_1math_1_1_c_matrix_fixed_numeric.html">CMatrixFixedNumeric</a><KFTYPE,OBS_SIZE,FEAT_SIZE> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ada78f649a58aa64c3db6c8b436b5ceba">mrpt::bayes::CKalmanFilterCapable::KFMatrix_OxF</a></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00175">175</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a521a75ae040b78365ebf57f97967bc84"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::KFMatrix_OxO" ref="a521a75ae040b78365ebf57f97967bc84" args="" -->
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<td class="memname">typedef <a class="el" href="classmrpt_1_1math_1_1_c_matrix_fixed_numeric.html">CMatrixFixedNumeric</a><KFTYPE,OBS_SIZE,OBS_SIZE> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a521a75ae040b78365ebf57f97967bc84">mrpt::bayes::CKalmanFilterCapable::KFMatrix_OxO</a></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00165">165</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="ab0ddb67c05616e7fc1fcabe9cc04c753"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::KFMatrix_OxV" ref="ab0ddb67c05616e7fc1fcabe9cc04c753" args="" -->
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<td class="memname">typedef <a class="el" href="classmrpt_1_1math_1_1_c_matrix_fixed_numeric.html">CMatrixFixedNumeric</a><KFTYPE,OBS_SIZE,VEH_SIZE> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ab0ddb67c05616e7fc1fcabe9cc04c753">mrpt::bayes::CKalmanFilterCapable::KFMatrix_OxV</a></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00176">176</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a7277a24dd3aae42b96708b4669e74a12"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::KFMatrix_VxF" ref="a7277a24dd3aae42b96708b4669e74a12" args="" -->
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<td class="memname">typedef <a class="el" href="classmrpt_1_1math_1_1_c_matrix_fixed_numeric.html">CMatrixFixedNumeric</a><KFTYPE,VEH_SIZE,FEAT_SIZE> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a7277a24dd3aae42b96708b4669e74a12">mrpt::bayes::CKalmanFilterCapable::KFMatrix_VxF</a></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00170">170</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a60912a7a33cd8e77605e4761848609b2"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::KFMatrix_VxO" ref="a60912a7a33cd8e77605e4761848609b2" args="" -->
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<td class="memname">typedef <a class="el" href="classmrpt_1_1math_1_1_c_matrix_fixed_numeric.html">CMatrixFixedNumeric</a><KFTYPE,VEH_SIZE,OBS_SIZE> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a60912a7a33cd8e77605e4761848609b2">mrpt::bayes::CKalmanFilterCapable::KFMatrix_VxO</a></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00169">169</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a8dd3e63dd847fd98ef59b472e46cad2c"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::KFMatrix_VxV" ref="a8dd3e63dd847fd98ef59b472e46cad2c" args="" -->
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<td class="memname">typedef <a class="el" href="classmrpt_1_1math_1_1_c_matrix_fixed_numeric.html">CMatrixFixedNumeric</a><KFTYPE,VEH_SIZE,VEH_SIZE> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a8dd3e63dd847fd98ef59b472e46cad2c">mrpt::bayes::CKalmanFilterCapable::KFMatrix_VxV</a></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00164">164</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="ad66228d2cc13705900669ccbab41f3d9"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::kftype" ref="ad66228d2cc13705900669ccbab41f3d9" args="" -->
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<td class="memname">typedef KFTYPE <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad66228d2cc13705900669ccbab41f3d9">mrpt::bayes::CKalmanFilterCapable::kftype</a></td>
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<p>The numeric type used in the Kalman Filter (default=double) </p>
<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00157">157</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a9a120b286d286048985b059aa721e0c1"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::KFVector" ref="a9a120b286d286048985b059aa721e0c1" args="" -->
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<td class="memname">typedef <a class="el" href="structmrpt_1_1dynamicsize__vector.html">mrpt::dynamicsize_vector</a><KFTYPE> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a9a120b286d286048985b059aa721e0c1">mrpt::bayes::CKalmanFilterCapable::KFVector</a></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00161">161</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="aa0e71d2a45e5d935eba18d817bb31cf9"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::vector_KFArray_OBS" ref="aa0e71d2a45e5d935eba18d817bb31cf9" args="" -->
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<td class="memname">typedef <a class="el" href="structmrpt_1_1aligned__containers.html">mrpt::aligned_containers</a><<a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6c1b24a2c7b35e77411888a06de4e59d">KFArray_OBS</a>>::vector_t <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#aa0e71d2a45e5d935eba18d817bb31cf9">mrpt::bayes::CKalmanFilterCapable::vector_KFArray_OBS</a></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00181">181</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<hr/><h2>Constructor & Destructor Documentation</h2>
<a class="anchor" id="a44fae14dfa9b912420b6465fb7e8f7c5"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::CKalmanFilterCapable" ref="a44fae14dfa9b912420b6465fb7e8f7c5" args="()" -->
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<td class="memname">mrpt::bayes::CKalmanFilterCapable::CKalmanFilterCapable </td>
<td>(</td>
<td class="paramname"></td><td>)</td>
<td><code> [inline]</code></td>
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<p>Default constructor </p>
<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00428">428</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a19b3a038e3ba1d3994eab0a08db733b5"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::~CKalmanFilterCapable" ref="a19b3a038e3ba1d3994eab0a08db733b5" args="()" -->
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<td class="memname">virtual mrpt::bayes::CKalmanFilterCapable::~CKalmanFilterCapable </td>
<td>(</td>
<td class="paramname"></td><td>)</td>
<td><code> [inline, virtual]</code></td>
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<p>Destructor. </p>
<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00429">429</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<hr/><h2>Member Function Documentation</h2>
<a class="anchor" id="abaeadbd68b3a68d35e530a29b7c193b2"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::get_action_size" ref="abaeadbd68b3a68d35e530a29b7c193b2" args="()" -->
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<td class="memname">static size_t mrpt::bayes::CKalmanFilterCapable::get_action_size </td>
<td>(</td>
<td class="paramname"></td><td>)</td>
<td><code> [inline, static]</code></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00152">152</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="ad19146ca0b12bed3c2aace9329ece0fd"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::get_feature_size" ref="ad19146ca0b12bed3c2aace9329ece0fd" args="()" -->
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<td class="memname">static size_t mrpt::bayes::CKalmanFilterCapable::get_feature_size </td>
<td>(</td>
<td class="paramname"></td><td>)</td>
<td><code> [inline, static]</code></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00151">151</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="ae1bc73d658a9a0e1ca3615a523624434"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::get_observation_size" ref="ae1bc73d658a9a0e1ca3615a523624434" args="()" -->
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<td class="memname">static size_t mrpt::bayes::CKalmanFilterCapable::get_observation_size </td>
