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https://github.com/introlab/rtabmap.git
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matlabbe
9c1e117384
* Sparse Bayes * updated perf test * improved tests with real data * Making sparse works in incremental mapping * bookkeeping optimization * small opt * refactoring * splitting dense and sparse in different classes to make the code more lisible * cleanup comments * fixing CI * Making all Bayes tests testing both dense and sparse * Added multisession_3it integration test (test memory management, multisession and dense/sparse bayes in that settings) * optimized sparse when transfer/retrieval happens (was slower than dense for that case) * Testing retrieval param variants * Updated multisession_3it integration tests to compare loop closure hypotheses * bump version * Fixed ui sum of prediction * adding g2o gtsam to linux ci * cleanup * added debug crash log for ci * Simplified Bayes/SparsePrediction description * Dont show too dense for sparse on small maps (e.g., when we just started a new map) * fixing amd64v3 issue with gtsam on ci ubuntu 26 * Dot not auto switch to dense based on map size. * updating test range * added coverage tests * Adressing coverage * ignore one line in coverage for purpose
92 lines
3.6 KiB
C++
92 lines
3.6 KiB
C++
/*
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Copyright (c) 2010-2016, Mathieu Labbe - IntRoLab - Universite de Sherbrooke
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All rights reserved.
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions are met:
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* Redistributions of source code must retain the above copyright
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notice, this list of conditions and the following disclaimer.
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* Redistributions in binary form must reproduce the above copyright
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notice, this list of conditions and the following disclaimer in the
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documentation and/or other materials provided with the distribution.
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* Neither the name of the Universite de Sherbrooke nor the
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names of its contributors may be used to endorse or promote products
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derived from this software without specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY
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DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
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(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
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ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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#ifndef RTABMAP_BAYES_DENSEPREDICTION_H_
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#define RTABMAP_BAYES_DENSEPREDICTION_H_
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#include "bayes/PredictionModel.h"
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#include <opencv2/core/core.hpp>
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#include <vector>
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namespace rtabmap {
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class Memory;
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namespace bayes {
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/**
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* @brief The prediction as a matrix, one column per location.
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*
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* The matrix costs the number of locations squared, whatever the graph puts in it, which on a
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* large map is most of what the Bayes filter holds and most of what an iteration reads. See
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* SparsePrediction for the form that keeps only the values.
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*/
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class DensePrediction
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{
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public:
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bool empty() const {return matrix_.empty();}
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const cv::Mat & matrix() const {return matrix_;}
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/// The locations the matrix is built for, which the incremental update carries over.
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const std::vector<int> & ids() const {return ids_;}
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void clear() {matrix_ = cv::Mat(); ids_.clear();}
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/**
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* @brief Builds the matrix for @p ids and keeps it, along with the ids it is built for.
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*
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* The matrix already there is carried over when it is built for locations @p ids only
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* appends to; otherwise every column is built again.
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*
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* @param fullUpdate Rebuilds every column rather than carrying the matrix over.
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* @param cache Filled with the neighborhoods, for a later incremental update.
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*/
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const cv::Mat & generate(const PredictionModel & model, const Memory * memory,
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const std::vector<int> & ids, bool fullUpdate, NeighborsCache * cache);
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/// prior = prediction x posterior.
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void multiply(const std::vector<float> & posterior, std::vector<float> & prior) const;
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unsigned long memoryUsed() const;
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private:
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cv::Mat generateFull(const PredictionModel & model, const Memory * memory,
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const std::vector<int> & ids, NeighborsCache * cache) const;
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cv::Mat update(const PredictionModel & model, const Memory * memory,
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const std::vector<int> & oldIds, const std::vector<int> & newIds,
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NeighborsCache * cache) const;
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cv::Mat matrix_;
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std::vector<int> ids_;
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};
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} // namespace bayes
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} // namespace rtabmap
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#endif /* RTABMAP_BAYES_DENSEPREDICTION_H_ */
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