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Preview for PR #1748 9fec2bd86e
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@@ -180,42 +180,42 @@ $(document).ready(function(){initNavTree('BayesFilter_8h_source.html',''); initR
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<div class="line"><a id="l00144" name="l00144"></a><span class="lineno"> 144</span> </div>
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<div class="line"><a id="l00149" name="l00149"></a><span class="lineno"><a class="line" href="classrtabmap_1_1BayesFilter.html#a1f479b2d242f05eef69532f454dc851f"> 149</a></span> <span class="keywordtype">float</span> <a class="code hl_function" href="classrtabmap_1_1BayesFilter.html#a1f479b2d242f05eef69532f454dc851f">getVirtualPlacePrior</a>() <span class="keyword">const</span>;</div>
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<div class="line"><a id="l00150" name="l00150"></a><span class="lineno"> 150</span> </div>
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<div class="line"><a id="l00155" name="l00155"></a><span class="lineno"><a class="line" href="classrtabmap_1_1BayesFilter.html#ab13c8acecfbb4fcfff4e809e2b0a7538"> 155</a></span> <span class="keyword">const</span> std::vector<double> & <a class="code hl_function" href="classrtabmap_1_1BayesFilter.html#ab13c8acecfbb4fcfff4e809e2b0a7538">getPredictionLC</a>() <span class="keyword">const</span>;</div>
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<div class="line"><a id="l00156" name="l00156"></a><span class="lineno"> 156</span> </div>
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<div class="line"><a id="l00161" name="l00161"></a><span class="lineno"><a class="line" href="classrtabmap_1_1BayesFilter.html#a18a6d97fe3088a4a356a85358f641376"> 161</a></span> std::string <a class="code hl_function" href="classrtabmap_1_1BayesFilter.html#a18a6d97fe3088a4a356a85358f641376">getPredictionLCStr</a>() <span class="keyword">const</span>;</div>
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<div class="line"><a id="l00162" name="l00162"></a><span class="lineno"> 162</span> </div>
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<div class="line"><a id="l00176" name="l00176"></a><span class="lineno"><a class="line" href="classrtabmap_1_1BayesFilter.html#a489031236b91dbaba0ff192f90799cd4"> 176</a></span> cv::Mat <a class="code hl_function" href="classrtabmap_1_1BayesFilter.html#a489031236b91dbaba0ff192f90799cd4">generatePrediction</a>(<span class="keyword">const</span> <a class="code hl_class" href="classrtabmap_1_1Memory.html">Memory</a> * memory, <span class="keyword">const</span> std::vector<int> & ids);</div>
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<div class="line"><a id="l00177" name="l00177"></a><span class="lineno"> 177</span> </div>
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<div class="line"><a id="l00182" name="l00182"></a><span class="lineno"><a class="line" href="classrtabmap_1_1BayesFilter.html#a60ad13fd18ac4e595359f3c86c430a80"> 182</a></span> <span class="keywordtype">unsigned</span> <span class="keywordtype">long</span> <a class="code hl_function" href="classrtabmap_1_1BayesFilter.html#a60ad13fd18ac4e595359f3c86c430a80">getMemoryUsed</a>() <span class="keyword">const</span>;</div>
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<div class="line"><a id="l00183" name="l00183"></a><span class="lineno"> 183</span> </div>
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<div class="line"><a id="l00184" name="l00184"></a><span class="lineno"> 184</span><span class="keyword">private</span>:</div>
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<div class="line"><a id="l00188" name="l00188"></a><span class="lineno"> 188</span> <span class="keywordtype">bool</span> posteriorHasSameIds(<span class="keyword">const</span> std::vector<int> & ids) <span class="keyword">const</span>;</div>
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<div class="line"><a id="l00189" name="l00189"></a><span class="lineno"> 189</span> </div>
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<div class="line"><a id="l00195" name="l00195"></a><span class="lineno"> 195</span> <span class="keywordtype">void</span> updatePosterior(<span class="keyword">const</span> <a class="code hl_class" href="classrtabmap_1_1Memory.html">Memory</a> * memory, <span class="keyword">const</span> std::map<int, float> & likelihood);</div>
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<div class="line"><a id="l00196" name="l00196"></a><span class="lineno"> 196</span> </div>
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<div class="line"><a id="l00197" name="l00197"></a><span class="lineno"> 197</span><span class="keyword">private</span>:</div>
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<div class="line"><a id="l00198" name="l00198"></a><span class="lineno"> 198</span> std::vector<int> _posteriorIds; </div>
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<div class="line"><a id="l00199" name="l00199"></a><span class="lineno"> 199</span> std::vector<float> _posteriorValues; </div>
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<div class="line"><a id="l00200" name="l00200"></a><span class="lineno"> 200</span> std::vector<int> _likelihoodIds; </div>
