OptimizerGTSAM.cpp

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/*
Copyright (c) 2010-2016, Mathieu Labbe - IntRoLab - Universite de Sherbrooke
All rights reserved.

Redistribution and use in source and binary forms, with or without
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      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.

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#include "rtabmap/core/Graph.h"

#include <rtabmap/utilite/ULogger.h>
#include <rtabmap/utilite/UStl.h>
#include <rtabmap/utilite/UMath.h>
#include <rtabmap/utilite/UConversion.h>
#include <rtabmap/utilite/UTimer.h>
#include <set>

#include <rtabmap/core/OptimizerGTSAM.h>

#ifdef RTABMAP_GTSAM
#include <gtsam/geometry/Pose2.h>
#include <gtsam/geometry/Pose3.h>
#include <gtsam/inference/Key.h>
#include <gtsam/inference/Symbol.h>
#include <gtsam/slam/PriorFactor.h>
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/GaussNewtonOptimizer.h>
#include <gtsam/nonlinear/DoglegOptimizer.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/nonlinear/NonlinearOptimizer.h>
#include <gtsam/nonlinear/Marginals.h>
#include <gtsam/nonlinear/Values.h>

#ifdef RTABMAP_VERTIGO
#include "vertigo/gtsam/betweenFactorMaxMix.h"
#include "vertigo/gtsam/betweenFactorSwitchable.h"
#include "vertigo/gtsam/switchVariableLinear.h"
#include "vertigo/gtsam/switchVariableSigmoid.h"
#endif
#endif // end RTABMAP_GTSAM

namespace rtabmap {

bool OptimizerGTSAM::available()
{
#ifdef RTABMAP_GTSAM
        return true;
#else
        return false;
#endif
}

std::map<int, Transform> OptimizerGTSAM::optimize(
                int rootId,
                const std::map<int, Transform> & poses,
                const std::multimap<int, Link> & edgeConstraints,
                std::list<std::map<int, Transform> > * intermediateGraphes,
                double * finalError,
                int * iterationsDone)
{
        std::map<int, Transform> optimizedPoses;
#ifdef RTABMAP_GTSAM

#ifndef RTABMAP_VERTIGO
        if(this->isRobust())
        {
                UWARN("Vertigo robust optimization is not available! Robust optimization is now disabled.");
                setRobust(false);
        }
#endif

        UDEBUG("Optimizing graph...");
        if(edgeConstraints.size()>=1 && poses.size()>=2 && iterations() > 0)
        {
                gtsam::NonlinearFactorGraph graph;

                //prior first pose
                UASSERT(uContains(poses, rootId));
                const Transform & initialPose = poses.at(rootId);
                if(isSlam2d())
                {
                        gtsam::noiseModel::Diagonal::shared_ptr priorNoise = gtsam::noiseModel::Diagonal::Sigmas(gtsam::Vector3(0.01, 0.01, 0.01));
                        graph.add(gtsam::PriorFactor<gtsam::Pose2>(rootId, gtsam::Pose2(initialPose.x(), initialPose.y(), initialPose.theta()), priorNoise));
                }
                else
                {
                        gtsam::noiseModel::Diagonal::shared_ptr priorNoise = gtsam::noiseModel::Diagonal::Sigmas((gtsam::Vector(6) << 1e-6, 1e-6, 1e-6, 1e-4, 1e-4, 1e-4).finished());
                        graph.add(gtsam::PriorFactor<gtsam::Pose3>(rootId, gtsam::Pose3(initialPose.toEigen4d()), priorNoise));
                }

                UDEBUG("fill poses to gtsam...");
                gtsam::Values initialEstimate;
                for(std::map<int, Transform>::const_iterator iter = poses.begin(); iter!=poses.end(); ++iter)
                {
                        UASSERT(!iter->second.isNull());
                        if(isSlam2d())
                        {
                                initialEstimate.insert(iter->first, gtsam::Pose2(iter->second.x(), iter->second.y(), iter->second.theta()));
                        }
                        else
                        {
                                initialEstimate.insert(iter->first, gtsam::Pose3(iter->second.toEigen4d()));
                        }
                }

                UDEBUG("fill edges to gtsam...");
                int switchCounter = poses.rbegin()->first+1;
                for(std::multimap<int, Link>::const_iterator iter=edgeConstraints.begin(); iter!=edgeConstraints.end(); ++iter)
                {
                        int id1 = iter->first;
                        int id2 = iter->second.to();

                        UASSERT(!iter->second.transform().isNull());

