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Preview for PR #1748 bc3753bf036c2b3622aa1043ef6fab9f275719db

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<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>
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<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>
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