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<div class="line"><a id="l00201" name="l00201"></a><span class="lineno"> 201</span> std::vector<float> _likelihoodValues; </div>
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<div class="line"><a id="l00202" name="l00202"></a><span class="lineno"> 202</span> std::vector<float> _priorValues; </div>
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<div class="line"><a id="l00203" name="l00203"></a><span class="lineno"> 203</span> </div>
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<div class="line"><a id="l00204" name="l00204"></a><span class="lineno"> 204</span> bayes::PredictionModel * _model; </div>
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<div class="line"><a id="l00205" name="l00205"></a><span class="lineno"> 205</span> bayes::DensePrediction * _dense; </div>
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<div class="line"><a id="l00206" name="l00206"></a><span class="lineno"> 206</span> bayes::SparsePrediction * _sparse; </div>
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<div class="line"><a id="l00207" name="l00207"></a><span class="lineno"> 207</span> std::map<int, std::map<int, int> > _neighborsIndex; </div>
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<div class="line"><a id="l00157" name="l00157"></a><span class="lineno"><a class="line" href="classrtabmap_1_1BayesFilter.html#a8d65b71c1a2230fdefae41d90f73c0d5"> 157</a></span> <span class="keywordtype">bool</span> <a class="code hl_function" href="classrtabmap_1_1BayesFilter.html#a8d65b71c1a2230fdefae41d90f73c0d5">isPredictionSparse</a>() <span class="keyword">const</span>;</div>
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<div class="line"><a id="l00158" name="l00158"></a><span class="lineno"> 158</span> </div>
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<div class="line"><a id="l00163" name="l00163"></a><span class="lineno"><a class="line" href="classrtabmap_1_1BayesFilter.html#ab13c8acecfbb4fcfff4e809e2b0a7538"> 163</a></span> <span class="keyword">const</span> std::vector<double> & <a class="code hl_function" href="classrtabmap_1_1BayesFilter.html#ab13c8acecfbb4fcfff4e809e2b0a7538">getPredictionLC</a>() <span class="keyword">const</span>;</div>
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<div class="line"><a id="l00164" name="l00164"></a><span class="lineno"> 164</span> </div>
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<div class="line"><a id="l00169" name="l00169"></a><span class="lineno"><a class="line" href="classrtabmap_1_1BayesFilter.html#a18a6d97fe3088a4a356a85358f641376"> 169</a></span> std::string <a class="code hl_function" href="classrtabmap_1_1BayesFilter.html#a18a6d97fe3088a4a356a85358f641376">getPredictionLCStr</a>() <span class="keyword">const</span>;</div>
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<div class="line"><a id="l00170" name="l00170"></a><span class="lineno"> 170</span> </div>
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<div class="line"><a id="l00186" name="l00186"></a><span class="lineno"><a class="line" href="classrtabmap_1_1BayesFilter.html#a489031236b91dbaba0ff192f90799cd4"> 186</a></span> cv::Mat <a class="code hl_function" href="classrtabmap_1_1BayesFilter.html#a489031236b91dbaba0ff192f90799cd4">generatePrediction</a>(<span class="keyword">const</span> <a class="code hl_class" href="classrtabmap_1_1Memory.html">Memory</a> * memory, <span class="keyword">const</span> std::vector<int> & ids);</div>
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<div class="line"><a id="l00187" name="l00187"></a><span class="lineno"> 187</span> </div>
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<div class="line"><a id="l00192" name="l00192"></a><span class="lineno"><a class="line" href="classrtabmap_1_1BayesFilter.html#a60ad13fd18ac4e595359f3c86c430a80"> 192</a></span> <span class="keywordtype">unsigned</span> <span class="keywordtype">long</span> <a class="code hl_function" href="classrtabmap_1_1BayesFilter.html#a60ad13fd18ac4e595359f3c86c430a80">getMemoryUsed</a>() <span class="keyword">const</span>;</div>
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<div class="line"><a id="l00193" name="l00193"></a><span class="lineno"> 193</span> </div>
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<div class="line"><a id="l00194" name="l00194"></a><span class="lineno"> 194</span><span class="keyword">private</span>:</div>
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<div class="line"><a id="l00200" name="l00200"></a><span class="lineno"> 200</span> <span class="keywordtype">void</span> updatePosterior(<span class="keyword">const</span> <a class="code hl_class" href="classrtabmap_1_1Memory.html">Memory</a> * memory, <span class="keyword">const</span> std::map<int, float> & likelihood);</div>
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<div class="line"><a id="l00201" name="l00201"></a><span class="lineno"> 201</span> </div>
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<div class="line"><a id="l00202" name="l00202"></a><span class="lineno"> 202</span><span class="keyword">private</span>:</div>
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<div class="line"><a id="l00203" name="l00203"></a><span class="lineno"> 203</span> std::vector<int> _posteriorIds; </div>
