PointToPointWithCov.cpp
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4 
5 Copyright (c) 2010--2012,
6 Fran├žois Pomerleau and Stephane Magnenat, ASL, ETHZ, Switzerland
7 You can contact the authors at <f dot pomerleau at gmail dot com> and
8 <stephane at magnenat dot net>
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35 
36 #include "ErrorMinimizersImpl.h"
37 #include "PointMatcherPrivate.h"
38 #include "Eigen/SVD"
39 
40 using namespace Eigen;
41 
42 template<typename T>
44  PointToPointErrorMinimizer<T>("PointToPointWithCovErrorMinimizer", availableParameters(), params),
45  sensorStdDev(Parametrizable::get<T>("sensorStdDev"))
46 {
47 }
48 
49 template<typename T>
51 {
52  ErrorElements mPts = mPts_const;
54 
55  this->covMatrix = this->estimateCovariance(mPts, result);
56 
57  return result;
58 }
59 
60 //TODO: rewrite this using ErrorElements struct
61 template<typename T>
63 {
64  const int max_nbr_point = mPts.reading.getNbPoints();
65 
66  Matrix covariance(Matrix::Zero(6,6));
67  Matrix J_hessian(Matrix::Zero(6,6));
68  Matrix d2J_dReadingdX(Matrix::Zero(6, max_nbr_point));
69  Matrix d2J_dReferencedX(Matrix::Zero(6, max_nbr_point));
70 
71  Vector reading_point(Vector::Zero(3));
72  Vector reference_point(Vector::Zero(3));
73  Vector normal(3);
74  Vector reading_direction(Vector::Zero(3));
75  Vector reference_direction(Vector::Zero(3));
76 
77  normal << 1.0, 1.0, 1.0; // Used for point-to-point computation
78 
79  T beta = -asin(transformation(2,0));
80  T alpha = atan2(transformation(2,1), transformation(2,2));
81  T gamma = atan2(transformation(1,0)/cos(beta), transformation(0,0)/cos(beta));
82  T t_x = transformation(0,3);
83  T t_y = transformation(1,3);
84  T t_z = transformation(2,3);
85 
86  Vector tmp_vector_6(Vector::Zero(6));
87 
88  int valid_points_count = 0;
89 
90  //TODO: add missing const
91  for(int i = 0; i < max_nbr_point; ++i)
92  {
93  //if (outlierWeights(0,i) > 0.0)
94  {
95  reading_point = mPts.reading.features.block(0,i,3,1);
96  //int reference_idx = matches.ids(0,i);
97  reference_point = mPts.reference.features.block(0,i,3,1);
98 
99  T reading_range = reading_point.norm();
100  reading_direction = reading_point / reading_range;
101  T reference_range = reference_point.norm();
102  reference_direction = reference_point / reference_range;
103 
104  T n_alpha = normal(2)*reading_direction(1) - normal(1)*reading_direction(2);
105  T n_beta = normal(0)*reading_direction(2) - normal(2)*reading_direction(0);
106  T n_gamma = normal(1)*reading_direction(0) - normal(0)*reading_direction(1);
107 
108  T E = normal(0)*(reading_point(0) - gamma*reading_point(1) + beta*reading_point(2) + t_x - reference_point(0));
109  E += normal(1)*(gamma*reading_point(0) + reading_point(1) - alpha*reading_point(2) + t_y - reference_point(1));
110  E += normal(2)*(-beta*reading_point(0) + alpha*reading_point(1) + reading_point(2) + t_z - reference_point(2));
111 
112  T N_reading = normal(0)*(reading_direction(0) - gamma*reading_direction(1) + beta*reading_direction(2));
113  N_reading += normal(1)*(gamma*reading_direction(0) + reading_direction(1) - alpha*reading_direction(2));
114  N_reading += normal(2)*(-beta*reading_direction(0) + alpha*reading_direction(1) + reading_direction(2));
115 
116  T N_reference = -(normal(0)*reference_direction(0) + normal(1)*reference_direction(1) + normal(2)*reference_direction(2));
117 
118  // update the hessian and d2J/dzdx
119  tmp_vector_6 << normal(0), normal(1), normal(2), reading_range * n_alpha, reading_range * n_beta, reading_range * n_gamma;
120 
121  J_hessian += tmp_vector_6 * tmp_vector_6.transpose();
122 
123  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);
124 
125  d2J_dReadingdX.block(0,valid_points_count,6,1) = tmp_vector_6;
126 
127  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;