<td>(</td>
<td class="paramname"></td><td>)</td>
<td><code> [inline, static]</code></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00150">150</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a6229386413a59a767a5197fca67e56cf"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::get_vehicle_size" ref="a6229386413a59a767a5197fca67e56cf" args="()" -->
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<td class="memname">static size_t mrpt::bayes::CKalmanFilterCapable::get_vehicle_size </td>
<td>(</td>
<td class="paramname"></td><td>)</td>
<td><code> [inline, static]</code></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00149">149</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="ae32eeb6c71cb7ea1acf21ae09ac19827"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::getLandmarkCov" ref="ae32eeb6c71cb7ea1acf21ae09ac19827" args="(size_t idx, KFMatrix_FxF &feat_cov) const " -->
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<td class="memname">void mrpt::bayes::CKalmanFilterCapable::getLandmarkCov </td>
<td>(</td>
<td class="paramtype">size_t </td>
<td class="paramname"><em>idx</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad8a2b4b92969f736bf6c147804b50614">KFMatrix_FxF</a> & </td>
<td class="paramname"><em>feat_cov</em> </td>
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<td>)</td>
<td></td><td> const<code> [inline]</code></td>
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<p>Returns the covariance of the idx'th landmark (not applicable to non-SLAM problems). </p>
<dl><dt><b>Exceptions:</b></dt><dd>
<table class="exception">
<tr><td class="paramname"><a class="el" href="classstd_1_1exception.html" title="STL class.">std::exception</a></td><td>On idx>= <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a1ac5b0adbbd3a0e5122ebb148ff3e7ad">getNumberOfLandmarksInTheMap()</a> </td></tr>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00196">196</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a4cc933fc0dacdd126b08b08591c617f4"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::getLandmarkMean" ref="a4cc933fc0dacdd126b08b08591c617f4" args="(size_t idx, KFArray_FEAT &feat) const " -->
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<td class="memname">void mrpt::bayes::CKalmanFilterCapable::getLandmarkMean </td>
<td>(</td>
<td class="paramtype">size_t </td>
<td class="paramname"><em>idx</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a0b5b72d68126a91918ce085789da06bd">KFArray_FEAT</a> & </td>
<td class="paramname"><em>feat</em> </td>
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<td>)</td>
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<p>Returns the mean of the estimated value of the idx'th landmark (not applicable to non-SLAM problems). </p>
<dl><dt><b>Exceptions:</b></dt><dd>
<table class="exception">
<tr><td class="paramname"><a class="el" href="classstd_1_1exception.html" title="STL class.">std::exception</a></td><td>On idx>= <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a1ac5b0adbbd3a0e5122ebb148ff3e7ad">getNumberOfLandmarksInTheMap()</a> </td></tr>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00189">189</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a1ac5b0adbbd3a0e5122ebb148ff3e7ad"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::getNumberOfLandmarksInTheMap" ref="a1ac5b0adbbd3a0e5122ebb148ff3e7ad" args="() const " -->
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<td class="memname">size_t mrpt::bayes::CKalmanFilterCapable::getNumberOfLandmarksInTheMap </td>
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<td class="paramname"></td><td>)</td>
<td> const<code> [inline]</code></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00153">153</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a4c2d71f07ef0501d157988ebccf9f3c0"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::getProfiler" ref="a4c2d71f07ef0501d157988ebccf9f3c0" args="()" -->
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<td class="memname"><a class="el" href="classmrpt_1_1utils_1_1_c_time_logger.html">mrpt::utils::CTimeLogger</a>& mrpt::bayes::CKalmanFilterCapable::getProfiler </td>
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<td><code> [inline]</code></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00431">431</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="aabe244779fd4694bc5e6d336874396de"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::getStateVectorLength" ref="aabe244779fd4694bc5e6d336874396de" args="() const " -->
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<td class="memname">size_t mrpt::bayes::CKalmanFilterCapable::getStateVectorLength </td>
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<td> const<code> [inline]</code></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00184">184</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a48adbb9852e76624b16e7fa115c19e65"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::isMapEmpty" ref="a48adbb9852e76624b16e7fa115c19e65" args="() const " -->
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<td class="memname">bool mrpt::bayes::CKalmanFilterCapable::isMapEmpty </td>
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<td> const<code> [inline]</code></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00154">154</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a8fb02067b7c9ff1210085bbd8ba25204"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::KF_aux_estimate_obs_Hx_jacobian" ref="a8fb02067b7c9ff1210085bbd8ba25204" args="(const KFArray_VEH &x, const std::pair< KFCLASS *, size_t > &dat, KFArray_OBS &out_x)" -->
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<td class="memname">static void mrpt::bayes::CKalmanFilterCapable::KF_aux_estimate_obs_Hx_jacobian </td>
<td>(</td>
<td class="paramtype">const <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad4f1667845ca7553925160142821cff0">KFArray_VEH</a> & </td>
<td class="paramname"><em>x</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype">const std::pair< <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6195a8efec17869133a1fd31105d6a64">KFCLASS</a> *, size_t > & </td>
<td class="paramname"><em>dat</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6c1b24a2c7b35e77411888a06de4e59d">KFArray_OBS</a> & </td>
<td class="paramname"><em>out_x</em> </td>
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<td>)</td>
<td></td><td><code> [inline, static, private]</code></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l01470">1470</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a07208df3280ab8f80b4952ab09bfd55a"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::KF_aux_estimate_obs_Hy_jacobian" ref="a07208df3280ab8f80b4952ab09bfd55a" args="(const KFArray_FEAT &x, const std::pair< KFCLASS *, size_t > &dat, KFArray_OBS &out_x)" -->
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<td class="memname">static void mrpt::bayes::CKalmanFilterCapable::KF_aux_estimate_obs_Hy_jacobian </td>
<td>(</td>
<td class="paramtype">const <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a0b5b72d68126a91918ce085789da06bd">KFArray_FEAT</a> & </td>
<td class="paramname"><em>x</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">const std::pair< <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6195a8efec17869133a1fd31105d6a64">KFCLASS</a> *, size_t > & </td>
<td class="paramname"><em>dat</em>, </td>
</tr>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6c1b24a2c7b35e77411888a06de4e59d">KFArray_OBS</a> & </td>
<td class="paramname"><em>out_x</em> </td>
</tr>
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<td></td>
<td>)</td>
<td></td><td><code> [inline, static, private]</code></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l01483">1483</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a713c4352da9cc10f827a52ed852a9e6a"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::KF_aux_estimate_trans_jacobian" ref="a713c4352da9cc10f827a52ed852a9e6a" args="(const KFArray_VEH &x, const std::pair< KFCLASS *, KFArray_ACT > &dat, KFArray_VEH &out_x)" -->
