PointToPlaneWithCov.cpp

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// kate: replace-tabs off; indent-width 4; indent-mode normal
// vim: ts=4:sw=4:noexpandtab
/*
 
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>
 
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.
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      documentation and/or other materials provided with the distribution.
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      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 <iostream>
 
#include "Eigen/SVD"
 
#include "ErrorMinimizersImpl.h"
#include "PointMatcherPrivate.h"
#include "Functions.h"
 
using namespace Eigen;
using namespace std;
 
typedef PointMatcherSupport::Parametrizable Parametrizable;
typedef PointMatcherSupport::Parametrizable P;
typedef Parametrizable::Parameters Parameters;
typedef Parametrizable::ParameterDoc ParameterDoc;
typedef Parametrizable::ParametersDoc ParametersDoc;
 
template<typename T>
PointToPlaneWithCovErrorMinimizer<T>::PointToPlaneWithCovErrorMinimizer(const Parameters& params):
        PointToPlaneErrorMinimizer<T>(PointToPlaneWithCovErrorMinimizer::availableParameters(), params),
        sensorStdDev(Parametrizable::get<T>("sensorStdDev"))
{
}
 
template<typename T>
typename PointMatcher<T>::TransformationParameters PointToPlaneWithCovErrorMinimizer<T>::compute(const ErrorElements& mPts_const)
{
        ErrorElements mPts = mPts_const;
    typename PointMatcher<T>::TransformationParameters out = PointToPlaneErrorMinimizer<T>::compute_in_place(mPts);
 
    this->covMatrix = this->estimateCovariance(mPts, out);
 
    return out;
}
 
template<typename T>
typename PointMatcher<T>::Matrix
PointToPlaneWithCovErrorMinimizer<T>::estimateCovariance(const ErrorElements& mPts, const TransformationParameters& transformation)
{
    const int max_nbr_point = mPts.reading.getNbPoints();
 
    Matrix covariance(Matrix::Zero(6,6));
    Matrix J_hessian(Matrix::Zero(6,6));
    Matrix d2J_dReadingdX(Matrix::Zero(6, max_nbr_point));
    Matrix d2J_dReferencedX(Matrix::Zero(6, max_nbr_point));
 
    Vector reading_point(Vector::Zero(3));
    Vector reference_point(Vector::Zero(3));
    Vector normal(3);
    Vector reading_direction(Vector::Zero(3));
    Vector reference_direction(Vector::Zero(3));
 
    //TODO: should be constView
    Matrix normals = mPts.reference.getDescriptorViewByName("normals");
 
    if (normals.rows() < 3)    // Make sure there are normals in DataPoints
        return std::numeric_limits<T>::max() * Matrix::Identity(6,6);
 
    T beta = -asin(transformation(2,0));
    T alpha = atan2(transformation(2,1), transformation(2,2));
    T gamma = atan2(transformation(1,0)/cos(beta), transformation(0,0)/cos(beta));
    T t_x = transformation(0,3);
    T t_y = transformation(1,3);
    T t_z = transformation(2,3);
 
    Vector tmp_vector_6(Vector::Zero(6));
 
    int valid_points_count = 0;
 
    //TODO: add missing const
    for(int i = 0; i < max_nbr_point; ++i)
    {
        //if (outlierWeights(0,i) > 0.0)
        {
            reading_point = mPts.reading.features.block(0,i,3,1);
            //int reference_idx = matches.ids(0,i);
            reference_point = mPts.reference.features.block(0,i,3,1);
 
            normal = normals.block(0,i,3,1);
 
            T reading_range = reading_point.norm();
            reading_direction = reading_point / reading_range;
            T reference_range = reference_point.norm();
            reference_direction = reference_point / reference_range;
 
            T n_alpha = normal(2)*reading_direction(1) - normal(1)*reading_direction(2);
            T n_beta = normal(0)*reading_direction(2) - normal(2)*reading_direction(0);
            T n_gamma = normal(1)*reading_direction(0) - normal(0)*reading_direction(1);
 
            T E = normal(0)*(reading_point(0) - gamma*reading_point(1) + beta*reading_point(2) + t_x - reference_point(0));
            E +=  normal(1)*(gamma*reading_point(0) + reading_point(1) - alpha*reading_point(2) + t_y - reference_point(1));
            E +=  normal(2)*(-beta*reading_point(0) + alpha*reading_point(1) + reading_point(2) + t_z - reference_point(2));
 
            T N_reading = normal(0)*(reading_direction(0) - gamma*reading_direction(1) + beta*reading_direction(2));
            N_reading +=  normal(1)*(gamma*reading_direction(0) + reading_direction(1) - alpha*reading_direction(2));
            N_reading +=  normal(2)*(-beta*reading_direction(0) + alpha*reading_direction(1) + reading_direction(2));
 
            T N_reference = -(normal(0)*reference_direction(0) + normal(1)*reference_direction(1) + normal(2)*reference_direction(2));
 
            // update the hessian and d2J/dzdx
            tmp_vector_6 << normal(0), normal(1), normal(2), reading_range * n_alpha, reading_range * n_beta, reading_range * n_gamma;
 
            J_hessian += tmp_vector_6 * tmp_vector_6.transpose();
 
            tmp_vector_6 << normal(0) * N_reading, normal(1) * N_reading, normal(2) * N_reading, n_alpha * (E + reading_range * N_reading), n_beta * (E + reading_range * N_reading), n_gamma * (E + reading_range * N_reading);
 
            d2J_dReadingdX.block(0,valid_points_count,6,1) = tmp_vector_6;
 
            tmp_vector_6 << normal(0) * N_reference, normal(1) * N_reference, normal(2) * N_reference, reference_range * n_alpha * N_reference, reference_range * n_beta * N_reference, reference_range * n_gamma * N_reference;
 
            d2J_dReferencedX.block(0,valid_points_count,6,1) = tmp_vector_6;
 
            valid_points_count++;
        } // if (outlierWeights(0,i) > 0.0)
    }
 
    Matrix d2J_dZdX(Matrix::Zero(6, 2 * valid_points_count));
    d2J_dZdX.block(0,0,6,valid_points_count) = d2J_dReadingdX.block(0,0,6,valid_points_count);
    d2J_dZdX.block(0,valid_points_count,6,valid_points_count) = d2J_dReferencedX.block(0,0,6,valid_points_count);
 
    Matrix inv_J_hessian = J_hessian.inverse();
 
    covariance = d2J_dZdX * d2J_dZdX.transpose();
    covariance = inv_J_hessian * covariance * inv_J_hessian;
 
    return (sensorStdDev * sensorStdDev) * covariance;
}
 
 
template<typename T>
typename PointMatcher<T>::Matrix PointToPlaneWithCovErrorMinimizer<T>::getCovariance() const
{
  return covMatrix;
}
 
template struct PointToPlaneWithCovErrorMinimizer<float>;
template struct PointToPlaneWithCovErrorMinimizer<double>;