128 
129  d2J_dReferencedX.block(0,valid_points_count,6,1) = tmp_vector_6;
130 
131  valid_points_count++;
132  } // if (outlierWeights(0,i) > 0.0)
133  }
134 
135  Matrix d2J_dZdX(Matrix::Zero(6, 2 * valid_points_count));
136  d2J_dZdX.block(0,0,6,valid_points_count) = d2J_dReadingdX.block(0,0,6,valid_points_count);
137  d2J_dZdX.block(0,valid_points_count,6,valid_points_count) = d2J_dReferencedX.block(0,0,6,valid_points_count);
138 
139  Matrix inv_J_hessian = J_hessian.inverse();
140 
141  covariance = d2J_dZdX * d2J_dZdX.transpose();
142  covariance = inv_J_hessian * covariance * inv_J_hessian;
143 
144  return (sensorStdDev * sensorStdDev) * covariance;
145 }
146 
147 template<typename T>
149 {
150  return covMatrix;
151 }
152 
PointToPointWithCovErrorMinimizer::estimateCovariance
Matrix estimateCovariance(const ErrorElements &mPts, const TransformationParameters &transformation)
Definition: PointToPointWithCov.cpp:62
PointToPointWithCovErrorMinimizer::PointToPointWithCovErrorMinimizer
PointToPointWithCovErrorMinimizer(const Parameters &params=Parameters())
Definition: PointToPointWithCov.cpp:43
build_map.T
T
Definition: build_map.py:34
PointToPointErrorMinimizer::Matrix
PointMatcher< T >::Matrix Matrix
Definition: PointToPoint.h:54
PointToPointWithCovErrorMinimizer::getCovariance
virtual Matrix getCovariance() const
If not redefined by child class, return zero matrix.
Definition: PointToPointWithCov.cpp:148
PointMatcherPrivate.h
ErrorMinimizersImpl.h
PointToPointWithCovErrorMinimizer::compute
virtual TransformationParameters compute(const ErrorElements &mPts)
Find the transformation that minimizes the error given matched pair of points. This function most be ...
Definition: PointToPointWithCov.cpp:50
PointMatcher::ErrorMinimizer::ErrorElements
A structure holding data ready for minimization. The data are "normalized", for instance there are no...
Definition: PointMatcher.h:534
align_sequence.params
params
Definition: align_sequence.py:13
PointMatcher::ErrorMinimizer::ErrorElements::reading
DataPoints reading
reading point cloud
Definition: PointMatcher.h:536
PointMatcher::DataPoints::getNbPoints
unsigned getNbPoints() const
Return the number of points contained in the point cloud.
Definition: pointmatcher/DataPoints.cpp:158
PointMatcher::DataPoints::features
Matrix features
features of points in the cloud
Definition: PointMatcher.h:331
PointMatcher::ErrorMinimizer::ErrorElements::reference
DataPoints reference
reference point cloud
Definition: PointMatcher.h:537
PointToPointErrorMinimizer::Vector
PointMatcher< T >::Vector Vector
Definition: PointToPoint.h:53
PointMatcherSupport::Parametrizable
The superclass of classes that are constructed using generic parameters. This class provides the para...
Definition: Parametrizable.h:141
PointMatcherSupport::get
const M::mapped_type & get(const M &m, const typename M::key_type &k)
Definition: Bibliography.cpp:57
build_map.transformation
transformation
Definition: build_map.py:37
PointToPointErrorMinimizer::TransformationParameters
PointMatcher< T >::TransformationParameters TransformationParameters
Definition: PointToPoint.h:48
PointToPointErrorMinimizer::compute_in_place
TransformationParameters compute_in_place(ErrorElements &mPts)
Definition: PointToPoint.cpp:62
PointToPointErrorMinimizer
Definition: PointToPoint.h:42
PointToPointWithCovErrorMinimizer
Definition: PointToPointWithCov.h:43
PointToPointWithCovErrorMinimizer::Matrix
PointMatcher< T >::Matrix Matrix
Definition: PointToPointWithCov.h:50
PointToPointErrorMinimizer::Parameters
Parametrizable::Parameters Parameters
Definition: PointToPoint.h:45
PointMatcher::TransformationParameters
Matrix TransformationParameters
A matrix holding the parameters a transformation.
Definition: PointMatcher.h:182


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