Sparse Bayes (#1748)

* 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

corelib/src/bayes/DensePrediction.h

@@ -0,0 +1,91 @@
+/*
+Copyright (c) 2010-2016, Mathieu Labbe - IntRoLab - Universite de Sherbrooke
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+    * Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+    * Redistributions in binary form must reproduce the above copyright
+      notice, this list of conditions and the following disclaimer in the
+      documentation and/or other materials provided with the distribution.
+    * Neither the name of the Universite de Sherbrooke nor the
+      names of its contributors may be used to endorse or promote products
+      derived from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY
+DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+*/
+
+#ifndef RTABMAP_BAYES_DENSEPREDICTION_H_
+#define RTABMAP_BAYES_DENSEPREDICTION_H_
+
+#include "bayes/PredictionModel.h"
+
+#include <opencv2/core/core.hpp>
+#include <vector>
+
+namespace rtabmap {
+
+class Memory;
+
+namespace bayes {
+
+/**
+ * @brief The prediction as a matrix, one column per location.
+ *
+ * The matrix costs the number of locations squared, whatever the graph puts in it, which on a
+ * large map is most of what the Bayes filter holds and most of what an iteration reads. See
+ * SparsePrediction for the form that keeps only the values.
+ */
+class DensePrediction
+{
+public:
+	bool empty() const {return matrix_.empty();}
+	const cv::Mat & matrix() const {return matrix_;}
+
+	/// The locations the matrix is built for, which the incremental update carries over.
+	const std::vector<int> & ids() const {return ids_;}
+
+	void clear() {matrix_ = cv::Mat(); ids_.clear();}
+
+	/**
+	 * @brief Builds the matrix for @p ids and keeps it, along with the ids it is built for.
+	 *
+	 * The matrix already there is carried over when it is built for locations @p ids only
+	 * appends to; otherwise every column is built again.
+	 *
+	 * @param fullUpdate Rebuilds every column rather than carrying the matrix over.
+	 * @param cache Filled with the neighborhoods, for a later incremental update.
+	 */
+	const cv::Mat & generate(const PredictionModel & model, const Memory * memory,
+			const std::vector<int> & ids, bool fullUpdate, NeighborsCache * cache);
+
+	/// prior = prediction x posterior.
+	void multiply(const std::vector<float> & posterior, std::vector<float> & prior) const;
+
+	unsigned long memoryUsed() const;
+
+private:
+	cv::Mat generateFull(const PredictionModel & model, const Memory * memory,
+			const std::vector<int> & ids, NeighborsCache * cache) const;
+	cv::Mat update(const PredictionModel & model, const Memory * memory,
+			const std::vector<int> & oldIds, const std::vector<int> & newIds,
+			NeighborsCache * cache) const;
+
+	cv::Mat matrix_;
+	std::vector<int> ids_;
+};
+
+} // namespace bayes
+} // namespace rtabmap
+
+#endif /* RTABMAP_BAYES_DENSEPREDICTION_H_ */