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<div class="line"><a id="l00204" name="l00204"></a><span class="lineno"> 204</span> std::vector<float> _posteriorValues; </div>
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<div class="line"><a id="l00205" name="l00205"></a><span class="lineno"> 205</span> std::vector<int> _likelihoodIds; </div>
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<div class="line"><a id="l00206" name="l00206"></a><span class="lineno"> 206</span> std::vector<float> _likelihoodValues; </div>
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<div class="line"><a id="l00207" name="l00207"></a><span class="lineno"> 207</span> std::vector<float> _priorValues; </div>
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<div class="line"><a id="l00208" name="l00208"></a><span class="lineno"> 208</span> </div>
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<div class="line"><a id="l00209" name="l00209"></a><span class="lineno"> 209</span> <span class="keywordtype">bool</span> _fullPredictionUpdate; </div>
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<div class="line"><a id="l00210" name="l00210"></a><span class="lineno"> 210</span> <span class="keywordtype">bool</span> _sparsePrediction; </div>
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<div class="line"><a id="l00211" name="l00211"></a><span class="lineno"> 211</span> <span class="keywordtype">bool</span> _predictionChanged; </div>
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<div class="line"><a id="l00212" name="l00212"></a><span class="lineno"> 212</span> <span class="keywordtype">bool</span> _sparsePredictionRejected; </div>
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<div class="line"><a id="l00213" name="l00213"></a><span class="lineno"> 213</span>};</div>
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<div class="line"><a id="l00209" name="l00209"></a><span class="lineno"> 209</span> bayes::PredictionModel * _model; </div>
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<div class="line"><a id="l00210" name="l00210"></a><span class="lineno"> 210</span> bayes::DensePrediction * _dense; </div>
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<div class="line"><a id="l00211" name="l00211"></a><span class="lineno"> 211</span> bayes::SparsePrediction * _sparse; </div>
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<div class="line"><a id="l00212" name="l00212"></a><span class="lineno"> 212</span> std::map<int, std::map<int, int> > _neighborsIndex; </div>
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<div class="line"><a id="l00213" name="l00213"></a><span class="lineno"> 213</span> </div>
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<div class="line"><a id="l00214" name="l00214"></a><span class="lineno"> 214</span> <span class="keywordtype">bool</span> _fullPredictionUpdate; </div>
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<div class="line"><a id="l00215" name="l00215"></a><span class="lineno"> 215</span> <span class="keywordtype">bool</span> _sparsePrediction; </div>
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<div class="line"><a id="l00216" name="l00216"></a><span class="lineno"> 216</span> <span class="keywordtype">bool</span> _predictionChanged; </div>
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<div class="line"><a id="l00217" name="l00217"></a><span class="lineno"> 217</span> <span class="keywordtype">bool</span> _sparsePredictionRejected; </div>
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<div class="line"><a id="l00218" name="l00218"></a><span class="lineno"> 218</span>};</div>
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</div>
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<div class="line"><a id="l00214" name="l00214"></a><span class="lineno"> 214</span> </div>
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<div class="line"><a id="l00215" name="l00215"></a><span class="lineno"> 215</span>} <span class="comment">// namespace rtabmap</span></div>
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<div class="line"><a id="l00219" name="l00219"></a><span class="lineno"> 219</span> </div>
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<div class="line"><a id="l00220" name="l00220"></a><span class="lineno"> 220</span>} <span class="comment">// namespace rtabmap</span></div>
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</div>
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<div class="line"><a id="l00216" name="l00216"></a><span class="lineno"> 216</span> </div>
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<div class="line"><a id="l00217" name="l00217"></a><span class="lineno"> 217</span><span class="preprocessor">#endif </span><span class="comment">/* BAYESFILTER_H_ */</span><span class="preprocessor"></span></div>
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<div class="line"><a id="l00221" name="l00221"></a><span class="lineno"> 221</span> </div>
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<div class="line"><a id="l00222" name="l00222"></a><span class="lineno"> 222</span><span class="preprocessor">#endif </span><span class="comment">/* BAYESFILTER_H_ */</span><span class="preprocessor"></span></div>