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<td class="memname">static void mrpt::bayes::CKalmanFilterCapable::KF_aux_estimate_trans_jacobian </td>
<td>(</td>
<td class="paramtype">const <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad4f1667845ca7553925160142821cff0">KFArray_VEH</a> & </td>
<td class="paramname"><em>x</em>, </td>
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<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">const std::pair< <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6195a8efec17869133a1fd31105d6a64">KFCLASS</a> *, <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6454c68e3c45da92e2bbb3e8aa8f6f70">KFArray_ACT</a> > & </td>
<td class="paramname"><em>dat</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad4f1667845ca7553925160142821cff0">KFArray_VEH</a> & </td>
<td class="paramname"><em>out_x</em> </td>
</tr>
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<td></td>
<td>)</td>
<td></td><td><code> [inline, static, private]</code></td>
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<p>Auxiliary functions for Jacobian numeric estimation. </p>
<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l01461">1461</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a987165d6a5082835f2645d32a62b43cd"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::OnGetAction" ref="a987165d6a5082835f2645d32a62b43cd" args="(KFArray_ACT &out_u) const =0" -->
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<td class="memname">virtual void mrpt::bayes::CKalmanFilterCapable::OnGetAction </td>
<td>(</td>
<td class="paramtype"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6454c68e3c45da92e2bbb3e8aa8f6f70">KFArray_ACT</a> & </td>
<td class="paramname"><em>out_u</em></td><td>)</td>
<td> const<code> [protected, pure virtual]</code></td>
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<p>Must return the action vector u. </p>
<dl><dt><b>Parameters:</b></dt><dd>
<table class="params">
<tr><td class="paramname">out_u</td><td>The action vector which will be passed to OnTransitionModel </td></tr>
</table>
</dd>
</dl>
<p>Implemented in <a class="el" href="classmrpt_1_1slam_1_1_c_range_bearing_k_f_s_l_a_m.html#afd5c9c6ce16f81f5639e511bc1e474f8">mrpt::slam::CRangeBearingKFSLAM</a>, and <a class="el" href="classmrpt_1_1slam_1_1_c_range_bearing_k_f_s_l_a_m2_d.html#abc536347b81003fc8c3e0902a435e151">mrpt::slam::CRangeBearingKFSLAM2D</a>.</p>
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<a class="anchor" id="a644a7ca4a46f46b704c123671a0b6bc7"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::OnGetObservationNoise" ref="a644a7ca4a46f46b704c123671a0b6bc7" args="(KFMatrix_OxO &out_R) const =0" -->
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<td class="memname">virtual void mrpt::bayes::CKalmanFilterCapable::OnGetObservationNoise </td>
<td>(</td>
<td class="paramtype"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a521a75ae040b78365ebf57f97967bc84">KFMatrix_OxO</a> & </td>
<td class="paramname"><em>out_R</em></td><td>)</td>
<td> const<code> [protected, pure virtual]</code></td>
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<p>Return the observation NOISE covariance matrix, that is, the model of the Gaussian additive noise of the sensor. </p>
<dl><dt><b>Parameters:</b></dt><dd>
<table class="params">
<tr><td class="paramname">out_R</td><td>The noise covariance matrix. It might be non diagonal, but it'll usually be. </td></tr>
</table>
</dd>
</dl>
<dl class="note"><dt><b>Note:</b></dt><dd>Upon call, it can be assumed that the previous contents of out_R are all zeros. </dd></dl>
<p>Implemented in <a class="el" href="classmrpt_1_1slam_1_1_c_range_bearing_k_f_s_l_a_m.html#afd569099f5505026e762547e143dc629">mrpt::slam::CRangeBearingKFSLAM</a>, and <a class="el" href="classmrpt_1_1slam_1_1_c_range_bearing_k_f_s_l_a_m2_d.html#a79de30811b3c0aa710cdebf7521215fb">mrpt::slam::CRangeBearingKFSLAM2D</a>.</p>
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<a class="anchor" id="a74490d79d5d8fbf499021af5f66d593b"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::OnGetObservationsAndDataAssociation" ref="a74490d79d5d8fbf499021af5f66d593b" args="(vector_KFArray_OBS &out_z, mrpt::vector_int &out_data_association, const vector_KFArray_OBS &in_all_predictions, const KFMatrix &in_S, const vector_size_t &in_lm_indices_in_S, const KFMatrix_OxO &in_R)=0" -->
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<td class="memname">virtual void mrpt::bayes::CKalmanFilterCapable::OnGetObservationsAndDataAssociation </td>
<td>(</td>
<td class="paramtype"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#aa0e71d2a45e5d935eba18d817bb31cf9">vector_KFArray_OBS</a> & </td>
<td class="paramname"><em>out_z</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype"><a class="el" href="namespacemrpt.html#ac2e04951e7bd82f53b6ecaa0fd8a2662">mrpt::vector_int</a> & </td>
<td class="paramname"><em>out_data_association</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype">const <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#aa0e71d2a45e5d935eba18d817bb31cf9">vector_KFArray_OBS</a> & </td>
<td class="paramname"><em>in_all_predictions</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype">const <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad214112622ea8531c9a7c052f93600bd">KFMatrix</a> & </td>
<td class="paramname"><em>in_S</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype">const <a class="el" href="namespacemrpt.html#ad4d2b1efd37ed750302c76ebbcfc310d">vector_size_t</a> & </td>
<td class="paramname"><em>in_lm_indices_in_S</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype">const <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a521a75ae040b78365ebf57f97967bc84">KFMatrix_OxO</a> & </td>
<td class="paramname"><em>in_R</em> </td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td><code> [protected, pure virtual]</code></td>
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<p>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. </p>
<dl><dt><b>Parameters:</b></dt><dd>
<table class="params">
<tr><td class="paramname">out_z</td><td>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. </td></tr>
<tr><td class="paramname">out_data_association</td><td>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,<a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a1ac5b0adbbd3a0e5122ebb148ff3e7ad">getNumberOfLandmarksInTheMap()</a>-1]), or -1 if it is a new map element and we want to insert it at the end of this KF iteration. </td></tr>
<tr><td class="paramname">in_all_predictions</td><td>A vector with the prediction of ALL the landmarks in the map. Note that, in contrast, in_S only comprises a subset of all the landmarks. </td></tr>
<tr><td class="paramname">in_S</td><td>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". </td></tr>
<tr><td class="paramname">in_lm_indices_in_S</td><td>The indices of the map landmarks (range [0,<a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a1ac5b0adbbd3a0e5122ebb148ff3e7ad">getNumberOfLandmarksInTheMap()</a>-1]) that can be found in the matrix in_S.</td></tr>
</table>
</dd>
</dl>
<p>This method will be called just once for each complete KF iteration. </p>
<dl class="note"><dt><b>Note:</b></dt><dd>It is assumed that the observations are independent, i.e. there are NO cross-covariances between them. </dd></dl>
<p>Implemented in <a class="el" href="classmrpt_1_1slam_1_1_c_range_bearing_k_f_s_l_a_m.html#a74f93f0a9a398aee4e51de1689127d11">mrpt::slam::CRangeBearingKFSLAM</a>, and <a class="el" href="classmrpt_1_1slam_1_1_c_range_bearing_k_f_s_l_a_m2_d.html#a1c01bffffcf1384d0863f2000e019258">mrpt::slam::CRangeBearingKFSLAM2D</a>.</p>
