390 RTABMAP_PARAM(Bayes, VirtualPlacePriorThr, float, 0.9, "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).");
391 RTABMAP_PARAM_STR(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", "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.");
392 RTABMAP_PARAM(Bayes, FullPredictionUpdate, bool, false, "Regenerate all the prediction matrix on each iteration (otherwise only removed/added ids are updated).");
-
393 RTABMAP_PARAM(Bayes, SparsePrediction, bool, true, 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.", kBayesPredictionLC().c_str(), kBayesPredictionLC().c_str()).c_str());
+
393 RTABMAP_PARAM(Bayes, SparsePrediction, bool, true, 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.", kBayesPredictionLC().c_str()).c_str());
394
395// Verify hypotheses
396 RTABMAP_PARAM(VhEp, Enabled, bool, false, uFormat("Verify visual loop closure hypothesis by computing a fundamental matrix. This is done prior to transformation computation when %s is enabled.", kRGBDEnabled().c_str()));
diff --git a/preview/pr-1748/api/latest/classrtabmap_1_1Parameters.html b/preview/pr-1748/api/latest/classrtabmap_1_1Parameters.html
index 61f5a742..8aa6a705 100644
--- a/preview/pr-1748/api/latest/classrtabmap_1_1Parameters.html
+++ b/preview/pr-1748/api/latest/classrtabmap_1_1Parameters.html
@@ -1587,7 +1587,7 @@ Static Public Member Functions
Type of parameter Bayes/FullPredictionUpdate , as a string: bool .
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.", kBayesPredictionLC().c_str(), kBayesPredictionLC().c_str()).c_str() Default value: true ( bool ).
+
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.", kBayesPredictionLC().c_str()).c_str() Default value: true ( bool ).
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.", kBayesPredictionLC().c_str(), kBayesPredictionLC().c_str()).c_str() Default value: true ( bool ).
+
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.", kBayesPredictionLC().c_str()).c_str() Default value: true ( bool ).
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 Bayes/PredictionLC, 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 Bayes/PredictionLC 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.
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 Bayes/PredictionLC sum to less than 1, as the prediction is then not sparse.