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@@ -263,13 +263,13 @@ Kp</h1>
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<th class="markdownTableHeadNone">Key </th><th class="markdownTableHeadNone">Type </th><th class="markdownTableHeadNone">Default </th><th class="markdownTableHeadNone">Description </th></tr>
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<td class="markdownTableBodyNone"><a class="anchor" id="param_KpNNStrategy"></a><a class="el" href="classrtabmap_1_1Parameters.html#a994321ccf32f126b6781aa1925a5fa66">Kp/NNStrategy</a> </td><td class="markdownTableBodyNone">int </td><td class="markdownTableBodyNone"><code>1</code> </td><td class="markdownTableBodyNone">kNNFlannNaive=0, kNNFlannKdTree=1, kNNFlannLSH=2, kNNBruteForce=3, kNNBruteForceGPU=4 </td></tr>
<td class="markdownTableBodyNone"><a class="anchor" id="param_KpNNStrategy"></a><a class="el" href="classrtabmap_1_1Parameters.html#a994321ccf32f126b6781aa1925a5fa66">Kp/NNStrategy</a> </td><td class="markdownTableBodyNone">int </td><td class="markdownTableBodyNone"><code>1</code> </td><td class="markdownTableBodyNone">FLANN Linear=0, FLANN KdTree=1, FLANN LSH=2, Brute Force=3, Brute Force GPU=4, FLANN KdTree Single=5, NanoFLANN KdTree=6 </td></tr>
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<td class="markdownTableBodyNone"><a class="anchor" id="param_KpIncrementalDictionary"></a><a class="el" href="classrtabmap_1_1Parameters.html#adea90352705c00f23bd3f90d72b7fbdd">Kp/IncrementalDictionary</a> </td><td class="markdownTableBodyNone">bool </td><td class="markdownTableBodyNone"><code>true</code> </td><td class="markdownTableBodyNone"></td></tr>
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<td class="markdownTableBodyNone"><a class="anchor" id="param_KpIncrementalFlann"></a><a class="el" href="classrtabmap_1_1Parameters.html#a092387f2a424c8db4f2482742b0b0fb9">Kp/IncrementalFlann</a> </td><td class="markdownTableBodyNone">bool </td><td class="markdownTableBodyNone"><code>true</code> </td><td class="markdownTableBodyNone">When using FLANN based strategy, add/remove points to its index without always rebuilding the index (the index is built only when the dictionary increases of the factor <a class="el" href="parameters.html#param_KpFlannRebalancingFactor">Kp/FlannRebalancingFactor</a> in size). </td></tr>
<td class="markdownTableBodyNone"><a class="anchor" id="param_KpIncrementalFlann"></a><a class="el" href="classrtabmap_1_1Parameters.html#a092387f2a424c8db4f2482742b0b0fb9">Kp/IncrementalFlann</a> </td><td class="markdownTableBodyNone">bool </td><td class="markdownTableBodyNone"><code>true</code> </td><td class="markdownTableBodyNone">When using FLANN based strategy, add/remove points to its index without always rebuilding the index (the index is only rebuilt when too many of its features have been removed, see <a class="el" href="parameters.html#param_KpFlannRebalancingFactor">Kp/FlannRebalancingFactor</a>). </td></tr>
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<td class="markdownTableBodyNone"><a class="anchor" id="param_KpFlannRebalancingFactor"></a><a class="el" href="classrtabmap_1_1Parameters.html#a17550d1f1b862a621c409d4e12c7696b">Kp/FlannRebalancingFactor</a> </td><td class="markdownTableBodyNone">float </td><td class="markdownTableBodyNone"><code>2.0</code> </td><td class="markdownTableBodyNone">Factor used when rebuilding the incremental FLANN index (see <a class="el" href="parameters.html#param_KpIncrementalFlann">Kp/IncrementalFlann</a>). Set &lt;=1 to disable. </td></tr>