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<div class="ttc" id="aclassrtabmap_1_1BayesFilter_html"><div class="ttname"><a href="classrtabmap_1_1BayesFilter.html">rtabmap::BayesFilter</a></div><div class="ttdoc">Recursive Bayesian filter for loop-closure hypothesis estimation in RTAB-Map.</div><div class="ttdef"><b>Definition</b> <a href="BayesFilter_8h_source.html#l00081">BayesFilter.h:82</a></div></div>
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<div class="ttc" id="aclassrtabmap_1_1BayesFilter_html_a18a6d97fe3088a4a356a85358f641376"><div class="ttname"><a href="classrtabmap_1_1BayesFilter.html#a18a6d97fe3088a4a356a85358f641376">rtabmap::BayesFilter::getPredictionLCStr</a></div><div class="ttdeci">std::string getPredictionLCStr() const</div><div class="ttdoc">Returns the loop-closure prediction model as a space-separated string.</div></div>
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<div class="ttc" id="aclassrtabmap_1_1BayesFilter_html_a1f479b2d242f05eef69532f454dc851f"><div class="ttname"><a href="classrtabmap_1_1BayesFilter.html#a1f479b2d242f05eef69532f454dc851f">rtabmap::BayesFilter::getVirtualPlacePrior</a></div><div class="ttdeci">float getVirtualPlacePrior() const</div><div class="ttdoc">Returns the virtual place prior threshold.</div></div>
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@@ -225,6 +225,7 @@ $(document).ready(function(){initNavTree('BayesFilter_8h_source.html',''); initR
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<div class="ttc" id="aclassrtabmap_1_1BayesFilter_html_a4b476a9fa8b1c67562be4134c936081c"><div class="ttname"><a href="classrtabmap_1_1BayesFilter.html#a4b476a9fa8b1c67562be4134c936081c">rtabmap::BayesFilter::getPosteriorIds</a></div><div class="ttdeci">const std::vector< int > & getPosteriorIds() const</div><div class="ttdoc">The locations the posterior is over, ascending by id.</div><div class="ttdef"><b>Definition</b> <a href="BayesFilter_8h_source.html#l00138">BayesFilter.h:138</a></div></div>
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<div class="ttc" id="aclassrtabmap_1_1BayesFilter_html_a60ad13fd18ac4e595359f3c86c430a80"><div class="ttname"><a href="classrtabmap_1_1BayesFilter.html#a60ad13fd18ac4e595359f3c86c430a80">rtabmap::BayesFilter::getMemoryUsed</a></div><div class="ttdeci">unsigned long getMemoryUsed() const</div><div class="ttdoc">Estimates memory usage of this object and its internal containers.</div></div>
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<div class="ttc" id="aclassrtabmap_1_1BayesFilter_html_a6d7d48b32c853f46eee9290d5c82cd28"><div class="ttname"><a href="classrtabmap_1_1BayesFilter.html#a6d7d48b32c853f46eee9290d5c82cd28">rtabmap::BayesFilter::parseParameters</a></div><div class="ttdeci">virtual void parseParameters(const ParametersMap &parameters)</div><div class="ttdoc">Updates internal settings from the parameter map.</div></div>
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<div class="ttc" id="aclassrtabmap_1_1BayesFilter_html_a8d65b71c1a2230fdefae41d90f73c0d5"><div class="ttname"><a href="classrtabmap_1_1BayesFilter.html#a8d65b71c1a2230fdefae41d90f73c0d5">rtabmap::BayesFilter::isPredictionSparse</a></div><div class="ttdeci">bool isPredictionSparse() const</div><div class="ttdoc">Whether the prediction is being kept in its sparse form rather than as a matrix.</div></div>
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<div class="ttc" id="aclassrtabmap_1_1BayesFilter_html_aa3ec78130d67d9cca8c2f11c31bc28d2"><div class="ttname"><a href="classrtabmap_1_1BayesFilter.html#aa3ec78130d67d9cca8c2f11c31bc28d2">rtabmap::BayesFilter::computePosterior</a></div><div class="ttdeci">bool computePosterior(const Memory *memory, const std::map< int, float > &likelihood)</div><div class="ttdoc">Runs one Bayes filter iteration (prediction + update).</div></div>
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<div class="ttc" id="aclassrtabmap_1_1BayesFilter_html_aaebf1bee8c904dabdf1d8ea0f446c143"><div class="ttname"><a href="classrtabmap_1_1BayesFilter.html#aaebf1bee8c904dabdf1d8ea0f446c143">rtabmap::BayesFilter::getPosteriorValues</a></div><div class="ttdeci">const std::vector< float > & getPosteriorValues() const</div><div class="ttdoc">The probability of each location of getPosteriorIds(), in the same order.</div><div class="ttdef"><b>Definition</b> <a href="BayesFilter_8h_source.html#l00143">BayesFilter.h:143</a></div></div>
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<div class="ttc" id="aclassrtabmap_1_1BayesFilter_html_ab13c8acecfbb4fcfff4e809e2b0a7538"><div class="ttname"><a href="classrtabmap_1_1BayesFilter.html#ab13c8acecfbb4fcfff4e809e2b0a7538">rtabmap::BayesFilter::getPredictionLC</a></div><div class="ttdeci">const std::vector< double > & getPredictionLC() const</div><div class="ttdoc">Returns the loop-closure prediction model as a vector of values.</div></div>
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@@ -415,7 +415,7 @@ $(document).ready(function(){initNavTree('Parameters_8h_source.html',''); initRe
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<div class="line"><a id="l00388" name="l00388"></a><span class="lineno"> 388</span> </div>
