Preview for PR #1748 bc3753bf036c2b3622aa1043ef6fab9f275719db

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@@ -417,7 +417,7 @@ $(document).ready(function(){initNavTree('Parameters_8h_source.html',''); initRe
 <div class="line"><a id="l00390" name="l00390"></a><span class="lineno"><a class="line" href="classrtabmap_1_1Parameters.html#aeb4f9762df3e027594ed16c2028d537e">  390</a></span>    RTABMAP_PARAM(Bayes, VirtualPlacePriorThr, <span class="keywordtype">float</span>, 0.9,  <span class="stringliteral">&quot;Virtual place prior. Considering that we are at a new place, this is the prior probability to move again to a new place (unvisited location). The prior probability to move to a previously visited location is 1 - VirtualPlacePriorThr (split equally against all previously visited locations).&quot;</span>);</div>
 <div class="line"><a id="l00391" name="l00391"></a><span class="lineno"><a class="line" href="classrtabmap_1_1Parameters.html#a4c911439d8366cb6a15cc106d051ba70">  391</a></span>    RTABMAP_PARAM_STR(Bayes, PredictionLC, <span class="stringliteral">&quot;0.1 0.36 0.30 0.16 0.062 0.0151 0.00255 0.000324 2.5e-05 1e-06 4.8e-08 1.2e-09 1.9e-11 2.2e-13 1.7e-15 8.5e-18 2.9e-20 6.9e-23&quot;</span>, <span class="stringliteral">&quot;Prediction of loop closures (Gaussian-like, here with sigma=1.6) - Format: {VirtualPlaceProb, LoopClosureProb, NeighborLvl1, NeighborLvl2, ...}. Considering we are at a previously visited location, the first value is the probability to move to a new place (unvisited location), the second value is the probability to stay at the same location, the third value is the probability to move to a neighbor or loop closure at the first depth level, the fourth value is the probability to move to a neighbor or loop closure at the second depth level, etc. If the sum of the values is not 1, the difference is normalized against all remaining visited locations. Normally, the sum of these values should be 1.&quot;</span>);</div>
 <div class="line"><a id="l00392" name="l00392"></a><span class="lineno"><a class="line" href="classrtabmap_1_1Parameters.html#acb494af5ed2d49e5d97c7df1b6a38a18">  392</a></span>    RTABMAP_PARAM(Bayes, FullPredictionUpdate, <span class="keywordtype">bool</span>, <span class="keyword">false</span>, <span class="stringliteral">&quot;Regenerate all the prediction matrix on each iteration (otherwise only removed/added ids are updated).&quot;</span>);</div>
-<div class="line"><a id="l00393" name="l00393"></a><span class="lineno"><a class="line" href="classrtabmap_1_1Parameters.html#a4c7834c4fbf9488336a0d1eb54ba6596">  393</a></span>    RTABMAP_PARAM(Bayes, SparsePrediction, <span class="keywordtype">bool</span>, <span class="keyword">true</span>, <a class="code hl_function" href="UConversion_8h.html#a59ad5e2b3368d3dd19d73a24159388ce">uFormat</a>(<span class="stringliteral">&quot;Multiply the prediction matrix with the last posterior using a sparse (compressed sparse row) view of the prediction instead of a dense matrix multiplication. A column of the prediction matrix only holds the neighbors within the depth of %s, so on a large map the matrix is mostly zeros and the dense multiplication spends all of its time reading them. The sparse view is rebuilt only when the prediction matrix changes, thus it costs nothing in localization mode over a fixed graph. Ignored (the dense multiplication is used) when the values of %s sum to less than 1, as the missing probability is then spread over every zero of each column and the matrix is no longer sparse.&quot;</span>, kBayesPredictionLC().c_str(), kBayesPredictionLC().c_str()).c_str());</div>
+<div class="line"><a id="l00393" name="l00393"></a><span class="lineno"><a class="line" href="classrtabmap_1_1Parameters.html#a4c7834c4fbf9488336a0d1eb54ba6596">  393</a></span>    RTABMAP_PARAM(Bayes, SparsePrediction, <span class="keywordtype">bool</span>, <span class="keyword">true</span>, <a class="code hl_function" href="UConversion_8h.html#a59ad5e2b3368d3dd19d73a24159388ce">uFormat</a>(<span class="stringliteral">&quot;Use a sparse representation of the prediction instead of a dense matrix, which significantly reduces memory usage and processing time on large maps. Ignored when the values of %s sum to less than 1, as the prediction is then not sparse.&quot;</span>, kBayesPredictionLC().c_str()).c_str());</div>
 <div class="line"><a id="l00394" name="l00394"></a><span class="lineno">  394</span> </div>