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<a class="anchor" id="a647a12e0844df2ead68949d5ed33967e"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::OnInverseObservationModel" ref="a647a12e0844df2ead68949d5ed33967e" args="(const KFArray_OBS &in_z, KFArray_FEAT &out_yn, KFMatrix_FxV &out_dyn_dxv, KFMatrix_FxO &out_dyn_dhn) const " -->
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<td class="memname">virtual void mrpt::bayes::CKalmanFilterCapable::OnInverseObservationModel </td>
<td>(</td>
<td class="paramtype">const <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6c1b24a2c7b35e77411888a06de4e59d">KFArray_OBS</a> & </td>
<td class="paramname"><em>in_z</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a0b5b72d68126a91918ce085789da06bd">KFArray_FEAT</a> & </td>
<td class="paramname"><em>out_yn</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#affb2aa897e6434f6572d48ff1be1988e">KFMatrix_FxV</a> & </td>
<td class="paramname"><em>out_dyn_dxv</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a253bf3d53d25e20ec0592868efdba607">KFMatrix_FxO</a> & </td>
<td class="paramname"><em>out_dyn_dhn</em> </td>
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<td>)</td>
<td></td><td> const<code> [inline, protected, virtual]</code></td>
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<p>If applicable to the given problem, this method implements the inverse observation model needed to extend the "map" with a new "element". </p>
<dl><dt><b>Parameters:</b></dt><dd>
<table class="params">
<tr><td class="paramname">in_z</td><td>The observation vector whose inverse sensor model is to be computed. This is actually one of the vector<> returned by <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a74490d79d5d8fbf499021af5f66d593b" title="This is called between the KF prediction step and the update step, and the application must return th...">OnGetObservationsAndDataAssociation()</a>. </td></tr>
<tr><td class="paramname">out_yn</td><td>The F-length vector with the inverse observation model <img class="formulaInl" alt="$ y_n=y(x,z_n) $" src="form_94.png"/>. </td></tr>
<tr><td class="paramname">out_dyn_dxv</td><td>The <img class="formulaInl" alt="$F \times V$" src="form_95.png"/> Jacobian of the inv. sensor model wrt the robot pose <img class="formulaInl" alt="$ \frac{\partial y_n}{\partial x_v} $" src="form_96.png"/>. </td></tr>
<tr><td class="paramname">out_dyn_dhn</td><td>The <img class="formulaInl" alt="$F \times O$" src="form_97.png"/> Jacobian of the inv. sensor model wrt the observation vector <img class="formulaInl" alt="$ \frac{\partial y_n}{\partial h_n} $" src="form_98.png"/>.</td></tr>
</table>
</dd>
</dl>
<ul>
<li>O: OBS_SIZE</li>
<li>V: VEH_SIZE</li>
<li>F: FEAT_SIZE</li>
</ul>
<dl class="note"><dt><b>Note:</b></dt><dd>OnNewLandmarkAddedToMap will be also called after calling this method if a landmark is actually being added to the map. </dd></dl>
<dl class="deprecated"><dt><b><a class="el" href="deprecated.html#_deprecated000001">Deprecated:</a></b></dt><dd>This version of the method is deprecated. The alternative method is preferred to allow a greater flexibility. </dd></dl>
<p>Reimplemented in <a class="el" href="classmrpt_1_1slam_1_1_c_range_bearing_k_f_s_l_a_m.html#a61b31c958f5ef1f119588538818de59c">mrpt::slam::CRangeBearingKFSLAM</a>, and <a class="el" href="classmrpt_1_1slam_1_1_c_range_bearing_k_f_s_l_a_m2_d.html#a4f261c0cc2bce890b65fdbc07f1cdb25">mrpt::slam::CRangeBearingKFSLAM2D</a>.</p>
<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00350">350</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="af7c70d63832d8d45d8c5de0ed6041109"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::OnInverseObservationModel" ref="af7c70d63832d8d45d8c5de0ed6041109" args="(const KFArray_OBS &in_z, KFArray_FEAT &out_yn, KFMatrix_FxV &out_dyn_dxv, KFMatrix_FxO &out_dyn_dhn, KFMatrix_FxF &out_dyn_dhn_R_dyn_dhnT, bool &out_use_dyn_dhn_jacobian) const " -->
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<td class="memname">virtual void mrpt::bayes::CKalmanFilterCapable::OnInverseObservationModel </td>
<td>(</td>
<td class="paramtype">const <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6c1b24a2c7b35e77411888a06de4e59d">KFArray_OBS</a> & </td>
<td class="paramname"><em>in_z</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a0b5b72d68126a91918ce085789da06bd">KFArray_FEAT</a> & </td>
<td class="paramname"><em>out_yn</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#affb2aa897e6434f6572d48ff1be1988e">KFMatrix_FxV</a> & </td>
<td class="paramname"><em>out_dyn_dxv</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a253bf3d53d25e20ec0592868efdba607">KFMatrix_FxO</a> & </td>
<td class="paramname"><em>out_dyn_dhn</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad8a2b4b92969f736bf6c147804b50614">KFMatrix_FxF</a> & </td>
<td class="paramname"><em>out_dyn_dhn_R_dyn_dhnT</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype">bool & </td>
<td class="paramname"><em>out_use_dyn_dhn_jacobian</em> </td>
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<td>)</td>
<td></td><td> const<code> [inline, protected, virtual]</code></td>
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<p>If applicable to the given problem, this method implements the inverse observation model needed to extend the "map" with a new "element". </p>
<p>The uncertainty in the new map feature comes from two parts: one from the vehicle uncertainty (through the out_dyn_dxv Jacobian), and another from the uncertainty in the observation itself. By default, out_use_dyn_dhn_jacobian=true on call, and if it's left at "true", the base KalmanFilter class will compute the uncertainty of the landmark relative position from out_dyn_dhn. Only in some problems (e.g. MonoSLAM), it'll be needed for the application to directly return the covariance matrix <em>out_dyn_dhn_R_dyn_dhnT</em>, which is the equivalent to:</p>
<p><img class="formulaInl" alt="$ \frac{\partial y_n}{\partial h_n} R \frac{\partial y_n}{\partial h_n}^\top $" src="form_99.png"/>.</p>
<p>but may be computed from additional terms, or whatever needed by the user.</p>
<dl><dt><b>Parameters:</b></dt><dd>
<table class="params">
<tr><td class="paramname">in_z</td><td>The observation vector whose inverse sensor model is to be computed. This is actually one of the vector<> returned by <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a74490d79d5d8fbf499021af5f66d593b" title="This is called between the KF prediction step and the update step, and the application must return th...">OnGetObservationsAndDataAssociation()</a>. </td></tr>
<tr><td class="paramname">out_yn</td><td>The F-length vector with the inverse observation model <img class="formulaInl" alt="$ y_n=y(x,z_n) $" src="form_94.png"/>. </td></tr>
<tr><td class="paramname">out_dyn_dxv</td><td>The <img class="formulaInl" alt="$F \times V$" src="form_95.png"/> Jacobian of the inv. sensor model wrt the robot pose <img class="formulaInl" alt="$ \frac{\partial y_n}{\partial x_v} $" src="form_96.png"/>. </td></tr>
<tr><td class="paramname">out_dyn_dhn</td><td>The <img class="formulaInl" alt="$F \times O$" src="form_97.png"/> Jacobian of the inv. sensor model wrt the observation vector <img class="formulaInl" alt="$ \frac{\partial y_n}{\partial h_n} $" src="form_98.png"/>. </td></tr>
<tr><td class="paramname">out_dyn_dhn_R_dyn_dhnT</td><td>See the discussion above.</td></tr>
</table>
</dd>
</dl>
<ul>
<li>O: OBS_SIZE</li>
<li>V: VEH_SIZE</li>
<li>F: FEAT_SIZE</li>
</ul>
<dl class="note"><dt><b>Note:</b></dt><dd>OnNewLandmarkAddedToMap will be also called after calling this method if a landmark is actually being added to the map. </dd></dl>
<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00383">383</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a41be072d4693b55225b484c249db5fc2"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::OnNewLandmarkAddedToMap" ref="a41be072d4693b55225b484c249db5fc2" args="(const size_t in_obsIdx, const size_t in_idxNewFeat)" -->