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<div class="line"><a id="l00389" name="l00389"></a><span class="lineno"> 389</span> <span class="comment">// BayesFilter</span></div>
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<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">"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)."</span>);</div>
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<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">"0.1 0.36 0.30 0.16 0.062 0.0151 0.00255 0.000324 2.5e-05 1.3e-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"</span>, <span class="stringliteral">"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."</span>);</div>
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<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">"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"</span>, <span class="stringliteral">"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."</span>);</div>
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<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">"Regenerate all the prediction matrix on each iteration (otherwise only removed/added ids are updated)."</span>);</div>
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<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">"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."</span>, kBayesPredictionLC().c_str(), kBayesPredictionLC().c_str()).c_str());</div>
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<div class="line"><a id="l00394" name="l00394"></a><span class="lineno"> 394</span> </div>
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@@ -113,10 +113,11 @@ $(document).ready(function(){initNavTree('classrtabmap_1_1BayesFilter.html','');
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||||
<tr class="even"><td class="entry"><a class="el" href="classrtabmap_1_1BayesFilter.html#ab13c8acecfbb4fcfff4e809e2b0a7538">getPredictionLC</a>() const</td><td class="entry"><a class="el" href="classrtabmap_1_1BayesFilter.html">rtabmap::BayesFilter</a></td><td class="entry"></td></tr>
|
||||
<tr class="odd"><td class="entry"><a class="el" href="classrtabmap_1_1BayesFilter.html#a18a6d97fe3088a4a356a85358f641376">getPredictionLCStr</a>() const</td><td class="entry"><a class="el" href="classrtabmap_1_1BayesFilter.html">rtabmap::BayesFilter</a></td><td class="entry"></td></tr>
|
||||
<tr class="even"><td class="entry"><a class="el" href="classrtabmap_1_1BayesFilter.html#a1f479b2d242f05eef69532f454dc851f">getVirtualPlacePrior</a>() const</td><td class="entry"><a class="el" href="classrtabmap_1_1BayesFilter.html">rtabmap::BayesFilter</a></td><td class="entry"></td></tr>
|
||||
<tr class="odd"><td class="entry"><a class="el" href="classrtabmap_1_1BayesFilter.html#a6d7d48b32c853f46eee9290d5c82cd28">parseParameters</a>(const ParametersMap &parameters)</td><td class="entry"><a class="el" href="classrtabmap_1_1BayesFilter.html">rtabmap::BayesFilter</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
|
||||
<tr class="even"><td class="entry"><a class="el" href="classrtabmap_1_1BayesFilter.html#ad415ee334d31eebc3685e61862f32573">reset</a>()</td><td class="entry"><a class="el" href="classrtabmap_1_1BayesFilter.html">rtabmap::BayesFilter</a></td><td class="entry"></td></tr>
|
||||
<tr class="odd"><td class="entry"><a class="el" href="classrtabmap_1_1BayesFilter.html#a3255a54c1d2fc5b9a6db91fb38d591ae">setPredictionLC</a>(const std::string &prediction)</td><td class="entry"><a class="el" href="classrtabmap_1_1BayesFilter.html">rtabmap::BayesFilter</a></td><td class="entry"></td></tr>
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<tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>~BayesFilter</b>() (defined in <a class="el" href="classrtabmap_1_1BayesFilter.html">rtabmap::BayesFilter</a>)</td><td class="entry"><a class="el" href="classrtabmap_1_1BayesFilter.html">rtabmap::BayesFilter</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
|
||||
<tr class="odd"><td class="entry"><a class="el" href="classrtabmap_1_1BayesFilter.html#a8d65b71c1a2230fdefae41d90f73c0d5">isPredictionSparse</a>() const</td><td class="entry"><a class="el" href="classrtabmap_1_1BayesFilter.html">rtabmap::BayesFilter</a></td><td class="entry"></td></tr>
|
||||
<tr class="even"><td class="entry"><a class="el" href="classrtabmap_1_1BayesFilter.html#a6d7d48b32c853f46eee9290d5c82cd28">parseParameters</a>(const ParametersMap &parameters)</td><td class="entry"><a class="el" href="classrtabmap_1_1BayesFilter.html">rtabmap::BayesFilter</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
|
||||
<tr class="odd"><td class="entry"><a class="el" href="classrtabmap_1_1BayesFilter.html#ad415ee334d31eebc3685e61862f32573">reset</a>()</td><td class="entry"><a class="el" href="classrtabmap_1_1BayesFilter.html">rtabmap::BayesFilter</a></td><td class="entry"></td></tr>
|
||||
<tr class="even"><td class="entry"><a class="el" href="classrtabmap_1_1BayesFilter.html#a3255a54c1d2fc5b9a6db91fb38d591ae">setPredictionLC</a>(const std::string &prediction)</td><td class="entry"><a class="el" href="classrtabmap_1_1BayesFilter.html">rtabmap::BayesFilter</a></td><td class="entry"></td></tr>
|
||||
