Point Cloud Library (PCL) 1.12.0
transformation_from_correspondences.hpp
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37
38#pragma once
39
40#include <pcl/common/transformation_from_correspondences.h>
41
42
43namespace pcl
44{
45
46inline void
48{
51 mean1_.fill(0);
52 mean2_.fill(0);
53 covariance_.fill(0);
54}
55
56
57inline void
58TransformationFromCorrespondences::add (const Eigen::Vector3f& point, const Eigen::Vector3f& corresponding_point,
59 float weight)
60{
61 if (weight==0.0f)
62 return;
63
65 accumulated_weight_ += weight;
66 float alpha = weight/accumulated_weight_;
67
68 Eigen::Vector3f diff1 = point - mean1_, diff2 = corresponding_point - mean2_;
69 covariance_ = (1.0f-alpha)*(covariance_ + alpha * (diff2 * diff1.transpose()));
70
71 mean1_ += alpha*(diff1);
72 mean2_ += alpha*(diff2);
73}
74
75
76inline Eigen::Affine3f
78{
79 //Eigen::JacobiSVD<Eigen::Matrix<float, 3, 3> > svd (covariance_, Eigen::ComputeFullU | Eigen::ComputeFullV);
80 Eigen::JacobiSVD<Eigen::Matrix<float, 3, 3> > svd (covariance_, Eigen::ComputeFullU | Eigen::ComputeFullV);
81 const Eigen::Matrix<float, 3, 3>& u = svd.matrixU(),
82 & v = svd.matrixV();
83 Eigen::Matrix<float, 3, 3> s;
84 s.setIdentity();
85 if (u.determinant()*v.determinant() < 0.0f)
86 s(2,2) = -1.0f;
87
88 Eigen::Matrix<float, 3, 3> r = u * s * v.transpose();
89 Eigen::Vector3f t = mean2_ - r*mean1_;
90
91 Eigen::Affine3f ret;
92 ret(0,0)=r(0,0); ret(0,1)=r(0,1); ret(0,2)=r(0,2); ret(0,3)=t(0);
93 ret(1,0)=r(1,0); ret(1,1)=r(1,1); ret(1,2)=r(1,2); ret(1,3)=t(1);
94 ret(2,0)=r(2,0); ret(2,1)=r(2,1); ret(2,2)=r(2,2); ret(2,3)=t(2);
95 ret(3,0)=0.0f; ret(3,1)=0.0f; ret(3,2)=0.0f; ret(3,3)=1.0f;
96
97 return (ret);
98}
99
100} // namespace pcl
101
Eigen::Affine3f getTransformation()
Calculate the transformation that will best transform the points into their correspondences.
void add(const Eigen::Vector3f &point, const Eigen::Vector3f &corresponding_point, float weight=1.0)
Add a new sample.
void reset()
Reset the object to work with a new data set.