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<td class="memname">virtual void mrpt::bayes::CKalmanFilterCapable::OnNewLandmarkAddedToMap </td>
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<td class="paramtype">const size_t </td>
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<td class="paramtype">const size_t </td>
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<p>If applicable to the given problem, do here any special handling of adding a new landmark to the map. </p>
<dl><dt><b>Parameters:</b></dt><dd>
<table class="params">
<tr><td class="paramname">in_obsIndex</td><td>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. </td></tr>
<tr><td class="paramname">in_idxNewFeat</td><td>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. </td></tr>
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</dd>
</dl>
<dl class="see"><dt><b>See also:</b></dt><dd><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a647a12e0844df2ead68949d5ed33967e" title="If applicable to the given problem, this method implements the inverse observation model needed to ex...">OnInverseObservationModel</a> </dd></dl>
<p>Reimplemented in <a class="el" href="classmrpt_1_1slam_1_1_c_range_bearing_k_f_s_l_a_m.html#a21d5fd42e86d10792e530aef75d9baf2">mrpt::slam::CRangeBearingKFSLAM</a>, and <a class="el" href="classmrpt_1_1slam_1_1_c_range_bearing_k_f_s_l_a_m2_d.html#a1073193e03d2f4ff2038fa84ccdd4653">mrpt::slam::CRangeBearingKFSLAM2D</a>.</p>
<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00403">403</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a8a5246ad413ba2f6121b9f7f261283b8"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::OnNormalizeStateVector" ref="a8a5246ad413ba2f6121b9f7f261283b8" args="()" -->
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<td class="memname">virtual void mrpt::bayes::CKalmanFilterCapable::OnNormalizeStateVector </td>
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<td><code> [inline, protected, virtual]</code></td>
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<p>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. </p>
<p>Reimplemented in <a class="el" href="classmrpt_1_1slam_1_1_c_range_bearing_k_f_s_l_a_m.html#abcc9fe624ee79796dd0db27b4b29f084">mrpt::slam::CRangeBearingKFSLAM</a>, and <a class="el" href="classmrpt_1_1slam_1_1_c_range_bearing_k_f_s_l_a_m2_d.html#aa3d99ee710bbdbdcd21bc2512ef52f9d">mrpt::slam::CRangeBearingKFSLAM2D</a>.</p>
<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00412">412</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a45e3d11533732ae6fdc6f0d8a0c6a16e"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::OnObservationJacobians" ref="a45e3d11533732ae6fdc6f0d8a0c6a16e" args="(const size_t &idx_landmark_to_predict, KFMatrix_OxV &Hx, KFMatrix_OxF &Hy) const " -->
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<td class="memname">virtual void mrpt::bayes::CKalmanFilterCapable::OnObservationJacobians </td>
<td>(</td>
<td class="paramtype">const size_t & </td>
<td class="paramname"><em>idx_landmark_to_predict</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ab0ddb67c05616e7fc1fcabe9cc04c753">KFMatrix_OxV</a> & </td>
<td class="paramname"><em>Hx</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ada78f649a58aa64c3db6c8b436b5ceba">KFMatrix_OxF</a> & </td>
<td class="paramname"><em>Hy</em> </td>
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<td>)</td>
<td></td><td> const<code> [inline, protected, virtual]</code></td>
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<p>Implements the observation Jacobians <img class="formulaInl" alt="$ \frac{\partial h_i}{\partial x} $" src="form_92.png"/> and (when applicable) <img class="formulaInl" alt="$ \frac{\partial h_i}{\partial y_i} $" src="form_93.png"/>. </p>
<dl><dt><b>Parameters:</b></dt><dd>
<table class="params">
<tr><td class="paramname">idx_landmark_to_predict</td><td>The index of the landmark in the map whose prediction is expected as output. For non SLAM-like problems, this will be zero and the expected output is for the whole state vector. </td></tr>
<tr><td class="paramname">Hx</td><td>The output Jacobian <img class="formulaInl" alt="$ \frac{\partial h_i}{\partial x} $" src="form_92.png"/>. </td></tr>
<tr><td class="paramname">Hy</td><td>The output Jacobian <img class="formulaInl" alt="$ \frac{\partial h_i}{\partial y_i} $" src="form_93.png"/>. </td></tr>
</table>
</dd>
</dl>
<p>Reimplemented in <a class="el" href="classmrpt_1_1slam_1_1_c_range_bearing_k_f_s_l_a_m.html#a16b2fccffc944e6e0af70a17b8c234e8">mrpt::slam::CRangeBearingKFSLAM</a>, and <a class="el" href="classmrpt_1_1slam_1_1_c_range_bearing_k_f_s_l_a_m2_d.html#accfee66b72edc2288df16e0686792879">mrpt::slam::CRangeBearingKFSLAM2D</a>.</p>
<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00311">311</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="acbba7768aa0513f67e30628787e429c2"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::OnObservationJacobiansNumericGetIncrements" ref="acbba7768aa0513f67e30628787e429c2" args="(KFArray_VEH &out_veh_increments, KFArray_FEAT &out_feat_increments) const " -->
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<td class="memname">virtual void mrpt::bayes::CKalmanFilterCapable::OnObservationJacobiansNumericGetIncrements </td>
<td>(</td>
<td class="paramtype"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad4f1667845ca7553925160142821cff0">KFArray_VEH</a> & </td>
<td class="paramname"><em>out_veh_increments</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a0b5b72d68126a91918ce085789da06bd">KFArray_FEAT</a> & </td>
<td class="paramname"><em>out_feat_increments</em> </td>
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<td>)</td>
<td></td><td> const<code> [inline, protected, virtual]</code></td>
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<p>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. </p>
<p>Reimplemented in <a class="el" href="classmrpt_1_1slam_1_1_c_range_bearing_k_f_s_l_a_m2_d.html#a6527eb56311535ab9b84dc48f3faf9b0">mrpt::slam::CRangeBearingKFSLAM2D</a>.</p>
<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00322">322</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a27de563ddb767f0cf75ff7a53e532b1a"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::OnObservationModel" ref="a27de563ddb767f0cf75ff7a53e532b1a" args="(const mrpt::vector_size_t &idx_landmarks_to_predict, vector_KFArray_OBS &out_predictions) const =0" -->
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<td class="memname">virtual void mrpt::bayes::CKalmanFilterCapable::OnObservationModel </td>
<td>(</td>
<td class="paramtype">const <a class="el" href="namespacemrpt.html#ad4d2b1efd37ed750302c76ebbcfc310d">mrpt::vector_size_t</a> & </td>
<td class="paramname"><em>idx_landmarks_to_predict</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#aa0e71d2a45e5d935eba18d817bb31cf9">vector_KFArray_OBS</a> & </td>
<td class="paramname"><em>out_predictions</em> </td>
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<td>)</td>
<td></td><td> const<code> [protected, pure virtual]</code></td>
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<p>Implements the observation prediction <img class="formulaInl" alt="$ h_i(x) $" src="form_91.png"/>. </p>
<dl><dt><b>Parameters:</b></dt><dd>
<table class="params">
<tr><td class="paramname">idx_landmark_to_predict</td><td>The indices of the landmarks in the map whose predictions are expected as output. For non SLAM-like problems, this input value is undefined and the application should just generate one observation for the given problem. </td></tr>
<tr><td class="paramname">out_predictions</td><td>The predicted observations. </td></tr>
</table>
</dd>
</dl>
<p>Implemented in <a class="el" href="classmrpt_1_1slam_1_1_c_range_bearing_k_f_s_l_a_m.html#ad985328f6b30810aee2da489f505ddb1">mrpt::slam::CRangeBearingKFSLAM</a>, and <a class="el" href="classmrpt_1_1slam_1_1_c_range_bearing_k_f_s_l_a_m2_d.html#aa8a021990239ac720ee2c44dd2b715e9">mrpt::slam::CRangeBearingKFSLAM2D</a>.</p>