<tr bgcolor="#f0f0f0" class="odd"><td class="entry"><b>~BayesFilter</b>() (defined in <a class="el" href="classrtabmap_1_1BayesFilter.html">rtabmap::BayesFilter</a>)</td><td class="entry"><a class="el" href="classrtabmap_1_1BayesFilter.html">rtabmap::BayesFilter</a></td><td class="entry"><span class="mlabel">virtual</span></td></tr>
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</table></div><!-- contents -->
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</div><!-- doc-content -->
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<!-- start footer part -->
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@@ -137,6 +137,9 @@ void </td><td class="memItemRight" valign="bottom"><b>reset</b> ()</td></tr
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<tr class="memitem:a1f479b2d242f05eef69532f454dc851f" id="r_a1f479b2d242f05eef69532f454dc851f"><td class="memItemLeft" align="right" valign="top">float </td><td class="memItemRight" valign="bottom"><a class="el" href="classrtabmap_1_1BayesFilter.html#a1f479b2d242f05eef69532f454dc851f">getVirtualPlacePrior</a> () const</td></tr>
|
||||
<tr class="memdesc:a1f479b2d242f05eef69532f454dc851f"><td class="mdescLeft"> </td><td class="mdescRight">Returns the virtual place prior threshold. <br /></td></tr>
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<tr class="separator:a1f479b2d242f05eef69532f454dc851f"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a8d65b71c1a2230fdefae41d90f73c0d5" id="r_a8d65b71c1a2230fdefae41d90f73c0d5"><td class="memItemLeft" align="right" valign="top">bool </td><td class="memItemRight" valign="bottom"><a class="el" href="classrtabmap_1_1BayesFilter.html#a8d65b71c1a2230fdefae41d90f73c0d5">isPredictionSparse</a> () const</td></tr>
|
||||
<tr class="memdesc:a8d65b71c1a2230fdefae41d90f73c0d5"><td class="mdescLeft"> </td><td class="mdescRight">Whether the prediction is being kept in its sparse form rather than as a matrix. <br /></td></tr>
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<tr class="separator:a8d65b71c1a2230fdefae41d90f73c0d5"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:ab13c8acecfbb4fcfff4e809e2b0a7538" id="r_ab13c8acecfbb4fcfff4e809e2b0a7538"><td class="memItemLeft" align="right" valign="top">const std::vector< double > & </td><td class="memItemRight" valign="bottom"><a class="el" href="classrtabmap_1_1BayesFilter.html#ab13c8acecfbb4fcfff4e809e2b0a7538">getPredictionLC</a> () const</td></tr>
|
||||
<tr class="memdesc:ab13c8acecfbb4fcfff4e809e2b0a7538"><td class="mdescLeft"> </td><td class="mdescRight">Returns the loop-closure prediction model as a vector of values. <br /></td></tr>
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<tr class="separator:ab13c8acecfbb4fcfff4e809e2b0a7538"><td class="memSeparator" colspan="2"> </td></tr>
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@@ -380,6 +383,26 @@ void </td><td class="memItemRight" valign="bottom"><b>reset</b> ()</td></tr
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||||
<p>Returns the virtual place prior threshold. </p>
|
||||
<dl class="section return"><dt>Returns</dt><dd>Value in [0, 1] used when building the virtual place row of the prediction matrix. </dd></dl>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
<a id="a8d65b71c1a2230fdefae41d90f73c0d5" name="a8d65b71c1a2230fdefae41d90f73c0d5"></a>
|
||||
<h2 class="memtitle"><span class="permalink"><a href="#a8d65b71c1a2230fdefae41d90f73c0d5">◆ </a></span>isPredictionSparse()</h2>
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<div class="memitem">
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<div class="memproto">
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<table class="memname">
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||||
<tr>
|
||||
<td class="memname">bool rtabmap::BayesFilter::isPredictionSparse </td>
|
||||
<td>(</td>
|
||||
<td class="paramname"></td><td>)</td>
|
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<td> const</td>
|
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</tr>
|
||||
</table>
|
||||
</div><div class="memdoc">
|
||||
|
||||
<p>Whether the prediction is being kept in its sparse form rather than as a matrix. </p>
|
||||
<p>False when <a class="el" href="classrtabmap_1_1Parameters.html#ae8141b0f2cc33d7e47b8a5f4fc558bc1">Parameters::kBayesSparsePrediction()</a> is disabled, and while it is enabled over a prediction measured as too dense for the sparse form to be worth it. </p>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
<a id="ab13c8acecfbb4fcfff4e809e2b0a7538" name="ab13c8acecfbb4fcfff4e809e2b0a7538"></a>
|
||||
@@ -450,6 +473,7 @@ void </td><td class="memItemRight" valign="bottom"><b>reset</b> ()</td></tr
|
||||
|
||||
<p>Builds or updates the prediction (transition) matrix for the given signature ids. </p>
|
||||
<p>Rows and columns correspond to <code>ids</code>. Neighbor links are queried from <a class="el" href="classrtabmap_1_1Memory.html">Memory</a> to fill transition probabilities according to <a class="el" href="classrtabmap_1_1BayesFilter.html#ab13c8acecfbb4fcfff4e809e2b0a7538">getPredictionLC()</a>. When <code>ids</code> match the current posterior keys, the cached matrix may be returned without recomputation.</p>
|
||||
