se2_l2_ransac.cpp

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/* +------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)            |
   |                          http://www.mrpt.org/                          |
   |                                                                        |
   | Copyright (c) 2005-2018, Individual contributors, see AUTHORS file     |
   | See: http://www.mrpt.org/Authors - All rights reserved.                |
   | Released under BSD License. See details in http://www.mrpt.org/License |
   +------------------------------------------------------------------------+ */
 
#include "tfest-precomp.h"  // Precompiled headers
 
#include <mrpt/tfest/se2.h>
#include <mrpt/poses/CPosePDFGaussian.h>
#include <mrpt/poses/CPoint2DPDFGaussian.h>
#include <mrpt/random.h>
#include <mrpt/math/geometry.h>
#include <mrpt/math/distributions.h>
#include <mrpt/system/CTimeLogger.h>
#include <iostream>
 
using namespace mrpt;
using namespace mrpt::tfest;
using namespace mrpt::random;
using namespace mrpt::poses;
using namespace mrpt::math;
using namespace std;
 
//#define AVOID_MULTIPLE_CORRESPONDENCES
 
// mark this pair as "selected" so it won't be picked again:
void markAsPicked(
        const TMatchingPair& c, std::vector<bool>& alreadySelectedThis,
        std::vector<bool>& alreadySelectedOther
#ifdef AVOID_MULTIPLE_CORRESPONDENCES
        ,
        const std::vector<std::vector<int>>& listDuplicatedLandmarksThis
#endif
        )
{
        ASSERTDEB_(c.this_idx < alreadySelectedThis.size());
        ASSERTDEB_(c.other_idx < alreadySelectedOther.size());
 
#ifndef AVOID_MULTIPLE_CORRESPONDENCES
        alreadySelectedThis[c.this_idx] = true;
        alreadySelectedOther[c.other_idx] = true;
#else
        for (std::vector<int>::iterator it1 =
                         listDuplicatedLandmarksThis[c.this_idx].begin();
                 it1 != listDuplicatedLandmarksThis[c.this_idx].end(); it1++)
                alreadySelectedThis[*it1] = true;
        for (std::vector<int>::iterator it2 =
                         listDuplicatedLandmarksOther[c.other_idx].begin();
                 it2 != listDuplicatedLandmarksOther[c.other_idx].end(); it2++)
                alreadySelectedOther[*it2] = true;
#endif
}
 
/*---------------------------------------------------------------
 
                                        robustRigidTransformation
 
  The technique was described in the paper:
        J.L. Blanco, J. Gonzalez-Jimenez and J.A. Fernandez-Madrigal.
        "A robust, multi-hypothesis approach to matching occupancy grid maps".
        Robotica, available on CJO2013. doi:10.1017/S0263574712000732.
        http://journals.cambridge.org/action/displayAbstract?aid=8815308
 
 This works as follows:
        - Repeat "results.ransac_iters" times:
                - Randomly pick TWO correspondences from the set "in_correspondences".
                - Compute the associated rigid transformation.
                - For "ransac_maxSetSize" randomly selected correspondences, test for
 "consensus" with the current group:
                        - If if is compatible (ransac_maxErrorXY, ransac_maxErrorPHI), grow
 the "consensus set"
                        - If not, do not add it.
  ---------------------------------------------------------------*/
bool tfest::se2_l2_robust(
        const mrpt::tfest::TMatchingPairList& in_correspondences,
        const double normalizationStd, const TSE2RobustParams& params,
        TSE2RobustResult& results)
{
//#define DO_PROFILING
 
#ifdef DO_PROFILING
        CTimeLogger timlog;
#endif
 
        const size_t nCorrs = in_correspondences.size();
 
        // Default: 2 * normalizationStd ("noise level")
        const double MAX_RMSE_TO_END =
                (params.max_rmse_to_end <= 0 ? 2 * normalizationStd
                                                                         : params.max_rmse_to_end);
 
        MRPT_START
 
        // Asserts:
        if (nCorrs < params.ransac_minSetSize)
        {
                // Nothing to do!
                results.transformation.clear();
                results.largestSubSet = TMatchingPairList();
                return false;
        }
 
#ifdef DO_PROFILING
        timlog.enter("ransac.find_max*");
#endif
        // Find the max. index of "this" and "other:
        unsigned int maxThis = 0, maxOther = 0;
        for (TMatchingPairList::const_iterator matchIt = in_correspondences.begin();
                 matchIt != in_correspondences.end(); ++matchIt)
        {
                maxThis = max(maxThis, matchIt->this_idx);
                maxOther = max(maxOther, matchIt->other_idx);
        }
#ifdef DO_PROFILING
        timlog.leave("ransac.find_max*");
#endif
 