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<a class="anchor" id="a5a3514fd81d6f81b8ff7b2c72dfae33e"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::OnPostIteration" ref="a5a3514fd81d6f81b8ff7b2c72dfae33e" args="()" -->
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<td class="memname">virtual void mrpt::bayes::CKalmanFilterCapable::OnPostIteration </td>
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<td class="paramname"></td><td>)</td>
<td><code> [inline, protected, virtual]</code></td>
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<p>This method is called after finishing one KF iteration and before returning from <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#aeebd325f91acbf7d27134c8c7388649e" title="The main entry point, executes one complete step: prediction + update.">runOneKalmanIteration()</a>. </p>
<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00419">419</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a505fa1e6eb8a300e0e1171a26b4f5b15"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::OnPreComputingPredictions" ref="a505fa1e6eb8a300e0e1171a26b4f5b15" args="(const vector_KFArray_OBS &in_all_prediction_means, mrpt::vector_size_t &out_LM_indices_to_predict) const " -->
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<td class="memname">virtual void mrpt::bayes::CKalmanFilterCapable::OnPreComputingPredictions </td>
<td>(</td>
<td class="paramtype">const <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#aa0e71d2a45e5d935eba18d817bb31cf9">vector_KFArray_OBS</a> & </td>
<td class="paramname"><em>in_all_prediction_means</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype"><a class="el" href="namespacemrpt.html#ad4d2b1efd37ed750302c76ebbcfc310d">mrpt::vector_size_t</a> & </td>
<td class="paramname"><em>out_LM_indices_to_predict</em> </td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td> const<code> [inline, protected, virtual]</code></td>
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<p>This will be called before OnGetObservationsAndDataAssociation to allow the application to reduce the number of covariance landmark predictions to be made. </p>
<p>For example, features which are known to be "out of sight" shouldn't be added to the output list to speed up the calculations. </p>
<dl><dt><b>Parameters:</b></dt><dd>
<table class="params">
<tr><td class="paramname">in_all_prediction_means</td><td>The mean of each landmark predictions; the computation or not of the corresponding covariances is what we're trying to determined with this method. </td></tr>
<tr><td class="paramname">out_LM_indices_to_predict</td><td>The list of landmark indices in the map [0,<a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a1ac5b0adbbd3a0e5122ebb148ff3e7ad">getNumberOfLandmarksInTheMap()</a>-1] that should be predicted. </td></tr>
</table>
</dd>
</dl>
<dl class="note"><dt><b>Note:</b></dt><dd>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. </dd></dl>
<dl class="see"><dt><b>See also:</b></dt><dd>OnGetObservations, OnDataAssociation </dd></dl>
<p>Reimplemented in <a class="el" href="classmrpt_1_1slam_1_1_c_range_bearing_k_f_s_l_a_m.html#af56a4f99f7f558fb7ac383b5d5c1febc">mrpt::slam::CRangeBearingKFSLAM</a>, and <a class="el" href="classmrpt_1_1slam_1_1_c_range_bearing_k_f_s_l_a_m2_d.html#a5d32610c2440162beb39617879d1b79c">mrpt::slam::CRangeBearingKFSLAM2D</a>.</p>
<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00261">261</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a2875f05e00d512746e6a39244fb28561"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::OnSubstractObservationVectors" ref="a2875f05e00d512746e6a39244fb28561" args="(KFArray_OBS &A, const KFArray_OBS &B) const " -->
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<td class="memname">virtual void mrpt::bayes::CKalmanFilterCapable::OnSubstractObservationVectors </td>
<td>(</td>
<td class="paramtype"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6c1b24a2c7b35e77411888a06de4e59d">KFArray_OBS</a> & </td>
<td class="paramname"><em>A</em>, </td>
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<td></td>
<td class="paramtype">const <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6c1b24a2c7b35e77411888a06de4e59d">KFArray_OBS</a> & </td>
<td class="paramname"><em>B</em> </td>
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<td>)</td>
<td></td><td> const<code> [inline, protected, virtual]</code></td>
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<p>Computes A=A-B, which may need to be re-implemented depending on the topology of the individual scalar components (eg, angles). </p>
<p>Reimplemented in <a class="el" href="classmrpt_1_1slam_1_1_c_range_bearing_k_f_s_l_a_m.html#a4debf44b3dd2ea88b9f5c37c2d0fd024">mrpt::slam::CRangeBearingKFSLAM</a>, and <a class="el" href="classmrpt_1_1slam_1_1_c_range_bearing_k_f_s_l_a_m2_d.html#ac209037604fb37a25d7b56f4c1fb4781">mrpt::slam::CRangeBearingKFSLAM2D</a>.</p>
<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00332">332</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a9102af2605891c4b76ef1216013da46d"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::OnTransitionJacobian" ref="a9102af2605891c4b76ef1216013da46d" args="(KFMatrix_VxV &out_F) const " -->
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<td class="memname">virtual void mrpt::bayes::CKalmanFilterCapable::OnTransitionJacobian </td>
<td>(</td>
<td class="paramtype"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a8dd3e63dd847fd98ef59b472e46cad2c">KFMatrix_VxV</a> & </td>
<td class="paramname"><em>out_F</em></td><td>)</td>
<td> const<code> [inline, protected, virtual]</code></td>
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<p>Implements the transition Jacobian <img class="formulaInl" alt="$ \frac{\partial f}{\partial x} $" src="form_29.png"/>. </p>
<dl><dt><b>Parameters:</b></dt><dd>
<table class="params">
<tr><td class="paramname">out_F</td><td>Must return the Jacobian. The returned matrix must be <img class="formulaInl" alt="$V \times V$" src="form_89.png"/> with V being either the size of the whole state vector (for non-SLAM problems) or VEH_SIZE (for SLAM problems). </td></tr>
</table>
</dd>
</dl>
<p>Reimplemented in <a class="el" href="classmrpt_1_1slam_1_1_c_range_bearing_k_f_s_l_a_m.html#abfa7d23c6a1b88b6877345ec68fac4b9">mrpt::slam::CRangeBearingKFSLAM</a>, and <a class="el" href="classmrpt_1_1slam_1_1_c_range_bearing_k_f_s_l_a_m2_d.html#a40f4a9cbe853526f05abbeb93b535102">mrpt::slam::CRangeBearingKFSLAM2D</a>.</p>
<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00236">236</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a6546398d63e8298f0b0a59a86e2395a7"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::OnTransitionJacobianNumericGetIncrements" ref="a6546398d63e8298f0b0a59a86e2395a7" args="(KFArray_VEH &out_increments) const " -->
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<td class="memname">virtual void mrpt::bayes::CKalmanFilterCapable::OnTransitionJacobianNumericGetIncrements </td>
<td>(</td>
<td class="paramtype"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad4f1667845ca7553925160142821cff0">KFArray_VEH</a> & </td>
<td class="paramname"><em>out_increments</em></td><td>)</td>
<td> const<code> [inline, protected, virtual]</code></td>
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<p>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. </p>
<p>Reimplemented in <a class="el" href="classmrpt_1_1slam_1_1_c_range_bearing_k_f_s_l_a_m2_d.html#afb9ddb3cff1724e22e616fe0610681ab">mrpt::slam::CRangeBearingKFSLAM2D</a>.</p>
<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00243">243</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a006b2619c480312a774ba3443efbe0a5"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::OnTransitionModel" ref="a006b2619c480312a774ba3443efbe0a5" args="(const KFArray_ACT &in_u, KFArray_VEH &inout_x, bool &out_skipPrediction) const =0" -->
