PointToPointSimilarity.cpp

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/*

Copyright (c) 2010--2012,
François Pomerleau and Stephane Magnenat, ASL, ETHZ, Switzerland
You can contact the authors at <f dot pomerleau at gmail dot com> and
<stephane at magnenat dot net>

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#include "ErrorMinimizersImpl.h"
#include "PointMatcherPrivate.h"
#include "Eigen/SVD"

using namespace Eigen;

template<typename T>
typename PointMatcher<T>::TransformationParameters PointToPointSimilarityErrorMinimizer<T>::compute(const ErrorElements& mPts_const)
{
        
        // Copy error element to use as storage later
        // TODO: check that, might worth it to only copy useful parts
        ErrorElements mPts = mPts_const;
        
        // now minimize on kept points
        const int dimCount(mPts.reading.features.rows());
        //const int ptsCount(mPts.reading.features.cols()); //Both point clouds have now the same number of (matched) point
        
        
        // Compute the (weighted) mean of each point cloud
        //TODO: change the member weights to be a Vector
        const Vector w = mPts.weights.row(0);
        const T w_sum_inv = T(1.)/w.sum();
        const Vector meanReading =
                (mPts.reading.features.topRows(dimCount-1).array().rowwise() * w.array().transpose()).rowwise().sum() * w_sum_inv;
        const Vector meanReference =
                (mPts.reference.features.topRows(dimCount-1).array().rowwise() * w.array().transpose()).rowwise().sum() * w_sum_inv;
        
        
        // Remove the mean from the point clouds
        mPts.reading.features.topRows(dimCount-1).colwise() -= meanReading;
        mPts.reference.features.topRows(dimCount-1).colwise() -= meanReference;
        
        const T sigma = mPts.reading.features.topRows(dimCount-1).colwise().squaredNorm().cwiseProduct(w.transpose()).sum();
        
        // Singular Value Decomposition
        const Matrix m(mPts.reference.features.topRows(dimCount-1) * w.asDiagonal()
                       * mPts.reading.features.topRows(dimCount-1).transpose());
        const JacobiSVD<Matrix> svd(m, ComputeThinU | ComputeThinV);
        Matrix rotMatrix(svd.matrixU() * svd.matrixV().transpose());
        typedef typename JacobiSVD<Matrix>::SingularValuesType SingularValuesType;
        SingularValuesType singularValues = svd.singularValues();
        // It is possible to get a reflection instead of a rotation. In this case, we
        // take the second best solution, guaranteed to be a rotation. For more details,
        // read the tech report: "Least-Squares Rigid Motion Using SVD", Olga Sorkine
        // http://igl.ethz.ch/projects/ARAP/svd_rot.pdf
        if (rotMatrix.determinant() < 0.)
        {
                Matrix tmpV = svd.matrixV().transpose();
                tmpV.row(dimCount-2) *= -1.;
                rotMatrix = svd.matrixU() * tmpV;
                singularValues(dimCount-2) *= -1.;
        }
        T scale = singularValues.sum() / sigma;
        if (sigma < 0.0001) scale = T(1);
        const Vector trVector(meanReference - scale * rotMatrix * meanReading);
        
        Matrix result(Matrix::Identity(dimCount, dimCount));
        result.topLeftCorner(dimCount-1, dimCount-1) = scale * rotMatrix;
        result.topRightCorner(dimCount-1, 1) = trVector;
        
        return result;
}

template<typename T>
T PointToPointSimilarityErrorMinimizer<T>::getResidualError(
        const DataPoints& filteredReading,
        const DataPoints& filteredReference,
        const OutlierWeights& outlierWeights,
        const Matches& matches) const
{
        assert(matches.ids.rows() > 0);
        
        // Fetch paired points
        typename ErrorMinimizer::ErrorElements mPts(filteredReading, filteredReference, outlierWeights, matches);
        
        return PointToPointErrorMinimizer<T>::computeResidualError(mPts);
}

template<typename T>
T PointToPointSimilarityErrorMinimizer<T>::getOverlap() const
{
        //NOTE: computing overlap of 2 point clouds can be complicated due to
        // the sparse nature of the representation. Here is only an estimate
        // of the true overlap.
        const int nbPoints = this->lastErrorElements.reading.features.cols();
        const int dim = this->lastErrorElements.reading.features.rows();
        if(nbPoints == 0)
        {
                throw std::runtime_error("Error, last error element empty. Error minimizer needs to be called at least once before using this method.");
        }
        
        if (!this->lastErrorElements.reading.descriptorExists("simpleSensorNoise"))
        {
                LOG_INFO_STREAM("PointToPointSimilarityErrorMinimizer - warning, no sensor noise found. Using best estimate given outlier rejection instead.");
                return this->getWeightedPointUsedRatio();
        }
        
        const BOOST_AUTO(noises, this->lastErrorElements.reading.getDescriptorViewByName("simpleSensorNoise"));
        
        const Vector dists = (this->lastErrorElements.reading.features.topRows(dim-1) - this->lastErrorElements.reference.features.topRows(dim-1)).colwise().norm();
        const T mean = dists.sum()/nbPoints;
        
        int count = 0;
        for(int i=0; i < nbPoints; i++)
        {
                if(dists(i) < (mean + noises(0,i)))
                {
                        count++;
                }
        }
        
        return (T)count/(T)nbPoints;
}

template struct PointToPointSimilarityErrorMinimizer<float>;
template struct PointToPointSimilarityErrorMinimizer<double>;