#ifdef DO_PROFILING
        timlog.enter("ransac.count_unique_corrs");
#endif
 
        // Fill out 2 arrays indicating whether each element has a correspondence:
        std::vector<bool> hasCorrThis(maxThis + 1, false);
        std::vector<bool> hasCorrOther(maxOther + 1, false);
        unsigned int howManyDifCorrs = 0;
        for (TMatchingPairList::const_iterator matchIt = in_correspondences.begin();
                 matchIt != in_correspondences.end(); ++matchIt)
        {
                if (!hasCorrThis[matchIt->this_idx] &&
                        !hasCorrOther[matchIt->other_idx])
                {
                        hasCorrThis[matchIt->this_idx] = true;
                        hasCorrOther[matchIt->other_idx] = true;
                        howManyDifCorrs++;
                }
        }
#ifdef DO_PROFILING
        timlog.leave("ransac.count_unique_corrs");
#endif
 
        // Clear the set of output particles:
        results.transformation.clear();
 
        // If there are less different correspondences than the minimum required,
        // quit:
        if (howManyDifCorrs < params.ransac_minSetSize)
        {
                // Nothing we can do here!!! :~$
                results.transformation.clear();
                results.largestSubSet = TMatchingPairList();
                return false;
        }
 
#ifdef AVOID_MULTIPLE_CORRESPONDENCES
        unsigned k;
        // Find duplicated landmarks (from SIFT features with different
        // descriptors,etc...)
        //   this is to avoid establishing multiple correspondences for the same
        //   physical point!
        // ------------------------------------------------------------------------------------------------
        std::vector<std::vector<int>> listDuplicatedLandmarksThis(maxThis + 1);
        ASSERT_(nCorrs >= 1);
        for (k = 0; k < nCorrs - 1; k++)
        {
                std::vector<int> duplis;
                for (unsigned j = k; j < nCorrs - 1; j++)
                {
                        if (in_correspondences[k].this_x == in_correspondences[j].this_x &&
                                in_correspondences[k].this_y == in_correspondences[j].this_y &&
                                in_correspondences[k].this_z == in_correspondences[j].this_z)
                                duplis.push_back(in_correspondences[j].this_idx);
                }
                listDuplicatedLandmarksThis[in_correspondences[k].this_idx] = duplis;
        }
 
        std::vector<std::vector<int>> listDuplicatedLandmarksOther(maxOther + 1);
        for (k = 0; k < nCorrs - 1; k++)
        {
                std::vector<int> duplis;
                for (unsigned j = k; j < nCorrs - 1; j++)
                {
                        if (in_correspondences[k].other_x ==
                                        in_correspondences[j].other_x &&
                                in_correspondences[k].other_y ==
                                        in_correspondences[j].other_y &&
                                in_correspondences[k].other_z == in_correspondences[j].other_z)
                                duplis.push_back(in_correspondences[j].other_idx);
                }
                listDuplicatedLandmarksOther[in_correspondences[k].other_idx] = duplis;
        }
#endif
 
        std::deque<TMatchingPairList> alreadyAddedSubSets;
 
        CPosePDFGaussian referenceEstimation;
        CPoint2DPDFGaussian pt_this;
 
        const double ransac_consistency_test_chi2_quantile = 0.99;
        const double chi2_thres_dim1 =
                mrpt::math::chi2inv(ransac_consistency_test_chi2_quantile, 1);
 
        // -------------------------
        //              The RANSAC loop
        // -------------------------
        size_t largest_consensus_yet = 0;  // Used for dynamic # of steps
        double largestSubSet_RMSE = std::numeric_limits<double>::max();
 
        results.ransac_iters = params.ransac_nSimulations;
        const bool use_dynamic_iter_number = results.ransac_iters == 0;
        if (use_dynamic_iter_number)
        {
                ASSERT_(
                        params.probability_find_good_model > 0 &&
                        params.probability_find_good_model < 1);
                // Set an initial # of iterations:
                results.ransac_iters = 10;  // It doesn't matter actually, since will be
                // changed in the first loop
        }
 
        std::vector<bool> alreadySelectedThis, alreadySelectedOther;
 
        if (!params.ransac_algorithmForLandmarks)
        {
                alreadySelectedThis.assign(maxThis + 1, false);
                alreadySelectedOther.assign(maxOther + 1, false);
        }
        // else -> It will be done anyway inside the for() below
 