 <div class="line"><a id="l00395" name="l00395"></a><span class="lineno">  395</span>    <span class="comment">// Verify hypotheses</span></div>
 <div class="line"><a id="l00396" name="l00396"></a><span class="lineno"><a class="line" href="classrtabmap_1_1Parameters.html#a55d159a1f4d432f77b50cde8b642439e">  396</a></span>    RTABMAP_PARAM(VhEp, Enabled, <span class="keywordtype">bool</span>, <span class="keyword">false</span>,       <a class="code hl_function" href="UConversion_8h.html#a59ad5e2b3368d3dd19d73a24159388ce">uFormat</a>(<span class="stringliteral">&quot;Verify visual loop closure hypothesis by computing a fundamental matrix. This is done prior to transformation computation when %s is enabled.&quot;</span>, kRGBDEnabled().c_str()));</div>

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@@ -1587,7 +1587,7 @@ Static Public Member Functions</h2></td></tr>
 <tr class="memdesc:a1b03bdfcb282bf6fa5e8a0e31e9a8525"><td class="mdescLeft">&#160;</td><td class="mdescRight">Type of parameter Bayes/FullPredictionUpdate , as a string: bool .  <br /></td></tr>
 <tr class="separator:a1b03bdfcb282bf6fa5e8a0e31e9a8525"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="memitem:ae8141b0f2cc33d7e47b8a5f4fc558bc1" id="r_ae8141b0f2cc33d7e47b8a5f4fc558bc1"><td class="memItemLeft" align="right" valign="top">static std::string&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classrtabmap_1_1Parameters.html#ae8141b0f2cc33d7e47b8a5f4fc558bc1">kBayesSparsePrediction</a> ()</td></tr>
-<tr class="memdesc:ae8141b0f2cc33d7e47b8a5f4fc558bc1"><td class="mdescLeft">&#160;</td><td class="mdescRight">Key of parameter Bayes/SparsePrediction : uFormat("Multiply the prediction matrix with the last posterior using a sparse (compressed sparse row) view of the prediction instead of a dense matrix multiplication. A column of the prediction matrix only holds the neighbors within the depth of %s, so on a large map the matrix is mostly zeros and the dense multiplication spends all of its time reading them. The sparse view is rebuilt only when the prediction matrix changes, thus it costs nothing in localization mode over a fixed graph. Ignored (the dense multiplication is used) when the values of %s sum to less than 1, as the missing probability is then spread over every zero of each column and the matrix is no longer sparse.", <a class="el" href="classrtabmap_1_1Parameters.html#aabfee3d6757a2269d7bbbbd3ebc20fb1" title="Key of parameter Bayes/PredictionLC : &quot;Prediction of loop closures (Gaussian-like,...">kBayesPredictionLC()</a>.c_str(), <a class="el" href="classrtabmap_1_1Parameters.html#aabfee3d6757a2269d7bbbbd3ebc20fb1" title="Key of parameter Bayes/PredictionLC : &quot;Prediction of loop closures (Gaussian-like,...">kBayesPredictionLC()</a>.c_str()).c_str() Default value: true ( bool ).  <br /></td></tr>
+<tr class="memdesc:ae8141b0f2cc33d7e47b8a5f4fc558bc1"><td class="mdescLeft">&#160;</td><td class="mdescRight">Key of parameter Bayes/SparsePrediction : uFormat("Use a sparse representation of the prediction instead of a dense matrix, which significantly reduces memory usage and processing time on large maps. Ignored when the values of %s sum to less than 1, as the prediction is then not sparse.", <a class="el" href="classrtabmap_1_1Parameters.html#aabfee3d6757a2269d7bbbbd3ebc20fb1" title="Key of parameter Bayes/PredictionLC : &quot;Prediction of loop closures (Gaussian-like,...">kBayesPredictionLC()</a>.c_str()).c_str() Default value: true ( bool ).  <br /></td></tr>
 <tr class="separator:ae8141b0f2cc33d7e47b8a5f4fc558bc1"><td class="memSeparator" colspan="2">&#160;</td></tr>
 <tr class="memitem:a4c7834c4fbf9488336a0d1eb54ba6596" id="r_a4c7834c4fbf9488336a0d1eb54ba6596"><td class="memItemLeft" align="right" valign="top">static bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classrtabmap_1_1Parameters.html#a4c7834c4fbf9488336a0d1eb54ba6596">defaultBayesSparsePrediction</a> ()</td></tr>
 <tr class="memdesc:a4c7834c4fbf9488336a0d1eb54ba6596"><td class="mdescLeft">&#160;</td><td class="mdescRight">Default value of parameter Bayes/SparsePrediction : true .  <br /></td></tr>
@@ -20033,7 +20033,7 @@ The <a class="el" href="parameters.html">Parameter reference</a> page, which lis