<p>When the prediction is being kept sparse, the matrix is expanded from it rather than kept: it costs the memory that keeping the prediction sparse is saving, so ask for it to read, dump or compare the prediction, not on every iteration.</p>
|
||||
<dl class="params"><dt>Parameters</dt><dd>
|
||||
<table class="params">
|
||||
<tr><td class="paramname">memory</td><td>Working memory instance (must not be null). </td></tr>
|
||||
@@ -457,7 +481,7 @@ void </td><td class="memItemRight" valign="bottom"><b>reset</b> ()</td></tr
|
||||
</table>
|
||||
</dd>
|
||||
</dl>
|
||||
<dl class="section return"><dt>Returns</dt><dd>Square CV_32FC1 matrix of size ids.size() × ids.size(), or an empty matrix when <a class="el" href="classrtabmap_1_1Parameters.html#ae8141b0f2cc33d7e47b8a5f4fc558bc1">Parameters::kBayesSparsePrediction()</a> is enabled and the prediction is being kept in its sparse form, in which case no matrix exists to return. </dd></dl>
|
||||
<dl class="section return"><dt>Returns</dt><dd>Square CV_32FC1 matrix of size ids.size() × ids.size(). </dd></dl>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -8,6 +8,7 @@ var classrtabmap_1_1BayesFilter =
|
||||
[ "getPosteriorIds", "classrtabmap_1_1BayesFilter.html#a4b476a9fa8b1c67562be4134c936081c", null ],
|
||||
[ "getPosteriorValues", "classrtabmap_1_1BayesFilter.html#aaebf1bee8c904dabdf1d8ea0f446c143", null ],
|
||||
[ "getVirtualPlacePrior", "classrtabmap_1_1BayesFilter.html#a1f479b2d242f05eef69532f454dc851f", null ],
|
||||
[ "isPredictionSparse", "classrtabmap_1_1BayesFilter.html#a8d65b71c1a2230fdefae41d90f73c0d5", null ],
|
||||
[ "getPredictionLC", "classrtabmap_1_1BayesFilter.html#ab13c8acecfbb4fcfff4e809e2b0a7538", null ],
|
||||
[ "getPredictionLCStr", "classrtabmap_1_1BayesFilter.html#a18a6d97fe3088a4a356a85358f641376", null ],
|
||||
[ "generatePrediction", "classrtabmap_1_1BayesFilter.html#a489031236b91dbaba0ff192f90799cd4", null ],
|
||||
|
||||
@@ -1569,10 +1569,10 @@ Static Public Member Functions</h2></td></tr>
|
||||
<tr class="memdesc:adf0155fc5e015ad6504d3772243f325b"><td class="mdescLeft"> </td><td class="mdescRight">Type of parameter Bayes/VirtualPlacePriorThr , as a string: float . <br /></td></tr>
|
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<tr class="separator:adf0155fc5e015ad6504d3772243f325b"><td class="memSeparator" colspan="2"> </td></tr>
|
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<tr class="memitem:aabfee3d6757a2269d7bbbbd3ebc20fb1" id="r_aabfee3d6757a2269d7bbbbd3ebc20fb1"><td class="memItemLeft" align="right" valign="top">static std::string </td><td class="memItemRight" valign="bottom"><a class="el" href="classrtabmap_1_1Parameters.html#aabfee3d6757a2269d7bbbbd3ebc20fb1">kBayesPredictionLC</a> ()</td></tr>
|
||||
<tr class="memdesc:aabfee3d6757a2269d7bbbbd3ebc20fb1"><td class="mdescLeft"> </td><td class="mdescRight">Key of parameter Bayes/PredictionLC : "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." Default value: "0.1 0.36 0.30 0.16 0.062 0.0151 0.00255 0.000324 2.5e-05 1.3e-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" (string). <br /></td></tr>
|
||||
<tr class="memdesc:aabfee3d6757a2269d7bbbbd3ebc20fb1"><td class="mdescLeft"> </td><td class="mdescRight">Key of parameter Bayes/PredictionLC : "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." Default value: "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" (string). <br /></td></tr>
|
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<tr class="separator:aabfee3d6757a2269d7bbbbd3ebc20fb1"><td class="memSeparator" colspan="2"> </td></tr>
|
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<tr class="memitem:a4c911439d8366cb6a15cc106d051ba70" id="r_a4c911439d8366cb6a15cc106d051ba70"><td class="memItemLeft" align="right" valign="top">static std::string </td><td class="memItemRight" valign="bottom"><a class="el" href="classrtabmap_1_1Parameters.html#a4c911439d8366cb6a15cc106d051ba70">defaultBayesPredictionLC</a> ()</td></tr>
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||||
<tr class="memdesc:a4c911439d8366cb6a15cc106d051ba70"><td class="mdescLeft"> </td><td class="mdescRight">Default value of parameter Bayes/PredictionLC : "0.1 0.36 0.30 0.16 0.062 0.0151 0.00255 0.000324 2.5e-05 1.3e-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" . <br /></td></tr>
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||||
<tr class="memdesc:a4c911439d8366cb6a15cc106d051ba70"><td class="mdescLeft"> </td><td class="mdescRight">Default value of parameter Bayes/PredictionLC : "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" . <br /></td></tr>
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<tr class="separator:a4c911439d8366cb6a15cc106d051ba70"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:acea3564948945be8a1d5d3627b33bcb0" id="r_acea3564948945be8a1d5d3627b33bcb0"><td class="memItemLeft" align="right" valign="top">static std::string </td><td class="memItemRight" valign="bottom"><a class="el" href="classrtabmap_1_1Parameters.html#acea3564948945be8a1d5d3627b33bcb0">typeBayesPredictionLC</a> ()</td></tr>
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||||
<tr class="memdesc:acea3564948945be8a1d5d3627b33bcb0"><td class="mdescLeft"> </td><td class="mdescRight">Type of parameter Bayes/PredictionLC , as a string: string. <br /></td></tr>