        // First: Build a permutation of the correspondences to pick from it
        // sequentially:
        std::vector<size_t> corrsIdxs(nCorrs), corrsIdxsPermutation;
        for (size_t i = 0; i < nCorrs; i++) corrsIdxs[i] = i;
 
        size_t iter_idx;
        for (iter_idx = 0; iter_idx < results.ransac_iters;
                 iter_idx++)  // results.ransac_iters can be dynamic
        {
#ifdef DO_PROFILING
                CTimeLoggerEntry tle(timlog, "ransac.iter");
#endif
 
#ifdef DO_PROFILING
                timlog.enter("ransac.permute");
#endif
                getRandomGenerator().permuteVector(corrsIdxs, corrsIdxsPermutation);
 
#ifdef DO_PROFILING
                timlog.leave("ransac.permute");
#endif
 
                TMatchingPairList subSet;
 
                // Select a subset of correspondences at random:
                if (params.ransac_algorithmForLandmarks)
                {
#ifdef DO_PROFILING
                        timlog.enter("ransac.reset_selection_marks");
#endif
                        alreadySelectedThis.assign(maxThis + 1, false);
                        alreadySelectedOther.assign(maxOther + 1, false);
#ifdef DO_PROFILING
                        timlog.leave("ransac.reset_selection_marks");
#endif
                }
                else
                {
                        // For points: Do not repeat the corrs, and take the number of corrs
                        // as weights
                }
 
// Try to build a subsetof "ransac_maxSetSize" (maximum) elements that achieve
// consensus:
// ------------------------------------------------------------------------------------------
#ifdef DO_PROFILING
                timlog.enter("ransac.inner_loops");
#endif
                for (unsigned int j = 0;
                         j < nCorrs && subSet.size() < params.ransac_maxSetSize; j++)
                {
                        const size_t idx = corrsIdxsPermutation[j];
 
                        const TMatchingPair& corr_j = in_correspondences[idx];
 
                        // Don't pick the same features twice!
                        if (alreadySelectedThis[corr_j.this_idx] ||
                                alreadySelectedOther[corr_j.other_idx])
                                continue;
 
                        // Additional user-provided filter:
                        if (params.user_individual_compat_callback)
                        {
                                mrpt::tfest::TPotentialMatch pm;
                                pm.idx_this = corr_j.this_idx;
                                pm.idx_other = corr_j.other_idx;
                                if (!params.user_individual_compat_callback(pm))
                                        continue;  // Skip this one!
                        }
 
                        if (subSet.size() < 2)
                        {
                                // ------------------------------------------------------------------------------------------------------
                                // If we are within the first two correspondences, just add them
                                // to the subset:
                                // ------------------------------------------------------------------------------------------------------
                                subSet.push_back(corr_j);
                                markAsPicked(corr_j, alreadySelectedThis, alreadySelectedOther);
 
                                if (subSet.size() == 2)
                                {
                                        // Consistency Test: From
 
                                        // Check the feasibility of this pair "idx1"-"idx2":
                                        //  The distance between the pair of points in MAP1 must be
                                        //  very close
                                        //   to that of their correspondences in MAP2:
                                        const double corrs_dist1 =
                                                mrpt::math::distanceBetweenPoints(
                                                        subSet[0].this_x, subSet[0].this_y,
                                                        subSet[1].this_x, subSet[1].this_y);
 
                                        const double corrs_dist2 =
                                                mrpt::math::distanceBetweenPoints(
                                                        subSet[0].other_x, subSet[0].other_y,
                                                        subSet[1].other_x, subSet[1].other_y);
 
                                        // Is is a consistent possibility?
                                        //  We use a chi2 test (see paper for the derivation)
                                        const double corrs_dist_chi2 =
                                                square(square(corrs_dist1) - square(corrs_dist2)) /
                                                (8.0 * square(normalizationStd) *
                                                 (square(corrs_dist1) + square(corrs_dist2)));
 
                                        bool is_acceptable = (corrs_dist_chi2 < chi2_thres_dim1);
 
                                        if (is_acceptable)
                                        {
                                                // Perform estimation:
                                                tfest::se2_l2(subSet, referenceEstimation);
                                                // Normalized covariance: scale!
                                                referenceEstimation.cov *= square(normalizationStd);
 
                                                // Additional filter:
                                                //  If the correspondences as such the transformation
                                                //  has a high ambiguity, we discard it!
                                                is_acceptable =
                                                        (referenceEstimation.cov(2, 2) <
                                                         square(DEG2RAD(5.0f)));
                                        }
 
