PointToPoint.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>
PointToPointErrorMinimizer<T>::PointToPointErrorMinimizer() :
PointMatcher<T>::ErrorMinimizer("PointToPointErrorMinimizer",
                                                                                                                                ParametersDoc(),
                                                                                                                                Parameters()) {}
 
template<typename T>
PointToPointErrorMinimizer<T>::PointToPointErrorMinimizer(const std::string& className, const ParametersDoc paramsDoc, const Parameters& params):
        ErrorMinimizer(className, paramsDoc, params)
{
}
 
template<typename T>
typename PointMatcher<T>::TransformationParameters PointToPointErrorMinimizer<T>::compute(const ErrorElements& mPts_const)
{
        ErrorElements mPts = mPts_const;
        return compute_in_place(mPts);
}
 
template<typename T>
typename PointMatcher<T>::TransformationParameters PointToPointErrorMinimizer<T>::compute_in_place(ErrorElements& mPts) {
        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
        
        
        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;
        
        // 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());
        // 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;
        }
        const Vector trVector(meanReference - rotMatrix * meanReading);
        
        Matrix result(Matrix::Identity(dimCount, dimCount));
        result.topLeftCorner(dimCount-1, dimCount-1) = rotMatrix;
        result.topRightCorner(dimCount-1, 1) = trVector;
        
        return result;
}
 
template<typename T>
T PointToPointErrorMinimizer<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::computeResidualError(mPts);
}
 
template<typename T>
T PointToPointErrorMinimizer<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("PointToPointErrorMinimizer - 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<typename T>
T PointToPointErrorMinimizer<T>::computeResidualError(const ErrorElements& mPts)
{
        //typedef typename PointMatcher<T>::Matrix Matrix;
        
        const Matrix deltas = mPts.reading.features - mPts.reference.features;
        
        // return sum of the norm of each delta
        Matrix deltaNorms = deltas.colwise().norm();
        return deltaNorms.sum();
}
 
template struct PointToPointErrorMinimizer<float>;
template struct PointToPointErrorMinimizer<double>;