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@@ -19859,7 +19859,7 @@ The <a class="el" href="parameters.html">Parameter reference</a> page, which lis
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</div><div class="memdoc">
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<p>Key of parameter Bayes/PredictionLC : "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." Default value: "0.1 0.36 0.30 0.16 0.062 0.0151 0.00255 0.000324 2.5e-05 1.3e-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" (string). </p>
|
||||
<p>Key of parameter Bayes/PredictionLC : "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." Default value: "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" (string). </p>
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<p class="definition">Definition at line <a class="el" href="Parameters_8h_source.html#l00391">391</a> of file <a class="el" href="Parameters_8h_source.html">Parameters.h</a>.</p>
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<p>Default value of parameter Bayes/PredictionLC : "0.1 0.36 0.30 0.16 0.062 0.0151 0.00255 0.000324 2.5e-05 1.3e-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" . </p>
|
||||
<p>Default value of parameter Bayes/PredictionLC : "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" . </p>
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<p class="definition">Definition at line <a class="el" href="Parameters_8h_source.html#l00391">391</a> of file <a class="el" href="Parameters_8h_source.html">Parameters.h</a>.</p>
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@@ -167,6 +167,7 @@ $(document).ready(function(){initNavTree('functions_func_i.html',''); initResiza
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<li>isOdomGravityUsed() : <a class="el" href="classrtabmap_1_1Memory.html#ab29b6d90e79694d7fdfb56574476141f">rtabmap::Memory</a></li>
|
||||
<li>isPaused() : <a class="el" href="classrtabmap_1_1SensorCaptureThread.html#a435f89b6a3186462f6ee860afa88644c">rtabmap::SensorCaptureThread</a></li>
|
||||
<li>isPointVisibleFromCameras() : <a class="el" href="classrtabmap_1_1SensorData.html#a30f069ee8ffa263ec2c18bc714713e03">rtabmap::SensorData</a></li>
|
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<li>isPredictionSparse() : <a class="el" href="classrtabmap_1_1BayesFilter.html#a8d65b71c1a2230fdefae41d90f73c0d5">rtabmap::BayesFilter</a></li>
|
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<li>isReadOnly() : <a class="el" href="classrtabmap_1_1Memory.html#ad6d7488b61f8bedf6c9e3f4019a6b7f4">rtabmap::Memory</a></li>
|
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<li>isRectificationMapInitialized() : <a class="el" href="classrtabmap_1_1CameraModel.html#a80cacb16d347e89980369cb2e02f3d48">rtabmap::CameraModel</a>, <a class="el" href="classrtabmap_1_1StereoCameraModel.html#aa35a73db2fc272f0f33b40220b75fa43">rtabmap::StereoCameraModel</a></li>
|
||||
<li>isRGBDMode() : <a class="el" href="classrtabmap_1_1Rtabmap.html#a691de75b6964ab48d7f1ea53ea40f4c1">rtabmap::Rtabmap</a></li>
|
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|
||||
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<li>isPaused() : <a class="el" href="classrtabmap_1_1SensorCaptureThread.html#a435f89b6a3186462f6ee860afa88644c">rtabmap::SensorCaptureThread</a></li>
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@@ -567,7 +567,7 @@ Bayes</h1>
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<tr class="markdownTableRowOdd">
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<td class="markdownTableBodyNone"><a class="anchor" id="param_BayesVirtualPlacePriorThr"></a><a class="el" href="classrtabmap_1_1Parameters.html#af5f568511dbc4d9213d21acf1cedd65e">Bayes/VirtualPlacePriorThr</a> </td><td class="markdownTableBodyNone">float </td><td class="markdownTableBodyNone"><code>0.9</code> </td><td class="markdownTableBodyNone">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). </td></tr>
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<tr class="markdownTableRowEven">
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<td class="markdownTableBodyNone"><a class="anchor" id="param_BayesPredictionLC"></a><a class="el" href="classrtabmap_1_1Parameters.html#aabfee3d6757a2269d7bbbbd3ebc20fb1">Bayes/PredictionLC</a> </td><td class="markdownTableBodyNone">string </td><td class="markdownTableBodyNone"><code>0.1 0.36 0.30 0.16 0.062 0.0151 0.00255 0.000324 2.5e-05 1.3e-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</code> </td><td class="markdownTableBodyNone">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. </td></tr>
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<td class="markdownTableBodyNone"><a class="anchor" id="param_BayesPredictionLC"></a><a class="el" href="classrtabmap_1_1Parameters.html#aabfee3d6757a2269d7bbbbd3ebc20fb1">Bayes/PredictionLC</a> </td><td class="markdownTableBodyNone">string </td><td class="markdownTableBodyNone"><code>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</code> </td><td class="markdownTableBodyNone">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. </td></tr>
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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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Reference in New Issue
Block a user