                                        if (!is_acceptable)
                                        {
                                                // Remove this correspondence & try again with a
                                                // different pair:
                                                subSet.erase(subSet.begin() + (subSet.size() - 1));
                                        }
                                        else
                                        {
                                                // Only mark as picked if we're really keeping it:
                                                markAsPicked(
                                                        corr_j, alreadySelectedThis, alreadySelectedOther);
                                        }
                                }
                        }
                        else
                        {
#ifdef DO_PROFILING
                                timlog.enter("ransac.test_consistency");
#endif
 
                                // ------------------------------------------------------------------------------------------------------
                                // The normal case:
                                //  - test for "consensus" with the current group:
                                //              - If it is compatible (ransac_maxErrorXY,
                                // ransac_maxErrorPHI), grow the "consensus set"
                                //              - If not, do not add it.
                                // ------------------------------------------------------------------------------------------------------
 
                                // Test for the mahalanobis distance between:
                                //  "referenceEstimation (+) point_other" AND "point_this"
                                referenceEstimation.composePoint(
                                        mrpt::math::TPoint2D(corr_j.other_x, corr_j.other_y),
                                        pt_this);
 
                                const double maha_dist = pt_this.mahalanobisDistanceToPoint(
                                        corr_j.this_x, corr_j.this_y);
 
                                const bool passTest =
                                        maha_dist < params.ransac_mahalanobisDistanceThreshold;
 
                                if (passTest)
                                {
                                        // OK, consensus passed:
                                        subSet.push_back(corr_j);
                                        markAsPicked(
                                                corr_j, alreadySelectedThis, alreadySelectedOther);
                                }
// else -> Test failed
 
#ifdef DO_PROFILING
                                timlog.leave("ransac.test_consistency");
#endif
                        }  // end else "normal case"
 
                }  // end for j
#ifdef DO_PROFILING
                timlog.leave("ransac.inner_loops");
#endif
 
                const bool has_to_eval_RMSE =
                        (subSet.size() >= params.ransac_minSetSize);
 
                // Compute the RMSE of this matching and the corresponding
                // transformation (only if we'll use this value below)
                double this_subset_RMSE = 0;
                if (has_to_eval_RMSE)
                {
#ifdef DO_PROFILING
                        CTimeLoggerEntry tle(timlog, "ransac.comp_rmse");
#endif
 
                        // Recompute referenceEstimation from all the corrs:
                        tfest::se2_l2(subSet, referenceEstimation);
                        // Normalized covariance: scale!
                        referenceEstimation.cov *= square(normalizationStd);
 
                        for (size_t k = 0; k < subSet.size(); k++)
                        {
                                double gx, gy;
                                referenceEstimation.mean.composePoint(
                                        subSet[k].other_x, subSet[k].other_y, gx, gy);
 
                                this_subset_RMSE +=
                                        mrpt::math::distanceSqrBetweenPoints<double>(
                                                subSet[k].this_x, subSet[k].this_y, gx, gy);
                        }
                        this_subset_RMSE /= std::max(static_cast<size_t>(1), subSet.size());
                }
                else
                {
                        this_subset_RMSE = std::numeric_limits<double>::max();
                }
 
                // Save the estimation result as a "particle", only if the subSet
                // contains
                //  "ransac_minSetSize" elements at least:
                if (subSet.size() >= params.ransac_minSetSize)
                {
                        // If this subset was previously added to the SOG, just increment
                        // its weight
                        //  and do not add a new mode:
                        int indexFound = -1;
 
                        // JLBC Added DEC-2007: An alternative (optional) method to fuse
                        // Gaussian modes:
                        if (!params.ransac_fuseByCorrsMatch)
                        {
                                // Find matching by approximate match in the X,Y,PHI means
                                // -------------------------------------------------------------------
                                for (size_t i = 0; i < results.transformation.size(); i++)
                                {
                                        double diffXY =
                                                results.transformation.get(i).mean.distanceTo(
                                                        referenceEstimation.mean);
                                        double diffPhi = fabs(
                                                math::wrapToPi(
                                                        results.transformation.get(i).mean.phi() -
                                                        referenceEstimation.mean.phi()));
                                        if (diffXY < params.ransac_fuseMaxDiffXY &&
                                                diffPhi < params.ransac_fuseMaxDiffPhi)
                                        {
                                                // printf("Match by distance found: distXY:%f distPhi=%f
                                                // deg\n",diffXY,RAD2DEG(diffPhi));
                                                indexFound = i;
                                                break;
                                        }
                                }
                        }
                        else
                        {
                                // Find matching mode by exact match in the list of
                                // correspondences:
                                // -------------------------------------------------------------------
                                // Sort "subSet" in order to compare them easily!
                                // std::sort( subSet.begin(), subSet.end() );
 