<td class="markdownTableBodyNone"><a class="anchor" id="param_KpFlannRebalancingFactor"></a><a class="el" href="classrtabmap_1_1Parameters.html#a17550d1f1b862a621c409d4e12c7696b">Kp/FlannRebalancingFactor</a> </td><td class="markdownTableBodyNone">float </td><td class="markdownTableBodyNone"><code>2.0</code> </td><td class="markdownTableBodyNone">Rebuild the incremental FLANN index (see <a class="el" href="parameters.html#param_KpIncrementalFlann">Kp/IncrementalFlann</a>) once the ratio (factor-1)/factor of its features has been removed, e.g. half of them for a factor of 2. Rebuilding frees the memory of the removed features and speeds up the searches. Features are mostly removed when memory management is enabled (<a class="el" href="parameters.html#param_RtabmapTimeThr">Rtabmap/TimeThr</a> or <a class="el" href="parameters.html#param_RtabmapMemoryThr">Rtabmap/MemoryThr</a>). Set to 1 to never rebuild, which also uses less memory as the features don't have to be referenced one by one. </td></tr>
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<td class="markdownTableBodyNone"><a class="anchor" id="param_KpByteToFloat"></a><a class="el" href="classrtabmap_1_1Parameters.html#a98de5152ce8f3bc8d1f197d20d4e778a">Kp/ByteToFloat</a> </td><td class="markdownTableBodyNone">bool </td><td class="markdownTableBodyNone"><code>false</code> </td><td class="markdownTableBodyNone">For <a class="el" href="parameters.html#param_KpNNStrategy">Kp/NNStrategy</a>=1, binary descriptors are converted to float by converting each byte to float instead of converting each bit to float. When converting bytes instead of bits, less memory is used and search is faster at the cost of slightly less accurate matching. </td></tr>
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@@ -1331,7 +1331,7 @@ Vis</h1>
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<td class="markdownTableBodyNone"><a class="anchor" id="param_VisCorType"></a><a class="el" href="classrtabmap_1_1Parameters.html#a1f1c5f9198da6ec1d43a36621b0e6687">Vis/CorType</a> </td><td class="markdownTableBodyNone">int </td><td class="markdownTableBodyNone"><code>0</code> </td><td class="markdownTableBodyNone">Correspondences computation approach: 0=Features Matching, 1=Optical Flow </td></tr>
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<td class="markdownTableBodyNone"><a class="anchor" id="param_VisCorNNType"></a><a class="el" href="classrtabmap_1_1Parameters.html#a341b0c15fe1fba640ad3aee76019a618">Vis/CorNNType</a> </td><td class="markdownTableBodyNone">int </td><td class="markdownTableBodyNone"><code>1</code> </td><td class="markdownTableBodyNone">[<a class="el" href="parameters.html#param_VisCorType">Vis/CorType</a>=0] kNNFlannNaive=0, kNNFlannKdTree=1, kNNFlannLSH=2, kNNBruteForce=3, kNNBruteForceGPU=4, BruteForceCrossCheck=5, SuperGlue=6, GMS=7. Used for features matching approach. </td></tr>
<td class="markdownTableBodyNone"><a class="anchor" id="param_VisCorNNType"></a><a class="el" href="classrtabmap_1_1Parameters.html#a341b0c15fe1fba640ad3aee76019a618">Vis/CorNNType</a> </td><td class="markdownTableBodyNone">int </td><td class="markdownTableBodyNone"><code>1</code> </td><td class="markdownTableBodyNone">[<a class="el" href="parameters.html#param_VisCorType">Vis/CorType</a>=0] FLANN Linear=0, FLANN KdTree=1, FLANN LSH=2, Brute Force=3, Brute Force GPU=4, Brute Force Cross Check=5, SuperGlue=6, GMS=7, FLANN KdTree Single=8, NanoFLANN KdTree=9. Used for features matching approach. </td></tr>
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<td class="markdownTableBodyNone"><a class="anchor" id="param_VisCorNNDR"></a><a class="el" href="classrtabmap_1_1Parameters.html#a381898b11ddb3d389c76a027f5e49bfd">Vis/CorNNDR</a> </td><td class="markdownTableBodyNone">float </td><td class="markdownTableBodyNone"><code>0.8</code> </td><td class="markdownTableBodyNone">[<a class="el" href="parameters.html#param_VisCorType">Vis/CorType</a>=0] NNDR: nearest neighbor distance ratio. Used for knn features matching approach. </td></tr>
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