#ifdef RTABMAP_VERTIGO
                        if(this->isRobust() &&
                           iter->second.type()!=Link::kNeighbor &&
                           iter->second.type() != Link::kNeighborMerged)
                        {
                                // create new switch variable
                                // Sunderhauf IROS 2012:
                                // "Since it is reasonable to initially accept all loop closure constraints,
                                //  a proper and convenient initial value for all switch variables would be
                                //  sij = 1 when using the linear switch function"
                                double prior = 1.0;
                                initialEstimate.insert(gtsam::Symbol('s',switchCounter), vertigo::SwitchVariableLinear(prior));

                                // create switch prior factor
                                // "If the front-end is not able to assign sound individual values
                                //  for Ξij , it is save to set all Ξij = 1, since this value is close
                                //  to the individual optimal choice of Ξij for a large range of
                                //  outliers."
                                gtsam::noiseModel::Diagonal::shared_ptr switchPriorModel = gtsam::noiseModel::Diagonal::Sigmas(gtsam::Vector1(1.0));
                                graph.add(gtsam::PriorFactor<vertigo::SwitchVariableLinear> (gtsam::Symbol('s',switchCounter), vertigo::SwitchVariableLinear(prior), switchPriorModel));
                        }
#endif

                        if(isSlam2d())
                        {
                                Eigen::Matrix<double, 3, 3> information = Eigen::Matrix<double, 3, 3>::Identity();
                                if(!isCovarianceIgnored())
                                {
                                        // For some reasons, dividing by 1000 avoids some exceptions (maybe too large numbers on optimization)
                                        information(0,0) = iter->second.infMatrix().at<double>(0,0)/1000.0; // x-x
                                        information(0,1) = iter->second.infMatrix().at<double>(0,1)/1000.0; // x-y
                                        information(0,2) = iter->second.infMatrix().at<double>(0,5)/1000.0; // x-theta
                                        information(1,0) = iter->second.infMatrix().at<double>(1,0)/1000.0; // y-x
                                        information(1,1) = iter->second.infMatrix().at<double>(1,1)/1000.0; // y-y
                                        information(1,2) = iter->second.infMatrix().at<double>(1,5)/1000.0; // y-theta
                                        information(2,0) = iter->second.infMatrix().at<double>(5,0)/1000.0; // theta-x
                                        information(2,1) = iter->second.infMatrix().at<double>(5,1)/1000.0; // theta-y
                                        information(2,2) = iter->second.infMatrix().at<double>(5,5)/1000.0; // theta-theta
                                }
                                gtsam::noiseModel::Gaussian::shared_ptr model = gtsam::noiseModel::Gaussian::Information(information);

#ifdef RTABMAP_VERTIGO
                                if(this->isRobust() &&
                                   iter->second.type()!=Link::kNeighbor &&
                                   iter->second.type() != Link::kNeighborMerged)
                                {
                                        // create switchable edge factor
                                        graph.add(vertigo::BetweenFactorSwitchableLinear<gtsam::Pose2>(id1, id2, gtsam::Symbol('s', switchCounter++), gtsam::Pose2(iter->second.transform().x(), iter->second.transform().y(), iter->second.transform().theta()), model));
                                }
                                else
#endif
                                {
                                        graph.add(gtsam::BetweenFactor<gtsam::Pose2>(id1, id2, gtsam::Pose2(iter->second.transform().x(), iter->second.transform().y(), iter->second.transform().theta()), model));
                                }
                        }
                        else
                        {
                                Eigen::Matrix<double, 6, 6> information = Eigen::Matrix<double, 6, 6>::Identity();
                                if(!isCovarianceIgnored())
                                {
                                        memcpy(information.data(), iter->second.infMatrix().data, iter->second.infMatrix().total()*sizeof(double));
                                        // For some reasons, dividing by 1000 avoids some exceptions (maybe too large numbers on optimization)
                                        information = information / 1000.0;
                                }

                                gtsam::noiseModel::Gaussian::shared_ptr model = gtsam::noiseModel::Gaussian::Information(information);

#ifdef RTABMAP_VERTIGO
                                if(this->isRobust() &&
                                   iter->second.type()!=Link::kNeighbor &&
                                   iter->second.type() != Link::kNeighborMerged)
                                {
                                        // create switchable edge factor
                                        graph.add(vertigo::BetweenFactorSwitchableLinear<gtsam::Pose3>(id1, id2, gtsam::Symbol('s', switchCounter++), gtsam::Pose3(iter->second.transform().toEigen4d()), model));
                                }
                                else
#endif
                                {
                                        graph.add(gtsam::BetweenFactor<gtsam::Pose3>(id1, id2, gtsam::Pose3(iter->second.transform().toEigen4d()), model));
                                }
                        }
                }