                                // Try to find matching corrs:
                                for (size_t i = 0; i < alreadyAddedSubSets.size(); i++)
                                {
                                        if (subSet == alreadyAddedSubSets[i])
                                        {
                                                indexFound = i;
                                                break;
                                        }
                                }
                        }
 
                        if (indexFound != -1)
                        {
                                // This is an already added mode:
                                if (params.ransac_algorithmForLandmarks)
                                        results.transformation.get(indexFound).log_w = log(
                                                1 + exp(results.transformation.get(indexFound).log_w));
                                else
                                        results.transformation.get(indexFound).log_w =
                                                log(subSet.size() +
                                                        exp(results.transformation.get(indexFound).log_w));
                        }
                        else
                        {
                                // Add a new mode to the SOG:
                                alreadyAddedSubSets.push_back(subSet);
 
                                CPosePDFSOG::TGaussianMode newSOGMode;
                                if (params.ransac_algorithmForLandmarks)
                                        newSOGMode.log_w = 0;  // log(1);
                                else
                                        newSOGMode.log_w = log(static_cast<double>(subSet.size()));
 
                                newSOGMode.mean = referenceEstimation.mean;
                                newSOGMode.cov = referenceEstimation.cov;
 
                                // Add a new mode to the SOG!
                                results.transformation.push_back(newSOGMode);
                        }
                }  // end if subSet.size()>=ransac_minSetSize
 
                const size_t ninliers = subSet.size();
                if (largest_consensus_yet < ninliers)
                {
                        largest_consensus_yet = ninliers;
 
                        // Dynamic # of steps:
                        if (use_dynamic_iter_number)
                        {
                                // Update estimate of nCorrs, the number of trials to ensure we
                                // pick,
                                // with probability p, a data set with no outliers.
                                const double fracinliers =
                                        ninliers /
                                        static_cast<double>(howManyDifCorrs);  // corrsIdxs.size());
                                double pNoOutliers =
                                        1 - pow(fracinliers, static_cast<double>(
                                                                                         2.0 /*minimumSizeSamplesToFit*/));
 
                                pNoOutliers = std::max(
                                        std::numeric_limits<double>::epsilon(),
                                        pNoOutliers);  // Avoid division by -Inf
                                pNoOutliers = std::min(
                                        1.0 - std::numeric_limits<double>::epsilon(),
                                        pNoOutliers);  // Avoid division by 0.
                                // Number of
                                results.ransac_iters =
                                        log(1 - params.probability_find_good_model) /
                                        log(pNoOutliers);
 
                                results.ransac_iters = std::max(
                                        results.ransac_iters, params.ransac_min_nSimulations);
 
                                if (params.verbose)
                                        cout << "[tfest::RANSAC] Iter #" << iter_idx
                                                 << ":est. # iters=" << results.ransac_iters
                                                 << " pNoOutliers=" << pNoOutliers
                                                 << " #inliers: " << ninliers << endl;
                        }
                }
 
                // Save the largest subset:
                if (subSet.size() >= params.ransac_minSetSize &&
                        this_subset_RMSE < largestSubSet_RMSE)
                {
                        if (params.verbose)
                                cout << "[tfest::RANSAC] Iter #" << iter_idx
                                         << " Better subset: " << subSet.size()
                                         << " inliers, RMSE=" << this_subset_RMSE << endl;
 
                        results.largestSubSet = subSet;
                        largestSubSet_RMSE = this_subset_RMSE;
                }
 
                // Is the found subset good enough?
                if (subSet.size() >= params.ransac_minSetSize &&
                        this_subset_RMSE < MAX_RMSE_TO_END)
                {
                        break;  // end RANSAC iterations.
                }
 
#ifdef DO_PROFILING
                timlog.leave("ransac.iter");
#endif
        }  // end for each iteration
 
        if (params.verbose)
                cout << "[tfest::RANSAC] Finished after " << iter_idx
                         << " iterations.\n";
 
        // Set the weights of the particles to sum the unity:
        results.transformation.normalizeWeights();
 
        // Done!
 
        MRPT_END_WITH_CLEAN_UP(
                printf("nCorrs=%u\n", static_cast<unsigned int>(nCorrs));
                printf("Saving '_debug_in_correspondences.txt'...");
                in_correspondences.dumpToFile("_debug_in_correspondences.txt");
                printf("Ok\n"); printf("Saving '_debug_results.transformation.txt'...");
                results.transformation.saveToTextFile(
                        "_debug_results.transformation.txt");
                printf("Ok\n"););
 
        return true;
}