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-<p>Key of parameter Bayes/SparsePrediction : uFormat("Multiply the prediction matrix with the last posterior using a sparse (compressed sparse row) view of the prediction instead of a dense matrix multiplication. A column of the prediction matrix only holds the neighbors within the depth of %s, so on a large map the matrix is mostly zeros and the dense multiplication spends all of its time reading them. The sparse view is rebuilt only when the prediction matrix changes, thus it costs nothing in localization mode over a fixed graph. Ignored (the dense multiplication is used) when the values of %s sum to less than 1, as the missing probability is then spread over every zero of each column and the matrix is no longer sparse.", <a class="el" href="classrtabmap_1_1Parameters.html#aabfee3d6757a2269d7bbbbd3ebc20fb1" title="Key of parameter Bayes/PredictionLC : &quot;Prediction of loop closures (Gaussian-like,...">kBayesPredictionLC()</a>.c_str(), <a class="el" href="classrtabmap_1_1Parameters.html#aabfee3d6757a2269d7bbbbd3ebc20fb1" title="Key of parameter Bayes/PredictionLC : &quot;Prediction of loop closures (Gaussian-like,...">kBayesPredictionLC()</a>.c_str()).c_str() Default value: true ( bool ). </p>
+<p>Key of parameter Bayes/SparsePrediction : uFormat("Use a sparse representation of the prediction instead of a dense matrix, which significantly reduces memory usage and processing time on large maps. Ignored when the values of %s sum to less than 1, as the prediction is then not sparse.", <a class="el" href="classrtabmap_1_1Parameters.html#aabfee3d6757a2269d7bbbbd3ebc20fb1" title="Key of parameter Bayes/PredictionLC : &quot;Prediction of loop closures (Gaussian-like,...">kBayesPredictionLC()</a>.c_str()).c_str() Default value: true ( bool ). </p>
 
 <p class="definition">Definition at line <a class="el" href="Parameters_8h_source.html#l00393">393</a> of file <a class="el" href="Parameters_8h_source.html">Parameters.h</a>.</p>
 

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@@ -571,7 +571,7 @@ Bayes</h1>
 <tr class="markdownTableRowOdd">
 <td class="markdownTableBodyNone"><a class="anchor" id="param_BayesFullPredictionUpdate"></a><a class="el" href="classrtabmap_1_1Parameters.html#a08f0a214eb15975386b5d06be56e38b4">Bayes/FullPredictionUpdate</a>   </td><td class="markdownTableBodyNone">bool   </td><td class="markdownTableBodyNone"><code>false</code>   </td><td class="markdownTableBodyNone">Regenerate all the prediction matrix on each iteration (otherwise only removed/added ids are updated).    </td></tr>
 <tr class="markdownTableRowEven">
-<td class="markdownTableBodyNone"><a class="anchor" id="param_BayesSparsePrediction"></a><a class="el" href="classrtabmap_1_1Parameters.html#ae8141b0f2cc33d7e47b8a5f4fc558bc1">Bayes/SparsePrediction</a>   </td><td class="markdownTableBodyNone">bool   </td><td class="markdownTableBodyNone"><code>true</code>   </td><td class="markdownTableBodyNone">Multiply the prediction matrix with the last posterior using a sparse (compressed sparse row) view of the prediction instead of a dense matrix multiplication. A column of the prediction matrix only holds the neighbors within the depth of <a class="el" href="parameters.html#param_BayesPredictionLC">Bayes/PredictionLC</a>, so on a large map the matrix is mostly zeros and the dense multiplication spends all of its time reading them. The sparse view is rebuilt only when the prediction matrix changes, thus it costs nothing in localization mode over a fixed graph. Ignored (the dense multiplication is used) when the values of <a class="el" href="parameters.html#param_BayesPredictionLC">Bayes/PredictionLC</a> sum to less than 1, as the missing probability is then spread over every zero of each column and the matrix is no longer sparse.   </td></tr>
+<td class="markdownTableBodyNone"><a class="anchor" id="param_BayesSparsePrediction"></a><a class="el" href="classrtabmap_1_1Parameters.html#ae8141b0f2cc33d7e47b8a5f4fc558bc1">Bayes/SparsePrediction</a>   </td><td class="markdownTableBodyNone">bool   </td><td class="markdownTableBodyNone"><code>true</code>   </td><td class="markdownTableBodyNone">Use a sparse representation of the prediction instead of a dense matrix, which significantly reduces memory usage and processing time on large maps. Ignored when the values of <a class="el" href="parameters.html#param_BayesPredictionLC">Bayes/PredictionLC</a> sum to less than 1, as the prediction is then not sparse.   </td></tr>
 </table>
 <p><a class="anchor" id="parameters_VhEp"></a></p>
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