                UDEBUG("create optimizer");
                gtsam::GaussNewtonParams parameters;
                parameters.relativeErrorTol = epsilon();
                parameters.maxIterations = iterations();
                gtsam::GaussNewtonOptimizer optimizer(graph, initialEstimate, parameters);
                //gtsam::LevenbergMarquardtParams parametersLev;
                //parametersLev.relativeErrorTol = epsilon();
                //parametersLev.maxIterations = iterations();
                //gtsam::LevenbergMarquardtOptimizer optimizer(graph, initialEstimate, parametersLev);
                //gtsam::DoglegParams parametersDogleg;
                //parametersDogleg.relativeErrorTol = epsilon();
                //parametersDogleg.maxIterations = iterations();
                //gtsam::DoglegOptimizer optimizer(graph, initialEstimate, parametersDogleg);

                UINFO("GTSAM optimizing begin (max iterations=%d, robust=%d)", iterations(), isRobust()?1:0);
                UTimer timer;
                int it = 0;
                double lastError = 0.0;
                for(int i=0; i<iterations(); ++i)
                {
                        if(intermediateGraphes && i > 0)
                        {
                                std::map<int, Transform> tmpPoses;
                                for(gtsam::Values::const_iterator iter=optimizer.values().begin(); iter!=optimizer.values().end(); ++iter)
                                {
                                        if(iter->value.dim() > 1)
                                        {
                                                if(isSlam2d())
                                                {
                                                        gtsam::Pose2 p = iter->value.cast<gtsam::Pose2>();
                                                        tmpPoses.insert(std::make_pair((int)iter->key, Transform(p.x(), p.y(), p.theta())));
                                                }
                                                else
                                                {
                                                        gtsam::Pose3 p = iter->value.cast<gtsam::Pose3>();
                                                        tmpPoses.insert(std::make_pair((int)iter->key, Transform::fromEigen4d(p.matrix())));
                                                }
                                        }
                                }
                                intermediateGraphes->push_back(tmpPoses);
                        }
                        try
                        {
                                optimizer.iterate();
                                ++it;
                        }
                        catch(gtsam::IndeterminantLinearSystemException & e)
                        {
                                UERROR("GTSAM exception caught: %s", e.what());
                                return optimizedPoses;
                        }

                        // early stop condition
                        double error = optimizer.error();
                        UDEBUG("iteration %d error =%f", i+1, error);
                        double errorDelta = lastError - error;
                        if(i>0 && errorDelta < this->epsilon())
                        {
                                if(errorDelta < 0)
                                {
                                        UDEBUG("Negative improvement?! Ignore and continue optimizing... (%f < %f)", errorDelta, this->epsilon());
                                }
                                else
                                {
                                        UINFO("Stop optimizing, not enough improvement (%f < %f)", errorDelta, this->epsilon());
                                        break;
                                }
                        }
                        else if(i==0 && error < this->epsilon())
                        {
                                UINFO("Stop optimizing, error is already under epsilon (%f < %f)", error, this->epsilon());
                                break;
                        }
                        lastError = error;
                }
                if(finalError)
                {
                        *finalError = lastError;
                }
                if(iterationsDone)
                {
                        *iterationsDone = it;
                }
                UINFO("GTSAM optimizing end (%d iterations done, error=%f (initial=%f final=%f), time=%f s)", optimizer.iterations(), optimizer.error(), graph.error(initialEstimate), graph.error(optimizer.values()), timer.ticks());

                for(gtsam::Values::const_iterator iter=optimizer.values().begin(); iter!=optimizer.values().end(); ++iter)
                {
                        if(iter->value.dim() > 1)
                        {
                                if(isSlam2d())
                                {
                                        gtsam::Pose2 p = iter->value.cast<gtsam::Pose2>();
                                        optimizedPoses.insert(std::make_pair((int)iter->key, Transform(p.x(), p.y(), p.theta())));
                                }
                                else
                                {
                                        gtsam::Pose3 p = iter->value.cast<gtsam::Pose3>();
                                        optimizedPoses.insert(std::make_pair((int)iter->key, Transform::fromEigen4d(p.matrix())));
                                }
                        }
                }
        }
        else if(poses.size() == 1 || iterations() <= 0)
        {
                optimizedPoses = poses;
        }
        else
        {
                UWARN("This method should be called at least with 1 pose!");
        }
        UDEBUG("Optimizing graph...end!");
#else
        UERROR("Not built with GTSAM support!");
#endif
        return optimizedPoses;
}

} /* namespace rtabmap */