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<td class="memname">virtual void mrpt::bayes::CKalmanFilterCapable::OnTransitionModel </td>
<td>(</td>
<td class="paramtype">const <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6454c68e3c45da92e2bbb3e8aa8f6f70">KFArray_ACT</a> & </td>
<td class="paramname"><em>in_u</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad4f1667845ca7553925160142821cff0">KFArray_VEH</a> & </td>
<td class="paramname"><em>inout_x</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">bool & </td>
<td class="paramname"><em>out_skipPrediction</em> </td>
</tr>
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<td></td>
<td>)</td>
<td></td><td> const<code> [protected, pure virtual]</code></td>
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<p>Implements the transition model <img class="formulaInl" alt="$ \hat{x}_{k|k-1} = f( \hat{x}_{k-1|k-1}, u_k ) $" src="form_86.png"/>. </p>
<dl><dt><b>Parameters:</b></dt><dd>
<table class="params">
<tr><td class="paramname">in_u</td><td>The vector returned by OnGetAction. </td></tr>
<tr><td class="paramname">inout_x</td><td>At input has <p class="formulaDsp">
<img class="formulaDsp" alt="\[ \hat{x}_{k-1|k-1} \]" src="form_87.png"/>
</p>
, at output must have <img class="formulaInl" alt="$ \hat{x}_{k|k-1} $" src="form_88.png"/> . </td></tr>
<tr><td class="paramname">out_skip</td><td>Set this to true if for some reason you want to skip the prediction step (to do not modify either the vector or the covariance). Default:false </td></tr>
</table>
</dd>
</dl>
<dl class="note"><dt><b>Note:</b></dt><dd>Even if you return "out_skip=true", the value of "inout_x" MUST be updated as usual (this is to allow numeric approximation of Jacobians). </dd></dl>
<p>Implemented in <a class="el" href="classmrpt_1_1slam_1_1_c_range_bearing_k_f_s_l_a_m.html#a068baea8f069e9016c9a1e7336cfdfd1">mrpt::slam::CRangeBearingKFSLAM</a>, and <a class="el" href="classmrpt_1_1slam_1_1_c_range_bearing_k_f_s_l_a_m2_d.html#a3ea632d9fe8529f381c4c6d259a2ff9d">mrpt::slam::CRangeBearingKFSLAM2D</a>.</p>
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<a class="anchor" id="a77cdfd45b0173035594248d605b49306"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::OnTransitionNoise" ref="a77cdfd45b0173035594248d605b49306" args="(KFMatrix_VxV &out_Q) const =0" -->
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<td class="memname">virtual void mrpt::bayes::CKalmanFilterCapable::OnTransitionNoise </td>
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<td class="paramtype"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a8dd3e63dd847fd98ef59b472e46cad2c">KFMatrix_VxV</a> & </td>
<td class="paramname"><em>out_Q</em></td><td>)</td>
<td> const<code> [protected, pure virtual]</code></td>
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<p>Implements the transition noise covariance <img class="formulaInl" alt="$ Q_k $" src="form_90.png"/>. </p>
<dl><dt><b>Parameters:</b></dt><dd>
<table class="params">
<tr><td class="paramname">out_Q</td><td>Must return the covariance matrix. The returned matrix must be of the same size than the jacobian from OnTransitionJacobian </td></tr>
</table>
</dd>
</dl>
<p>Implemented in <a class="el" href="classmrpt_1_1slam_1_1_c_range_bearing_k_f_s_l_a_m.html#a671e5190f4701458ae0a784c8d65ffa8">mrpt::slam::CRangeBearingKFSLAM</a>, and <a class="el" href="classmrpt_1_1slam_1_1_c_range_bearing_k_f_s_l_a_m2_d.html#a492735723c8e453b8bda0acbb6d4f271">mrpt::slam::CRangeBearingKFSLAM2D</a>.</p>
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<a class="anchor" id="ab78281b5d70d6e295a8527a10fea66de"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::printf_debug" ref="ab78281b5d70d6e295a8527a10fea66de" args="(const char *frmt,...)" -->
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<td class="memname">static void mrpt::utils::CDebugOutputCapable::printf_debug </td>
<td>(</td>
<td class="paramtype">const char * </td>
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<p>Sends a formated text to "debugOut" if not NULL, or to cout otherwise. </p>
<p>Referenced by <a class="el" href="_c_levenberg_marquardt_8h_source.html#l00098">mrpt::math::CLevenbergMarquardtTempl::execute()</a>.</p>
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<a class="anchor" id="aeebd325f91acbf7d27134c8c7388649e"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::runOneKalmanIteration" ref="aeebd325f91acbf7d27134c8c7388649e" args="()" -->
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<td class="memname">void mrpt::bayes::CKalmanFilterCapable::runOneKalmanIteration </td>
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<p>The main entry point, executes one complete step: prediction + update. </p>
<p>It is protected since derived classes must provide a problem-specific entry point for users. The exact order in which this method calls the virtual method is explained in <a href="http://www.mrpt.org/Kalman_Filters">http://www.mrpt.org/Kalman_Filters</a> </p>
<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00459">459</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<hr/><h2>Friends And Related Function Documentation</h2>
<a class="anchor" id="a1f7c0688afe03ffd6f14c4e9f4e5a050"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::detail::CRunOneKalmanIteration_addNewLandmarks" ref="a1f7c0688afe03ffd6f14c4e9f4e5a050" args="" -->
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<td class="memname">friend struct <a class="el" href="structmrpt_1_1bayes_1_1detail_1_1_c_run_one_kalman_iteration__add_new_landmarks.html">detail::CRunOneKalmanIteration_addNewLandmarks</a><code> [friend]</code></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l01499">1499</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<hr/><h2>Member Data Documentation</h2>
<a class="anchor" id="a90762ef91865e4f300226f31cb187828"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::all_predictions" ref="a90762ef91865e4f300226f31cb187828" args="" -->
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<td class="memname"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#aa0e71d2a45e5d935eba18d817bb31cf9">vector_KFArray_OBS</a> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a90762ef91865e4f300226f31cb187828">mrpt::bayes::CKalmanFilterCapable::all_predictions</a><code> [private]</code></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00440">440</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="ad9c15457835641e93026e505b3c44ac7"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::aux_K_dh_dx" ref="ad9c15457835641e93026e505b3c44ac7" args="" -->
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<td class="memname"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad214112622ea8531c9a7c052f93600bd">KFMatrix</a> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad9c15457835641e93026e505b3c44ac7">mrpt::bayes::CKalmanFilterCapable::aux_K_dh_dx</a><code> [private]</code></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00451">451</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a581ce072177eef66789bf8024f3c2ed7"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::dh_dx" ref="a581ce072177eef66789bf8024f3c2ed7" args="" -->
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<td class="memname"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad214112622ea8531c9a7c052f93600bd">KFMatrix</a> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a581ce072177eef66789bf8024f3c2ed7">mrpt::bayes::CKalmanFilterCapable::dh_dx</a><code> [private]</code></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00442">442</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a6dcad04e08c1483c1ed5a0e5c7e5ef79"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::dh_dx_full" ref="a6dcad04e08c1483c1ed5a0e5c7e5ef79" args="" -->
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<td class="memname"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad214112622ea8531c9a7c052f93600bd">KFMatrix</a> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a6dcad04e08c1483c1ed5a0e5c7e5ef79">mrpt::bayes::CKalmanFilterCapable::dh_dx_full</a><code> [private]</code></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00443">443</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a8eca88f65c4032f3179b8e2e5d13250b"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::dh_dx_full_obs" ref="a8eca88f65c4032f3179b8e2e5d13250b" args="" -->
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<td class="memname"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad214112622ea8531c9a7c052f93600bd">KFMatrix</a> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a8eca88f65c4032f3179b8e2e5d13250b">mrpt::bayes::CKalmanFilterCapable::dh_dx_full_obs</a><code> [private]</code></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00450">450</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a099172f5abfc72517b79cf221200b9f2"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::idxs" ref="a099172f5abfc72517b79cf221200b9f2" args="" -->
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<td class="memname"><a class="el" href="namespacemrpt.html#ad4d2b1efd37ed750302c76ebbcfc310d">vector_size_t</a> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a099172f5abfc72517b79cf221200b9f2">mrpt::bayes::CKalmanFilterCapable::idxs</a><code> [private]</code></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00444">444</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="aac0376b299cfa494e755b1ddfb512b7e"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::K" ref="aac0376b299cfa494e755b1ddfb512b7e" args="" -->
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<td class="memname"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad214112622ea8531c9a7c052f93600bd">KFMatrix</a> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#aac0376b299cfa494e755b1ddfb512b7e">mrpt::bayes::CKalmanFilterCapable::K</a><code> [private]</code></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00448">448</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a88651951bad5ea93ae84d848aeed6414"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::KF_options" ref="a88651951bad5ea93ae84d848aeed6414" args="" -->
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<td class="memname"><a class="el" href="structmrpt_1_1bayes_1_1_t_k_f__options.html">TKF_options</a> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a88651951bad5ea93ae84d848aeed6414">mrpt::bayes::CKalmanFilterCapable::KF_options</a></td>
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<p>Generic options for the Kalman Filter algorithm itself. </p>
<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00433">433</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a15c291ca9eb40996381dc0e60f01d533"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::m_pkk" ref="a15c291ca9eb40996381dc0e60f01d533" args="" -->
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<td class="memname"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad214112622ea8531c9a7c052f93600bd">KFMatrix</a> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a15c291ca9eb40996381dc0e60f01d533">mrpt::bayes::CKalmanFilterCapable::m_pkk</a><code> [protected]</code></td>
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<p>The system full covariance matrix. </p>
<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00205">205</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="a3eff55c428065c52bd2f9418e3944e48"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::m_timLogger" ref="a3eff55c428065c52bd2f9418e3944e48" args="" -->
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<td class="memname"><a class="el" href="classmrpt_1_1utils_1_1_c_time_logger.html">mrpt::utils::CTimeLogger</a> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a3eff55c428065c52bd2f9418e3944e48">mrpt::bayes::CKalmanFilterCapable::m_timLogger</a><code> [protected]</code></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00209">209</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="ada33322b67cea44a95948712be1c131b"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::m_user_didnt_implement_jacobian" ref="ada33322b67cea44a95948712be1c131b" args="" -->
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<td class="memname">bool <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ada33322b67cea44a95948712be1c131b">mrpt::bayes::CKalmanFilterCapable::m_user_didnt_implement_jacobian</a><code> [mutable, private]</code></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l01458">1458</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<td class="memname"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a9a120b286d286048985b059aa721e0c1">KFVector</a> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a7e8948c9d209c23517c45c6494b46147">mrpt::bayes::CKalmanFilterCapable::m_xkk</a><code> [protected]</code></td>
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<p>The system state vector. </p>
<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00204">204</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<td class="memname"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad214112622ea8531c9a7c052f93600bd">KFMatrix</a> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a65e1419380dea75082a0aa0a09f04332">mrpt::bayes::CKalmanFilterCapable::Pkk_subset</a><code> [private]</code></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00446">446</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<td class="memname"><a class="el" href="namespacemrpt.html#ad4d2b1efd37ed750302c76ebbcfc310d">vector_size_t</a> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a87c0cfc640df569e33a97749951a6e06">mrpt::bayes::CKalmanFilterCapable::predictLMidxs</a><code> [private]</code></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00441">441</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<td class="memname"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad214112622ea8531c9a7c052f93600bd">KFMatrix</a> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a65d721081bd1d54c1d05d88c4279fe42">mrpt::bayes::CKalmanFilterCapable::S</a><code> [private]</code></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00445">445</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<td class="memname"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#ad214112622ea8531c9a7c052f93600bd">KFMatrix</a> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#a19e1025c17ff21c2714fd5bcb9d58a9b">mrpt::bayes::CKalmanFilterCapable::S_1</a><code> [private]</code></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00449">449</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<a class="anchor" id="aa43fef4c1e7af3dee7ad10053132f47b"></a><!-- doxytag: member="mrpt::bayes::CKalmanFilterCapable::Z" ref="aa43fef4c1e7af3dee7ad10053132f47b" args="" -->
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<td class="memname"><a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#aa0e71d2a45e5d935eba18d817bb31cf9">vector_KFArray_OBS</a> <a class="el" href="classmrpt_1_1bayes_1_1_c_kalman_filter_capable.html#aa43fef4c1e7af3dee7ad10053132f47b">mrpt::bayes::CKalmanFilterCapable::Z</a><code> [private]</code></td>
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<p>Definition at line <a class="el" href="_c_kalman_filter_capable_8h_source.html#l00447">447</a> of file <a class="el" href="_c_kalman_filter_capable_8h_source.html">CKalmanFilterCapable.h</a>.</p>
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<br><hr><br> <table border="0" width="100%"> <tr> <td> Page generated by <a href="http://www.doxygen.org" target="_blank">Doxygen 1.7.5</a> for MRPT 0.9.5 SVN: at Sun Sep 25 17:20:18 UTC 2011</td><td></td> <td width="100"> </td> <td width="150"> </td></tr> </table> </body></html>
